**Part 1**

**Physiology of Sports Medicine** 

**1** 

*France* 

**Measurement and Physiological** 

**Rate During Exercise (LIPOXmax)** 

*Université de Montpellier 2, Montpellier, CHRU Montpellier,* 

*Physiques et Sportives, Université de Perpignan Via Domitia,* 

*3Laboratoire EA4556 Epsylon, Dynamique des Capacités Humaines et* 

Jean-Frédéric Brun1, Emmanuelle Varlet-Marie2, Ahmed Jérôme Romain3 and Jacques Mercier1

*1U1046, INSERM, Université de Montpellier 1,* 

*des Conduites de Santé (Montpellier)* 

*Département de Physiologie Clinique, Montpellier,* 

**Relevance of the Maximal Lipid Oxidation** 

*2Laboratoire Performance Santé Altitude, Sciences et Techniques des Activités* 

The intensity of exercise that elicits a maximal oxidation of lipids has been termed LIPOXmax, FATOXmax or FATmax. The three acronyms refer to three original protocols of exercise calorimetry which have been proposed almost simultaneously and it is thus interesting to maintain the three names in this review in order to avoid confusion. The difference among the three protocols is presented in table 1. Since our team has developed the technique called LIPOXmax (Perez-Martin et al., 2001; Brun et al., 2009b;) this acronym will be more employed in this chapter, keeping in mind that LIPOXmax, FATOXmax or

As will be reviewed in this paper, the measurement of LIPOXmax by graded exercise calorimetry is a reproducible measurement, although modifiable by several physiological conditions (training, previous exercise or meal). Its measurement closely predicts what will be oxidized over 45-60 min of low to medium intensity training performed at the corresponding intensity. It might be a marker of metabolic fitness, and is tightly correlated to mitochondrial function. LIPOXmax is related to catecholamine status and the growthhormone IGF-I axis, and occurs in athletes below the lactate and the ventilatory threshold (on the average around 40% VO2max). Its changes are related to alterations in muscular levels of citrate synthase, and to the mitochondrial ability to oxidize fatty acids. A meta-analysis shows that training at this level is efficient in sedentary subjects for reducing fat mass, sparing fat-free mass, increasing the ability to oxidize lipids during exercise, reducing blood glucose and Hba1c in type 2 diabetes, and decreasing circulating cholesterol. In athletes, various profiles are observed, with a high ability to oxidize lipids in endurance-trained athletes and in some samples of athletes trained for sprint or intermittent exercise a profile

FATmax represent obviously the same physiological concept.

showing a predominant use of carbohydrates.

**1. Introduction** 

## **Measurement and Physiological Relevance of the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax)**

Jean-Frédéric Brun1, Emmanuelle Varlet-Marie2, Ahmed Jérôme Romain3 and Jacques Mercier1 *1U1046, INSERM, Université de Montpellier 1, Université de Montpellier 2, Montpellier, CHRU Montpellier, Département de Physiologie Clinique, Montpellier, 2Laboratoire Performance Santé Altitude, Sciences et Techniques des Activités Physiques et Sportives, Université de Perpignan Via Domitia, 3Laboratoire EA4556 Epsylon, Dynamique des Capacités Humaines et des Conduites de Santé (Montpellier) France* 

## **1. Introduction**

The intensity of exercise that elicits a maximal oxidation of lipids has been termed LIPOXmax, FATOXmax or FATmax. The three acronyms refer to three original protocols of exercise calorimetry which have been proposed almost simultaneously and it is thus interesting to maintain the three names in this review in order to avoid confusion. The difference among the three protocols is presented in table 1. Since our team has developed the technique called LIPOXmax (Perez-Martin et al., 2001; Brun et al., 2009b;) this acronym will be more employed in this chapter, keeping in mind that LIPOXmax, FATOXmax or FATmax represent obviously the same physiological concept.

As will be reviewed in this paper, the measurement of LIPOXmax by graded exercise calorimetry is a reproducible measurement, although modifiable by several physiological conditions (training, previous exercise or meal). Its measurement closely predicts what will be oxidized over 45-60 min of low to medium intensity training performed at the corresponding intensity. It might be a marker of metabolic fitness, and is tightly correlated to mitochondrial function. LIPOXmax is related to catecholamine status and the growthhormone IGF-I axis, and occurs in athletes below the lactate and the ventilatory threshold (on the average around 40% VO2max). Its changes are related to alterations in muscular levels of citrate synthase, and to the mitochondrial ability to oxidize fatty acids. A meta-analysis shows that training at this level is efficient in sedentary subjects for reducing fat mass, sparing fat-free mass, increasing the ability to oxidize lipids during exercise, reducing blood glucose and Hba1c in type 2 diabetes, and decreasing circulating cholesterol. In athletes, various profiles are observed, with a high ability to oxidize lipids in endurance-trained athletes and in some samples of athletes trained for sprint or intermittent exercise a profile showing a predominant use of carbohydrates.

Measurement and Physiological Relevance of

relationship.

a. Muscular contractile activity by its own may use lipids as a source of energy.

b. Progressive rise in lipid oxidation with exercise duration

c. Compensatory rise in lipid oxidation after high intensity

exercise

d. Long term regular exercise may increase the ability to oxidize lipids at rest

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 5

2001; Brun et al., 2007, 2011). Accordingly, several teams have developed this measurement and attempted to train patients at a level determined by this exploration, as reviewed below. **« CROSSOVER" CONCEPT**

**Lipid oxidation CHO oxidation**

**endurance training**

During steady state exercise performed at low intensity, fat is oxidized at an almost constant rate (Bensimhon et al., 2006; Meyer et al., 2007), and there is an intensity of exercise that elicits the maximum oxidation of lipids termed

When exercise is heavy and prolonged enough to result in glycogen depletion, there is a shift toward lipids and their oxidation gradually increases (Ahlborg

This phenomenon is rather slow in mild to medium intensity exercise when

High intensity exercise oxidizes almost exclusively CHO but is frequently followed by a compensatory rise in lipid oxidation which compensates more

Long term regular exercise may shift the balance of substrates oxidized over 24 hr toward oxidative use of higher quantities of lipids (Talanian et al., 2007). A training-induced increase in the ability to oxidize lipids over 24-hr is statistically a predictor of exercise-induced weight loss (Barwell et al., 2009).

or less for the lipids not oxidized during exercise (Folch et al., 2001; Melanson et al., 2002), but it is inconsistent and frequently quite low (Malatesta et al., 2009; Lazer et al., 2010), even more if exercise is

**% of VO2max**

Fig. 1. The crossover concept: the balance of substrates at exercise is a function of exercise intensity, the proportion of lipids used for oxidation continuously decreasing when intensity increases, while CHO become the predominant fuel (>70%) above the "crossover point" (approximately 50% VO2max, see text. This increase in CHO oxidation down-regulates lipid oxidation despite sustained lipolysis. Above the crossover point glycogen utilization scales exponentially. Endurance training, energy supply, overtraining, dietary manipulation and previous exercise modify this pattern. Most trained athletes exhibit a right-shift in this

et al., 1974; Bergman et al., 1999; Watt et al.; 2003).

the duration of this exercise does not exceed 1 hr.

Table 2. Effects of exercise on lipid oxidation: exercise may increase the oxidative use of lipids by at least 4 mechanisms (after Brun et al., 2011). According to Warren the most important and reliable of these mechanisms is the oxidation during exercise performed

**detraining previous meal**

*maximal fat oxidation rate* (MFO ).

discontinuous (Warren et al., 2009).

around the LIPOXmax or below. (Warren et al., 2009).


Table 1. Definition of LIPOXmax, FATOXmax or FATmax.

#### **2. The physiological basis for measuring lipid oxidation during exercise**

#### **2.1 Balance of substrate oxidation during exercise: The "crossover concept"**

Pioneering studies (Zuntz et al., 1901; Krogh et al., 1920; Christensen et al., 1939) have demonstrated that a mixture of carbohydrates and fat is used by the muscle as a fuel at rest and during exercise, and that the ratio between VCO2 and VO2 was a reflect of the relative proportion of lipids and CHO used for oxidation. It was clear already at this time that exercise intensity, exercise duration and prior diet modified this balance of substrates.

Recent studies have evidenced that quantitatively, the most important substrate oxidized at the level of the exercising muscle is glucose (Bergman et al., 1999; Friedlander et al., 2007). The maximal rate of CHO oxidation during exercise is about two fold higher than that of lipids (Sahlin et al., 2008). However, when substrate metabolism is assessed on the whole body, lipids remain a major source of fuel at rest and during exercise. At rest, lipids provide >50% of the energy requirements, and they remain an important source of energy during low to middle intensity exercise, while CHO become the main substrate at high intensity (>80% VO2max) (Jeukendrup et al., 1998). As summarized in table 2, exercise may induce a significant amount of lipid oxidation by at least 4 mechanisms (Brun et al., 2011).

During the last quarter of the XXth century the literature became conflictual with several authors emphasizing the importance of carbohydrates and the others the importance of lipids. This controversy was actually clarified by the heuristic proposal of the "crossover concept" by George Brooks (Brooks et al., 1994). The "crossover concept" is an attempt to integrate the seemingly divergent effects of exercise intensity, nutritional status, gender, age and prior endurance training on the balance of carbohydrates and lipids used as a fuel during sustained exercise. It predicts that although an increase in exercise intensity results in a preferential use of CHO, endurance training shifts the balance of substrates during exercise toward a stronger reliance upon lipids (Fig.1).

The idea of developing a simple reliable exercise-test for assessing this balance of substrates thus emerged as a logical consequence of these fundamental studies (Perez-Martin et al.,

et al., 2009b;

**2. The physiological basis for measuring lipid oxidation during exercise 2.1 Balance of substrate oxidation during exercise: The "crossover concept"** 

significant amount of lipid oxidation by at least 4 mechanisms (Brun et al., 2011).

During the last quarter of the XXth century the literature became conflictual with several authors emphasizing the importance of carbohydrates and the others the importance of lipids. This controversy was actually clarified by the heuristic proposal of the "crossover concept" by George Brooks (Brooks et al., 1994). The "crossover concept" is an attempt to integrate the seemingly divergent effects of exercise intensity, nutritional status, gender, age and prior endurance training on the balance of carbohydrates and lipids used as a fuel during sustained exercise. It predicts that although an increase in exercise intensity results in a preferential use of CHO, endurance training shifts the balance of substrates during

The idea of developing a simple reliable exercise-test for assessing this balance of substrates thus emerged as a logical consequence of these fundamental studies (Perez-Martin et al.,

Pioneering studies (Zuntz et al., 1901; Krogh et al., 1920; Christensen et al., 1939) have demonstrated that a mixture of carbohydrates and fat is used by the muscle as a fuel at rest and during exercise, and that the ratio between VCO2 and VO2 was a reflect of the relative proportion of lipids and CHO used for oxidation. It was clear already at this time that exercise intensity, exercise duration and prior diet modified this balance of substrates. Recent studies have evidenced that quantitatively, the most important substrate oxidized at the level of the exercising muscle is glucose (Bergman et al., 1999; Friedlander et al., 2007). The maximal rate of CHO oxidation during exercise is about two fold higher than that of lipids (Sahlin et al., 2008). However, when substrate metabolism is assessed on the whole body, lipids remain a major source of fuel at rest and during exercise. At rest, lipids provide >50% of the energy requirements, and they remain an important source of energy during low to middle intensity exercise, while CHO become the main substrate at high intensity (>80% VO2max) (Jeukendrup et al., 1998). As summarized in table 2, exercise may induce a

3 min 6 min 5 min

Perez-Martin et al., 2001; Brun

Power intensity at which the derivative of the curve of lipid oxidation versus power is equal to zero (eg, top of the bell-shaped curve)

usually % of theoretical maximal power; also % extrapolated maximal oxygen uptake (%VO2max ACSM)] or % maximal oxygen uptake (%VO2max) determined by a previous test

Chenevière et al., 2009b

This model includes three independent variables (dilatation, symmetry, and translation). This SIN model has been reported to allow a more accurate calculation of Fatmin/LIPOXzero

Fatmax, MFO, dilatation, symmetry and translation

**acronym FATOXmax FATmax LIPOXmax SIN model**

Achten et al., 2002, 2003, 2004; Jeukendrup, 2003; Venables et al., 2005

Visual determination

% of maximal oxygen uptake (%VO2max ) MFO in g. min-1

Table 1. Definition of LIPOXmax, FATOXmax or FATmax.

exercise toward a stronger reliance upon lipids (Fig.1).

initial publication

Duration of steps

Calculation

Expression of results

Dériaz et al., 2001

5-6 min (until steady state)

Visual determination

% of maximal oxygen uptake (%VO2max MFO in kJ.min-1

2001; Brun et al., 2007, 2011). Accordingly, several teams have developed this measurement and attempted to train patients at a level determined by this exploration, as reviewed below.

## **« CROSSOVER" CONCEPT**

**% of VO2max**

Fig. 1. The crossover concept: the balance of substrates at exercise is a function of exercise intensity, the proportion of lipids used for oxidation continuously decreasing when intensity increases, while CHO become the predominant fuel (>70%) above the "crossover point" (approximately 50% VO2max, see text. This increase in CHO oxidation down-regulates lipid oxidation despite sustained lipolysis. Above the crossover point glycogen utilization scales exponentially. Endurance training, energy supply, overtraining, dietary manipulation and previous exercise modify this pattern. Most trained athletes exhibit a right-shift in this relationship.


Table 2. Effects of exercise on lipid oxidation: exercise may increase the oxidative use of lipids by at least 4 mechanisms (after Brun et al., 2011). According to Warren the most important and reliable of these mechanisms is the oxidation during exercise performed around the LIPOXmax or below. (Warren et al., 2009).

Measurement and Physiological Relevance of

stores (Dick, 2009).

**3.1 Methodological aspects** 

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 7

Interleukin-6 (IL-6) coming from the adipose tissue and the muscle acts as an energy sensor and thus activates AMP-activated kinase, resulting in enhanced glucose disposal, lipolysis and fat oxidation (Hoene et al., 2008). Adiponectin increases muscular lipid oxidation via phosphorylation of AMPK (Dick, 2009). Leptin increases muscle fat oxidation and decreases muscle fat uptake, thereby decreasing intramyocellular lipid

Although the information on this issue remains limited, it is clear that the level of maximal oxidation of lipids is related to some of these hormonal regulators : norepinephrine, whose training induced changes are positively correlated to an improvement in LIPOXmax (Bordenave et al., 2008) and growth hormone, whose deficit decreases it, a defect that can be corrected by growth hormone replacement (Brandou et al., 2006a). Downstream GH, IGF-I has also been reported to be correlated to LIPOXmax in soccer players as shown on Fig 5 (Brun et al., 1999), reflecting either a parallel effect of training on muscle fuel partitioning or IGF-I release, or an action of IGF-I (or GH via IGF) on muscular lipid oxidation. Other endocrine

axes are surely also involved but this issue is poorly known and remains to be studied.

As reminded above, the classic picture of Brooks and Mercier's "crossover concept" (Brooks & Mercier, 1994) has led to the development of an exercise-test suitable for routinely assessing this balance of substrates (Perez-Martin & Mercier, 2001; Brun et al., 2007). Based on our previous studies on calorimetry during long duration steady-state workloads (Manetta et al., 2002a, 2002b; Manetta et al., 2005) we developped a test (Perez-Martin et al., 2001) consisting of five 6-min submaximal steps, in which we assumed that a steady-state

We proposed (Perez-Martin et al., 2001) a diagnostic test including four or five 6-minutes workloads, that may be followed by a series of fast increases in power intensity until the tolerable maximum under these conditions is reached. This final incremental part of the test can be avoided in very sedentary patients and the maximal level can be indirectly evaluated by the linear extrapolation according to the ACSM guidelines (VO2max ACSM) (Aucouturier et al., 2009). The test is performed on an ergometric bicycle connected to an analyzer allowing the analysis of the gaseous exchange cycles by cycle. EKG monitoring and measurements of VO2, VCO2, and respiratory exchange ratio (RER) are performed during the test. After a period of 3 minutes at rest, and another period of initial warm-up at 20% of the predicted maximal power (PMP) for 3 minutes, the 6-min workloads set at approximately 30, 40, 50 and 60% of PMP are performed. The phase of recovery comprises two periods during which a monitoring of respiratory and cardiac parameters is maintained: active recovery at 20% of the PMP during 1 minute; passive recovery (*ie*, rest) during the 2 following minutes. At the end of each stage, during the fifth and sixth minutes, values of VO2 and VCO2 are recorded. These values are used the calculation of the respective rates of oxidation of carbohydrates and lipids by applying the classical

Carbohydrates (mg/min) = 4.585 VCO2 – 3.2255 VO2 (1)

Lipid Oxidation (mg/min) = -1.7012 VCO2 + 1.6946 VO2 (2)

**3. Technical aspects of exercise graded calorimetry** 

for gas exchanges was obtained during the 2 last minutes.

stoichiometric equations of indirect calorimetry:

#### **2.2 Mechanisms of substrate (fat vs CHO) selection during muscular activity**

According to the data presented above, fat is the major energy supply for the muscle below 25% of VO2max, since in this condition very few glycogen is employed as a source of energy (Romijn et al., 1993). Then, when exercise intensity increases, glycogen will rapidly become the predominant fuel. However, fat oxidation will still increase until the LIPOXmax/FATOXmax is reached. Above this level fat oxidation decreases. Interestingly, this decrease in fat oxidation coincides with lactate increase above baseline, as demonstrated in healthy adolescents during incremental cycling (Tolfrey et al., 2010).

The cellular mechanism of this decrease has been reviewed elsewhere (Sahlin et al., 2008) and is still incompletely understood. Theoretically, lipid supply by lipolysis, lipid entrance in muscle cell, lipid entrance in mitochondria, and mitochondrial fat processing may all be limiting steps. Experiments show that extracellular lipid supply is not limiting, since lipid oxidation decreases even if additional fat is provided to the cell. Limiting steps seem to be the entrance in mitochondria, governed by CPT-1, which can be inhibited by Malonyl-CoA and lactate (Starritt et al., 2000), and possibly downstream CPT-I other mitochondrial enzymes such as Acyl-CoA synthase and electron transport chain. All these steps are sensitive to the rate of CHO oxidation and thus a rise in CHO oxidation seems to depress lipid oxidation despite availability of fat and presence of all the enzymes of fat oxidation.

Experiments using intravenous infusion of labeled long-chain fatty acids in endurancetrained men cycling for 40 min at steady state at 50% of VO2max clearly demonstrate that carbohydrate availability directly regulates fat oxidation during exercise. An increased glycolytic flux results in a direct inhibition of long-chain fatty acid oxidation (Coyle et al., 1997). Conversely, there is a wide body of evidence that glycogen depletion reverses this inhibition and thus increases fat oxidation, as observed during long duration glycogendepleting exercise.

These processes are governed by cellular factors, that are under the influence of the central nervous system and circulating hormones (Ahlborg et al., 1974; Kiens & Richter, 1998; Kirvan et al., 1988; Thompson et al., 1998). Intracellular pathways have been reviewed elsewhere and this area of knowledge seems to be rapidly expanding. The activation of the AMPK (AMP-dependent kinase) pathway, together with a subsequent increase in the fatty acid oxidation, appear to constitute the main mechanism of action of these hormones in the regulation of lipid metabolism (Koulmann & Bigard, 2006). To summarize the main hormonal regulators of muscular lipid oxidation, epinephrine increases lipolysis (beta effect) and increases glucose oxidation in muscle (de Glisezinski et al., 2009). Norepinephrine increases lipid oxidation in muscle (Poehlman et al., 1994). Cortisol increases adipogenesis and lipolysis, and decreases non-insulin mediated glucose uptake. β-endorphin induces a lipolysis that can be blunted by naloxone (Richter et al., 1983, 1987). Growth hormone (GH) stimulates lipolysis and ketogenesis (Møller et al., 1990b). In the muscle and the liver, GH stimulates triglyceride uptake, by enhancing lipoprotein lipase expression, and its subsequent storage (Vijayakumar et al., 2010). GH also increases whole body lipid oxidation and nonoxidative glucose utilization and decreases glucose oxidation (Møller et al., 1990a). We have shown that GH-deficient individuals have a lower LIPOXmax and MFO that is restored after GH treatment (Brandou et al., 2006a). Dowstream GH, IGF-I that mediates many of the anabolic actions of growth hormone stimulates muscle protein synthesis, promotes glycogen storage and enhances lipolysis (Guha et al., 2009).

Interleukin-6 (IL-6) coming from the adipose tissue and the muscle acts as an energy sensor and thus activates AMP-activated kinase, resulting in enhanced glucose disposal, lipolysis and fat oxidation (Hoene et al., 2008). Adiponectin increases muscular lipid oxidation via phosphorylation of AMPK (Dick, 2009). Leptin increases muscle fat oxidation and decreases muscle fat uptake, thereby decreasing intramyocellular lipid stores (Dick, 2009).

Although the information on this issue remains limited, it is clear that the level of maximal oxidation of lipids is related to some of these hormonal regulators : norepinephrine, whose training induced changes are positively correlated to an improvement in LIPOXmax (Bordenave et al., 2008) and growth hormone, whose deficit decreases it, a defect that can be corrected by growth hormone replacement (Brandou et al., 2006a). Downstream GH, IGF-I has also been reported to be correlated to LIPOXmax in soccer players as shown on Fig 5 (Brun et al., 1999), reflecting either a parallel effect of training on muscle fuel partitioning or IGF-I release, or an action of IGF-I (or GH via IGF) on muscular lipid oxidation. Other endocrine axes are surely also involved but this issue is poorly known and remains to be studied.

## **3. Technical aspects of exercise graded calorimetry**

## **3.1 Methodological aspects**

6 An International Perspective on Topics in Sports Medicine and Sports Injury

According to the data presented above, fat is the major energy supply for the muscle below 25% of VO2max, since in this condition very few glycogen is employed as a source of energy (Romijn et al., 1993). Then, when exercise intensity increases, glycogen will rapidly become the predominant fuel. However, fat oxidation will still increase until the LIPOXmax/FATOXmax is reached. Above this level fat oxidation decreases. Interestingly, this decrease in fat oxidation coincides with lactate increase above baseline, as demonstrated

The cellular mechanism of this decrease has been reviewed elsewhere (Sahlin et al., 2008) and is still incompletely understood. Theoretically, lipid supply by lipolysis, lipid entrance in muscle cell, lipid entrance in mitochondria, and mitochondrial fat processing may all be limiting steps. Experiments show that extracellular lipid supply is not limiting, since lipid oxidation decreases even if additional fat is provided to the cell. Limiting steps seem to be the entrance in mitochondria, governed by CPT-1, which can be inhibited by Malonyl-CoA and lactate (Starritt et al., 2000), and possibly downstream CPT-I other mitochondrial enzymes such as Acyl-CoA synthase and electron transport chain. All these steps are sensitive to the rate of CHO oxidation and thus a rise in CHO oxidation seems to depress lipid oxidation despite availability of fat and presence of all the enzymes of fat oxidation. Experiments using intravenous infusion of labeled long-chain fatty acids in endurancetrained men cycling for 40 min at steady state at 50% of VO2max clearly demonstrate that carbohydrate availability directly regulates fat oxidation during exercise. An increased glycolytic flux results in a direct inhibition of long-chain fatty acid oxidation (Coyle et al., 1997). Conversely, there is a wide body of evidence that glycogen depletion reverses this inhibition and thus increases fat oxidation, as observed during long duration glycogen-

These processes are governed by cellular factors, that are under the influence of the central nervous system and circulating hormones (Ahlborg et al., 1974; Kiens & Richter, 1998; Kirvan et al., 1988; Thompson et al., 1998). Intracellular pathways have been reviewed elsewhere and this area of knowledge seems to be rapidly expanding. The activation of the AMPK (AMP-dependent kinase) pathway, together with a subsequent increase in the fatty acid oxidation, appear to constitute the main mechanism of action of these hormones in the regulation of lipid metabolism (Koulmann & Bigard, 2006). To summarize the main hormonal regulators of muscular lipid oxidation, epinephrine increases lipolysis (beta effect) and increases glucose oxidation in muscle (de Glisezinski et al., 2009). Norepinephrine increases lipid oxidation in muscle (Poehlman et al., 1994). Cortisol increases adipogenesis and lipolysis, and decreases non-insulin mediated glucose uptake. β-endorphin induces a lipolysis that can be blunted by naloxone (Richter et al., 1983, 1987). Growth hormone (GH) stimulates lipolysis and ketogenesis (Møller et al., 1990b). In the muscle and the liver, GH stimulates triglyceride uptake, by enhancing lipoprotein lipase expression, and its subsequent storage (Vijayakumar et al., 2010). GH also increases whole body lipid oxidation and nonoxidative glucose utilization and decreases glucose oxidation (Møller et al., 1990a). We have shown that GH-deficient individuals have a lower LIPOXmax and MFO that is restored after GH treatment (Brandou et al., 2006a). Dowstream GH, IGF-I that mediates many of the anabolic actions of growth hormone stimulates muscle protein synthesis,

**2.2 Mechanisms of substrate (fat vs CHO) selection during muscular activity** 

in healthy adolescents during incremental cycling (Tolfrey et al., 2010).

promotes glycogen storage and enhances lipolysis (Guha et al., 2009).

depleting exercise.

As reminded above, the classic picture of Brooks and Mercier's "crossover concept" (Brooks & Mercier, 1994) has led to the development of an exercise-test suitable for routinely assessing this balance of substrates (Perez-Martin & Mercier, 2001; Brun et al., 2007). Based on our previous studies on calorimetry during long duration steady-state workloads (Manetta et al., 2002a, 2002b; Manetta et al., 2005) we developped a test (Perez-Martin et al., 2001) consisting of five 6-min submaximal steps, in which we assumed that a steady-state for gas exchanges was obtained during the 2 last minutes.

We proposed (Perez-Martin et al., 2001) a diagnostic test including four or five 6-minutes workloads, that may be followed by a series of fast increases in power intensity until the tolerable maximum under these conditions is reached. This final incremental part of the test can be avoided in very sedentary patients and the maximal level can be indirectly evaluated by the linear extrapolation according to the ACSM guidelines (VO2max ACSM) (Aucouturier et al., 2009). The test is performed on an ergometric bicycle connected to an analyzer allowing the analysis of the gaseous exchange cycles by cycle. EKG monitoring and measurements of VO2, VCO2, and respiratory exchange ratio (RER) are performed during the test. After a period of 3 minutes at rest, and another period of initial warm-up at 20% of the predicted maximal power (PMP) for 3 minutes, the 6-min workloads set at approximately 30, 40, 50 and 60% of PMP are performed. The phase of recovery comprises two periods during which a monitoring of respiratory and cardiac parameters is maintained: active recovery at 20% of the PMP during 1 minute; passive recovery (*ie*, rest) during the 2 following minutes. At the end of each stage, during the fifth and sixth minutes, values of VO2 and VCO2 are recorded. These values are used the calculation of the respective rates of oxidation of carbohydrates and lipids by applying the classical stoichiometric equations of indirect calorimetry:

$$\text{Carbonhydroxes (mg/min)} = 4.585 \text{ VCO}\_2 - 3.2255 \text{ VO}\_2 \tag{1}$$

$$\text{LipidOxidation (mg/min)} = \text{-1.7012 VCO}\_2 + \text{1.6946 VO}\_2 \tag{2}$$

Measurement and Physiological Relevance of

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 9

Recently a more sophisticated mathematical model (sine model, SIN) was proposed in order to describe fat oxidation kinetics as a function the relative exercise intensity [% of maximal oxygen uptake (%VO2max)] during graded exercise and to determine the exercise intensity elicits maximal fat oxidation and the intensity at which the fat oxidation becomes negligible. This model which will not be developed here includes three independent variables (dilatation, symmetry, and translation). This SIN model exhibits the same precision as other methods currently used in the determination of LIPOXmax and has been reported to allow a

Actually, there is now a large body of literature to support the validity of such protocols of exercise calorimetry (Jeukendrup & Wallis, 2005). The theoretical concern was that, when exercise is performed above the lactate threshold, there is an extra CO2 production which can be assumed to interfere with the calculations (MacRae et al., 1995). In fact, below 75% of the VO2max, this increase in CO2 has no measurable effect on calorimetric calculations (Romijn et al., 1992), so that these calculations predict closely oxidation rates measured by stable isotope labeling (Christmass et al., 1999). Clearly, even at high intensity exercise,

A controversial issue appears to be: how to express the results. The crude power and/or heart rate at which lipid oxidation reaches its maximum is the most useful information if one aims at undertaking a targeted training procedure. The difficulty arises when units for reporting data in scientific studies are discussed. A percentage of the actual VO2max is a logic solution, and was used by the team of A. Jeukendrup (Achten et al., 2002, 2003) but this requires to perform another exercise test designed for a precise measurement of VO2max. Alternatively, in the initial protocol proposed by Perez-Martin (Perez-Martin & Mercier, 2001), after the four or five 6-min steps used for calorimetry, a rapid incremental protocol until the maximal level was proposed. However, after 24 or 30 min of exercise, subjects may be tired and unable to reach the actual maximum level which would thus be sometimes underestimated. In fact, in our team, we often express our results as a percentage of the theoretical maximal power calculated with Wasserman's equation. This method allows avoiding a maximal stress, which is sometimes perceived as very harmful by sedentary and obese individuals, and thus improves the acceptability of the test. Two French studies have challenged this approach. Aucouturier and coworkers (Aucouturier et al., 2009) report that a calculation of VO2max according to the American College of Sports Medicine (ACSM) recommendations from submaximal VO2 values provides a satisfactory evaluation of the actual VO2max while theoretical VO2max values given by Wasserman's equation are sometimes misleading in such subjects. These authors thus propose to express the LIPOXmax as a percentage of VO2max ACSM. This approach was also employed by Lazzer (Lazzer et al., 2010). Michallet et al (Michallet et al., 2008) insisted on the fact that the theoretical design of the test with steps set at 20, 30, 40, 50 and 60% of theoretical maximal aerobic power can be inaccurate, and that a good protocol should include steps at a respiratory exchange ratio below and above 0.9, this value being that of the "crossover point". In a very recent study the team of E Bouhlel proposes an improvement that markedly increases the reproducibility and thus presumably the precision of the measurement : the authors propose a previous determination of the VO2max with a maximal exercise test and then set the power intensity of the steps of the calorimetry according to this test (Gmada et al, 2011). This study has the interest to further demonstrate the precision and reproducibility of the method and to propose a protocol suitable for research purposes, but for the assessment of series of patients

more accurate calculation of Fatmin/LIPOXzero (Chenevière et al., 2009b).

respiratory gases are mostly the reflect of the balance of substrate oxidation.

These calculations are performed on values of the 5-6th minutes of each step, since at this CO2 production from bicarbonate buffers compensating for the production of lactic acid becomes negligible. The increment in carbohydrate oxidation above basal values appears to be roughly a linear function of the developed power and the slope of this relation is calculated, providing the *glucidic cost of the watt* (Aloulou, 2002)*.* The increase in lipid oxidation adopts the shape of a bell-shaped curve: after a peak, lipid oxidation decreases at the highest power intensities.

The exact mechanism of this reduction in the use of the lipids at the highest power intensities is actually imperfectly known: a reduction in lipolysis is likely to explain a part of it, together with a shift of metabolic pathways within the muscle fiber. The empirical formula of indirect calorimetry that gives the lipid oxidation rate is, as reminded above:

$$\text{Lipid oxidation (mg/min)} = \text{-1.7 VCO}\_2 + \text{1.7 VO}\_2 \tag{3}$$

It is easy to deduce from this formula that the relation between power (P) and oxidation of lipids (Lox) displays a bell-shaped curve of the form:

$$\text{Lox} = \text{A.P.(1-RER)} \tag{4}$$

The smoothing of this curve enables us to calculate the power intensity at which lipid oxidation becomes maximal, which is the point where the derivative of this curve becomes equal to zero. Therefore the LIPOXmax calculation is only an application of the classical empirical equation of lipid oxidation used in calorimetry.

Fig. 2. Calculation of the LIPOXmax: The curve of lipid oxidation (mg/min) is given by the empirical formula of calorimetry Lipox = -1.7 VCO2 + 1.7 VO2 . This curve Lipox = A.P (1- RER) (see text) can be derived and the point where its derivative equals zero is the top of the bell-shaped curve and thus represents the LIPOXmax. Actually in some subjects this is a broad zone and in others a narrow range of power intensities.

These calculations are performed on values of the 5-6th minutes of each step, since at this CO2 production from bicarbonate buffers compensating for the production of lactic acid becomes negligible. The increment in carbohydrate oxidation above basal values appears to be roughly a linear function of the developed power and the slope of this relation is calculated, providing the *glucidic cost of the watt* (Aloulou, 2002)*.* The increase in lipid oxidation adopts the shape of a bell-shaped curve: after a peak, lipid oxidation decreases at

The exact mechanism of this reduction in the use of the lipids at the highest power intensities is actually imperfectly known: a reduction in lipolysis is likely to explain a part of it, together with a shift of metabolic pathways within the muscle fiber. The empirical formula of indirect calorimetry that gives the lipid oxidation rate is, as reminded above:

It is easy to deduce from this formula that the relation between power (P) and oxidation of

The smoothing of this curve enables us to calculate the power intensity at which lipid oxidation becomes maximal, which is the point where the derivative of this curve becomes equal to zero. Therefore the LIPOXmax calculation is only an application of the classical

0 10 20 30 40 50 60 70 80

Fig. 2. Calculation of the LIPOXmax: The curve of lipid oxidation (mg/min) is given by the empirical formula of calorimetry Lipox = -1.7 VCO2 + 1.7 VO2 . This curve Lipox = A.P (1- RER) (see text) can be derived and the point where its derivative equals zero is the top of the bell-shaped curve and thus represents the LIPOXmax. Actually in some subjects this is a

**% maximal power output**

Lipid oxidation (mg/min) = -1.7 VCO2 + 1.7 VO2 (3)

**LIPOXmax**

Lox = A.P (1-RER) (4)

LIPOX = f(P)

d(LIPOX)/dP

the highest power intensities.

lipids (Lox) displays a bell-shaped curve of the form:

empirical equation of lipid oxidation used in calorimetry.


broad zone and in others a narrow range of power intensities.



0

**LIPID OXIDATION** 

**mg/min**

5

10

15

20

Recently a more sophisticated mathematical model (sine model, SIN) was proposed in order to describe fat oxidation kinetics as a function the relative exercise intensity [% of maximal oxygen uptake (%VO2max)] during graded exercise and to determine the exercise intensity elicits maximal fat oxidation and the intensity at which the fat oxidation becomes negligible. This model which will not be developed here includes three independent variables (dilatation, symmetry, and translation). This SIN model exhibits the same precision as other methods currently used in the determination of LIPOXmax and has been reported to allow a more accurate calculation of Fatmin/LIPOXzero (Chenevière et al., 2009b).

Actually, there is now a large body of literature to support the validity of such protocols of exercise calorimetry (Jeukendrup & Wallis, 2005). The theoretical concern was that, when exercise is performed above the lactate threshold, there is an extra CO2 production which can be assumed to interfere with the calculations (MacRae et al., 1995). In fact, below 75% of the VO2max, this increase in CO2 has no measurable effect on calorimetric calculations (Romijn et al., 1992), so that these calculations predict closely oxidation rates measured by stable isotope labeling (Christmass et al., 1999). Clearly, even at high intensity exercise, respiratory gases are mostly the reflect of the balance of substrate oxidation.

A controversial issue appears to be: how to express the results. The crude power and/or heart rate at which lipid oxidation reaches its maximum is the most useful information if one aims at undertaking a targeted training procedure. The difficulty arises when units for reporting data in scientific studies are discussed. A percentage of the actual VO2max is a logic solution, and was used by the team of A. Jeukendrup (Achten et al., 2002, 2003) but this requires to perform another exercise test designed for a precise measurement of VO2max. Alternatively, in the initial protocol proposed by Perez-Martin (Perez-Martin & Mercier, 2001), after the four or five 6-min steps used for calorimetry, a rapid incremental protocol until the maximal level was proposed. However, after 24 or 30 min of exercise, subjects may be tired and unable to reach the actual maximum level which would thus be sometimes underestimated. In fact, in our team, we often express our results as a percentage of the theoretical maximal power calculated with Wasserman's equation. This method allows avoiding a maximal stress, which is sometimes perceived as very harmful by sedentary and obese individuals, and thus improves the acceptability of the test. Two French studies have challenged this approach. Aucouturier and coworkers (Aucouturier et al., 2009) report that a calculation of VO2max according to the American College of Sports Medicine (ACSM) recommendations from submaximal VO2 values provides a satisfactory evaluation of the actual VO2max while theoretical VO2max values given by Wasserman's equation are sometimes misleading in such subjects. These authors thus propose to express the LIPOXmax as a percentage of VO2max ACSM. This approach was also employed by Lazzer (Lazzer et al., 2010). Michallet et al (Michallet et al., 2008) insisted on the fact that the theoretical design of the test with steps set at 20, 30, 40, 50 and 60% of theoretical maximal aerobic power can be inaccurate, and that a good protocol should include steps at a respiratory exchange ratio below and above 0.9, this value being that of the "crossover point". In a very recent study the team of E Bouhlel proposes an improvement that markedly increases the reproducibility and thus presumably the precision of the measurement : the authors propose a previous determination of the VO2max with a maximal exercise test and then set the power intensity of the steps of the calorimetry according to this test (Gmada et al, 2011). This study has the interest to further demonstrate the precision and reproducibility of the method and to propose a protocol suitable for research purposes, but for the assessment of series of patients

Measurement and Physiological Relevance of

90% of VO2max (Brun et al, 2011c).

is correlated to muscle physiological status.

**3.3 How short can be the steps of an exercise calorimetry?** 

apnea… etc)

**exercise calorimetry** 

Meyer et al., 2007).

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 11

each kg of muscle to burn lipids; it provides an index which has been shown to be predictive of the effects of exercise on weight loss (Lavault et al., 2011) as indicated below. A MFO lower than 5 mg.min-1.kg-1 muscle mass predicts poor exercise induced weight loss while as a higher MFO value predicts more efficient exercise induced weight loss. MFO ranges on the average between 38 and 1073 mg/min and the boundary of the lowest quartile is 140 mg/min. The LIPOXmax occurs at a very variable level between 3.6 and 101.5% of Pmaxth so that the boundary of the lowest quartile is 22% (ie, it is at 64.01% ± 0.52% of FCmaxth the boundary of the lowest quartile is 58%. Expressed in % of the reserve heart rate *ie* 44.5% of VO2max. Thus targeting, on theoretical grounds, these values ±5 % would be actually set at the LIPOXmax in only 30-40% of subjects, ie 60-70% of patients would not be trained at the expected level. The crossover point occurs on average at 32% of Wmaxth so that the boundary of the lowest quartile is 23.4%. This corresponds to 45% of VO2max (Brun et al., 2009b). Therefore, in an average French population, the LIPOXmax occurs around 30% of Wmaxth ie 45% of VO2max. In sedentary obese and diabetic patients, there is now considerable evidence that this level is more or less lowered and is sometimes extremely low. The point where there are no longer lipids oxidized (LIPOXzero or FATmax) is at 80% of Pmax ie 85-

In addition as shown on Table 4, the LIPOXmax is shifted to lower intensities and the MFO is decreased in many situations referred as "glucodependence" (obesity, diabetes, sleep

**3.2 Physiological relevance of the balance of substrates at exercise as assessed with** 

During steady-state exercise at low intensity (LIPOXmax or below), lipid oxidation remains stable at the level predicted by exercise calorimetry over 45 min or more (Jean et al., 2007;

When higher intensities are reached (60% VO2max or more) there is a gradual increase in lipid oxidation when the duration of exercise increases. This enhanced fat oxidation results from a decrease in muscle glycogen content which diminishes the availability of CHO in the exercising muscle. For example, a 2hr exercise at 60% VO2max induces a 77% reduction in muscle glycogen depletion (Thomson et al., 1979). The shift to lipids has been shown to

Exercise calorimetry thus can be used as a basis for targeted training, as discussed below. On the other hand, the ability to oxidize lipids during exercise is likely to reflect a profile of "metabolic fitness" that is impaired in some diseases and improved by training, and which

The basic assumption that underlies exercise calorimetry is that blood lactate generation during exercise has minimal influence on RER after 3-4 minutes of exercise performed at a steady state. In this condition, the extra-CO2 production from blood HCO3- buffers can indeed be regarded as negligible. One can calculate that even the fastest increase (approximately 2 mmol-1min-1) in blood lactate produces an increase of VCO2 by only 3%. Indeed, if we assume that the volume of distribution of lactate is proportional by a factor of 100 ml.kg-1 to body mass and thus represents approximately 8 L, this would mobilize 16 mmol HCO3- and generate, over 6 min, roughly 1.8 CO2 l.min-1. Under these conditions,

occur when there is a reduction of 30-40% of glycogen stores (Kirwan et al, 1988).

or athletes it is clearly necessary to rely upon a single test, ie, calorimetry if we want to measure te balance of substrates.

Fig. 3. Examples of individual exercise calorimetries: left; obese woman with "glucodependence" (ie, poor ability to oxidize lipids at exercise) with a peak of lipid oxidation at 135 mg/min located at a power intensity of 34 watts (40% % VO2max ACSM) ; right, overweight patient who oxidizes 235 mg/min of lipids at a LIPOXmax of 68 watts, (55% % VO2max ACSM.) In the last subject, the LIPOX zone is quite wide, indicating that lipids are oxidize over a wide range of exercise intensities. In the first subject it is restricted to a narrow area. The two curves of lipid oxidation are plotted together on the lower pannel, showing their difference in profile according to the theoretical maximal working capacity. Similar discrepancies can be found in athletes.

The maximal fat oxidation rate (MFO) has been expressed in mg/min (Perez-Martin & Mercier, 2001; Dumortier et al., 2002; Brandou et al., 2003, 2005, 2006a, 2006b), g/min (Achten et al., 2003; Achten & Jeukendrup, 2004; Jeukendrup, 2003), mg/min/kg body weight, mg/min/kg fat free mass, and more recently in mg/min/kg muscle mass (Lavault et al., 2011). Muscle can be evaluated from bioimpedance analysis with a validated equation (Janssen et al., 2000), and expression of MFO in mg/min/kg muscle offers at least two advantages: it helps to delineate the effects of training on muscle mass and on the ability of

or athletes it is clearly necessary to rely upon a single test, ie, calorimetry if we want to

0 20 40 60 80 100 **% THEORETICAL MAXIMAL POWER**

oxidation at 135 mg/min located at a power intensity of 34 watts (40% % VO2max ACSM) ; right, overweight patient who oxidizes 235 mg/min of lipids at a LIPOXmax of 68 watts, (55% % VO2max ACSM.) In the last subject, the LIPOX zone is quite wide, indicating that lipids are oxidize over a wide range of exercise intensities. In the first subject it is restricted to a narrow area. The two curves of lipid oxidation are plotted together on the lower pannel, showing their difference in profile according to the theoretical maximal working capacity.

The maximal fat oxidation rate (MFO) has been expressed in mg/min (Perez-Martin & Mercier, 2001; Dumortier et al., 2002; Brandou et al., 2003, 2005, 2006a, 2006b), g/min (Achten et al., 2003; Achten & Jeukendrup, 2004; Jeukendrup, 2003), mg/min/kg body weight, mg/min/kg fat free mass, and more recently in mg/min/kg muscle mass (Lavault et al., 2011). Muscle can be evaluated from bioimpedance analysis with a validated equation (Janssen et al., 2000), and expression of MFO in mg/min/kg muscle offers at least two advantages: it helps to delineate the effects of training on muscle mass and on the ability of

**K Cal/m in**

> 0 20 50 80 95 **POWER (watts)**

GLU LIP

measure te balance of substrates.

GLU LIP

**KCal/m in**

> 0 20 40 60 80 **POWER (watts)**

> > 0

Fig. 3. Examples of individual exercise calorimetries: left; obese woman with "glucodependence" (ie, poor ability to oxidize lipids at exercise) with a peak of lipid

50

100

150

**LIPID OXIDATIONmg/min**

Similar discrepancies can be found in athletes.

200

250

each kg of muscle to burn lipids; it provides an index which has been shown to be predictive of the effects of exercise on weight loss (Lavault et al., 2011) as indicated below. A MFO lower than 5 mg.min-1.kg-1 muscle mass predicts poor exercise induced weight loss while as a higher MFO value predicts more efficient exercise induced weight loss. MFO ranges on the average between 38 and 1073 mg/min and the boundary of the lowest quartile is 140 mg/min. The LIPOXmax occurs at a very variable level between 3.6 and 101.5% of Pmaxth so that the boundary of the lowest quartile is 22% (ie, it is at 64.01% ± 0.52% of FCmaxth the boundary of the lowest quartile is 58%. Expressed in % of the reserve heart rate *ie* 44.5% of VO2max. Thus targeting, on theoretical grounds, these values ±5 % would be actually set at the LIPOXmax in only 30-40% of subjects, ie 60-70% of patients would not be trained at the expected level. The crossover point occurs on average at 32% of Wmaxth so that the boundary of the lowest quartile is 23.4%. This corresponds to 45% of VO2max (Brun et al., 2009b). Therefore, in an average French population, the LIPOXmax occurs around 30% of Wmaxth ie 45% of VO2max. In sedentary obese and diabetic patients, there is now considerable evidence that this level is more or less lowered and is sometimes extremely low. The point where there are no longer lipids oxidized (LIPOXzero or FATmax) is at 80% of Pmax ie 85- 90% of VO2max (Brun et al, 2011c).

In addition as shown on Table 4, the LIPOXmax is shifted to lower intensities and the MFO is decreased in many situations referred as "glucodependence" (obesity, diabetes, sleep apnea… etc)

#### **3.2 Physiological relevance of the balance of substrates at exercise as assessed with exercise calorimetry**

During steady-state exercise at low intensity (LIPOXmax or below), lipid oxidation remains stable at the level predicted by exercise calorimetry over 45 min or more (Jean et al., 2007; Meyer et al., 2007).

When higher intensities are reached (60% VO2max or more) there is a gradual increase in lipid oxidation when the duration of exercise increases. This enhanced fat oxidation results from a decrease in muscle glycogen content which diminishes the availability of CHO in the exercising muscle. For example, a 2hr exercise at 60% VO2max induces a 77% reduction in muscle glycogen depletion (Thomson et al., 1979). The shift to lipids has been shown to occur when there is a reduction of 30-40% of glycogen stores (Kirwan et al, 1988).

Exercise calorimetry thus can be used as a basis for targeted training, as discussed below. On the other hand, the ability to oxidize lipids during exercise is likely to reflect a profile of "metabolic fitness" that is impaired in some diseases and improved by training, and which is correlated to muscle physiological status.

## **3.3 How short can be the steps of an exercise calorimetry?**

The basic assumption that underlies exercise calorimetry is that blood lactate generation during exercise has minimal influence on RER after 3-4 minutes of exercise performed at a steady state. In this condition, the extra-CO2 production from blood HCO3- buffers can indeed be regarded as negligible. One can calculate that even the fastest increase (approximately 2 mmol-1min-1) in blood lactate produces an increase of VCO2 by only 3%. Indeed, if we assume that the volume of distribution of lactate is proportional by a factor of 100 ml.kg-1 to body mass and thus represents approximately 8 L, this would mobilize 16 mmol HCO3- and generate, over 6 min, roughly 1.8 CO2 l.min-1. Under these conditions,

Measurement and Physiological Relevance of

0

80 Difference


0

40

50

100

150

200

250

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 13

0 50 100 150 200 250

Mean difference : 4,51 [ -14,8 to 23,8 ] Difference plot N = 11 <sup>40</sup> <sup>80</sup> <sup>120</sup> <sup>160</sup> <sup>200</sup> <sup>240</sup> Mean -80

Initial studies on exercise calorimetry unanimously reported a fair reliability, which seems to be confirmed by daily clinical practice. The coefficient of variation (CV) for the LIPOXmax (at that time it was manually determined) was found to be 11.4% (Perez-Martin et al., 2001) and with Achten and Jeukendrup's procedure in 10 males tested three times it was 9.6% ie ±0.23 l/min (Achten et al., 2003). Similarly Michallet in 14 subjects aged 19-50 years found with the current LIPOXmax procedure a CV equal to 8.7%. The crossover point PCX appeared somewhat less reproducible with a CV of 17% (Michallet et al., 2006). However, Meyer investigating this methodology, reported variability as high as ±0.91 l/min that was supposed to be too wide (Meyer et al., 2009). Meyer's paper actually investigated the reproducibility in non-standardized conditions concerning recent exercise and food intake, two major modifiers of the balance of substrates, and therefore his conclusions are restricted to subjects tested in similarly non standardized conditions. More recently a careful methodological study proposing a more standardized approach based on prior determination of VO2max by a maximal exercise test evidenced an even better reproducibility as low as 5.02% (Gmada et al, 2011). Therefore, on the whole, it is clear that the LIPOXmax is a fairly reproducible measurement, unless conditions of measure are not standardized for

Fig. 4. Correlation and Bland-Altman plot showing the agreement between the measurement of MFO with LIPOXmax protocol and the average lipid oxidation rate maintained over 45 min during a steady state exercise set at this intensity level.

**3.4 Factors of variation and reliability of LIPOXmax/FATmax** 

**r= 0.859**

y = 1.2524x – 17.6

VCO2 would increase by less than 0.06 l.min-1, *ie* roughly 3%. Thus, the increase in RER in these exercise conditions is almost completely explained by the balance between oxidized carbohydrates and lipids, independent of blood lactate. The validity of this calorimetric approach is further confirmed by a classical work of Romijn (Romijn et al., 1992) who showed in highly trained sportsmen that up to 80-85% VO2max calorimetric calculations based on respiratory exchanges during exercise closely fit with much more sophisticated measurements using stable isotopes (MacRae et al., 1995). Concerning proteins, if one compares exercise bouts at 33 and 66% of VO2max it can be demonstrated that their use for oxidation remains stable at the various levels of exercise, supporting the basal assumption that the balance of substrates may be interpreted in terms of respective percentage of oxidized fat and carbohydrates.

We have presented above our procedure based on 6-minutes workloads. However, other investigators (Achten et al., 2002) have simultaneously developed a procedure based on 3 minutes "ultra-short" workloads. This latter method has been validated by its promoters in athletes and healthy sedentary subjects (Achten et al., 2002, 2003). Actually, there was a paucity of data about its validity in very sedentary patients, in whom it usually takes more time to obtain a steady state of respiratory exchanges. We recently compared calorimetry data obtained with this procedure (2nd-3rd minutes) with the one presented above (5-6th minutes) and found that values measured during the 3 minutes steps are poorly correlated with values measured during the 6 minutes steps, due to an overestimation of steady state RER that can be as high as 0.35. This shift results in an average overestimation of carbohydrate oxidation of 15.8 mg/min (this difference can reach 1200 mg/min). Besides, lipid oxidations are poorly correlated between the two methods. Therefore, among very sedentary patients in whom these tests are used for targeting physical activity, 3-min steps appear too short to allow accurate calorimetric calculations. Our protocol based on 6 minutes workloads seems preferable (Bordenave et al., 2007).

As already developed above, Romijn (Romijn et al., 1992) compared, in highly trained endurance cyclists, calorimetric results and isotopic measurement during exercise tests up to 85% VO2max and showed that at this level calorimetry is fully reliable. However, a look at the figures of this paper shows that the steady state of RER occurs after 4 min and is not obtained after 2 minutes. In addition, we recently showed that the estimate of lipid oxidation by this method during the 5th and 6th minutes of a 6 min step predicts fairly well the actual lipid oxidation rate that would be observed over 45 minutes performed at the same level (Fig.4). The mean difference between the predicted value and the measured value is only 4.51±8.7 mg/min (Jean et al., 2007). Meyer (Meyer et al., 2007) also reported that VO2 used for fat oxidation after 6 min closely predicted fat oxidation measured between 30 and 40 min of a constant-load exercise performed at the same intensity. These two observations further support the use of the 6-min steps procedure rather than the 3-min steps procedure proposed by the team of Jeukendrup (Achten et al., 2002) that seems to be accurate mostly for sports medicine and exercise physiology but less reliable in sedentary subjects.

A recent study further addressed this issue in prepubertal children. Comparison of 10 min and 3 min steps showed that the 3 min procedure yielded a satisfactory assessment of the power intensity where the maximum was reached (55% VO2peak) with 95% satisfactory limits of agreement ± 7% VO2peak, but that the value of the lipid oxidation rate was less precisely assessed in this population with the 3 min procedure. The authors concluded that, in children, the 3 min procedure provides a valid estimation of the power intensity but was less precise for assessing the flow rate (Zakrzewski & Tolfrey, 2011).

VCO2 would increase by less than 0.06 l.min-1, *ie* roughly 3%. Thus, the increase in RER in these exercise conditions is almost completely explained by the balance between oxidized carbohydrates and lipids, independent of blood lactate. The validity of this calorimetric approach is further confirmed by a classical work of Romijn (Romijn et al., 1992) who showed in highly trained sportsmen that up to 80-85% VO2max calorimetric calculations based on respiratory exchanges during exercise closely fit with much more sophisticated measurements using stable isotopes (MacRae et al., 1995). Concerning proteins, if one compares exercise bouts at 33 and 66% of VO2max it can be demonstrated that their use for oxidation remains stable at the various levels of exercise, supporting the basal assumption that the balance of substrates may be interpreted in terms of respective percentage of

We have presented above our procedure based on 6-minutes workloads. However, other investigators (Achten et al., 2002) have simultaneously developed a procedure based on 3 minutes "ultra-short" workloads. This latter method has been validated by its promoters in athletes and healthy sedentary subjects (Achten et al., 2002, 2003). Actually, there was a paucity of data about its validity in very sedentary patients, in whom it usually takes more time to obtain a steady state of respiratory exchanges. We recently compared calorimetry data obtained with this procedure (2nd-3rd minutes) with the one presented above (5-6th minutes) and found that values measured during the 3 minutes steps are poorly correlated with values measured during the 6 minutes steps, due to an overestimation of steady state RER that can be as high as 0.35. This shift results in an average overestimation of carbohydrate oxidation of 15.8 mg/min (this difference can reach 1200 mg/min). Besides, lipid oxidations are poorly correlated between the two methods. Therefore, among very sedentary patients in whom these tests are used for targeting physical activity, 3-min steps appear too short to allow accurate calorimetric calculations. Our protocol based on 6-

As already developed above, Romijn (Romijn et al., 1992) compared, in highly trained endurance cyclists, calorimetric results and isotopic measurement during exercise tests up to 85% VO2max and showed that at this level calorimetry is fully reliable. However, a look at the figures of this paper shows that the steady state of RER occurs after 4 min and is not obtained after 2 minutes. In addition, we recently showed that the estimate of lipid oxidation by this method during the 5th and 6th minutes of a 6 min step predicts fairly well the actual lipid oxidation rate that would be observed over 45 minutes performed at the same level (Fig.4). The mean difference between the predicted value and the measured value is only 4.51±8.7 mg/min (Jean et al., 2007). Meyer (Meyer et al., 2007) also reported that VO2 used for fat oxidation after 6 min closely predicted fat oxidation measured between 30 and 40 min of a constant-load exercise performed at the same intensity. These two observations further support the use of the 6-min steps procedure rather than the 3-min steps procedure proposed by the team of Jeukendrup (Achten et al., 2002) that seems to be accurate mostly

for sports medicine and exercise physiology but less reliable in sedentary subjects.

less precise for assessing the flow rate (Zakrzewski & Tolfrey, 2011).

A recent study further addressed this issue in prepubertal children. Comparison of 10 min and 3 min steps showed that the 3 min procedure yielded a satisfactory assessment of the power intensity where the maximum was reached (55% VO2peak) with 95% satisfactory limits of agreement ± 7% VO2peak, but that the value of the lipid oxidation rate was less precisely assessed in this population with the 3 min procedure. The authors concluded that, in children, the 3 min procedure provides a valid estimation of the power intensity but was

oxidized fat and carbohydrates.

minutes workloads seems preferable (Bordenave et al., 2007).

Fig. 4. Correlation and Bland-Altman plot showing the agreement between the measurement of MFO with LIPOXmax protocol and the average lipid oxidation rate maintained over 45 min during a steady state exercise set at this intensity level.

## **3.4 Factors of variation and reliability of LIPOXmax/FATmax**

Initial studies on exercise calorimetry unanimously reported a fair reliability, which seems to be confirmed by daily clinical practice. The coefficient of variation (CV) for the LIPOXmax (at that time it was manually determined) was found to be 11.4% (Perez-Martin et al., 2001) and with Achten and Jeukendrup's procedure in 10 males tested three times it was 9.6% ie ±0.23 l/min (Achten et al., 2003). Similarly Michallet in 14 subjects aged 19-50 years found with the current LIPOXmax procedure a CV equal to 8.7%. The crossover point PCX appeared somewhat less reproducible with a CV of 17% (Michallet et al., 2006). However, Meyer investigating this methodology, reported variability as high as ±0.91 l/min that was supposed to be too wide (Meyer et al., 2009). Meyer's paper actually investigated the reproducibility in non-standardized conditions concerning recent exercise and food intake, two major modifiers of the balance of substrates, and therefore his conclusions are restricted to subjects tested in similarly non standardized conditions. More recently a careful methodological study proposing a more standardized approach based on prior determination of VO2max by a maximal exercise test evidenced an even better reproducibility as low as 5.02% (Gmada et al, 2011). Therefore, on the whole, it is clear that the LIPOXmax is a fairly reproducible measurement, unless conditions of measure are not standardized for

Measurement and Physiological Relevance of

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 15

exercise in hot environments. Reversal after

ability to oxidize lipids during exercise but in some sports like soccer, a preferential

lower power intensities and MFO lowered.

compared to subjects matched for body mass index (difference not found by others)

Training improves both apnea-index and

Over the 60-70 the literature is full of papers showing that endurance training allows fat to become the predominant substrate for endurance exercise, while other leading authors in that time emphasized the importance of CHO-derived energy stores for exercise performance (see review in Brooks & Mercier, 1994). According to the initial formulation of the crossover concept, it could be expected that endurance athletes would exhibit a profile of "lipid oxidizers" proportional to their fitness and the efficacy of their training. Most of the exercise calorimetry studies in athletes confirm this early statement. They show that on the average endurance-trained athletes oxidize more lipids. Data from cross-sectional and longitudinal studies have supported the notion that training reduces the reliance on CHO as an energy source, thereby increasing fat oxidation during submaximal exercise (Achten et al., 2004). In pre- to early pubertal children, brisk walking or slow running promotes higher fat oxidation (Zakrzewski & Tolfrey, 2011). A specific study on the effects of endurance training in women shows that endurance-trained women had a higher fat oxidation rate, but their peak values occur at a very similar intensity (56±3% VO2max) compared with the untrained women (53±2% VO2max) (Stisen et al., 2006). González-Haro and coworkers have fairly evidenced in high competitive level triathletes and cyclists various profiles of high

(Friedlander et al., 1998a, 1998b; Chenevière et al., 2011 ; Brun et al., 2009a ; Zakrzewski & Tolfrey,

Febbraio et al, 1994; del Coso

(Bergman & Brooks, 1999; Achten et al., 2003; Venables et al., 2005; González-Haro et al., 2007; Varlet-Marie et al.,

(Perez-Martin et al., 2001; Sardinoux et al., 2009)

(Malin et al., 2010).

(Ghanassia et al., 2006; Mogensen et al., 2009)

(Desplan et al., 2010).

2011b).

et al, 2010

2006).

on average their LIPOXmax occurs at higher power intensity This difference is confirmed in all studies but is actually quite moderate and has probably little relevance On the opposite, fat oxidation is higher in pre- to early pubertal boys compared with girls at similar relative (but

**Modifying factor Effect references** 

not absolute) intensities

acclimation and training.

use of CHO is observed

decrease its postexercise rise

type 1 diabetes Lower ability to oxidize lipids (Brun et al., 2008).

lipid oxidation at exercise

**4.1 Endurance training improves the ability to oxidize fat during exercise** 

temperature Shift to preferential CHO oxidation during

highly trained athletes Most of them exhibit a markedly high

Obesity and diabetes LIPOXmax values markedly shifted to

type 2 diabetes Lower ability to oxidize lipids when

Table 4. Factors of variation of LIPOXmax/FATmax

Metformin increases fat oxidation during exercise and

sleep apnea syndrome Lower ability to oxidize lipids at exercise.

gender Women oxidize slightly more lipids and

the major factors of variation such as exercise or prior meal (see Table 3). This last remark is important because, like all physiological parameters, the LIPOXmax can be acutely modified by several factors (see Table 4).


Table 3. reproducibility studies of the LIPOXmax/FATmax: reproducibility is fair unless patients are not fasting and not standardized for recent previous exercise, and reproducibility is even greater if the protocol is more standardized

## **4. LIPOXmax/FATmax in sports medicine**

As reviewed below, most literature on the LIPOXmax/Fatmax deals with alterations of this parameter in patients and its potential interest for exercise targeting. However, there are some reports suggesting that this parameter has some interest in athletes.


the major factors of variation such as exercise or prior meal (see Table 3). This last remark is important because, like all physiological parameters, the LIPOXmax can be acutely modified

**reproducibility remarks** 

(Perez-Martin et al., 2001) CV= 11.4% Early LIPOXmax protocol, visual

(Meyer et al., 2009) Variability ±0.91 l/min Not standardized for prior exercise and

Table 3. reproducibility studies of the LIPOXmax/FATmax: reproducibility is fair unless

As reviewed below, most literature on the LIPOXmax/Fatmax deals with alterations of this parameter in patients and its potential interest for exercise targeting. However, there are

decreased MFO and shifted LIPOXmax to a

decreases fat oxidation during exercise, even if the diet is consumed for only 2 to 3 days, due to reduced muscle glycogen

prepubertal children and gradually decrease throughout puberty to reach adult

adults and in pre- to early pubertal

values at the end of puberty

patients are not fasting and not standardized for recent previous exercise, and

some reports suggesting that this parameter has some interest in athletes.

**Modifying factor Effect references** 

slightly lower intensity

stores

puberty LIPOXmax and MFO are higher in

type of exercise Higher during running than cycling in

children

reproducibility is even greater if the protocol is more standardized

(Michallet et al., 2006) CV = 8.7% Current LIPOXmax protocol

(Achten et al., 2003) CV= 9.6% ie ±0.23 l/min FATmax protocol

determination

Standardized determination after prior maximal test to determine VO2max markedly increases reproducibility of

> Bergman & Brooks, 1999; Jeukendrup, 2003; Friedlander et al., 2007

(Brandou et al, 2006b; Riddell et al., 2008 ; (Zakrzewski &

(Coyle et al., 2001).

Tolfrey, 2011b).

2011a).

(Achten et al., 2003; Zakrzewski & Tolfrey,

the LIPOXmax protocol

feeding

MFO slightly increased (Chenevière et al., 2009a)

by several factors (see Table 4).

**Author (reference) Parameters of** 

(Gmada et al, 2011) CV= 5.02%

previous meal taken less than

high-fat diets in which more than 60% of the energy is derived from fat

previous exercise performed just before the exercise

3 hr before

calorimetry

**4. LIPOXmax/FATmax in sports medicine** 


Table 4. Factors of variation of LIPOXmax/FATmax

#### **4.1 Endurance training improves the ability to oxidize fat during exercise**

Over the 60-70 the literature is full of papers showing that endurance training allows fat to become the predominant substrate for endurance exercise, while other leading authors in that time emphasized the importance of CHO-derived energy stores for exercise performance (see review in Brooks & Mercier, 1994). According to the initial formulation of the crossover concept, it could be expected that endurance athletes would exhibit a profile of "lipid oxidizers" proportional to their fitness and the efficacy of their training. Most of the exercise calorimetry studies in athletes confirm this early statement. They show that on the average endurance-trained athletes oxidize more lipids. Data from cross-sectional and longitudinal studies have supported the notion that training reduces the reliance on CHO as an energy source, thereby increasing fat oxidation during submaximal exercise (Achten et al., 2004). In pre- to early pubertal children, brisk walking or slow running promotes higher fat oxidation (Zakrzewski & Tolfrey, 2011). A specific study on the effects of endurance training in women shows that endurance-trained women had a higher fat oxidation rate, but their peak values occur at a very similar intensity (56±3% VO2max) compared with the untrained women (53±2% VO2max) (Stisen et al., 2006). González-Haro and coworkers have fairly evidenced in high competitive level triathletes and cyclists various profiles of high

Measurement and Physiological Relevance of

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 17

Fig. 6. Comparison of the power at which occur the crossover point and the LIPOXmax in control subjects and in various groups of athletes. \*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.05 (female rugby players vs. female control subjects);

When they were expressed as percentages of theoretical maximal power this ranking became: cyclists > rugbywomen > rugbymen > female controls > male controls > soccer players (Figure 7). Raw lipid oxidation rates at the level of the LIPOXmax ranked as follows (Figure 8): rugbymen > cyclists > rugbywomen > sedentary male controls > soccer. If lipid oxidation is expressed per kg of body weight this ranking becames: cyclists > rugbymen > rugbywomen >

Fig. 7. Comparison of the crossover point and the LIPOXmax, expressed in % of Wmax, in control subjects and in various groups of athletes. \*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.0001 (female rugby players vs. female control subjects);

sedentary female controls > sedentary male controls > soccer players (Figure 9).

++p<0.0001 (female rugby players vs. male rugby players)

p<0.05; p<0.0001 (cyclists vs. male rugby players)

lipid oxidation which differ among sports (González-Haro et al., 2007). However, the reason for the inter-individual variability of these parameters remains poorly understood (Achten & Jeukendrup, 2003, 2004; Brun et al., 2000; Jeukendrup & Wallis, 2005). Clearly, energetic pathways favorized by specific training programs may be markedly different among sports.

#### **4.2 Endocrine correlates of this profile of high lipid oxidation**

In soccer players relationships between the GH-IGF-I axis and the LIPOXmax were reported (Brun et al., 1999). These correlations are likely to reflect either a parallel effect of training on muscle fuel partitioning or IGF-I release, or an action of IGF-I (or GH via IGF) on muscular lipid oxidation (Fig. 5).

Fig. 5. Correlation between Insulin-like growth factor 1 (IGF-I) levels and the LIPOXmax in soccer players (Brun et al., 1999).

#### **4.3 Are there 'glucodependent' sports**

While low intensity training, as shown above, increases lipid oxidation, high intensity training has been reported to improve the ability to oxidize carbohydrates (Manetta et al., 2002a, 2002b).

Varlet-Marie et al (Varlet-Marie et al., 2006) described the profile of lipid oxidation in 90 trained athletes: 28 cyclists, 32 male soccer players, 19 male rugby players, 11 rugbywomen (national level in soccer and male rugby and regional level in cyclism and female rugby) and 41 healthy sedentary volunteers. All athletes had been involved in regular training for several years (>3 years), and trained 10.69 ± 0.9 hr/wk. The soccer team performed over the year a combination of endurance training under the form of interval training, strength training, speed training, skill and tactical training, in various proportions according to the period. Rugbymen and rugbywomen underwent an heavy training mostly based on strength training. The cyclists performed 14 hours of cycling (*ie*, about 450 km) per week during a nine-month training period. During the first month, training sessions were performed at low intensity with a specific target (below their ventilatory threshold: VT). During the other months, they added interval-training sessions to their endurance training, wherein they performed at high intensity with a specific target heart (above their VT).

When expressed as raw power values, the LIPOXmax and the crossover point ranked as follows: rugbymen > cyclists > male controls > rugbywomen > female controls > male soccer players (Figure 6).

lipid oxidation which differ among sports (González-Haro et al., 2007). However, the reason for the inter-individual variability of these parameters remains poorly understood (Achten & Jeukendrup, 2003, 2004; Brun et al., 2000; Jeukendrup & Wallis, 2005). Clearly, energetic pathways favorized by specific training programs may be markedly different among sports.

In soccer players relationships between the GH-IGF-I axis and the LIPOXmax were reported (Brun et al., 1999). These correlations are likely to reflect either a parallel effect of training on muscle fuel partitioning or IGF-I release, or an action of IGF-I (or GH via IGF) on muscular

**r=0.694 p<0.01**

0 10 20 30 40 50 60 70

**IGF-I** *nMol/L*

Fig. 5. Correlation between Insulin-like growth factor 1 (IGF-I) levels and the LIPOXmax in

While low intensity training, as shown above, increases lipid oxidation, high intensity training has been reported to improve the ability to oxidize carbohydrates (Manetta et al.,

Varlet-Marie et al (Varlet-Marie et al., 2006) described the profile of lipid oxidation in 90 trained athletes: 28 cyclists, 32 male soccer players, 19 male rugby players, 11 rugbywomen (national level in soccer and male rugby and regional level in cyclism and female rugby) and 41 healthy sedentary volunteers. All athletes had been involved in regular training for several years (>3 years), and trained 10.69 ± 0.9 hr/wk. The soccer team performed over the year a combination of endurance training under the form of interval training, strength training, speed training, skill and tactical training, in various proportions according to the period. Rugbymen and rugbywomen underwent an heavy training mostly based on strength training. The cyclists performed 14 hours of cycling (*ie*, about 450 km) per week during a nine-month training period. During the first month, training sessions were performed at low intensity with a specific target (below their ventilatory threshold: VT). During the other months, they added interval-training sessions to their endurance training, wherein they performed at high intensity with a specific target heart (above their VT). When expressed as raw power values, the LIPOXmax and the crossover point ranked as follows: rugbymen > cyclists > male controls > rugbywomen > female controls > male soccer

**4.2 Endocrine correlates of this profile of high lipid oxidation** 

lipid oxidation (Fig. 5).

**LIPOXmax (%Wmax)**

2002a, 2002b).

players (Figure 6).

soccer players (Brun et al., 1999).

**4.3 Are there 'glucodependent' sports** 

Fig. 6. Comparison of the power at which occur the crossover point and the LIPOXmax in control subjects and in various groups of athletes. \*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.05 (female rugby players vs. female control subjects); ++p<0.0001 (female rugby players vs. male rugby players)

When they were expressed as percentages of theoretical maximal power this ranking became: cyclists > rugbywomen > rugbymen > female controls > male controls > soccer players (Figure 7). Raw lipid oxidation rates at the level of the LIPOXmax ranked as follows (Figure 8): rugbymen > cyclists > rugbywomen > sedentary male controls > soccer. If lipid oxidation is expressed per kg of body weight this ranking becames: cyclists > rugbymen > rugbywomen > sedentary female controls > sedentary male controls > soccer players (Figure 9).

Fig. 7. Comparison of the crossover point and the LIPOXmax, expressed in % of Wmax, in control subjects and in various groups of athletes. \*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.0001 (female rugby players vs. female control subjects); p<0.05; p<0.0001 (cyclists vs. male rugby players)

Measurement and Physiological Relevance of

and requires more investigation.

**diabetes** 

**5.1 Scientific background** 

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 19

intensities, leading to the concept that training effects on the balance of substrates at exercise are reversed by overtraining (Aloulou et al, 2003). This issue remains poorly documented

Fig. 9. Lipid oxidation rates in control subjects and in various groups of athletes, expressed in mg/min/kg of body weight.\*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); p<0.0001 (cyclists vs. soccer players); ##p<0.0001 (male rugby players vs. soccer players)

**5. Interest of the LIPOXmax as a target for structured training in obesity and** 

It is now unanimously recognized that exercise is an efficient tool for : 1) preventing the onset of type 2 diabetes (Lindström et al., 2006; Kim et al., 2006); 2) improving blood glucose control (Marwick et al., 2009) and 3) preventing further weight regain in weight-reduced obese individuals (Bensimhon et al., 2006). Exercise is also beneficial for cardiovascular health, due to its positive effects on blood pressure (Pescatello et al., 2004; Pescatello, 2005), blood lipids (Kelley et al., 2006), inflammation (Fabre et al., 2002), blood viscosity (Brun et al., 2010b), mood

However the effects of exercise as a weight reducing procedure have been considered during many years as rather limited and almost negligible. It is beyond any doubt that regular exercise attenuates the metabolic drive to regain weight after long-term weight loss (MacLean et al, 2009). The interest of physical activity was thus mostly to prevent weight gain, to improve stabilization after slimming, and to reduce obesity-related co-morbidities

This traditional view has been challenged by studies demonstrating that even without any change in diet, exercise alone may reduce body weight (Ross et al., 2000; Slentz et al., 2004). This has been further evidenced by a recent meta-analysis that concludes that exercise on its own improves the effects of a diet by on average 1.4 kg (Wu et al., 2009). It seems now well demonstrated that exercise considered alone can reduce body weight. The last American consensus (Donnelly et al., 2009) indicates that more than 250 min of weekly moderateintensity physical activity is associated with clinically significant weight loss. Accordingly,

(Krogh et al., 2010) and cognitive function (Fabre et al., 2002; Angevaren et al., 2008).

but not to reduce weight by its own (Jakicic & Otto, 2005, 2006; Duclos et al., 2010).

This study evidences markedly different patterns of balance of substrates among groups of athletes. Clearly, cycling and rugby are rather characterized by high rates of lipid oxidation which peaks at high exercise intensities, while in soccer there is an early predominance of CHO.

The finding of a high ability to oxidize lipids in athletes submitted to regular endurance training, like cyclists, is consistent with previous literature (Achten & Jeukendrup, 2003). By contrast, it is interesting to notice in soccer players, a pattern of "glucodependence" that implies a reduced reliance on lipids at exercise. Although in our study we can only present data on soccer, this pattern is likely to occur in several sports. Since exercise training at high intensity (Manetta et al., 2002a, 2002b) and intermittent exercise (Perez-Martin et al., 2000) both increase the ratio between CHO and fat used for oxidation during muscular activity, this pattern may reflect an adaptation of muscle metabolism to short repeated bouts of high intensity. Interestingly, such a "glucodependence" is also found in obesity (Perez-Martin et al., 2001) and type 2 diabetes (Blanc et al., 2000). In this case it can be rapidly reversed by a few weeks of targeted exercise training at the level of the LIPOXmax (Dumortier et al., 2002, 2003). Since physical inactivity rapidly shifts the balance of substrates at rest towards a lower ratio of lipid/CHO used for oxidation (Blanc et al., 2000) it can be assumed that sedentarity explains at least in part the glucodependence of these patients.

Fig. 8. Lipid oxidation rates in control subjects and in various groups of athletes, expressed in mg/min. \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.0001 (female rugby players vs. female control subjects); ++p<0.0001 (female rugby players vs. male rugby players)

#### **4.4 Shifts in the balance of substrates during exercise with overtraining**

According to the energy pathway mostly involved in a type of activity, training increases thus the ability to oxidize either lipids or CHO. This was clearly evidenced in a study conducted on competitive road cyclists in whom high intensity endurance training increased the ability to oxidize CHO above the ventilatory threshold, while at the end of the season most patients exhibited symptoms of overreaching associated with a reversal of this increase in CHO oxidation (Manetta et al., 2002a). By contrast overreaching in endurance athletes submitted to exercise calorimetry showed lowered ability to oxidize fat at low

This study evidences markedly different patterns of balance of substrates among groups of athletes. Clearly, cycling and rugby are rather characterized by high rates of lipid oxidation which peaks at high exercise intensities, while in soccer there is an early predominance of

The finding of a high ability to oxidize lipids in athletes submitted to regular endurance training, like cyclists, is consistent with previous literature (Achten & Jeukendrup, 2003). By contrast, it is interesting to notice in soccer players, a pattern of "glucodependence" that implies a reduced reliance on lipids at exercise. Although in our study we can only present data on soccer, this pattern is likely to occur in several sports. Since exercise training at high intensity (Manetta et al., 2002a, 2002b) and intermittent exercise (Perez-Martin et al., 2000) both increase the ratio between CHO and fat used for oxidation during muscular activity, this pattern may reflect an adaptation of muscle metabolism to short repeated bouts of high intensity. Interestingly, such a "glucodependence" is also found in obesity (Perez-Martin et al., 2001) and type 2 diabetes (Blanc et al., 2000). In this case it can be rapidly reversed by a few weeks of targeted exercise training at the level of the LIPOXmax (Dumortier et al., 2002, 2003). Since physical inactivity rapidly shifts the balance of substrates at rest towards a lower ratio of lipid/CHO used for oxidation (Blanc et al., 2000) it can be assumed that

sedentarity explains at least in part the glucodependence of these patients.

Fig. 8. Lipid oxidation rates in control subjects and in various groups of athletes, expressed in mg/min. \*\*p<0.0001 (male athletes vs. male control subjects); ##p<0.0001 (male rugby players vs. soccer players); p<0.0001 (cyclists vs. soccer players); p<0.0001 (female rugby players

According to the energy pathway mostly involved in a type of activity, training increases thus the ability to oxidize either lipids or CHO. This was clearly evidenced in a study conducted on competitive road cyclists in whom high intensity endurance training increased the ability to oxidize CHO above the ventilatory threshold, while at the end of the season most patients exhibited symptoms of overreaching associated with a reversal of this increase in CHO oxidation (Manetta et al., 2002a). By contrast overreaching in endurance athletes submitted to exercise calorimetry showed lowered ability to oxidize fat at low

vs. female control subjects); ++p<0.0001 (female rugby players vs. male rugby players)

**4.4 Shifts in the balance of substrates during exercise with overtraining** 

CHO.

intensities, leading to the concept that training effects on the balance of substrates at exercise are reversed by overtraining (Aloulou et al, 2003). This issue remains poorly documented and requires more investigation.

Fig. 9. Lipid oxidation rates in control subjects and in various groups of athletes, expressed in mg/min/kg of body weight.\*p<0.05; \*\*p<0.0001 (male athletes vs. male control subjects); p<0.0001 (cyclists vs. soccer players); ##p<0.0001 (male rugby players vs. soccer players)

## **5. Interest of the LIPOXmax as a target for structured training in obesity and diabetes**

## **5.1 Scientific background**

It is now unanimously recognized that exercise is an efficient tool for : 1) preventing the onset of type 2 diabetes (Lindström et al., 2006; Kim et al., 2006); 2) improving blood glucose control (Marwick et al., 2009) and 3) preventing further weight regain in weight-reduced obese individuals (Bensimhon et al., 2006). Exercise is also beneficial for cardiovascular health, due to its positive effects on blood pressure (Pescatello et al., 2004; Pescatello, 2005), blood lipids (Kelley et al., 2006), inflammation (Fabre et al., 2002), blood viscosity (Brun et al., 2010b), mood (Krogh et al., 2010) and cognitive function (Fabre et al., 2002; Angevaren et al., 2008).

However the effects of exercise as a weight reducing procedure have been considered during many years as rather limited and almost negligible. It is beyond any doubt that regular exercise attenuates the metabolic drive to regain weight after long-term weight loss (MacLean et al, 2009). The interest of physical activity was thus mostly to prevent weight gain, to improve stabilization after slimming, and to reduce obesity-related co-morbidities but not to reduce weight by its own (Jakicic & Otto, 2005, 2006; Duclos et al., 2010).

This traditional view has been challenged by studies demonstrating that even without any change in diet, exercise alone may reduce body weight (Ross et al., 2000; Slentz et al., 2004). This has been further evidenced by a recent meta-analysis that concludes that exercise on its own improves the effects of a diet by on average 1.4 kg (Wu et al., 2009). It seems now well demonstrated that exercise considered alone can reduce body weight. The last American consensus (Donnelly et al., 2009) indicates that more than 250 min of weekly moderateintensity physical activity is associated with clinically significant weight loss. Accordingly,

Measurement and Physiological Relevance of

been oxidized (Strasser et al, 2007).

**5.2 Standardized vs personalized targeting?** 

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 21

There is an important discussion that underlies all the controversies about exercise prescription in chronic diseases. This is: should we use standard or personalized exercise prescriptions. Some current rules of prescription emphasize the need for taking into account the personal characteristics of each patient, but they are by essence "standardized", ie, they do not take into account the specific physiologic profile of each subject. All is based on the assumption that the most important mechanism underlying the metabolic effect of exercise is to generate an energy deficit, regardless of the actual quantity of lipids or CHO that have

Such a standardized approach was used in pneumology and cardiology, before it was challenged by a new paradigm: the "individualization concept". Personalized targeting of exercise has been promoted in respiratory and cardiac chronic diseases and was shown to provide better results (Vallet et al., 1997). The "Hippocratic" concept of superiority of the individualized approach is taken into account by a number of practitioners and appears in national guidelines (Guidelines, 2005). However, some guidelines do not mention it,

In metabolic diseases, such a discussion about the "individualization concept" has not yet been initiated. Usual French recommendations for exercise in diabetes (Gautier et al, 1998; Gautier, 2004) do not take into account the individual metabolic background. Authors only

Extending the individualization concept to obesity and diabetes raises the question of a specific individual target, and obviously the LIPOXmax/FATOXmax appears as a logic candidate for this purpose. Accordingly, several teams have undertaken the study of the

**5.3 Targeting endurance exercise close to the LIPOXmax vs higher intensity levels**  A topic which has generated a lot of discussion over the last decade is the selection of the optimal exercise protocol that could be used for the management of obesity and diabetes. Initially, low intensity endurance training (LI), as it was know to be the variety of exercise that oxidized the highest quantity of lipids, was logically proposed (Thompson et al., 1998). However, later focus was given on other kinds of exercise such high intensity endurance training (HI), resistance training (RT) and interval training (IT). All of them were evidenced to exert beneficial effects when applied to obese (Jakicic & Otto, 2005, 2006) and diabetic (Praet & van Loon, 2007, 2009) patients. On the whole, LI remains the easiest and the most widely evaluated procedure. It is also the best demonstrated as shown by a recent metaanalysis on exercise and diabetes that selected 34 from 645 papers. This paper confirmed that endurance exercise alone improves Hba1c (-0.6%), blood pressure, (-6.08 mmHg) , and triglycerides (-0.3 mmol/L), while RT has no effects demonstrable by meta-analysis on these

An overlook at the rapidly expanding body of knowledge in the field of molecular biology of muscle shows that either LI, HI, RT or IT are able to improve muscular function and to be helpful for the correction of metabolic disturbances (Burgomaster et al. 2008). It is however important to emphasize that RT and ET act on separate and antagonistic intracellular pathways (Koulmann & Bigard, 2006), hence they are independant tools that cannot be

considering that evidence for counseling is not sufficient (Rochester, 2003).

metabolic effects of exercise training targeted at this level.

parameters (Chudyk & Petrella, 2011).

expected to provide equivalent effects on muscle cells.

indicate a broad zone of % VO2max or heart rates assumed to be the most accurate.

lower weekly amounts of moderate-intensity exercise (between 150 and 250 min per week) is effective to prevent weight gain, but can provide only modest weight loss by their own. They can result in significant weight loss if associated to moderate diet restriction but, interestingly, not severe diet restriction.

The picture is thus slightly modified. Exercise appears nowadays as an effective means to reverse overweight, but its effects are shown to be very variable, sometimes impressive, but often poor. A major explanation for this heterogeneity is that exercise may induce marked compensatory changes in energy intake (King et al, 2008). Therefore exercise should be combined with a dietary approach based on correction of compensatory behaviors and errors (Bouchard et al, 1990; Caudwell, 2009). This approach is surely more logic than the traditional restriction which has some short-term efficiency but almost always result in a subsequent weight gain due to homeostatic mechanisms of fat mass preservation (MacLean, 2011).

What is the most important: duration or frequency? Chambliss (Chambliss, 2005) examined the effect of duration and frequency of exercise on weight loss and cardiorespiratory fitness in 201 previously sedentary, overweight women (Chambliss, 2005) over 12 months. He found a mean weight loss after 1 year was 8.9, 8.2, 6.3 and 7.0 kg, for the vigorous intensity/high duration, moderate intensity/high duration, moderate intensity/moderate duration, and vigorous intensity/moderate duration groups, respectively, but there was no effect of exercise duration or exercise intensity on changes in body weight or in BMI. Duration of exercise (at least 150 min/week in walking) was more important than vigorous versus moderate intensity in achieving these goals.

Most of the studies make little or no reference to the substrate (lipid or CHO) that is oxidized during exercise. However, there is a rationale to do so, as largely described above. Multiple studies have show that fatty acid handling and oxidation is impaired in skeletal muscle of obese, impaired glucose-tolerant, and T2D individuals (Blaak, 2004; Corpeleijn et al., 2008, 2009; Kelley et al., 1999; Mensink et al., 2001). This defect leads to propose exercise protocols aiming at restoring muscular ability to oxidize lipids. For this reason, LI protocols designed for oxidizing more lipids during exercise sessions were described by several authors (Blaak & Saris, 2002; Blaak, 2004) and were shown to improve both the ability to oxidize lipids and body composition (Schrauwen et al., 2002).

As shown on the meta-analysis of 12 LIPOXmax training studies, 3 or 4 weekly sessions of 45 min cycling at the LIPOXmax result in a weight loss of -2.25 % [confidence range -3.53 to - 0.97] which is at least as efficient as the various protocols studied in the literature (Romain et al., 2010). Therefore, LIPOXmax training is one of the strategies that can be proposed to reduce body weight in obese subjects. A comparison with other more classical protocols remains to be done.

The issue of the exercise protocol that should be recommended for weight maintenance remains incompletely studied. Cross sectional studies show that weight maintenance is improved with physical activity > 250 min per week. However, no evidence from welldesigned randomized controlled trials exists to judge the effectiveness of physical activity for the prevention of weight regain after weight loss (Donnelly et al., 2009). According to this consensus document, resistance training does not enhance weight loss but may increase fat-free mass and increase loss of fat mass and is associated with reductions in health risk. Existing evidence indicates that endurance PA or resistance training without weight loss improves health risk. There is no evidence that PA prevents or attenuates obesity-related detrimental changes (Donnelly et al., 2009).

## **5.2 Standardized vs personalized targeting?**

20 An International Perspective on Topics in Sports Medicine and Sports Injury

lower weekly amounts of moderate-intensity exercise (between 150 and 250 min per week) is effective to prevent weight gain, but can provide only modest weight loss by their own. They can result in significant weight loss if associated to moderate diet restriction but,

The picture is thus slightly modified. Exercise appears nowadays as an effective means to reverse overweight, but its effects are shown to be very variable, sometimes impressive, but often poor. A major explanation for this heterogeneity is that exercise may induce marked compensatory changes in energy intake (King et al, 2008). Therefore exercise should be combined with a dietary approach based on correction of compensatory behaviors and errors (Bouchard et al, 1990; Caudwell, 2009). This approach is surely more logic than the traditional restriction which has some short-term efficiency but almost always result in a subsequent

What is the most important: duration or frequency? Chambliss (Chambliss, 2005) examined the effect of duration and frequency of exercise on weight loss and cardiorespiratory fitness in 201 previously sedentary, overweight women (Chambliss, 2005) over 12 months. He found a mean weight loss after 1 year was 8.9, 8.2, 6.3 and 7.0 kg, for the vigorous intensity/high duration, moderate intensity/high duration, moderate intensity/moderate duration, and vigorous intensity/moderate duration groups, respectively, but there was no effect of exercise duration or exercise intensity on changes in body weight or in BMI. Duration of exercise (at least 150 min/week in walking) was more important than vigorous

Most of the studies make little or no reference to the substrate (lipid or CHO) that is oxidized during exercise. However, there is a rationale to do so, as largely described above. Multiple studies have show that fatty acid handling and oxidation is impaired in skeletal muscle of obese, impaired glucose-tolerant, and T2D individuals (Blaak, 2004; Corpeleijn et al., 2008, 2009; Kelley et al., 1999; Mensink et al., 2001). This defect leads to propose exercise protocols aiming at restoring muscular ability to oxidize lipids. For this reason, LI protocols designed for oxidizing more lipids during exercise sessions were described by several authors (Blaak & Saris, 2002; Blaak, 2004) and were shown to improve both the ability to

As shown on the meta-analysis of 12 LIPOXmax training studies, 3 or 4 weekly sessions of 45 min cycling at the LIPOXmax result in a weight loss of -2.25 % [confidence range -3.53 to - 0.97] which is at least as efficient as the various protocols studied in the literature (Romain et al., 2010). Therefore, LIPOXmax training is one of the strategies that can be proposed to reduce body weight in obese subjects. A comparison with other more classical protocols

The issue of the exercise protocol that should be recommended for weight maintenance remains incompletely studied. Cross sectional studies show that weight maintenance is improved with physical activity > 250 min per week. However, no evidence from welldesigned randomized controlled trials exists to judge the effectiveness of physical activity for the prevention of weight regain after weight loss (Donnelly et al., 2009). According to this consensus document, resistance training does not enhance weight loss but may increase fat-free mass and increase loss of fat mass and is associated with reductions in health risk. Existing evidence indicates that endurance PA or resistance training without weight loss improves health risk. There is no evidence that PA prevents or attenuates obesity-related

weight gain due to homeostatic mechanisms of fat mass preservation (MacLean, 2011).

interestingly, not severe diet restriction.

versus moderate intensity in achieving these goals.

oxidize lipids and body composition (Schrauwen et al., 2002).

remains to be done.

detrimental changes (Donnelly et al., 2009).

There is an important discussion that underlies all the controversies about exercise prescription in chronic diseases. This is: should we use standard or personalized exercise prescriptions. Some current rules of prescription emphasize the need for taking into account the personal characteristics of each patient, but they are by essence "standardized", ie, they do not take into account the specific physiologic profile of each subject. All is based on the assumption that the most important mechanism underlying the metabolic effect of exercise is to generate an energy deficit, regardless of the actual quantity of lipids or CHO that have been oxidized (Strasser et al, 2007).

Such a standardized approach was used in pneumology and cardiology, before it was challenged by a new paradigm: the "individualization concept". Personalized targeting of exercise has been promoted in respiratory and cardiac chronic diseases and was shown to provide better results (Vallet et al., 1997). The "Hippocratic" concept of superiority of the individualized approach is taken into account by a number of practitioners and appears in national guidelines (Guidelines, 2005). However, some guidelines do not mention it, considering that evidence for counseling is not sufficient (Rochester, 2003).

In metabolic diseases, such a discussion about the "individualization concept" has not yet been initiated. Usual French recommendations for exercise in diabetes (Gautier et al, 1998; Gautier, 2004) do not take into account the individual metabolic background. Authors only indicate a broad zone of % VO2max or heart rates assumed to be the most accurate.

Extending the individualization concept to obesity and diabetes raises the question of a specific individual target, and obviously the LIPOXmax/FATOXmax appears as a logic candidate for this purpose. Accordingly, several teams have undertaken the study of the metabolic effects of exercise training targeted at this level.

#### **5.3 Targeting endurance exercise close to the LIPOXmax vs higher intensity levels**

A topic which has generated a lot of discussion over the last decade is the selection of the optimal exercise protocol that could be used for the management of obesity and diabetes. Initially, low intensity endurance training (LI), as it was know to be the variety of exercise that oxidized the highest quantity of lipids, was logically proposed (Thompson et al., 1998). However, later focus was given on other kinds of exercise such high intensity endurance training (HI), resistance training (RT) and interval training (IT). All of them were evidenced to exert beneficial effects when applied to obese (Jakicic & Otto, 2005, 2006) and diabetic (Praet & van Loon, 2007, 2009) patients. On the whole, LI remains the easiest and the most widely evaluated procedure. It is also the best demonstrated as shown by a recent metaanalysis on exercise and diabetes that selected 34 from 645 papers. This paper confirmed that endurance exercise alone improves Hba1c (-0.6%), blood pressure, (-6.08 mmHg) , and triglycerides (-0.3 mmol/L), while RT has no effects demonstrable by meta-analysis on these parameters (Chudyk & Petrella, 2011).

An overlook at the rapidly expanding body of knowledge in the field of molecular biology of muscle shows that either LI, HI, RT or IT are able to improve muscular function and to be helpful for the correction of metabolic disturbances (Burgomaster et al. 2008). It is however important to emphasize that RT and ET act on separate and antagonistic intracellular pathways (Koulmann & Bigard, 2006), hence they are independant tools that cannot be expected to provide equivalent effects on muscle cells.

Measurement and Physiological Relevance of

oxidizes mostly carbohydrates.

(Hopkins et al, 2011).

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 23

A crucial issue is that the extent of fat loss in response to exercise training varies quite widely among individuals (Snyder et al., 1997; King et al., 2008; Byrne et al., 2006), even when differences in compliance to the exercise program and energy intake are accounted for. In other terms, exercise is very efficient to lose weight in some individuals while in others it fails to induce weight loss and even more may induce weight gain. Focus on the specific profile of responders and nonresponders helps to understand this variability. It

A role for fat oxidation is suggested by recent studies. In sedentary premenopausal women, a 7-week endurance-type exercise training program reaching progressively to 5 x 60 minutes per week at 65% -80% of predicted maximum heart rate resulted in a mean change in fat mass for the group was -0.97 kg (range +2.1 to -5.3 kg). The strongest correlate of change in fat mass was exercise energy expenditure, as expected. However, the change in fasting RER correlated significantly with the residual for change in fat mass after adjusting for the effects of both exercise energy expenditure and change in energy intake. This means that traininginduced increase in fat oxidation explains 7% of the variance of exercise-induced weight loss. In multiple regression analysis, exercise energy expenditure and change in fasting RER were the only statistically significant predictors of change in fat mass, together explaining 40.2% of the variance. Thus, fat loss in response to exercise training depends not only on exercise energy expenditure but also on exercise training-induced changes in RER at rest. Whether it is also the case for RER during exercise is suggested by recent studies (Lavault et al, 2011). This suggests that development of strategies to maximize exercise-induced fat loss

appears to be explained by two variables: eating behavior and fat oxidation.

may be useful for optimizing exercise-induced slimming (Barwell et al., 2009).

Another important issue in these exercise-based strategies would be to control changes in eating behavior (Bouchard et al, 1990). Muscular activity may induce a temporary postexercise anorexia, which is dose-dependent on the intensity and duration of the exercise (King et al, 1994; Westerterp-Plantenga et al, 1997). On the long term, the effect of exercise on eating behavior are complex and variable and largely explain the variability of weight loss responses. In fact, recent studies show that the effects of regular exercise on appetite regulation involves at least 2 processes: an increase in the overall (orexigenic) drive to eat and a concomitant increase in the satiating efficiency of a fixed meal (King et al, 2009). The former may be related to glycogen deficiency, which increases appetite (Melanson et al, 1999), so that exercise protocols that spare glycogen may avoid this increase in orexigenic drive (Hopkins et al, 2011). Exercise targeted at the LIPOXmax, due to its greater reliance on fat, is likely to spare more glycogen and thus to be less orexigenic than acute exercise that

Clearly, exercise may be an efficient and safe technique to lose fat as shown by the results obtained in responders. The study of non responders suggests that focus on the two parameters explaining the lower efficacy of exercise (fat oxidation and eating behavior) may help to improve the results. Targeting on lipid oxidation during exercise may be a way to better control these two parameters. More studies are needed to verify this concept

**5.4 LIPOX training improves mitochondrial respiration and enzymes of lipid oxidation**  Bordenave (Bordenave et al., 2008) described the effects of ten weeks of mild exercise training targeted at the LIPOXmax (45 min of cycling, twice a week). This training was not sufficient to significantly decrease weight, but it exhibited marked effects on whole body

At the cellular level (Koulmann & Bigard, 2006) endurance training induces a set of regulatory adaptations that improve mitochondrial function and protein synthesis and overall, the enzymes for both CHO and fat oxidation are increased. As a result of these cellular adaptations, exercise training improves whole body lipid metabolism and carbohydrate tolerance, thus consisting in a fully recognized tool for reducing both blood lipids and glucose (see below).

As reminded above, most exercise physiology papers describe exercise protocols applied at a given percentage of maximal aerobic capacity without reference to exercise calorimetry. When exercise is performed at 40% VO2max or below, it is likely to be performed in the LIPOXmax zone, but, since LIPOXmax is frequently much lower in obese subjects, a significant percentage of subjects are likely to exercise above this zone, in the range where lipid oxidation is close to zero and CHO almost becomes the exclusive energy source.

Therefore, LIPOX training represents a better defined exercise protocol, whose effects on lipid oxidation are predictable. Exercise performed above this zone results in more CHO oxidation. This CHO oxidation may be followed by some degree of fat oxidation after exercise but may frequently fail to induce this lipid oxidation rise. Although the energetic balance is assumed to result in both cases in a negative fat balance, these two types of exercise do not involve the same energetic pathways.

At the date of redaction of this article, there are several studies (or abstracts) published in peer-reviewed journals reporting the results of LIPOX training (table 4). Therefore, these effects of LIPOX training can now be well described on the basis of a recent meta-analysis (Romain et al., 2010) that included 16 studies shown in table 1, *ie*, 247 participants belonging to 5 different populations: obese teenagers, metabolic syndrome, HIV patients with lipodistrophy, type-2 diabetics, and psychiatric patients treated by neuroleptics. Study length ranged between 2 to 12 months. Weekly frequency of sessions ranged between 2 and 4. Preliminary results showed that LIPOXmax was shifted to a higher power intensity by 4.93 watts (95% confidence interval (CI) 4.74-5.13; p<0.0001). Weight decreased by -2.9 kg (95%CI: -4.1; -1.7; p<0.0001). Fat mass decreased by 1.7% (95% CI 1.82 – 1.64; p<0.0001), and waist circumference decreased by -4.9 cm [95%CI: -6.6; -3.2] (p<0.0001).

We have not included in this meta-analysis an interesting study by the team of A. Sartorio (Lazzer et al, 2011) that compared over three weeks energy-matched programs of low intensity (40% VO2max) and high intensity (60% VO2max) endurance, and showed that the exercise in the LIPOX zone was twice more efficient for fat loss. This protocol was not exactly targeted on the LIPOXmax but designed to train the subjects in this zone, and its results are in agreement with those pooled in the meta-analysis.

The results of these studies demonstrate the efficiency of training targeted at the LIPOXmax on weight loss, even over a short time period. In diabetics, HIV-infected patients, and psychiatric patients under neuroleptics, the efficiency of this procedure seems to be lower than in obesity or metabolic syndrome. As expected, the association with a diet improves the efficiency of this training. However, two thirds of the studies were without added diet and thus most of the weight-lowering effects of LIPOX training are likely to be due to the effects of exercise alone on energy balance and eating behavior. Therefore, it is clear that LIPOX training alone decreases body fat, even if no specific diet is applied. This effect is clearly and constantly evidenced in training protocols containing as few as 2 or 3 sessions per week. In fact, the number of sessions per week seems to improve the results and a dose-relationship can be postulated. However, this issue remains to be specifically investigated.

At the cellular level (Koulmann & Bigard, 2006) endurance training induces a set of regulatory adaptations that improve mitochondrial function and protein synthesis and overall, the enzymes for both CHO and fat oxidation are increased. As a result of these cellular adaptations, exercise training improves whole body lipid metabolism and carbohydrate tolerance, thus consisting in a fully recognized tool for reducing both blood

As reminded above, most exercise physiology papers describe exercise protocols applied at a given percentage of maximal aerobic capacity without reference to exercise calorimetry. When exercise is performed at 40% VO2max or below, it is likely to be performed in the LIPOXmax zone, but, since LIPOXmax is frequently much lower in obese subjects, a significant percentage of subjects are likely to exercise above this zone, in the range where lipid oxidation is close to zero and CHO almost becomes the exclusive energy source. Therefore, LIPOX training represents a better defined exercise protocol, whose effects on lipid oxidation are predictable. Exercise performed above this zone results in more CHO oxidation. This CHO oxidation may be followed by some degree of fat oxidation after exercise but may frequently fail to induce this lipid oxidation rise. Although the energetic balance is assumed to result in both cases in a negative fat balance, these two types of

At the date of redaction of this article, there are several studies (or abstracts) published in peer-reviewed journals reporting the results of LIPOX training (table 4). Therefore, these effects of LIPOX training can now be well described on the basis of a recent meta-analysis (Romain et al., 2010) that included 16 studies shown in table 1, *ie*, 247 participants belonging to 5 different populations: obese teenagers, metabolic syndrome, HIV patients with lipodistrophy, type-2 diabetics, and psychiatric patients treated by neuroleptics. Study length ranged between 2 to 12 months. Weekly frequency of sessions ranged between 2 and 4. Preliminary results showed that LIPOXmax was shifted to a higher power intensity by 4.93 watts (95% confidence interval (CI) 4.74-5.13; p<0.0001). Weight decreased by -2.9 kg (95%CI: -4.1; -1.7; p<0.0001). Fat mass decreased by 1.7% (95% CI 1.82 – 1.64; p<0.0001), and

We have not included in this meta-analysis an interesting study by the team of A. Sartorio (Lazzer et al, 2011) that compared over three weeks energy-matched programs of low intensity (40% VO2max) and high intensity (60% VO2max) endurance, and showed that the exercise in the LIPOX zone was twice more efficient for fat loss. This protocol was not exactly targeted on the LIPOXmax but designed to train the subjects in this zone, and its

The results of these studies demonstrate the efficiency of training targeted at the LIPOXmax on weight loss, even over a short time period. In diabetics, HIV-infected patients, and psychiatric patients under neuroleptics, the efficiency of this procedure seems to be lower than in obesity or metabolic syndrome. As expected, the association with a diet improves the efficiency of this training. However, two thirds of the studies were without added diet and thus most of the weight-lowering effects of LIPOX training are likely to be due to the effects of exercise alone on energy balance and eating behavior. Therefore, it is clear that LIPOX training alone decreases body fat, even if no specific diet is applied. This effect is clearly and constantly evidenced in training protocols containing as few as 2 or 3 sessions per week. In fact, the number of sessions per week seems to improve the results and a dose-relationship can be postulated. However,

lipids and glucose (see below).

exercise do not involve the same energetic pathways.

waist circumference decreased by -4.9 cm [95%CI: -6.6; -3.2] (p<0.0001).

results are in agreement with those pooled in the meta-analysis.

this issue remains to be specifically investigated.

A crucial issue is that the extent of fat loss in response to exercise training varies quite widely among individuals (Snyder et al., 1997; King et al., 2008; Byrne et al., 2006), even when differences in compliance to the exercise program and energy intake are accounted for. In other terms, exercise is very efficient to lose weight in some individuals while in others it fails to induce weight loss and even more may induce weight gain. Focus on the specific profile of responders and nonresponders helps to understand this variability. It appears to be explained by two variables: eating behavior and fat oxidation.

A role for fat oxidation is suggested by recent studies. In sedentary premenopausal women, a 7-week endurance-type exercise training program reaching progressively to 5 x 60 minutes per week at 65% -80% of predicted maximum heart rate resulted in a mean change in fat mass for the group was -0.97 kg (range +2.1 to -5.3 kg). The strongest correlate of change in fat mass was exercise energy expenditure, as expected. However, the change in fasting RER correlated significantly with the residual for change in fat mass after adjusting for the effects of both exercise energy expenditure and change in energy intake. This means that traininginduced increase in fat oxidation explains 7% of the variance of exercise-induced weight loss. In multiple regression analysis, exercise energy expenditure and change in fasting RER were the only statistically significant predictors of change in fat mass, together explaining 40.2% of the variance. Thus, fat loss in response to exercise training depends not only on exercise energy expenditure but also on exercise training-induced changes in RER at rest. Whether it is also the case for RER during exercise is suggested by recent studies (Lavault et al, 2011). This suggests that development of strategies to maximize exercise-induced fat loss may be useful for optimizing exercise-induced slimming (Barwell et al., 2009).

Another important issue in these exercise-based strategies would be to control changes in eating behavior (Bouchard et al, 1990). Muscular activity may induce a temporary postexercise anorexia, which is dose-dependent on the intensity and duration of the exercise (King et al, 1994; Westerterp-Plantenga et al, 1997). On the long term, the effect of exercise on eating behavior are complex and variable and largely explain the variability of weight loss responses. In fact, recent studies show that the effects of regular exercise on appetite regulation involves at least 2 processes: an increase in the overall (orexigenic) drive to eat and a concomitant increase in the satiating efficiency of a fixed meal (King et al, 2009). The former may be related to glycogen deficiency, which increases appetite (Melanson et al, 1999), so that exercise protocols that spare glycogen may avoid this increase in orexigenic drive (Hopkins et al, 2011). Exercise targeted at the LIPOXmax, due to its greater reliance on fat, is likely to spare more glycogen and thus to be less orexigenic than acute exercise that oxidizes mostly carbohydrates.

Clearly, exercise may be an efficient and safe technique to lose fat as shown by the results obtained in responders. The study of non responders suggests that focus on the two parameters explaining the lower efficacy of exercise (fat oxidation and eating behavior) may help to improve the results. Targeting on lipid oxidation during exercise may be a way to better control these two parameters. More studies are needed to verify this concept (Hopkins et al, 2011).

#### **5.4 LIPOX training improves mitochondrial respiration and enzymes of lipid oxidation**

Bordenave (Bordenave et al., 2008) described the effects of ten weeks of mild exercise training targeted at the LIPOXmax (45 min of cycling, twice a week). This training was not sufficient to significantly decrease weight, but it exhibited marked effects on whole body

Measurement and Physiological Relevance of

**5.7 LIPOXmax training maintains fat-free mass** 

situations such as anorexia nervosa are in progress.

tools and have distinct properties.

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 25

All the aforementioned studies show that LIPOXmax training maintains fat-free mass, and in some of them an increase is found. This is a constant finding, while protocols using higher intensities in patients give less consistent result on fat-free mass (Brandou et al., 2005; Brun et al., 2010a). Obviously, it is well demonstrated that correctly performed resistance training is an efficient way to improve fat free mass (Schoenfeld et al., 2010), but low intensity exercise has also been demonstrated to protect lean mass and to prevent protein breakdown. A 45-min walk on a treadmill at 40% VO2 peak induced short-term increases in muscle and plasma

In a study on dieting postmenopausal women with a total energy expenditure of 700 kcal/wk, *ie*, 8 kcal.kg body weight -1wk-1 with LI being at 45-50% if heart rate reserve (HRR) and HI at 70-75% (vigourous-intensity) of HRR (Nicklas et al., 2009) found that with a similar amount of total weight loss, lean mass is preserved with either moderate- or vigorous-intensity aerobic exercise performed during caloric restriction and concluded that FFM is equally preserved with LI and HI. Both resistance (RE) and endurance (EE) exercise are able to stimulate mixed skeletal muscle protein synthesis, but the phenotypes induced by RE (myofibrillar protein accretion) and EE (mitochondrial expression) training are different and this is probably due to differential stimulation of myofibrillar and mitochondrial protein synthesis (Koulmann et al, 2006; Wilkinson et al., 2008; Harber et al., 2009). A mechanism that may play a role in this protective effect of LI is glycogen sparing. It has been shown that CHO availability influences the rates of skeletal muscle and whole body protein synthesis, degradation and net balance during prolonged exercise in humans (Howarth et al., 2010). On the whole, although more investigation is required, LIPOXmax training is an efficient way to maintain or even to improve fat-free mass by increasing the mass of metabolically active muscle. At the beginning of training protocols in very sedentary patients, it may be used for this purpose. For example, studies in undernutrition

protein synthesis in both younger and older men (Sheffield-Moore et al., 2004).

**5.8 Comparison between high intensity (HI) and low intensity (LI) training** 

It is clear that during LI even more if it is targeted at the LIPOXmax quite important quantities of lipids are oxidized. By contrast, HI oxidizes mostly or exclusively CHO. HI is sometimes followed by a slight rise in oxidation of lipids, but this quantity of lipids oxidized postexercise remains low (Chenevière et al., 2009a; Warren et al., 2009) even more when HI is not continuous (interval training [INT]). Therefore, LI and HI or INT are not equivalent

This is clearly evidenced in a recent study comparing INT and LIPOX training in T2D (Brun et al., 2010a). 63 type-2 diabetics were compared over a period of 3 months without nutritional intervention: 39 were trained at the LIPOXmax determined with exercise calorimetry, 12 were submitted to a square wave endurance exercise-test (SWEET) protocol of training, and 12 untrained patients served as controls. After 3 months, both training procedures increased VO2max (SWEET training +42% vs LIPOXmax training +14%) and this effect was stronger with SWEET than with LIPOXmax. SWEET training reduced resting systolic blood pressure (-12mmHg) and total cholesterol (-0.29 mmol/l), while LIPOXmax training did not. Both procedures decreased weight and BMI. As expected, the LIPOXmax training improved the ability to oxidize lipids (maximum lipid oxidation rate +53 mg/min) shifted to a higher power intensity (+21 watts), decreased fat mass (-1 kg), increased fat-free

lipid oxidation and muscle oxidative capacities. Indeed, after training, the LIPOXmax was shifted to higher power intensity and the MFO was significantly increased compared with pre-training values (+51 mg.min-1). This study included biopsies and evidenced LIPOX training-induced improvements in mitochondrial respiration and citrate synthase activity. Changes in whole body lipid oxidation were associated with changes in parameters of muscle oxidation. In another study the 3-Hydroxyacyl-CoA dehydrogenase (HAD), an important enzyme that functions in mitochondrial fatty acid beta-oxidation by catalyzing the oxidation of straight chain 3-hydroxyacyl-CoAs, was studied in trained and untrained women. It was shown that HAD activity and fat oxidation rates were highly correlated indicating that training-induced adaptation in muscle fat oxidative capacity is an important factor for enhanced fat oxidation (Stisen et al., 2006).

#### **5.5 Does LIPOX training increase REE and resting fat oxidation?**

There is no study on the effects of LIPOX training on resting fat oxidation. Endurance training at higher levels yields conflicting results and it is likely that this effect is not constant with endurance protocols. A study by Van Aggel-Leijssen (Van Aggel-Leijssen et al., 2001) using a low-intensity exercise training program (40% VO2max, ie the LIPOXmax zone) three times per week for 12 weeks showed that this variety of training increased the contribution of fat oxidation to total energy expenditure during exercise but failed to do so at rest in obese women (Van Aggel-Leijssen et al., 2001). However, another study may suggest an effect of endurance training in the LIPOXmax zone or below on resting fat oxidation. A very mild exercise consisting in an increase of regular physical activity equivalent to 45 min of walking 3 days/week induces some improvements in lipid metabolism such as an increase in skeletal muscle protein expression of PPARdelta and UCP3 in type 2 diabetic patients (Fritz et al., 2006), and improves lipid oxidation without changes in mitochondrial function in type 2 diabetes (Trenell et al., 2008). This protocol of walking an extra 45 min per day over an 8-week period is an insufficient stimulus to induce detectable mitochondrial biogenesis but demonstrates physical activity-induced enhancement of resting lipid oxidation, independently of intramuscular lipid levels. A specific study on LIPOXmax training and lipid oxidation at rest in obese and nonobese people remains to be done.

#### **5.6 Low intensity training targeted at the LIPOXmax training improves inflammatory status**

Low-grade systemic inflammation is suggested to play a role in the development of a variety of chronic diseases including obesity, diabetes and cancer. A number of studies suggest that in these diseases regular exercise has anti-inflammatory effects therefore it may contribute to suppress systemic low-grade inflammation (Mattusch et al., 2000; Stewart et al., 2007; Goldhammer, et al., 2005). Overall, both endurance and resistance training decrease C-Reactive Protein (CRP) (Martins et al., 2010). In two studies, LIPOXmax training has been shown to decrease CRP (Ben Ounis et al., 2010; Brun et al, 2011b). In these studies, inflammatory parameters were also measured and changes in CRP were negatively related to those of lipid oxidation during exercise, suggesting that the improvement in the ability to oxidize lipids during exercise is associated with an anti-inflammatory effect. Further studies are needed.

#### **5.7 LIPOXmax training maintains fat-free mass**

24 An International Perspective on Topics in Sports Medicine and Sports Injury

lipid oxidation and muscle oxidative capacities. Indeed, after training, the LIPOXmax was shifted to higher power intensity and the MFO was significantly increased compared with pre-training values (+51 mg.min-1). This study included biopsies and evidenced LIPOX training-induced improvements in mitochondrial respiration and citrate synthase activity. Changes in whole body lipid oxidation were associated with changes in parameters of muscle oxidation. In another study the 3-Hydroxyacyl-CoA dehydrogenase (HAD), an important enzyme that functions in mitochondrial fatty acid beta-oxidation by catalyzing the oxidation of straight chain 3-hydroxyacyl-CoAs, was studied in trained and untrained women. It was shown that HAD activity and fat oxidation rates were highly correlated indicating that training-induced adaptation in muscle fat oxidative capacity is an important

There is no study on the effects of LIPOX training on resting fat oxidation. Endurance training at higher levels yields conflicting results and it is likely that this effect is not constant with endurance protocols. A study by Van Aggel-Leijssen (Van Aggel-Leijssen et al., 2001) using a low-intensity exercise training program (40% VO2max, ie the LIPOXmax zone) three times per week for 12 weeks showed that this variety of training increased the contribution of fat oxidation to total energy expenditure during exercise but failed to do so at rest in obese women (Van Aggel-Leijssen et al., 2001). However, another study may suggest an effect of endurance training in the LIPOXmax zone or below on resting fat oxidation. A very mild exercise consisting in an increase of regular physical activity equivalent to 45 min of walking 3 days/week induces some improvements in lipid metabolism such as an increase in skeletal muscle protein expression of PPARdelta and UCP3 in type 2 diabetic patients (Fritz et al., 2006), and improves lipid oxidation without changes in mitochondrial function in type 2 diabetes (Trenell et al., 2008). This protocol of walking an extra 45 min per day over an 8-week period is an insufficient stimulus to induce detectable mitochondrial biogenesis but demonstrates physical activity-induced enhancement of resting lipid oxidation, independently of intramuscular lipid levels. A specific study on LIPOXmax training and lipid oxidation at rest in obese and nonobese

**5.6 Low intensity training targeted at the LIPOXmax training improves inflammatory** 

Low-grade systemic inflammation is suggested to play a role in the development of a variety of chronic diseases including obesity, diabetes and cancer. A number of studies suggest that in these diseases regular exercise has anti-inflammatory effects therefore it may contribute to suppress systemic low-grade inflammation (Mattusch et al., 2000; Stewart et al., 2007; Goldhammer, et al., 2005). Overall, both endurance and resistance training decrease C-Reactive Protein (CRP) (Martins et al., 2010). In two studies, LIPOXmax training has been shown to decrease CRP (Ben Ounis et al., 2010; Brun et al, 2011b). In these studies, inflammatory parameters were also measured and changes in CRP were negatively related to those of lipid oxidation during exercise, suggesting that the improvement in the ability to oxidize lipids during exercise is associated with an anti-inflammatory effect. Further studies

factor for enhanced fat oxidation (Stisen et al., 2006).

people remains to be done.

**status** 

are needed.

**5.5 Does LIPOX training increase REE and resting fat oxidation?** 

All the aforementioned studies show that LIPOXmax training maintains fat-free mass, and in some of them an increase is found. This is a constant finding, while protocols using higher intensities in patients give less consistent result on fat-free mass (Brandou et al., 2005; Brun et al., 2010a). Obviously, it is well demonstrated that correctly performed resistance training is an efficient way to improve fat free mass (Schoenfeld et al., 2010), but low intensity exercise has also been demonstrated to protect lean mass and to prevent protein breakdown. A 45-min walk on a treadmill at 40% VO2 peak induced short-term increases in muscle and plasma protein synthesis in both younger and older men (Sheffield-Moore et al., 2004).

In a study on dieting postmenopausal women with a total energy expenditure of 700 kcal/wk, *ie*, 8 kcal.kg body weight -1wk-1 with LI being at 45-50% if heart rate reserve (HRR) and HI at 70-75% (vigourous-intensity) of HRR (Nicklas et al., 2009) found that with a similar amount of total weight loss, lean mass is preserved with either moderate- or vigorous-intensity aerobic exercise performed during caloric restriction and concluded that FFM is equally preserved with LI and HI. Both resistance (RE) and endurance (EE) exercise are able to stimulate mixed skeletal muscle protein synthesis, but the phenotypes induced by RE (myofibrillar protein accretion) and EE (mitochondrial expression) training are different and this is probably due to differential stimulation of myofibrillar and mitochondrial protein synthesis (Koulmann et al, 2006; Wilkinson et al., 2008; Harber et al., 2009). A mechanism that may play a role in this protective effect of LI is glycogen sparing. It has been shown that CHO availability influences the rates of skeletal muscle and whole body protein synthesis, degradation and net balance during prolonged exercise in humans (Howarth et al., 2010). On the whole, although more investigation is required, LIPOXmax training is an efficient way to maintain or even to improve fat-free mass by increasing the mass of metabolically active muscle. At the beginning of training protocols in very sedentary patients, it may be used for this purpose. For example, studies in undernutrition situations such as anorexia nervosa are in progress.

#### **5.8 Comparison between high intensity (HI) and low intensity (LI) training**

It is clear that during LI even more if it is targeted at the LIPOXmax quite important quantities of lipids are oxidized. By contrast, HI oxidizes mostly or exclusively CHO. HI is sometimes followed by a slight rise in oxidation of lipids, but this quantity of lipids oxidized postexercise remains low (Chenevière et al., 2009a; Warren et al., 2009) even more when HI is not continuous (interval training [INT]). Therefore, LI and HI or INT are not equivalent tools and have distinct properties.

This is clearly evidenced in a recent study comparing INT and LIPOX training in T2D (Brun et al., 2010a). 63 type-2 diabetics were compared over a period of 3 months without nutritional intervention: 39 were trained at the LIPOXmax determined with exercise calorimetry, 12 were submitted to a square wave endurance exercise-test (SWEET) protocol of training, and 12 untrained patients served as controls. After 3 months, both training procedures increased VO2max (SWEET training +42% vs LIPOXmax training +14%) and this effect was stronger with SWEET than with LIPOXmax. SWEET training reduced resting systolic blood pressure (-12mmHg) and total cholesterol (-0.29 mmol/l), while LIPOXmax training did not. Both procedures decreased weight and BMI. As expected, the LIPOXmax training improved the ability to oxidize lipids (maximum lipid oxidation rate +53 mg/min) shifted to a higher power intensity (+21 watts), decreased fat mass (-1 kg), increased fat-free


Table 5. Studies of training at the LIPOXmax/FATmax

Measurement and Physiological Relevance of

but not in the SWEET group.

pressure and increased VO2max.

the Maximal Lipid Oxidation Rate During Exercise (LIPOXmax) 27

mass (+1 kg), decreased waist circumference (-3.8 cm) and hip circumference (-2.2 cm) while SWEET training did not significantly modify any of those parameters. Over this short period, the effects of training on HbA1c were significant in the LIPOXmax group (-0.15%)

Roffey (Roffey, 2008) compared, in a randomized experiment, supervised cycling training at a constant-load FATmax intensity with high intensity interval training (HIIT) with intervals at 85% VO2max, both protocols being matched for total mechanical work volume (11250 kCal). Although both procedures reduced fat mass, the effect was twice more important in FATmax trained subjects than HIIT. A decrease in waist circumference and total cholesterol was evidenced with FATmax but not HIIT. Both procedures decreased systolic blood

Put together, these studies show that interval training improves aerobic working capacity, blood pressure and lipid profile, while low intensity endurance training (LIPOXmax training) improves the ability to oxidize lipids during exercise, increases fat free mass, decreases fat mass and decreases HbA1c. The benefits of these two procedures are thus quite different and

The psychological tolerability of LIPOXmax training, and more generally low intensity endurance training, compared to high intensity training is poorly known. There is no specific study about the psychological tolerance of LIPOXmax training but some information exists about the effects of prescribing moderate vs higher levels of intensity and frequency on adherence to exercise prescriptions (Perri et al., 2002). In 376 sedentary adults randomly assigned to walk 30 min per day at a frequency of either 3-4 or 5-7 days per week, at an intensity of either 45-55% or 65-75% of maximum heart rate reserve, analyses of percentage of prescribed exercise completed showed greater adherence in the moderate intensity condition. The authors concluded that prescribing a lower frequency increased the accumulation of exercise without a decline in adherence, whereas prescribing a higher

Interestingly, Roffey (Roffey, 2008) in his randomized work comparing FATmax training and HIIT, observed a number of clinically significant improvements in health-related

Both endurance as well as resistance-type training have been shown to improve whole body insulin sensitivity and/or glucose tolerance and are of therapeutic use in diabetic and insulin-resistant subjects (Praet et al., 2007). Prolonged endurance-type exercise training has been shown to improve insulin sensitivity in both young, elderly and/or insulin-resistant subjects, due to the concomitant induction of weight loss, the upregulation of skeletal muscle glucose transporters GLUT4 expression, improved nitric oxide-mediated skeletal muscle blood flow, reduced hormonal stimulation of hepatic glucose production, and the

Long-term resistance-type exercise interventions have also been reported to improve glucose tolerance and/or whole body insulin sensitivity. Other than the consecutive effects of each successive bout of exercise, resistance-type exercise training has been associated with a substantial gain in skeletal muscle mass, assumed improve whole body glucosedisposal capacity on the basis on the undemonstrated belief that the higher fat free mass, the

both are probably interesting to associate in the management of 2 type 2 diabetes.

intensity decreased adherence and resulted in the completion of less exercise.

quality of life in the FATmax but not the HIIT group.

**5.9 Which exercise for diabetes?** 

normalization of blood lipid profiles.

higher insulin sensitivity.

Table 5. Studies of training at the LIPOXmax/FATmax

mass (+1 kg), decreased waist circumference (-3.8 cm) and hip circumference (-2.2 cm) while SWEET training did not significantly modify any of those parameters. Over this short period, the effects of training on HbA1c were significant in the LIPOXmax group (-0.15%) but not in the SWEET group.

Roffey (Roffey, 2008) compared, in a randomized experiment, supervised cycling training at a constant-load FATmax intensity with high intensity interval training (HIIT) with intervals at 85% VO2max, both protocols being matched for total mechanical work volume (11250 kCal). Although both procedures reduced fat mass, the effect was twice more important in FATmax trained subjects than HIIT. A decrease in waist circumference and total cholesterol was evidenced with FATmax but not HIIT. Both procedures decreased systolic blood pressure and increased VO2max.

Put together, these studies show that interval training improves aerobic working capacity, blood pressure and lipid profile, while low intensity endurance training (LIPOXmax training) improves the ability to oxidize lipids during exercise, increases fat free mass, decreases fat mass and decreases HbA1c. The benefits of these two procedures are thus quite different and both are probably interesting to associate in the management of 2 type 2 diabetes.

The psychological tolerability of LIPOXmax training, and more generally low intensity endurance training, compared to high intensity training is poorly known. There is no specific study about the psychological tolerance of LIPOXmax training but some information exists about the effects of prescribing moderate vs higher levels of intensity and frequency on adherence to exercise prescriptions (Perri et al., 2002). In 376 sedentary adults randomly assigned to walk 30 min per day at a frequency of either 3-4 or 5-7 days per week, at an intensity of either 45-55% or 65-75% of maximum heart rate reserve, analyses of percentage of prescribed exercise completed showed greater adherence in the moderate intensity condition. The authors concluded that prescribing a lower frequency increased the accumulation of exercise without a decline in adherence, whereas prescribing a higher intensity decreased adherence and resulted in the completion of less exercise.

Interestingly, Roffey (Roffey, 2008) in his randomized work comparing FATmax training and HIIT, observed a number of clinically significant improvements in health-related quality of life in the FATmax but not the HIIT group.

## **5.9 Which exercise for diabetes?**

Both endurance as well as resistance-type training have been shown to improve whole body insulin sensitivity and/or glucose tolerance and are of therapeutic use in diabetic and insulin-resistant subjects (Praet et al., 2007). Prolonged endurance-type exercise training has been shown to improve insulin sensitivity in both young, elderly and/or insulin-resistant subjects, due to the concomitant induction of weight loss, the upregulation of skeletal muscle glucose transporters GLUT4 expression, improved nitric oxide-mediated skeletal muscle blood flow, reduced hormonal stimulation of hepatic glucose production, and the normalization of blood lipid profiles.

Long-term resistance-type exercise interventions have also been reported to improve glucose tolerance and/or whole body insulin sensitivity. Other than the consecutive effects of each successive bout of exercise, resistance-type exercise training has been associated with a substantial gain in skeletal muscle mass, assumed improve whole body glucosedisposal capacity on the basis on the undemonstrated belief that the higher fat free mass, the higher insulin sensitivity.

Measurement and Physiological Relevance of

Int J Sports Med, 24, 603-608.

*Nutrition*, Vol. 20, pp. 716-727.

*Sci Sport*, Vol. 17, pp. 315-317.

*Physiol*, Vol. 105, pp. 325-331.

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While a recent meta-analysis concluded that the effects of both procedures on glucose homeostasis were similar, achieving a reduction in HbA1c by 0.6 to 0.8 (Snowling & Hopkins, 2006; Praet & Van Loon, 2009), a more recent one, after rigourous selection of papers for their methodology, concluded that endurance exercise alone improves Hba1c, blood pressure, and triglycerides, while RT has no demonstrable effects (Chudyk & Petrella, 2011). This issue remains thus controversial, and clearly the best demonstrated method remains endurance training.

When applying endurance-type exercise, energy expenditure should be equivalent to 1.7-2.1 MJ (400-500 kcal) per exercise bout on 3 but preferably 4-5 days/wk, since many of the benefits of exercise are temporary. More vigorous exercise in uncomplicated insulinresistant states will further improve glycemic control and enhance cardiorespiratory fitness and microvascular function.

Endurance-type exercise combined with resistance (ie, intermittent intensity-type exercise) forms a lower cardiovascular challenge and improves functional performance capacity to a similar extent. Therefore, the combination of endurance- and resistance-type exercise is generally recommended, since its increases the diversity and, as such, the adherence to the exercise intervention program. This is in agreement with the above-reported study in which we compared INT and LIPOX training in T2D (Brun et al., 2010a) and which evidenced that SWEET training improves aerobic working capacity, blood pressure and lipid profile, while low intensity endurance training (LIPOXmax training) improves the ability to oxidize lipids at exercise, increases fat free mass, decreases fat mass and decreases HbA1c. This study shows that benefits of two procedures are rather different and both are probably interesting to associate in the management of type 2 diabetes. A study comparing LIPOX training to more traditional protocols is currently in progress.

Another important advance in this issue comes from a meta-analysis in diabetes (Umpierre et al, 2011) which shows that structured exercise is much more efficient for improving HbA1c than exercise advice alone, and that more than 150 minutes of endurance per week is twice more efficient than exercise performed less than 150 minutes per week.

## **6. Conclusions**

LIPOXmax/Fatmax is a parameter that can be measured with validated protocols. It appears to be a reproducible measurement, although modifiable by several physiological conditions (training, previous exercise or meal). Its measurement closely predicts the amount of lipids that will be oxidized over a 45-60 min of low to medium intensity training performed at the corresponding intensity. It might be a marker of metabolic fitness, and reflects mitochondrial respiration. LIPOXmax is related to catecholamine status and the growth-hormone IGF-1 axis. Its changes are related to alterations in muscular levels of citrate synthase, and to the mitochondrial ability to oxidize lipids during exercise, reducing blood pressure and HbA1c in type 2 diabetes and decreasing circulating cholesterol. Whether the specific targeting on lipid oxidation during exercise has beneficial effects superior to those obtained by a similar energy deficit obtained by other protocols of exercise is suggested by recent studies but remains a current matter of research.

Little is known about the usefulness of these parameters in sports, but classification of athletes according to their metabolic profile during exercise may help to understand their ability to perform endurance sports or short term all of out exercise, and to detect overtraining-related alterations in metabolic adaptation to exercise.

## **7. References**

28 An International Perspective on Topics in Sports Medicine and Sports Injury

While a recent meta-analysis concluded that the effects of both procedures on glucose homeostasis were similar, achieving a reduction in HbA1c by 0.6 to 0.8 (Snowling & Hopkins, 2006; Praet & Van Loon, 2009), a more recent one, after rigourous selection of papers for their methodology, concluded that endurance exercise alone improves Hba1c, blood pressure, and triglycerides, while RT has no demonstrable effects (Chudyk & Petrella, 2011). This issue remains thus controversial, and clearly the best demonstrated method

When applying endurance-type exercise, energy expenditure should be equivalent to 1.7-2.1 MJ (400-500 kcal) per exercise bout on 3 but preferably 4-5 days/wk, since many of the benefits of exercise are temporary. More vigorous exercise in uncomplicated insulinresistant states will further improve glycemic control and enhance cardiorespiratory fitness

Endurance-type exercise combined with resistance (ie, intermittent intensity-type exercise) forms a lower cardiovascular challenge and improves functional performance capacity to a similar extent. Therefore, the combination of endurance- and resistance-type exercise is generally recommended, since its increases the diversity and, as such, the adherence to the exercise intervention program. This is in agreement with the above-reported study in which we compared INT and LIPOX training in T2D (Brun et al., 2010a) and which evidenced that SWEET training improves aerobic working capacity, blood pressure and lipid profile, while low intensity endurance training (LIPOXmax training) improves the ability to oxidize lipids at exercise, increases fat free mass, decreases fat mass and decreases HbA1c. This study shows that benefits of two procedures are rather different and both are probably interesting to associate in the management of type 2 diabetes. A study comparing LIPOX training to

Another important advance in this issue comes from a meta-analysis in diabetes (Umpierre et al, 2011) which shows that structured exercise is much more efficient for improving HbA1c than exercise advice alone, and that more than 150 minutes of endurance per week is

LIPOXmax/Fatmax is a parameter that can be measured with validated protocols. It appears to be a reproducible measurement, although modifiable by several physiological conditions (training, previous exercise or meal). Its measurement closely predicts the amount of lipids that will be oxidized over a 45-60 min of low to medium intensity training performed at the corresponding intensity. It might be a marker of metabolic fitness, and reflects mitochondrial respiration. LIPOXmax is related to catecholamine status and the growth-hormone IGF-1 axis. Its changes are related to alterations in muscular levels of citrate synthase, and to the mitochondrial ability to oxidize lipids during exercise, reducing blood pressure and HbA1c in type 2 diabetes and decreasing circulating cholesterol. Whether the specific targeting on lipid oxidation during exercise has beneficial effects superior to those obtained by a similar energy deficit obtained by other protocols of exercise

Little is known about the usefulness of these parameters in sports, but classification of athletes according to their metabolic profile during exercise may help to understand their ability to perform endurance sports or short term all of out exercise, and to detect

twice more efficient than exercise performed less than 150 minutes per week.

is suggested by recent studies but remains a current matter of research.

overtraining-related alterations in metabolic adaptation to exercise.

remains endurance training.

and microvascular function.

**6. Conclusions** 

more traditional protocols is currently in progress.


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**2** 

*Brazil* 

**Glutamine and Glutamate Reference** 

Rodrigo Hohl1, Lázaro Alessandro Soares Nunes1,

Foued Salmen Spindola2 and Denise Vaz Macedo1 *1Laboratory of Exercise Biochemistry (LABEX), Biology Institute,* 

*2Laboratory of Biochemistry and Molecular Biology (LABIBI),* 

*University State of Campinas (UNICAMP),* 

*Federal University of Uberlândia (UFU)* 

**Intervals as a Clinical Tool to Detect Training Intolerance During Training and Overtraining** 

Rafael Alkmin Reis1, René Brenzikofer1, Rodrigo Perroni Ferraresso1,

A training process consists of a sum of repeated exercise sessions with gradual overloads that are performed in a systematised and programmed way. The workload can be manipulated through variables such as weight load resistance, speed, duration, pauses between stimuli, muscular action, movement speed, amplitude, weekly frequency, number of sessions per day, number of exercises per session and the combination of different

Exercise triggers the synthesis of several enzymes and structural proteins that adapt tissues, organs and systems to changes in cellular homeostasis, in a task-oriented way and depending on the exercise stimulus. This set of chronic physiological and metabolic changes, currently termed supercompensation, allows for a more efficient and sustainable physiological environment during voluntary physical activity. Supercompensation supplies energy economy for habitual physical activities or enhances the energy supply during exercises of high metabolic demands. Recently, our group demonstrated, using proteomic analyses of rat muscle, that only one stimulus of exhaustive, incremental exercise (approximately 30 min) is enough to produce an acute, generalised, metabolic response in the muscular fibre (Gandra et al., 2010). This probably occurs to minimise the stress that will occur in a subsequent exercise session and, in the long term, the cumulative effects of exercise on gene expression lead to specific muscle phenotypic alterations, which is a major

However, supercompensation is only achieved when the ratio between overload and recovery time is individually balanced. Damaged tissue structures resulting from the exercise stimulus are repaired during recovery, when rest and food intake are crucial for the energy supply that is required for the synthesis of new proteins and cellular components.

**1. Introduction** 

**1.1 Training and overtraining** 

exercises in the same session.

aspect of performance enhancement.


## **Glutamine and Glutamate Reference Intervals as a Clinical Tool to Detect Training Intolerance During Training and Overtraining**

Rodrigo Hohl1, Lázaro Alessandro Soares Nunes1, Rafael Alkmin Reis1, René Brenzikofer1, Rodrigo Perroni Ferraresso1, Foued Salmen Spindola2 and Denise Vaz Macedo1 *1Laboratory of Exercise Biochemistry (LABEX), Biology Institute, University State of Campinas (UNICAMP), 2Laboratory of Biochemistry and Molecular Biology (LABIBI), Federal University of Uberlândia (UFU) Brazil* 

## **1. Introduction**

40 An International Perspective on Topics in Sports Medicine and Sports Injury

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### **1.1 Training and overtraining**

A training process consists of a sum of repeated exercise sessions with gradual overloads that are performed in a systematised and programmed way. The workload can be manipulated through variables such as weight load resistance, speed, duration, pauses between stimuli, muscular action, movement speed, amplitude, weekly frequency, number of sessions per day, number of exercises per session and the combination of different exercises in the same session.

Exercise triggers the synthesis of several enzymes and structural proteins that adapt tissues, organs and systems to changes in cellular homeostasis, in a task-oriented way and depending on the exercise stimulus. This set of chronic physiological and metabolic changes, currently termed supercompensation, allows for a more efficient and sustainable physiological environment during voluntary physical activity. Supercompensation supplies energy economy for habitual physical activities or enhances the energy supply during exercises of high metabolic demands. Recently, our group demonstrated, using proteomic analyses of rat muscle, that only one stimulus of exhaustive, incremental exercise (approximately 30 min) is enough to produce an acute, generalised, metabolic response in the muscular fibre (Gandra et al., 2010). This probably occurs to minimise the stress that will occur in a subsequent exercise session and, in the long term, the cumulative effects of exercise on gene expression lead to specific muscle phenotypic alterations, which is a major aspect of performance enhancement.

However, supercompensation is only achieved when the ratio between overload and recovery time is individually balanced. Damaged tissue structures resulting from the exercise stimulus are repaired during recovery, when rest and food intake are crucial for the energy supply that is required for the synthesis of new proteins and cellular components.

Glutamine and Glutamate Reference Intervals as a Clinical

**1.2 Overtraining animal model** 

Tool to Detect Training Intolerance During Training and Overtraining 43

Experiments in humans must meet ethical requirements to protect the physical and emotional well-being of the volunteer subjects. Those subjects must also be aware of all possible benefits and disadvantages of the experimental protocol. Therefore, one must consider the risks of possible damage to the athlete's professional and social life when he or she is subjected to an OT induction protocol. Therefore, the study of OT in animal models is endorsed by The American Physiological Society (APS, 2006), which states that '...experimental protocols that use animal subjects are therefore developed when it would

Currently, animal models are used in all biological research areas. Claude Bernard (1865) advanced the principle of studying animal models and showed how findings in animal models could be translated to human physiology. A model is an imitation object that must have similar characteristics to the imitated object and the capacity to be manipulated without the limitations of the imitated object. Therefore, an OT animal model should display a set of similar alterations that would be expected in humans. In this vein, our group standardised an 11-week treadmill endurance training model using Wistar rats, where a gradual reduction in the recovery time between exercise sessions was introduced during the last three weeks (Hohl et al., 2009). Six incremental performance tests to exhaustion were

**Experimental Performance Training Training Number of Recovery between Weeks tests Speed (m/min) Time (min) Daily sessions training sessions (h)** 

1st no tests 15 20 1 24 2nd no tests 20 30 1 24 3th no tests 22.5 45 1 24 4th 2 25 60 1 24 5th to 7th no tests 25 60 1 24 8th 3 25 60 1 24 9th 4 25 60 2 4 10th 5 25 60 3 3 11th 6 25 60 4 2

This OT animal model was characterised by an adaptive training period (1st to 8th week) followed by a period of increased daily training sessions (9th, 10th and 11th weeks). This OT model is also unique because it allows for the comparison of two distinct groups of animals separated by performance *a posteriori*. One group show continuous performance improvement after 60 hours of complete rest, characterising the FOR state; and the other group show improvement followed by a sharp drop in performance that persists for two

Analysis of performance during training was the parameter used both for selection criteria and to define the experimental FOR and NFOR groups. Thus, similar changes observed in the training groups (i.e., FOR and NFOR) as compared to a control group likely reflect the common response to OT, whereas differences that are unique to the NFOR group reflect the intolerance of some rats to OT. This generates a performance drop that is related to the effects of OT on the intrinsic characteristics of each animal. As observed in Fig. 1, although

1 (--) (--) (--) (--)

not be appropriate to use human subjects for studies of exercise's impact.'

performed during the training protocol, which is described in Table 1*.*

Table 1. Overtraining animal model protocol

weeks, characterising the NFOR state (Fig. 1).

On the other hand, an excess of rest and a lack of exercise load may cause a loss of phenotypic adaptation, or performance stagnation. Therefore, athletes routinely use a continuous process of intense training to achieve maximal competitive performance (Bompa & Haff, 2009; Meeusen et al. 2006). The training load can be manipulated through substantial increases in duration, frequency, intensity or multiple variables simultaneously, along with a reduction of the regenerative period. However, a persistent imbalance between exercise load and recovery time can also lead to a state of chronic fatigue associated with previously acquired performance decrement, generally called overtraining (OT).

Throughout the years, different nomenclatures have been used to describe this loss of performance in previously well-adapted individuals, such as overtraining, overtraining syndrome (OTS), overreaching, non-functional overreaching (NFOR), staleness and chronic fatigue. Independent of the terminology, decreased performance seems to be the only critical feature of OT in human beings (Halson & Jeukendrup, 2004; Meeusen et al., 2006). Furthermore, OT may cause financial loss and emotional distress to trainers and athletes.

The European College of Sport Science proposed a change in terminology for OT (Meeusen et al., 2006). They defined OT as a continuous process of intense training that can generate different outcomes, depending on performance states. Upkeep, or a possible increase in performance after a brief recovery period (days to weeks), was named functional overreaching (FOR); meanwhile, a prolonged decay in performance, reversed only by a long regenerative period (weeks to months) was named non-functional overreaching (NFOR). Finally, the extreme state of the OT process was named OTS, where recovery may take years or may never happen.

The NFOR and OTS states can be associated with one or more symptoms, including accentuated catabolic state; physiological, immunological and biochemical alterations; increased incidence of injury; and mood alterations (Halson & Jeukendrup, 2004). Still, there is no set of conclusive characteristics that define the NFOR and OTS states. Diagnosis is only possible when a decrease in performance cannot be explained by other factors, such as high levels of muscle microtrauma (which is characterised by increased blood concentrations of muscle injury markers such as creatine kinase and lactate dehydrogenase), contusions, diseases, infections, allergies and abnormal cardiac symptoms (Meeusen et al., 2006). Elite athletes are susceptible to OT outcomes because they are constantly submitted to OT to maintain high physical performance during the training season. However, amateur sportsmen who do not respect the time for recovery between stimuli are also susceptible to undesirable OT outcomes.

There are many theories regarding the biological basis of the training–OT *continuum*, but the underlying mechanisms remain to be validated experimentally. Experimental difficulties that have impeded progress in this field include variability of research studies, the contradiction of applying a training program that aims to reduce functional physiological capacity and the lack of volunteer athletes willing to risk losing a season of training and competitions (Halson & Jeukendrup, 2004). These obstacles limit data collection to anecdotes from athletes who have been diagnosed as overtrained (Halson & Jeukendrup, 2004) due to the intensification of the training process (i.e., OT), which is routinely utilised by athletes who hope to improve their performance. Thus, physiological and psychological limits dictate a need for research that addresses the avoidance of the undesirable outcomes of OT, maximises recovery and successfully negotiates the fine line between high and excessive training loads (Kellmann, 2010).

## **1.2 Overtraining animal model**

42 An International Perspective on Topics in Sports Medicine and Sports Injury

On the other hand, an excess of rest and a lack of exercise load may cause a loss of phenotypic adaptation, or performance stagnation. Therefore, athletes routinely use a continuous process of intense training to achieve maximal competitive performance (Bompa & Haff, 2009; Meeusen et al. 2006). The training load can be manipulated through substantial increases in duration, frequency, intensity or multiple variables simultaneously, along with a reduction of the regenerative period. However, a persistent imbalance between exercise load and recovery time can also lead to a state of chronic fatigue associated with

Throughout the years, different nomenclatures have been used to describe this loss of performance in previously well-adapted individuals, such as overtraining, overtraining syndrome (OTS), overreaching, non-functional overreaching (NFOR), staleness and chronic fatigue. Independent of the terminology, decreased performance seems to be the only critical feature of OT in human beings (Halson & Jeukendrup, 2004; Meeusen et al., 2006). Furthermore, OT may cause financial loss and emotional distress to trainers and athletes. The European College of Sport Science proposed a change in terminology for OT (Meeusen et al., 2006). They defined OT as a continuous process of intense training that can generate different outcomes, depending on performance states. Upkeep, or a possible increase in performance after a brief recovery period (days to weeks), was named functional overreaching (FOR); meanwhile, a prolonged decay in performance, reversed only by a long regenerative period (weeks to months) was named non-functional overreaching (NFOR). Finally, the extreme state of the OT process was named OTS, where recovery may take years

The NFOR and OTS states can be associated with one or more symptoms, including accentuated catabolic state; physiological, immunological and biochemical alterations; increased incidence of injury; and mood alterations (Halson & Jeukendrup, 2004). Still, there is no set of conclusive characteristics that define the NFOR and OTS states. Diagnosis is only possible when a decrease in performance cannot be explained by other factors, such as high levels of muscle microtrauma (which is characterised by increased blood concentrations of muscle injury markers such as creatine kinase and lactate dehydrogenase), contusions, diseases, infections, allergies and abnormal cardiac symptoms (Meeusen et al., 2006). Elite athletes are susceptible to OT outcomes because they are constantly submitted to OT to maintain high physical performance during the training season. However, amateur sportsmen who do not respect the time for recovery between stimuli are also susceptible to

There are many theories regarding the biological basis of the training–OT *continuum*, but the underlying mechanisms remain to be validated experimentally. Experimental difficulties that have impeded progress in this field include variability of research studies, the contradiction of applying a training program that aims to reduce functional physiological capacity and the lack of volunteer athletes willing to risk losing a season of training and competitions (Halson & Jeukendrup, 2004). These obstacles limit data collection to anecdotes from athletes who have been diagnosed as overtrained (Halson & Jeukendrup, 2004) due to the intensification of the training process (i.e., OT), which is routinely utilised by athletes who hope to improve their performance. Thus, physiological and psychological limits dictate a need for research that addresses the avoidance of the undesirable outcomes of OT, maximises recovery and successfully negotiates the fine line between high and excessive

previously acquired performance decrement, generally called overtraining (OT).

or may never happen.

undesirable OT outcomes.

training loads (Kellmann, 2010).

Experiments in humans must meet ethical requirements to protect the physical and emotional well-being of the volunteer subjects. Those subjects must also be aware of all possible benefits and disadvantages of the experimental protocol. Therefore, one must consider the risks of possible damage to the athlete's professional and social life when he or she is subjected to an OT induction protocol. Therefore, the study of OT in animal models is endorsed by The American Physiological Society (APS, 2006), which states that '...experimental protocols that use animal subjects are therefore developed when it would not be appropriate to use human subjects for studies of exercise's impact.'

Currently, animal models are used in all biological research areas. Claude Bernard (1865) advanced the principle of studying animal models and showed how findings in animal models could be translated to human physiology. A model is an imitation object that must have similar characteristics to the imitated object and the capacity to be manipulated without the limitations of the imitated object. Therefore, an OT animal model should display a set of similar alterations that would be expected in humans. In this vein, our group standardised an 11-week treadmill endurance training model using Wistar rats, where a gradual reduction in the recovery time between exercise sessions was introduced during the last three weeks (Hohl et al., 2009). Six incremental performance tests to exhaustion were performed during the training protocol, which is described in Table 1*.*


Table 1. Overtraining animal model protocol

This OT animal model was characterised by an adaptive training period (1st to 8th week) followed by a period of increased daily training sessions (9th, 10th and 11th weeks). This OT model is also unique because it allows for the comparison of two distinct groups of animals separated by performance *a posteriori*. One group show continuous performance improvement after 60 hours of complete rest, characterising the FOR state; and the other group show improvement followed by a sharp drop in performance that persists for two weeks, characterising the NFOR state (Fig. 1).

Analysis of performance during training was the parameter used both for selection criteria and to define the experimental FOR and NFOR groups. Thus, similar changes observed in the training groups (i.e., FOR and NFOR) as compared to a control group likely reflect the common response to OT, whereas differences that are unique to the NFOR group reflect the intolerance of some rats to OT. This generates a performance drop that is related to the effects of OT on the intrinsic characteristics of each animal. As observed in Fig. 1, although

Glutamine and Glutamate Reference Intervals as a Clinical

individual training intolerance.

proliferation (Krebs, 1980).

Tool to Detect Training Intolerance During Training and Overtraining 45

for ten days. The authors concluded that 'reduced plasma glutamine concentrations may provide a good indication of severe exercise stress'*.* Smith and Norris (2000) reported a mean (± standard deviation (sd)) glutamine concentration of 522 ± 53 µM, glutamate concentration of 128 ± 19 µM and Gm/Ga ratio of 4.15 ± 0.57 'as the extreme values for athletes who have not met conditions of overtraining and are thus managing the training load imposed', and they also proposed that 'overreaching which can also lead to overtraining may occur when the Gm/Ga ratio is 3.58*'.* Additionally, Halson et al. (2003) reported a mean (± sd) glutamine concentration decrease from 631± 21 µM to 475 ± 40 µM, glutamate increase from 158 ± 18 µM to 235 ± 18 µM and Gm/Ga ratio decrease from 4.38 ± 0.49 µM to 2.13 ± 0.26 µM in 'overreached athletes' after two weeks of intense training loads. The terminologies and statements of 'severe exercise stress', 'who have not met conditions of overtraining' and 'overreaching' suggest that blood glutamine and/or glutamate levels change before the dangerous and undesirable outcomes of OT, here termed NFOR or OTS. We have also found the same pattern of plasma glutamine and glutamate responses in rats subjected to OT (Hohl et al., 2009). Hohl et al. (2009) showed a significantly higher glutamate blood concentration in the NFOR group compared to the FOR group. Moreover, blood glutamine level in the NFOR group suggested a trend to a lower plasma concentration than the FOR group. Therefore, the Gm/Ga ratio in the NFOR group was significantly lower than in the FOR group (3.1 ± 0.2 and 4.5 ± 0.9, respectively), confirming previous studies with humans. Together, these data suggest that changes in glutamine and glutamate levels may be early indicators of some critical aspects of metabolism related to the

**1.4 Glutamine and glutamate changes due to metabolic maladaptation** 

that the training program is becoming more harmful than helpful to the subject.

Changes in glutamine and glutamate concentrations may not be the direct cause of OT outcomes (Keast et al., 1995), but those changes may be linked to many different aspects of metabolism, which may contribute, in different magnitudes, to the undesirable OT outcomes during the training program. Significant plasma glutamine and glutamate changes could be an indication of undesirable maladaptation in progress, in other words,

In response to deleterious challenges, such as burn damage or surgery, plasma glutamine decreases, despite increased mobilisation from muscle (Lobley et al., 2001). This is probably due to increased metabolic usage by the immune system and the liver, due to immunological challenge. Of note, the skeletal muscle is the main glutamine exporter to the blood stream (Krebs, 1980), and glutamine is mainly metabolised by immune cells such as lymphocytes, macrophages and neutrophils, which all depend on glutaminolysis for cell

Smith (2000) proposed that OTS is a response to excessive muscle stress, which may induce a local acute inflammatory response that may evolve into chronic inflammation and can lead to systemic inflammation. Circulating monocytes are activated in response to muscle trauma, which may increase the demand for glutamine. In addition, the increases in proinflammatory cytokines IL-6 and Tumor necrosis factor (TNF-α) stimulate glutamine and alanine uptake in human hepatocytes (Fischer and Hasselgren, 1991). Increased glutamine uptake by the liver from blood also favours the synthesis of large quantities of inflammatory-related, acute-phase proteins, such as C-reactive protein and haptoglobin (Marks et al., 1996). Serum proteomic analyses have shown increases in other acute-phase

Fig. 1. Performance (mean ± sd) of the FOR (n = 11) and NFOR (n = 8) groups in the six performance tests performed during the 11-week training described in Table 1. \* Significant difference between test 6 and test 5 in the paired *t* test analysis of FOR and NFOR groups (p < 0.01 for FOR and p < 0.001 for NFOR).

OT is necessary to maximise the increase in performance, it is also detrimental to the adaptive process for some animals. Lehmann et al. (1993) reported that inter-individual variability in recovery potential, exercise capacity and stress training tolerance explains the different vulnerabilities of athletes to OT under identical training stimuli. Therefore, OT is a process that deserves careful and individualised control of appropriate training loads and recovery times.

The animal model proposed by Hohl et al. (2009) has been a useful tool in seeking tissue and blood biomarkers for use in studying undesirable OT outcomes (i.e., NFOR/OTS) that are associated with an animal's tolerance or intolerance to the same OT protocol. This method can be used to compare the effects of OT in FOR and NFOR group of rats.

#### **1.3 Glutamine and glutamate as potential biomarkers for training intolerance**

Some blood biomarkers have been proposed to be associated with OT in humans (Petibois et al., 2002), but there is currently no consensus for all OT cases. One possible biomarker that could be used is the ratio of the concentration of glutamine to glutamate (Gm/Ga) in the blood. A decreased Gm/Ga ratio, due to a decrease in the glutamine blood concentration (Parry-Billings et al., 1992) and/or an increase in glutamate concentration (Coutts et al., 2007; Smith & Norris, 2000) after exercise, has been observed in overtrained humans (Coutts et al., 2007; Halson et al., 2003; Smith & Norris, 2000).

Keast et al. (1995) reported mean plasma glutamine decay from 630 µM to 328 µM in five highly trained male subjects who underwent intensive interval training sessions twice daily

123456

**Performance tests**

OT is necessary to maximise the increase in performance, it is also detrimental to the adaptive process for some animals. Lehmann et al. (1993) reported that inter-individual variability in recovery potential, exercise capacity and stress training tolerance explains the different vulnerabilities of athletes to OT under identical training stimuli. Therefore, OT is a process that deserves careful and individualised control of appropriate training loads and

The animal model proposed by Hohl et al. (2009) has been a useful tool in seeking tissue and blood biomarkers for use in studying undesirable OT outcomes (i.e., NFOR/OTS) that are associated with an animal's tolerance or intolerance to the same OT protocol. This method

Some blood biomarkers have been proposed to be associated with OT in humans (Petibois et al., 2002), but there is currently no consensus for all OT cases. One possible biomarker that could be used is the ratio of the concentration of glutamine to glutamate (Gm/Ga) in the blood. A decreased Gm/Ga ratio, due to a decrease in the glutamine blood concentration (Parry-Billings et al., 1992) and/or an increase in glutamate concentration (Coutts et al., 2007; Smith & Norris, 2000) after exercise, has been observed in overtrained humans (Coutts

Keast et al. (1995) reported mean plasma glutamine decay from 630 µM to 328 µM in five highly trained male subjects who underwent intensive interval training sessions twice daily

can be used to compare the effects of OT in FOR and NFOR group of rats.

et al., 2007; Halson et al., 2003; Smith & Norris, 2000).

**1.3 Glutamine and glutamate as potential biomarkers for training intolerance** 

Fig. 1. Performance (mean ± sd) of the FOR (n = 11) and NFOR (n = 8) groups in the six performance tests performed during the 11-week training described in Table 1. \* Significant difference between test 6 and test 5 in the paired *t* test analysis of FOR and NFOR groups (p

\*

\*

 FOR NFOR

100

< 0.01 for FOR and p < 0.001 for NFOR).

200

300

400

**Performance (Kg.m)**

recovery times.

500

600

700

800

for ten days. The authors concluded that 'reduced plasma glutamine concentrations may provide a good indication of severe exercise stress'*.* Smith and Norris (2000) reported a mean (± standard deviation (sd)) glutamine concentration of 522 ± 53 µM, glutamate concentration of 128 ± 19 µM and Gm/Ga ratio of 4.15 ± 0.57 'as the extreme values for athletes who have not met conditions of overtraining and are thus managing the training load imposed', and they also proposed that 'overreaching which can also lead to overtraining may occur when the Gm/Ga ratio is 3.58*'.* Additionally, Halson et al. (2003) reported a mean (± sd) glutamine concentration decrease from 631± 21 µM to 475 ± 40 µM, glutamate increase from 158 ± 18 µM to 235 ± 18 µM and Gm/Ga ratio decrease from 4.38 ± 0.49 µM to 2.13 ± 0.26 µM in 'overreached athletes' after two weeks of intense training loads. The terminologies and statements of 'severe exercise stress', 'who have not met conditions of overtraining' and 'overreaching' suggest that blood glutamine and/or glutamate levels change before the dangerous and undesirable outcomes of OT, here termed NFOR or OTS. We have also found the same pattern of plasma glutamine and glutamate responses in rats subjected to OT (Hohl et al., 2009). Hohl et al. (2009) showed a significantly higher glutamate blood concentration in the NFOR group compared to the FOR group. Moreover, blood glutamine level in the NFOR group suggested a trend to a lower plasma concentration than the FOR group. Therefore, the Gm/Ga ratio in the NFOR group was significantly lower than in the FOR group (3.1 ± 0.2 and 4.5 ± 0.9, respectively), confirming previous studies with humans. Together, these data suggest that changes in glutamine and glutamate levels may be early indicators of some critical aspects of metabolism related to the individual training intolerance.

#### **1.4 Glutamine and glutamate changes due to metabolic maladaptation**

Changes in glutamine and glutamate concentrations may not be the direct cause of OT outcomes (Keast et al., 1995), but those changes may be linked to many different aspects of metabolism, which may contribute, in different magnitudes, to the undesirable OT outcomes during the training program. Significant plasma glutamine and glutamate changes could be an indication of undesirable maladaptation in progress, in other words, that the training program is becoming more harmful than helpful to the subject.

In response to deleterious challenges, such as burn damage or surgery, plasma glutamine decreases, despite increased mobilisation from muscle (Lobley et al., 2001). This is probably due to increased metabolic usage by the immune system and the liver, due to immunological challenge. Of note, the skeletal muscle is the main glutamine exporter to the blood stream (Krebs, 1980), and glutamine is mainly metabolised by immune cells such as lymphocytes, macrophages and neutrophils, which all depend on glutaminolysis for cell proliferation (Krebs, 1980).

Smith (2000) proposed that OTS is a response to excessive muscle stress, which may induce a local acute inflammatory response that may evolve into chronic inflammation and can lead to systemic inflammation. Circulating monocytes are activated in response to muscle trauma, which may increase the demand for glutamine. In addition, the increases in proinflammatory cytokines IL-6 and Tumor necrosis factor (TNF-α) stimulate glutamine and alanine uptake in human hepatocytes (Fischer and Hasselgren, 1991). Increased glutamine uptake by the liver from blood also favours the synthesis of large quantities of inflammatory-related, acute-phase proteins, such as C-reactive protein and haptoglobin (Marks et al., 1996). Serum proteomic analyses have shown increases in other acute-phase

Glutamine and Glutamate Reference Intervals as a Clinical

the training characteristics or sport modality.

avoid haemolysis and to keep the analytes stable.

Tool to Detect Training Intolerance During Training and Overtraining 47

The International Federation of Clinical Chemistry (IFCC) Expert Panel on Theory of Reference Values in 1986 established the terminology, analytical procedures and statistical analyses of reference intervals (Solberg, 1987a). A *reference individual* is an individual selected for comparison using defined criteria (Solberg, 1987a). For sport science studies, it is important to consider that physical training promotes significant alterations in blood cells, enzyme activities, and protein and metabolite concentrations (Lazarim et al., 2009; Nunes et al., 2010; Sawka 2000). The training characteristics, or sport modalities, can promote different adaptive responses that can be reflected in each analyte. For example, endurance athletes have lower haematocrit, haemoglobin and red blood cell count compared to individuals who perform strength training (Schumacher et al., 2002). In addition, biochemical and haematological biomarkers may be influenced by age, body mass, genotype, ethnicity, sex, diet, circadian rhythm (Ritchie & Palomaki, 2004) and biological variation (Nunes et al., 2010). Thus, the selection of a reference individual should include

The *reference population* consists of all possible *reference individuals* of a *reference sample group*. The IFCC recommend a minimum of 120 subjects to obtain reliable estimates and confidence intervals (Solberg, 1987a). This may be a problem when one decides to estimate reference intervals for team sports, such as soccer, volleyball or individual sports. One alternative is to obtain reference intervals in co-operation with laboratories that use the same methods for screening tests in athletes. Another important issue is the selection criteria for a reference sample group. It is important to consider the training level and adaptive state of each individual. In such a way, a performance test is important to characterise the subjects that will make up an exercised reference sample group. In addition, when the reference sample group is composed of professional athletes, it is important to consider possible variations of blood parameters during the training season and competition periods (Banfi et al., 2011). The *reference values* are the values obtained in reference individuals for an individual analyte (the constituent that will be analysed) (Solberg, 1987a). The reference values are sensitive to pre-analytical and analytical variation; therefore, sample collection and handling techniques that are adopted should be standardised to minimise the sources of error (Fraser, 2001). Some techniques must be observed to obtain the reference samples: i) taking samples at the same time of day, considering possible circadian variations of the analyte; ii) ensuring that the reference individuals have been subjected to the same conditions (i.e., fasting for at least 10 hours and no consumption of alcohol or medication for at least 2 days before testing, particularly anti-inflammatory drugs); iii) the individual's training load or fitness level should be standardised; iv) taking blood samples with a standard phlebotomy technique (e.g., placing the samples into the same type of collection tubes and preferably having the subjects in a sitting position); and v) no training or exercise for at least 48 hours before the collection to avoid the acute haemodilution effects that can occur on the blood samples (Sawka, 2000). Also, the sample transport and handling should be carefully monitored to

Analysis of reference samples sometimes requires many methodological steps. Therefore, applying the same equipment, reagents and calibrators is critical to ensure that the results are accurate. The analytical variation can be estimated by calculating the analytical coefficient of variation, obtained from the mean and standard deviation of the quality control analysis. The internal quality control should analyse a sample that simulates the

reference values samples (e.g., serum, plasma, whole blood, urine or saliva).

proteins in NFOR when compared to FOR rats (Lazarim et al., 2010), which may be linked to the decreased blood glutamine observed by Hohl et al. (2009).

Another complementary hypothesis to be considered for blood glutamine reduction is that a decrease in oxidative capacity is caused by muscle mitochondrial damage. The muscle glutamine synthetase (GS) requires α-oxoglutarate as co-substrate for glutamate synthesis that is actually used as GS substrate for glutamine synthesis with ATP and NH3 +. It was speculated that mitochondrial injuries could limit the availability of α-oxoglutarate formed by the Krebs cycle, thereby diminishing glutamine production inside the muscle (Rowbottom et al., 1995).

This hypothesis is supported by the uncommon reduction in citrate synthase (CS) activity that we found in the NFOR group from the OT animal model protocol (Hohl et al., 2009). The unexpected chronic performance drop associated with lower oxidative capacity in the NFOR group could be related to the increased generation of reactive oxygen species (ROS, e.g., superoxide anion [O2 ●-], hydrogen peroxide [H2O2] and hydroxyl radical [OH. ]). Ji et al. (1988) have shown that increased ROS production in muscles of rats during prolonged and exhaustive exercise causes an alteration in the intra-mitochondrial redox state. This occurs by the oxidation of thiol groups (-SH) of mitochondrial enzymes (e.g., CS, malate dehydrogenase and aminotransferase alanine), linking this alteration to the reductions in the activities of these enzymes for 48 hours after exercise. We observed oxidative stress in the NFOR rat group red gastrocnemius, along with decreased CS and mitochondrial Complex IV activities, 60 hours after the last training session (at 11th week in Table 1) (Hohl et al., 2010).

The increase in blood glutamate is less understood than the decrease in glutamine during OT. A possible explanation for the increase in blood glutamate in the NFOR/OTS states is that excessive skeletal muscle microtrauma causes a reduction in the electrochemical gradient in the muscle by increasing intracellular Na+ (Hack et al., 1996).Glutamate is carried in the cell by Na+-dependent transporters; therefore, increased intracellular Na+ will result in decreased glutamate carried into the cell (McGivan & Pastor-Anglada, 1994). This problem was verified in hypercatabolism (cachexia) (Hack et al., 1996), which entails a great loss of body cell mass. In addition, excess tissue trauma may be associated with reduced food intake, causing gluconeogenesis to be up-regulated in order to maintain blood glucose level (Smith, 2000). Because alanine and glutamine are the main precursors for gluconeogenesis, glutamine will decrease in this case (Wagenmakers, 1998). Muscle microtrauma and up-regulation of liver gluconeogenesis could link the blood glutamate increase and glutamine decrease in a feedback loop.

Although measuring blood glutamine and glutamate may be useful to monitor the effects of exercise programs, they can only be used to individualise medical/nutritional programs or exercise training interventions if the blood values are increased or decreased in relation to a well-defined reference population. So far, there have been no reports describing the reference intervals for blood glutamine and glutamate that can be applied in the exercise/sport sciences.

#### **1.5 Reference interval as a clinical tool**

The results of laboratory tests are often used in the clinic to diagnose, monitor or prevent many different pathological states. The most commonly used interpretation task is to compare individual blood parameter values with reference intervals that have been obtained from a defined population. Reference intervals refer to the range of values for a laboratory test that are observed in a specific population, typically described by upper and lower reference limits.

proteins in NFOR when compared to FOR rats (Lazarim et al., 2010), which may be linked to

Another complementary hypothesis to be considered for blood glutamine reduction is that a decrease in oxidative capacity is caused by muscle mitochondrial damage. The muscle glutamine synthetase (GS) requires α-oxoglutarate as co-substrate for glutamate synthesis that

that mitochondrial injuries could limit the availability of α-oxoglutarate formed by the Krebs cycle, thereby diminishing glutamine production inside the muscle (Rowbottom et al., 1995). This hypothesis is supported by the uncommon reduction in citrate synthase (CS) activity that we found in the NFOR group from the OT animal model protocol (Hohl et al., 2009). The unexpected chronic performance drop associated with lower oxidative capacity in the NFOR group could be related to the increased generation of reactive oxygen species (ROS,

(1988) have shown that increased ROS production in muscles of rats during prolonged and exhaustive exercise causes an alteration in the intra-mitochondrial redox state. This occurs by the oxidation of thiol groups (-SH) of mitochondrial enzymes (e.g., CS, malate dehydrogenase and aminotransferase alanine), linking this alteration to the reductions in the activities of these enzymes for 48 hours after exercise. We observed oxidative stress in the NFOR rat group red gastrocnemius, along with decreased CS and mitochondrial Complex IV activities, 60 hours

The increase in blood glutamate is less understood than the decrease in glutamine during OT. A possible explanation for the increase in blood glutamate in the NFOR/OTS states is that excessive skeletal muscle microtrauma causes a reduction in the electrochemical gradient in the muscle by increasing intracellular Na+ (Hack et al., 1996).Glutamate is carried in the cell by Na+-dependent transporters; therefore, increased intracellular Na+ will result in decreased glutamate carried into the cell (McGivan & Pastor-Anglada, 1994). This problem was verified in hypercatabolism (cachexia) (Hack et al., 1996), which entails a great loss of body cell mass. In addition, excess tissue trauma may be associated with reduced food intake, causing gluconeogenesis to be up-regulated in order to maintain blood glucose level (Smith, 2000). Because alanine and glutamine are the main precursors for gluconeogenesis, glutamine will decrease in this case (Wagenmakers, 1998). Muscle microtrauma and up-regulation of liver gluconeogenesis could link the blood glutamate

Although measuring blood glutamine and glutamate may be useful to monitor the effects of exercise programs, they can only be used to individualise medical/nutritional programs or exercise training interventions if the blood values are increased or decreased in relation to a well-defined reference population. So far, there have been no reports describing the reference intervals for blood glutamine and glutamate that can be applied in the

The results of laboratory tests are often used in the clinic to diagnose, monitor or prevent many different pathological states. The most commonly used interpretation task is to compare individual blood parameter values with reference intervals that have been obtained from a defined population. Reference intervals refer to the range of values for a laboratory test that are observed in a specific population, typically described by upper and lower reference limits.

●-], hydrogen peroxide [H2O2] and hydroxyl radical [OH.

+. It was speculated

]). Ji et al.

the decreased blood glutamine observed by Hohl et al. (2009).

e.g., superoxide anion [O2

exercise/sport sciences.

is actually used as GS substrate for glutamine synthesis with ATP and NH3

after the last training session (at 11th week in Table 1) (Hohl et al., 2010).

increase and glutamine decrease in a feedback loop.

**1.5 Reference interval as a clinical tool** 

The International Federation of Clinical Chemistry (IFCC) Expert Panel on Theory of Reference Values in 1986 established the terminology, analytical procedures and statistical analyses of reference intervals (Solberg, 1987a). A *reference individual* is an individual selected for comparison using defined criteria (Solberg, 1987a). For sport science studies, it is important to consider that physical training promotes significant alterations in blood cells, enzyme activities, and protein and metabolite concentrations (Lazarim et al., 2009; Nunes et al., 2010; Sawka 2000). The training characteristics, or sport modalities, can promote different adaptive responses that can be reflected in each analyte. For example, endurance athletes have lower haematocrit, haemoglobin and red blood cell count compared to individuals who perform strength training (Schumacher et al., 2002). In addition, biochemical and haematological biomarkers may be influenced by age, body mass, genotype, ethnicity, sex, diet, circadian rhythm (Ritchie & Palomaki, 2004) and biological variation (Nunes et al., 2010). Thus, the selection of a reference individual should include the training characteristics or sport modality.

The *reference population* consists of all possible *reference individuals* of a *reference sample group*. The IFCC recommend a minimum of 120 subjects to obtain reliable estimates and confidence intervals (Solberg, 1987a). This may be a problem when one decides to estimate reference intervals for team sports, such as soccer, volleyball or individual sports. One alternative is to obtain reference intervals in co-operation with laboratories that use the same methods for screening tests in athletes. Another important issue is the selection criteria for a reference sample group. It is important to consider the training level and adaptive state of each individual. In such a way, a performance test is important to characterise the subjects that will make up an exercised reference sample group. In addition, when the reference sample group is composed of professional athletes, it is important to consider possible variations of blood parameters during the training season and competition periods (Banfi et al., 2011).

The *reference values* are the values obtained in reference individuals for an individual analyte (the constituent that will be analysed) (Solberg, 1987a). The reference values are sensitive to pre-analytical and analytical variation; therefore, sample collection and handling techniques that are adopted should be standardised to minimise the sources of error (Fraser, 2001). Some techniques must be observed to obtain the reference samples: i) taking samples at the same time of day, considering possible circadian variations of the analyte; ii) ensuring that the reference individuals have been subjected to the same conditions (i.e., fasting for at least 10 hours and no consumption of alcohol or medication for at least 2 days before testing, particularly anti-inflammatory drugs); iii) the individual's training load or fitness level should be standardised; iv) taking blood samples with a standard phlebotomy technique (e.g., placing the samples into the same type of collection tubes and preferably having the subjects in a sitting position); and v) no training or exercise for at least 48 hours before the collection to avoid the acute haemodilution effects that can occur on the blood samples (Sawka, 2000). Also, the sample transport and handling should be carefully monitored to avoid haemolysis and to keep the analytes stable.

Analysis of reference samples sometimes requires many methodological steps. Therefore, applying the same equipment, reagents and calibrators is critical to ensure that the results are accurate. The analytical variation can be estimated by calculating the analytical coefficient of variation, obtained from the mean and standard deviation of the quality control analysis. The internal quality control should analyse a sample that simulates the reference values samples (e.g., serum, plasma, whole blood, urine or saliva).

Glutamine and Glutamate Reference Intervals as a Clinical

data input routine and intuitive interface.

glutamine level, glutamate level and Gm/Ga ratio.

**2. Material and methods** 

University (CAAE: 0200.0.146.000-08).

every additional 10''.

**2.1 Subjects** 

Tool to Detect Training Intolerance During Training and Overtraining 49

distribution by applying a goodness-of-fit test, such as an Anderson-Darling or Kolmogorov-Smirnov test (Solberg, 1986). The calculation will use the mean - 1.96 x sd to estimate the 2.5th percentile and mean + 1.96 x sd for the 97.5th percentile of the reference intervals (Horn & Pesce, 2003). The RefVal program, developed by Solberg (2004), is a computer program that performs many statistical routines described above, including procedures and algorithms in accordance with the IFCC recommendations. It has a simple

One difficulty in assessing the effects of training on blood parameters is the lack of appropriate reference intervals obtained from a reference population that practices regular and systematised physical activity and following the IFCC rules. The aim of this study was to obtain glutamine, glutamate and Gm/Ga reference intervals, according to IFCC rules, using automated equipment that does not require extensive laboratory skills. The reference population consisted of a cohort of young men who had increased performance in a 3000-m time trial test after four months of periodic training, when compared with their performances at the beginning of training. Secondly, we present a suggestion for a practical method to follow training effects that combines routine performance analyses with

Male volunteers (n=526), with an average age of 18 ± 1 years, participated in this study. All volunteers were students in the first stage of physical and educational preparation for a career in the army. The participants responded to a questionnaire about their use of medication and their complaints of pain and injuries caused by training. Those who were using medications or were injured were not included in the study. Volunteer subjects were duly informed about the research and signed a free informed consent form. They participated for nine months (February to October) in a regular and strictly controlled exercise program, which consisted predominantly of aerobic activities (high volume and low intensity) for three hours daily. They trained five days per week, with two days of rest. This work was approved by the Human Research Ethics Committee of the Campinas State

All subjects performed four freely paced 3000-m time trial tests during the training period. The tests were performed in February, April, May and October, respectively, with all subjects performing them within the same one-week period. The time trial test is a feasible way to test the endurance capacity of the subjects, considering the large sample size. Each subject ran 3000 m with a numbered wristband. At the end of the 3000-m trial, the subjects placed their wristbands on a pole. A total of five poles were placed at the end line, and one evaluator stopped a memory stopwatch every time a wristband was placed on the pole.

The students were highly motivated for the time trial test. The 3000-m time trial was graded by members of the army school, and students failed the year if they did not perform the 3000 m in at least 14' 59'' in the last test (October). A maximum grade (10) was obtained when the cadet performed below 11' 30'', and the grade dropped 0.5 per

**2.2 Performance test and subject selection for a reference population** 

Later, the sequences on the wristbands were matched with the stopwatch times.

After the reference sample assay, a histogram should be generated to inspect the distribution of the data (Fig. 2). We found that glutamine and glutamate show a Gaussian (Fig. 2A) and non-Gaussian distribution (Fig. 2B), respectively. In the visual histogram analysis, it is also possible to detect aberrant values (*outliers*) and to identify possible data errors. A number of statistical tests should also be performed to detect *outliers.* The IFCC do not recommend any particular method, but the Dixon test is commonly used, and it is relatively 'insensitive to moderate deviation from the Gaussian distribution' (Solberg, 1987b). However, this method often fails when several outliers are present. The *Horn's*  algorithm (Horn et al., 2001) attempts to solve this problem by employing two stages. In the first stage, the data are transformed in a Gaussian distribution, and in the second stage, the extreme values are detected based on 50% of the transformed sample (Horn et al., 2001). The aberrant values, which are identified as *outliers*, should be removed following rational criteria: the pre-analytical process should be re-evaluated, the analytical process should be checked or samples re-assayed to discharge possible mistakes (Solberg, 1987b).

Fig. 2. Histograms representing the reference distributions of glutamine (A) and glutamate (B) in a physically active population (n = 146). The black line represents a normal fit.

Several types of reference intervals have been proposed in the literature: inter-percentile interval, tolerance interval and prediction interval. Most frequently, the reference intervals are estimated to be the lower and upper percentiles in the central 95% of the results (Solberg 1987b). This procedure is recommended because the reference intervals are easily estimated by parametric and non-parametric procedures. Other percentiles (e.g., 90%) can be adopted to narrow the intervals.

The percentiles may be estimated by parametric (Gaussian distribution) or by nonparametric methods (non-Gaussian distribution). There are several non-parametric methods for estimating reference intervals. The rank-based method is simple to apply manually or by using a computer, and it is also recommended by the IFCC and well described by Reed et al. (1971). As parametric estimates are more precise, we can transform the non-Gaussian distribution into a Gaussian distribution by applying statistical techniques (e.g., logarithms or square roots of the values) (Solberg, 1987b). To calculate reference intervals by parametric methods, we first need to test a Gaussian distribution by applying a goodness-of-fit test, such as an Anderson-Darling or Kolmogorov-Smirnov test (Solberg, 1986). The calculation will use the mean - 1.96 x sd to estimate the 2.5th percentile and mean + 1.96 x sd for the 97.5th percentile of the reference intervals (Horn & Pesce, 2003). The RefVal program, developed by Solberg (2004), is a computer program that performs many statistical routines described above, including procedures and algorithms in accordance with the IFCC recommendations. It has a simple data input routine and intuitive interface.

One difficulty in assessing the effects of training on blood parameters is the lack of appropriate reference intervals obtained from a reference population that practices regular and systematised physical activity and following the IFCC rules. The aim of this study was to obtain glutamine, glutamate and Gm/Ga reference intervals, according to IFCC rules, using automated equipment that does not require extensive laboratory skills. The reference population consisted of a cohort of young men who had increased performance in a 3000-m time trial test after four months of periodic training, when compared with their performances at the beginning of training. Secondly, we present a suggestion for a practical method to follow training effects that combines routine performance analyses with glutamine level, glutamate level and Gm/Ga ratio.

## **2. Material and methods**

## **2.1 Subjects**

48 An International Perspective on Topics in Sports Medicine and Sports Injury

After the reference sample assay, a histogram should be generated to inspect the distribution of the data (Fig. 2). We found that glutamine and glutamate show a Gaussian (Fig. 2A) and non-Gaussian distribution (Fig. 2B), respectively. In the visual histogram analysis, it is also possible to detect aberrant values (*outliers*) and to identify possible data errors. A number of statistical tests should also be performed to detect *outliers.* The IFCC do not recommend any particular method, but the Dixon test is commonly used, and it is relatively 'insensitive to moderate deviation from the Gaussian distribution' (Solberg, 1987b). However, this method often fails when several outliers are present. The *Horn's*  algorithm (Horn et al., 2001) attempts to solve this problem by employing two stages. In the first stage, the data are transformed in a Gaussian distribution, and in the second stage, the extreme values are detected based on 50% of the transformed sample (Horn et al., 2001). The aberrant values, which are identified as *outliers*, should be removed following rational criteria: the pre-analytical process should be re-evaluated, the analytical process should be

checked or samples re-assayed to discharge possible mistakes (Solberg, 1987b).

A B

0

Fig. 2. Histograms representing the reference distributions of glutamine (A) and glutamate (B) in a physically active population (n = 146). The black line represents a normal fit.

Several types of reference intervals have been proposed in the literature: inter-percentile interval, tolerance interval and prediction interval. Most frequently, the reference intervals are estimated to be the lower and upper percentiles in the central 95% of the results (Solberg 1987b). This procedure is recommended because the reference intervals are easily estimated by parametric and non-parametric procedures. Other percentiles (e.g., 90%) can be adopted

The percentiles may be estimated by parametric (Gaussian distribution) or by nonparametric methods (non-Gaussian distribution). There are several non-parametric methods for estimating reference intervals. The rank-based method is simple to apply manually or by using a computer, and it is also recommended by the IFCC and well described by Reed et al. (1971). As parametric estimates are more precise, we can transform the non-Gaussian distribution into a Gaussian distribution by applying statistical techniques (e.g., logarithms or square roots of the values) (Solberg, 1987b). To calculate reference intervals by parametric methods, we first need to test a Gaussian

5

10

15

20

Frequency

25

30

35

20 30 40 50 60 70

Glutamate (µM)

450 500 550 600 650 700 750 800 850 900

Glutamine (µM)

0

to narrow the intervals.

5

10

15

20

Frequency

25

30

35

Male volunteers (n=526), with an average age of 18 ± 1 years, participated in this study. All volunteers were students in the first stage of physical and educational preparation for a career in the army. The participants responded to a questionnaire about their use of medication and their complaints of pain and injuries caused by training. Those who were using medications or were injured were not included in the study. Volunteer subjects were duly informed about the research and signed a free informed consent form. They participated for nine months (February to October) in a regular and strictly controlled exercise program, which consisted predominantly of aerobic activities (high volume and low intensity) for three hours daily. They trained five days per week, with two days of rest. This work was approved by the Human Research Ethics Committee of the Campinas State University (CAAE: 0200.0.146.000-08).

## **2.2 Performance test and subject selection for a reference population**

All subjects performed four freely paced 3000-m time trial tests during the training period. The tests were performed in February, April, May and October, respectively, with all subjects performing them within the same one-week period. The time trial test is a feasible way to test the endurance capacity of the subjects, considering the large sample size. Each subject ran 3000 m with a numbered wristband. At the end of the 3000-m trial, the subjects placed their wristbands on a pole. A total of five poles were placed at the end line, and one evaluator stopped a memory stopwatch every time a wristband was placed on the pole. Later, the sequences on the wristbands were matched with the stopwatch times.

The students were highly motivated for the time trial test. The 3000-m time trial was graded by members of the army school, and students failed the year if they did not perform the 3000 m in at least 14' 59'' in the last test (October). A maximum grade (10) was obtained when the cadet performed below 11' 30'', and the grade dropped 0.5 per every additional 10''.

Glutamine and Glutamate Reference Intervals as a Clinical

active, healthy men who responded to 4 months of training.

Tool to Detect Training Intolerance During Training and Overtraining 51

Figure 4 shows a zoom of a specific region of the graph and the selected students chosen to compose the reference population (points circled in gray). Thus, the glutamine and glutamate reference intervals presented here represent a population of young, physically

Fig. 4. Zoom applied on Fig.3. Comparison between 3000-m time trials performed in February (*x*-axis) and May (*y*-axis). The points circled in grey represent the individuals selected to establish the reference values for glutamine and glutamate. The dashed line

The performances of the students in the 3000-m time trial tests between May and October were used to choose candidates to test the use of plasma glutamine level, glutamate level and Gm/Ga ratio as biomarkers of tolerance or intolerance to the same training protocol. Twenty-five subjects who were above the identity line were randomly selected as the nonresponders to training (NRT), and an additional 25 subjects who fell below the identity line

All blood samples were collected after two days of rest to avoid the effects of hemodynamic variations and acute haemodilution that are induced by exercise. Blood samples were collected under standardised conditions. Eight millilitres of venous blood was collected in

represents the identity line.

**2.3 Collection of blood samples** 

were randomly selected as responders to training (RT).

Software was developed to facilitate the visualisation, identification and selection of subjects from this large sample. It is based on the use of scatter plots and allows the user, in an interactive way, to observe trends and patterns of the group's results, providing the position of the individual within the group and showing the current results of the subjects compared to previous results (Reis et al., 2011).

The example shown in Fig. 3 illustrates the comparison between the time achieved for a 3000-m time trial performed in February and May. This type of comparison allowed for visualisation of the progress of the students during the training period between tests. By clicking on 'Identity Line', a straight line is drawn with a slope equal to 1 and an intercept equal to 0, which divides the graph diagonally. This line represents the set of identical results in both tests. The points below the identity line represent the students who responded well to the physical training program, as they performed the 3000-m time trial with a lower time in May than in February. There were also a few students positioned above the identity line who, for some reason, performed the test more slowly in May than in February.

Fig. 3. Comparison between 3000-m time trials performed in February (*x*-axis) and May (*y*axis). The dashed line represents the identity line. The text box indicates the exact value obtained by subject 1790 on both dates.

Software was developed to facilitate the visualisation, identification and selection of subjects from this large sample. It is based on the use of scatter plots and allows the user, in an interactive way, to observe trends and patterns of the group's results, providing the position of the individual within the group and showing the current results of the subjects compared

The example shown in Fig. 3 illustrates the comparison between the time achieved for a 3000-m time trial performed in February and May. This type of comparison allowed for visualisation of the progress of the students during the training period between tests. By clicking on 'Identity Line', a straight line is drawn with a slope equal to 1 and an intercept equal to 0, which divides the graph diagonally. This line represents the set of identical results in both tests. The points below the identity line represent the students who responded well to the physical training program, as they performed the 3000-m time trial with a lower time in May than in February. There were also a few students positioned above the identity line who, for some reason, performed the test more slowly in May than in

Fig. 3. Comparison between 3000-m time trials performed in February (*x*-axis) and May (*y*axis). The dashed line represents the identity line. The text box indicates the exact value

to previous results (Reis et al., 2011).

obtained by subject 1790 on both dates.

February.

Figure 4 shows a zoom of a specific region of the graph and the selected students chosen to compose the reference population (points circled in gray). Thus, the glutamine and glutamate reference intervals presented here represent a population of young, physically active, healthy men who responded to 4 months of training.

Fig. 4. Zoom applied on Fig.3. Comparison between 3000-m time trials performed in February (*x*-axis) and May (*y*-axis). The points circled in grey represent the individuals selected to establish the reference values for glutamine and glutamate. The dashed line represents the identity line.

The performances of the students in the 3000-m time trial tests between May and October were used to choose candidates to test the use of plasma glutamine level, glutamate level and Gm/Ga ratio as biomarkers of tolerance or intolerance to the same training protocol. Twenty-five subjects who were above the identity line were randomly selected as the nonresponders to training (NRT), and an additional 25 subjects who fell below the identity line were randomly selected as responders to training (RT).

#### **2.3 Collection of blood samples**

All blood samples were collected after two days of rest to avoid the effects of hemodynamic variations and acute haemodilution that are induced by exercise. Blood samples were collected under standardised conditions. Eight millilitres of venous blood was collected in

Glutamine and Glutamate Reference Intervals as a Clinical

volunteers after four months of daily, periodic physical activity.

for healthy, physically active young men.

healthy, physically active young men.

**2.5 Statistical analysis** 

**3. Results** 

Tool to Detect Training Intolerance During Training and Overtraining 53

standard concentrations detected were 0.620 mM, 0.692 mM, 0.812 mM and 0.892 mM, respectively. The same procedure was done for glutamate: the added standard concentrations were 0.020 mM, 0.030 mM, 0.040 mM and 0.050 mM, and the standard concentrations detected were 0.021 mM, 0.030 mM, 0.039 mM and 0.053 mM, respectively.

Initially, all data from the 3000-m time trial tests were organised using Microsoft Excel and were checked for consistency between the recorded values and their identities with the evaluated subjects. After this initial organisation, all data were imported into Matlab® 7.0, which is the platform on which the software that was designed for this study was developed. Despite having been developed in this environment, the software was compiled and can run on any computer using Microsoft Windows and does not require the installation of Matlab® 7.0. The software, Origin 6.0, was used to perform statistical analyses and to generate graphs. An unpaired t test was used to compare RT and NRT glutamine and glutamate measurements; p < 0.05 was considered significant. The *Horn's* algorithm was applied to detect and remove outliers (Horn et al., 2001). The RefVal program (Solberg 2004), including practical approaches and formulas recommended by the IFCC, was used to calculate the 97.5th and 2.5th percentiles of the subjects and their respective 0.90 confidence intervals. This was achieved by using parametric estimates of the glutamine levels, glutamate levels and Gm/Ga ratios that were obtained from plasma samples of the 146

Table 2 shows the reference intervals (upper and lower limits) and the confidence intervals

Analysis Reference 0.90 Confidence Interval Subjects Outliers Subjects

Glutamine (µM) 566 - 798 546 - 586 782 - 813 146 514, 867, 875 143 Glutamate (µM) 31 - 59 30 - 32 55 - 62 146 26, 28, 68 143

Gm/Ga 12 - 23 12 - 12 22 - 24 146 10, 10, 26 143

Figure 5 presents the performance analyses of the test subjects for the 3000-m time trial, over the span of one training year. At the end of the training year (May x October, Fig. 4C) almost all subjects performed below 14' 59'' (cut off) and the 3000-m times were more homogenous than at the beginning of the year (February x April, Fig. 4A). However, the regression line in Fig. 4C approaches the line of identity due to the increase in the amount of time it took some

Table 2. Reference intervals for glutamine level, glutamate level and Gm/Ga ratio for

the test subjects (n = 222) to complete the 3000-m trial in October compared to May.

Interval (n) Ref. Interval

**2.5th - 97.5th 2.5th 97.5th** (n)

heparin tubes with a Vacuette® (Greiner Bio-one) gel separator to obtain plasma for glutamine and glutamate assays. Blood samples were collected in the morning after 12 hours of fasting with subjects in a seated position, transported at 4°C to the laboratory within 30 min, centrifuged under refrigeration (4°C) at 1,800 x *g* for 10 minutes, and then immediately separated and protected from light. Plasma samples were finally stored at - 80°C and analysed within 2 months.

#### **2.4 Assays for glutamine and glutamate measurements**

Glutamine and glutamate analyses were conducted with a dual-channel YSI 2700® Select Biochemistry automated analyser (Yellow Springs Instrument Co., Ohio, USA), according to the manufacturer's recommendations. This analyser uses a platinum electrode to measure the current generated by two enzyme-impregnated membranes. When a sample is injected into the sample chamber, the glutamine diffuses to the glutamine membrane, which contains glutaminase and glutamate oxidase. The glutamine is deaminated to glutamate and ammonia by glutaminase. In the presence of glutamate oxidase, glutamate is oxidised to hydrogen peroxide, α-ketoglutarate, and ammonia. The hydrogen peroxide is detected amperometrically at the platinum electrode surface. The current flow at the electrode is directly proportional to the hydrogen peroxide concentration and thus to the glutamate concentration. The glutamate in the sample is also oxidised at the glutamate and glutamine membranes by glutamate oxidase, producing hydrogen peroxide, α-ketoglutarate, and ammonia. Glutamine concentration is calculated as the concentration measured by the glutamine electrode minus that detected by the glutamate electrode.

We used 1 mM glutamine and 0.500 mM glutamate standards to calibrate the machine after every four measurements (sample size 65 µl). The standard solutions (5 mM) were provided by the manufacturer (Yellow Springs Instrument Co., Ohio, USA) and were diluted in Milli-Q water (Millipore Corporation, MA, USA).

The 146 samples used for the reference interval were analysed within two days, and the linearity of the enzyme membranes was evaluated every day before sample analysis. The 50 samples (RT, n = 25; NRT, n = 25) were analysed within the same day with other enzyme membranes and standard kits, after evaluation of membrane linearity. All plasma samples were measured in duplicate, and the mean of the duplicate runs was used in subsequent calculations. The linearity for glutamine and glutamate was calculated with diluted standards in three concentrations: 0.300 mM, 0.500 mM and 0.900 mM for glutamine and 0.05 mM, 0.100 mM and 0.200 mM for glutamate. In all linearity analyses, the correlation coefficient and the slope (r) were approximately 1.

We used one plasma pool to evaluate the within-day coefficient of variation (CVw) and four different plasma pools to evaluate the between-day coefficient of variation (CVb). The CVw of glutamine and glutamate were 0.60% and 1.20%, respectively (n = 20 using one plasma pool). The mean CVb, within three consecutive days, of glutamine and glutamate were 2.0% and 3.8%, respectively.

The accuracy of the YSI 2700D, when analysing the amino acids in non-deproteinated plasma, was evaluated by adding four different standard concentrations into four vials of the same plasma sample. Glutamine and glutamate standard concentrations that were diluted in plasma were thus calculated by subtracting the real glutamine and glutamate concentrations previously measured in plasma. We added 0.600 mM, 0.700 mM, 0.800 mM and 0.900 mM of glutamine standard into four different vials of one plasma sample, and the standard concentrations detected were 0.620 mM, 0.692 mM, 0.812 mM and 0.892 mM, respectively. The same procedure was done for glutamate: the added standard concentrations were 0.020 mM, 0.030 mM, 0.040 mM and 0.050 mM, and the standard concentrations detected were 0.021 mM, 0.030 mM, 0.039 mM and 0.053 mM, respectively.

## **2.5 Statistical analysis**

52 An International Perspective on Topics in Sports Medicine and Sports Injury

heparin tubes with a Vacuette® (Greiner Bio-one) gel separator to obtain plasma for glutamine and glutamate assays. Blood samples were collected in the morning after 12 hours of fasting with subjects in a seated position, transported at 4°C to the laboratory within 30 min, centrifuged under refrigeration (4°C) at 1,800 x *g* for 10 minutes, and then immediately separated and protected from light. Plasma samples were finally stored at -

Glutamine and glutamate analyses were conducted with a dual-channel YSI 2700® Select Biochemistry automated analyser (Yellow Springs Instrument Co., Ohio, USA), according to the manufacturer's recommendations. This analyser uses a platinum electrode to measure the current generated by two enzyme-impregnated membranes. When a sample is injected into the sample chamber, the glutamine diffuses to the glutamine membrane, which contains glutaminase and glutamate oxidase. The glutamine is deaminated to glutamate and ammonia by glutaminase. In the presence of glutamate oxidase, glutamate is oxidised to hydrogen peroxide, α-ketoglutarate, and ammonia. The hydrogen peroxide is detected amperometrically at the platinum electrode surface. The current flow at the electrode is directly proportional to the hydrogen peroxide concentration and thus to the glutamate concentration. The glutamate in the sample is also oxidised at the glutamate and glutamine membranes by glutamate oxidase, producing hydrogen peroxide, α-ketoglutarate, and ammonia. Glutamine concentration is calculated as the concentration measured by the

We used 1 mM glutamine and 0.500 mM glutamate standards to calibrate the machine after every four measurements (sample size 65 µl). The standard solutions (5 mM) were provided by the manufacturer (Yellow Springs Instrument Co., Ohio, USA) and were diluted in Milli-

The 146 samples used for the reference interval were analysed within two days, and the linearity of the enzyme membranes was evaluated every day before sample analysis. The 50 samples (RT, n = 25; NRT, n = 25) were analysed within the same day with other enzyme membranes and standard kits, after evaluation of membrane linearity. All plasma samples were measured in duplicate, and the mean of the duplicate runs was used in subsequent calculations. The linearity for glutamine and glutamate was calculated with diluted standards in three concentrations: 0.300 mM, 0.500 mM and 0.900 mM for glutamine and 0.05 mM, 0.100 mM and 0.200 mM for glutamate. In all linearity analyses, the correlation

We used one plasma pool to evaluate the within-day coefficient of variation (CVw) and four different plasma pools to evaluate the between-day coefficient of variation (CVb). The CVw of glutamine and glutamate were 0.60% and 1.20%, respectively (n = 20 using one plasma pool). The mean CVb, within three consecutive days, of glutamine and glutamate were 2.0%

The accuracy of the YSI 2700D, when analysing the amino acids in non-deproteinated plasma, was evaluated by adding four different standard concentrations into four vials of the same plasma sample. Glutamine and glutamate standard concentrations that were diluted in plasma were thus calculated by subtracting the real glutamine and glutamate concentrations previously measured in plasma. We added 0.600 mM, 0.700 mM, 0.800 mM and 0.900 mM of glutamine standard into four different vials of one plasma sample, and the

80°C and analysed within 2 months.

**2.4 Assays for glutamine and glutamate measurements** 

glutamine electrode minus that detected by the glutamate electrode.

Q water (Millipore Corporation, MA, USA).

coefficient and the slope (r) were approximately 1.

and 3.8%, respectively.

Initially, all data from the 3000-m time trial tests were organised using Microsoft Excel and were checked for consistency between the recorded values and their identities with the evaluated subjects. After this initial organisation, all data were imported into Matlab® 7.0, which is the platform on which the software that was designed for this study was developed. Despite having been developed in this environment, the software was compiled and can run on any computer using Microsoft Windows and does not require the installation of Matlab® 7.0. The software, Origin 6.0, was used to perform statistical analyses and to generate graphs. An unpaired t test was used to compare RT and NRT glutamine and glutamate measurements; p < 0.05 was considered significant. The *Horn's* algorithm was applied to detect and remove outliers (Horn et al., 2001). The RefVal program (Solberg 2004), including practical approaches and formulas recommended by the IFCC, was used to calculate the 97.5th and 2.5th percentiles of the subjects and their respective 0.90 confidence intervals. This was achieved by using parametric estimates of the glutamine levels, glutamate levels and Gm/Ga ratios that were obtained from plasma samples of the 146 volunteers after four months of daily, periodic physical activity.

## **3. Results**


Table 2 shows the reference intervals (upper and lower limits) and the confidence intervals for healthy, physically active young men.

Table 2. Reference intervals for glutamine level, glutamate level and Gm/Ga ratio for healthy, physically active young men.

Figure 5 presents the performance analyses of the test subjects for the 3000-m time trial, over the span of one training year. At the end of the training year (May x October, Fig. 4C) almost all subjects performed below 14' 59'' (cut off) and the 3000-m times were more homogenous than at the beginning of the year (February x April, Fig. 4A). However, the regression line in Fig. 4C approaches the line of identity due to the increase in the amount of time it took some the test subjects (n = 222) to complete the 3000-m trial in October compared to May.

Glutamine and Glutamate Reference Intervals as a Clinical

Tool to Detect Training Intolerance During Training and Overtraining 55

Figure 6 presents the blood glutamine and glutamate levels of responders and nonresponders to training in relation to the 97.5th and 2.5th percentile reference interval. Only four NRT subjects had Gm/Ga ratios below the reference interval, and one of them also

Fig. 6. Gm/Ga (A), glutamine (B) and glutamate (C) plasma levels (µM) of responders and non-responders to training (NRT; n = 25). Solid lines are the reference intervals. **X** identifies

This is the first study to establish reference intervals for glutamine, glutamate and Gm/Ga ratio in the plasma of test subjects, according to the IFCC recommendations. The reference intervals presented here are not comparable to other studies regarding the subject selection criteria or number (n= 143), sample preparation, sample storage or the equipment used for

one subject with low Gm/Ga ratio and glutamine.

glutamine and glutamate measurements (i.e., YSI 2700).

**4. Discussion** 

presented a glutamine concentration below the reference interval (subject "X").

Fig. 5. Comparisons of 3000-m time trial tests over the training year. In these charts, the identity lines are dashed and the regression lines are solid. The identity line is the reference for time changes between the two tests. Subjects who fell below the identity line showed lower 3000-m times from one test to the next, so the performance was improved. The regression line shows the trend between the two 3000-m time trial tests over the year. Dotted lines show the Army School's cutting time in 14'59''.

Table 3 shows the comparisons of glutamine, glutamate and Gm/Ga between the RT and NRT groups. We observed a tendency of lower glutamine, significantly higher glutamate and significantly lower Gm/Ga ratio in the NRT group compared to the RT group.


Table 3. Glutamine, glutamate and Gm/Ga ratio in the RT group vs. the NRT group. RT: Responders to training (n = 25). NRT: Non-responders to training (n = 25). \* Unpaired t test: significant difference between groups (glutamate, p = 0.002 and Gm/Ga, p = 0.0003).

Fig. 5. Comparisons of 3000-m time trial tests over the training year. In these charts, the identity lines are dashed and the regression lines are solid. The identity line is the reference for time changes between the two tests. Subjects who fell below the identity line showed lower 3000-m times from one test to the next, so the performance was improved. The regression line shows the trend between the two 3000-m time trial tests over the year.

Table 3 shows the comparisons of glutamine, glutamate and Gm/Ga between the RT and NRT groups. We observed a tendency of lower glutamine, significantly higher glutamate

Glutamine (µM) 669 ± 49 645 ± 63

Glutamate (µM) 41 ± 5 46 ± 6 \*

Gm/Ga 16 ± 2 14 ± 2 \*

Table 3. Glutamine, glutamate and Gm/Ga ratio in the RT group vs. the NRT group. RT: Responders to training (n = 25). NRT: Non-responders to training (n = 25). \* Unpaired t test: significant difference between groups (glutamate, p = 0.002 and Gm/Ga, p = 0.0003).

RT NRT

and significantly lower Gm/Ga ratio in the NRT group compared to the RT group.

Dotted lines show the Army School's cutting time in 14'59''.

Figure 6 presents the blood glutamine and glutamate levels of responders and nonresponders to training in relation to the 97.5th and 2.5th percentile reference interval. Only four NRT subjects had Gm/Ga ratios below the reference interval, and one of them also presented a glutamine concentration below the reference interval (subject "X").

Fig. 6. Gm/Ga (A), glutamine (B) and glutamate (C) plasma levels (µM) of responders and non-responders to training (NRT; n = 25). Solid lines are the reference intervals. **X** identifies one subject with low Gm/Ga ratio and glutamine.

## **4. Discussion**

This is the first study to establish reference intervals for glutamine, glutamate and Gm/Ga ratio in the plasma of test subjects, according to the IFCC recommendations. The reference intervals presented here are not comparable to other studies regarding the subject selection criteria or number (n= 143), sample preparation, sample storage or the equipment used for glutamine and glutamate measurements (i.e., YSI 2700).

Glutamine and Glutamate Reference Intervals as a Clinical

chronic training effect on glutamine metabolism.

program, then intolerance to training may have occurred.

established from endurance-trained subjects, as presented here.

well-trained subjects.

**reference intervals** 

Tool to Detect Training Intolerance During Training and Overtraining 57

minimum of 120 subjects (Solberg, 2004). However, those glutamine and glutamate values are slightly lower than the upper limits of the reference intervals found in this study (Table 2), probably due to the regular physical training effect. Particularly, glutamine may increase due to endurance training. Rowbottom et al. (1997) identified a positive correlation between the level of plasma glutamine and improved endurance performance. In addition, Kargotich et al. (2006) showed increased plasma Gm, VO2max and time to exhaustion after 6 weeks of endurance training (3 to 6 × 90-minute sessions per week at 70% VO2 max) in active, not

Plasma glutamine level is also influenced by acute exercise. The effect of acute exercise on plasma glutamine seems to depend on the intensity and duration of the exercise (Hiscock & Pedersen, 2002). For instance, intermittent, high-intensity bouts of activity decrease plasma glutamine (Walsh et al., 1998), but a single, short, high-intensity bout increases (Babij et al., 1983) or maintains glutamine (Sewell et al., 1994). Other studies have shown that blood glutamine decreases for 2–4 hours after acute exercise lasting more than 2 hours (Castell et al., 1997; Rohde et al., 1996). Therefore, to use the glutamine and glutamate reference intervals as a training monitoring tool, it is advised not to withdraw blood samples right after acute exercise of any type. Otherwise, the effect of acute exercise may overcome the

As well as being influenced by exercise, blood glutamine level may also change due carbohydrate (CHO) intake. Wagenmakers et al. (1991) showed that both plasma glutamine and glutamate decrease after glycogen depletion, compared to CHO loading during exercise. This change is probably due to increased deamination of amino acids. Blanchand et al. (2001) showed that resting mean plasma glutamine of test subjects was significantly higher after 14 days of rich CHO intake (70% of total energy; mean plasma glutamine of 857 µM) than in subjects with poor CHO intake (45% of total energy; mean plasma glutamine of 610 µM). In practice, athletes and coaches should carefully plan the CHO intake of the athlete because it can also negatively affect performance (Maughan et al., 1997). If performance decreases and glutamine falls below the reference interval, but CHO intake is sufficient for maintenance of liver and muscular glycogen levels during the training

For the reasons expressed so far, the reference intervals for glutamine and glutamate presented here should be used whether the measurement conditions (i.e., storage, preparation and assay technique) are reproduced faithfully. Sufficient CHO energy intake for the maintenance of glycogen stores is recommended in order to avoid glutamine/glutamate changes associated with performance decay related to poor nutrition. Overnight fasting and 8 to 12 hours of recovery from a previous training session should be considered before blood sample withdrawal. Finally, it is advised, for endurance training monitoring, to use glutamine, glutamate and Gm/Ga reference intervals that have been

**4.2 Training monitoring using self-paced time trials and glutamine and glutamate** 

To test the glutamine and glutamate reference intervals, 25 subjects were randomly selected as responders to training, due to their decreased times in the self-paced 3000-m time trial in October compared to May (subjects below identity line, Fig. 5C). The self-paced time trial is

#### **4.1 Methodological aspects and confounding factors in measuring plasma glutamine and glutamate**

To date, three different methods have been used to measure glutamine and glutamate concentrations: high-performance liquid chromatography (HPLC), the enzymatic method and a glutamine-dependent *Escherichia coli* bioassay (Hiscock & Pedersen, 2002). According to Hiscock and Pedersen (2002), the major difference amongst the three is in the concentrations of glutamine that is detected. When measured by the bioassay, the glutamine concentration is 40% higher than by HPLC. In addition, plasma glutamate measured by the enzymatic assay seems to be more than 200% higher than HPLC. The reason for disagreement amongst these methods is speculative. However, the discrepancies may be verified by noting the plasma glutamate values between 100 and 200 µM measured by the enzymatic assay (Halson et al., 2003; Keast et al., 1995; Smith & Norris, 2000) with values between 28 and 55 µM measured by HPLC (Abdulrazzaq & Ibrahim, 2001; de Jonge & Breuer, 1995; Van Eijk et al., 1994).

HPLC has been widely used in methodological studies that evaluate the stability of amino acids in non-deproteinated, deproteinated and neutralised samples at several storage temperatures (Abdulrazzaq & Ibrahim, 2001; de Jonge & Breuer, 1995; Van Eijk et al., 1994). Although HPLC is considered a reliable method for amino acid measurements, it also requires highly skilled laboratory personnel to guarantee reliability. According to the manufacturer (Yellow Springs Instrument Co., Ohio, USA), the YSI 2700 electrode method is highly correlated with HPLC glutamine measurements (slope 0.94 and r2=0.99), with the advantage of being much more user-friendly than the HPLC method. The reliability tests performed in this study also showed outstanding accuracy in measuring glutamine and glutamate standards in plasma, good linearity and small within-day and between-day variability.

Blood plasma storage and deproteination add systematic errors that can influence interlaboratorial comparisons of data (de Jonge & Breuer, 1996). Blood acid deproteination is frequently used to stop enzymatic activity; however, glutamine is particularly influenced by sample acid deproteination because a low storage pH accelerates the spontaneous degradation of glutamine in pyroglutamate (Khan et al., 1991). Nevertheless, the systematic error caused by deproteination is negligible when samples are stored below -70°C (Van Eijk et al., 1994). Glutamine and glutamate are stable for at least 6 months in non-deproteinated samples that are stored below -70°C (Van Eijk et al., 1994) but are not stable when stored at - 20°C for more than 4 weeks (Abdulrazzaq & Ibrahim, 2001; de Jonge & Breuer, 1996). Therefore, based on those previous studies, non-deproteinated plasma stored at -80°C was used in this study.

Plasma glutamine and glutamate seem to not be affected by sex or age. Van Eijk et al. (1994) used deproteinated plasma and HPLC as their measurement technique. They showed mean glutamine levels between 663 and 693 µM and mean glutamate levels between 49 and 55 µM in healthy males 20–69 years old, with no significant differences in those amino acids between males and females. Planche et al. (2002) showed no differences in plasma glutamine and or glutamate between healthy children (12–71 months) and healthy adults (age not specified) using the YSI 2700. They observed a mean value (range min–max) of 532 (485–577) µM for glutamine and 32 (28–43) µM for glutamate. Both Planche et al. (2002) and Van Eijk et al. (1994) used only 8–12 subjects, which are insufficient numbers to consider those glutamine and glutamate values as references, according to the IFCC, who recommend a

**4.1 Methodological aspects and confounding factors in measuring plasma glutamine** 

To date, three different methods have been used to measure glutamine and glutamate concentrations: high-performance liquid chromatography (HPLC), the enzymatic method and a glutamine-dependent *Escherichia coli* bioassay (Hiscock & Pedersen, 2002). According to Hiscock and Pedersen (2002), the major difference amongst the three is in the concentrations of glutamine that is detected. When measured by the bioassay, the glutamine concentration is 40% higher than by HPLC. In addition, plasma glutamate measured by the enzymatic assay seems to be more than 200% higher than HPLC. The reason for disagreement amongst these methods is speculative. However, the discrepancies may be verified by noting the plasma glutamate values between 100 and 200 µM measured by the enzymatic assay (Halson et al., 2003; Keast et al., 1995; Smith & Norris, 2000) with values between 28 and 55 µM measured by HPLC (Abdulrazzaq & Ibrahim, 2001; de Jonge &

HPLC has been widely used in methodological studies that evaluate the stability of amino acids in non-deproteinated, deproteinated and neutralised samples at several storage temperatures (Abdulrazzaq & Ibrahim, 2001; de Jonge & Breuer, 1995; Van Eijk et al., 1994). Although HPLC is considered a reliable method for amino acid measurements, it also requires highly skilled laboratory personnel to guarantee reliability. According to the manufacturer (Yellow Springs Instrument Co., Ohio, USA), the YSI 2700 electrode method is highly correlated with HPLC glutamine measurements (slope 0.94 and r2=0.99), with the advantage of being much more user-friendly than the HPLC method. The reliability tests performed in this study also showed outstanding accuracy in measuring glutamine and glutamate standards in plasma, good linearity and small within-day and between-day

Blood plasma storage and deproteination add systematic errors that can influence interlaboratorial comparisons of data (de Jonge & Breuer, 1996). Blood acid deproteination is frequently used to stop enzymatic activity; however, glutamine is particularly influenced by sample acid deproteination because a low storage pH accelerates the spontaneous degradation of glutamine in pyroglutamate (Khan et al., 1991). Nevertheless, the systematic error caused by deproteination is negligible when samples are stored below -70°C (Van Eijk et al., 1994). Glutamine and glutamate are stable for at least 6 months in non-deproteinated samples that are stored below -70°C (Van Eijk et al., 1994) but are not stable when stored at - 20°C for more than 4 weeks (Abdulrazzaq & Ibrahim, 2001; de Jonge & Breuer, 1996). Therefore, based on those previous studies, non-deproteinated plasma stored at -80°C was

Plasma glutamine and glutamate seem to not be affected by sex or age. Van Eijk et al. (1994) used deproteinated plasma and HPLC as their measurement technique. They showed mean glutamine levels between 663 and 693 µM and mean glutamate levels between 49 and 55 µM in healthy males 20–69 years old, with no significant differences in those amino acids between males and females. Planche et al. (2002) showed no differences in plasma glutamine and or glutamate between healthy children (12–71 months) and healthy adults (age not specified) using the YSI 2700. They observed a mean value (range min–max) of 532 (485–577) µM for glutamine and 32 (28–43) µM for glutamate. Both Planche et al. (2002) and Van Eijk et al. (1994) used only 8–12 subjects, which are insufficient numbers to consider those glutamine and glutamate values as references, according to the IFCC, who recommend a

**and glutamate** 

variability.

used in this study.

Breuer, 1995; Van Eijk et al., 1994).

minimum of 120 subjects (Solberg, 2004). However, those glutamine and glutamate values are slightly lower than the upper limits of the reference intervals found in this study (Table 2), probably due to the regular physical training effect. Particularly, glutamine may increase due to endurance training. Rowbottom et al. (1997) identified a positive correlation between the level of plasma glutamine and improved endurance performance. In addition, Kargotich et al. (2006) showed increased plasma Gm, VO2max and time to exhaustion after 6 weeks of endurance training (3 to 6 × 90-minute sessions per week at 70% VO2 max) in active, not well-trained subjects.

Plasma glutamine level is also influenced by acute exercise. The effect of acute exercise on plasma glutamine seems to depend on the intensity and duration of the exercise (Hiscock & Pedersen, 2002). For instance, intermittent, high-intensity bouts of activity decrease plasma glutamine (Walsh et al., 1998), but a single, short, high-intensity bout increases (Babij et al., 1983) or maintains glutamine (Sewell et al., 1994). Other studies have shown that blood glutamine decreases for 2–4 hours after acute exercise lasting more than 2 hours (Castell et al., 1997; Rohde et al., 1996). Therefore, to use the glutamine and glutamate reference intervals as a training monitoring tool, it is advised not to withdraw blood samples right after acute exercise of any type. Otherwise, the effect of acute exercise may overcome the chronic training effect on glutamine metabolism.

As well as being influenced by exercise, blood glutamine level may also change due carbohydrate (CHO) intake. Wagenmakers et al. (1991) showed that both plasma glutamine and glutamate decrease after glycogen depletion, compared to CHO loading during exercise. This change is probably due to increased deamination of amino acids. Blanchand et al. (2001) showed that resting mean plasma glutamine of test subjects was significantly higher after 14 days of rich CHO intake (70% of total energy; mean plasma glutamine of 857 µM) than in subjects with poor CHO intake (45% of total energy; mean plasma glutamine of 610 µM). In practice, athletes and coaches should carefully plan the CHO intake of the athlete because it can also negatively affect performance (Maughan et al., 1997). If performance decreases and glutamine falls below the reference interval, but CHO intake is sufficient for maintenance of liver and muscular glycogen levels during the training program, then intolerance to training may have occurred.

For the reasons expressed so far, the reference intervals for glutamine and glutamate presented here should be used whether the measurement conditions (i.e., storage, preparation and assay technique) are reproduced faithfully. Sufficient CHO energy intake for the maintenance of glycogen stores is recommended in order to avoid glutamine/glutamate changes associated with performance decay related to poor nutrition. Overnight fasting and 8 to 12 hours of recovery from a previous training session should be considered before blood sample withdrawal. Finally, it is advised, for endurance training monitoring, to use glutamine, glutamate and Gm/Ga reference intervals that have been established from endurance-trained subjects, as presented here.

#### **4.2 Training monitoring using self-paced time trials and glutamine and glutamate reference intervals**

To test the glutamine and glutamate reference intervals, 25 subjects were randomly selected as responders to training, due to their decreased times in the self-paced 3000-m time trial in October compared to May (subjects below identity line, Fig. 5C). The self-paced time trial is

Glutamine and Glutamate Reference Intervals as a Clinical

to the context of biological individuality and fitness level.

Gm/Ga ratios within the reference intervals (Fig. 6).

program because of insufficient workload.

**6. Acknowledgements** 

**5. Conclusion** 

Tool to Detect Training Intolerance During Training and Overtraining 59

On the other hand, when the performance decreases and the biomarker levels fall outside

Any blood biomarker that is measured during training monitoring would be useful when the detectable changes in blood happen before NFOR/OTS outcomes. In this sense, previous studies that have measured glutamine and glutamate before and after a period of intense training loads seem to agree that changes in these amino acids occur before the extreme OT outcomes, independent of the terminologies used to define them (Halson et al., 2003; Keast et al., 1995; Smith & Norris, 2000; Souza et al., 2005). Nevertheless, intense exercise is related

The occurrence of mean lower glutamine, a significantly lower Gm/Ga ratio and higher glutamate in the NRT group compared to the RT group may indicate slight differences in glutamine and glutamate metabolism (Table 2). However, mean glutamine, glutamate and Gm/Ga ratio of the NRT group were all within the population reference interval. Therefore, glutamine and glutamate mean values may be indicative of higher training stress or lower endurance capacity within a group but are meaningless regarding individual intervention. When the reference intervals were used, only four subjects from the NRT group showed Gm/Ga ratios below the reference interval, and one of them also showed low glutamine (Fig. 6). Of note, all 25 subjects in the RT group showed glutamine and glutamate levels, and

These results suggest that only those four subjects, and particularly the subject with low glutamine concentration (X-mark in Fig. 6), should continue the training program with close daily or weekly monitoring of their mood profiles. The remaining 21 NRT subjects probably were not responsive to the training program in the last four months; therefore, the 3000-m

For most trainers and sportsmen, the main sign that an athlete has developed NFOR/OTS is sustained poor performance and fatigue. Poor performance and fatigue, however, can also be due to many other factors, such as inadequate training sessions and poor nutrition, respectively, as well as to extraneous factors such as loss of confidence, pressure outside of the sport, and sleep disturbances. The reference intervals of glutamine, glutamate and Gm/Ga ratio presented here may therefore be useful tools to monitor adaptation to training and to thereby identify those athletes who show early signs of OT before prolonged fatigue. In addition, glutamine and glutamate blood concentrations may identify those athletes who are intolerant to a training program versus those who are not responsive to the training

The authors thank Ana Maria Marçal Porto and Ismair Teodoro Reis for technical support. This study was financially supported by the Sao Paulo State Research Support Foundation (FAPESP) and Campinas State University Foundation (FUNCAMP). Rodrigo Hohl

time trial test probably reflects stagnation and the inherent variation of the test.

the reference interval limits, then extended recovery should be considered.

**4.3 Practical use of glutamine, glutamate and Gm/Ga ratio reference intervals** 

influenced by an anticipatory component, which, in turn, is influenced by physiological inputs prior to exercise that are related to the fitness level, expected exercise distance/duration and previous experience of the test subject (Tucker, 2009). The army students were experienced in the 3000-m time trial because they had previously performed many trials throughout the year in addition to the four officially valid ones for the army school records. In addition, they were task-motivated, due to the internal competition amongst themselves and also to the army school grading. However, many students showed poorer performance in October compared to May (subjects above identity line, Fig. 5C), and therefore, an additional 25 subjects were randomly selected from this group to test the reference intervals. No additional symptoms, such as mood alterations or increased incidence of injuries, which would also be characteristic of NFOR/OTS, were observed (Meeusen et al., 2006); therefore, those subjects were considered non-responders to training.

The same exercise-training stimulus may be either efficient or insufficient in improving performance and physiological adaptation when applied to many different subjects. An insufficient training stimulus may cause unexpected stagnation or a mild decrease in performance in self-paced time trials of subjects who are not responding (i.e., adapting) to training. The pacing strategy also depends on external factors such as the environment, race situation and the influence of other competitors (Tucker, 2009). Therefore, decreased performance may not be caused only by physiological maladaptation, but may also be a result of stagnation and test variability. In this sense, the army training program does not aim for high performance levels, as in professional athletes; the goal, in this case, is to homogenise the fitness level of young cadets on their first step in an army career. To reduce the differences amongst the 3000-m time trials, all students were subjected to a similar training program and load.

Figures 5A to 5C show that the army training program was effective in improving the 3000 m time trial for those subjects with higher initial 3000-m times (above ~15 min). However, the training program was less effective in decreasing the 3000-m time trial for subjects who performed below 14 minutes. The scattered points behaviour suggest that the army endurance training program was an insufficient stimulus for students already performing the 3000-m time trial below ~14 min early in the season (Figs. 5A and B).

However, the self-pacing time trial is also the result of a complex, physiological, integrative model of exercise. It has been stated that 'pacing is controlled by the brain, which regulates exercise intensity and alters the adopted pacing strategy to ensure that potentially catastrophic derangements to homeostasis do not occur' (Noakes et al., 2005). This definition holds that during self-paced exercise, when one is able to select an exercise work rate, performance is regulated by a central governor (i.e., brain) to prevent changes in physiological systems that may be harmful during exercise (Noakes et al., 2005; Tucker, 2009). We hypothesize that glutamine and glutamate are related to many of the chronic and acute metabolic aspects related to exercise (topic 1.4 in this chapter) which could interfere with the central governor regulation. In this sense, glutamine and glutamate reference intervals may be used as additional tools for deciding between heavier training loads to avoid detraining/stagnation, or extended recovery to avoid maladaptation. In practice, when the performance decreases and the glutamine, glutamate or Gm/Ga ratio falls within the reference interval limits, then an adjustment in the training load should be considered. On the other hand, when the performance decreases and the biomarker levels fall outside the reference interval limits, then extended recovery should be considered.

## **4.3 Practical use of glutamine, glutamate and Gm/Ga ratio reference intervals**

Any blood biomarker that is measured during training monitoring would be useful when the detectable changes in blood happen before NFOR/OTS outcomes. In this sense, previous studies that have measured glutamine and glutamate before and after a period of intense training loads seem to agree that changes in these amino acids occur before the extreme OT outcomes, independent of the terminologies used to define them (Halson et al., 2003; Keast et al., 1995; Smith & Norris, 2000; Souza et al., 2005). Nevertheless, intense exercise is related to the context of biological individuality and fitness level.

The occurrence of mean lower glutamine, a significantly lower Gm/Ga ratio and higher glutamate in the NRT group compared to the RT group may indicate slight differences in glutamine and glutamate metabolism (Table 2). However, mean glutamine, glutamate and Gm/Ga ratio of the NRT group were all within the population reference interval. Therefore, glutamine and glutamate mean values may be indicative of higher training stress or lower endurance capacity within a group but are meaningless regarding individual intervention.

When the reference intervals were used, only four subjects from the NRT group showed Gm/Ga ratios below the reference interval, and one of them also showed low glutamine (Fig. 6). Of note, all 25 subjects in the RT group showed glutamine and glutamate levels, and Gm/Ga ratios within the reference intervals (Fig. 6).

These results suggest that only those four subjects, and particularly the subject with low glutamine concentration (X-mark in Fig. 6), should continue the training program with close daily or weekly monitoring of their mood profiles. The remaining 21 NRT subjects probably were not responsive to the training program in the last four months; therefore, the 3000-m time trial test probably reflects stagnation and the inherent variation of the test.

## **5. Conclusion**

58 An International Perspective on Topics in Sports Medicine and Sports Injury

influenced by an anticipatory component, which, in turn, is influenced by physiological inputs prior to exercise that are related to the fitness level, expected exercise distance/duration and previous experience of the test subject (Tucker, 2009). The army students were experienced in the 3000-m time trial because they had previously performed many trials throughout the year in addition to the four officially valid ones for the army school records. In addition, they were task-motivated, due to the internal competition amongst themselves and also to the army school grading. However, many students showed poorer performance in October compared to May (subjects above identity line, Fig. 5C), and therefore, an additional 25 subjects were randomly selected from this group to test the reference intervals. No additional symptoms, such as mood alterations or increased incidence of injuries, which would also be characteristic of NFOR/OTS, were observed (Meeusen et al., 2006); therefore, those subjects were

The same exercise-training stimulus may be either efficient or insufficient in improving performance and physiological adaptation when applied to many different subjects. An insufficient training stimulus may cause unexpected stagnation or a mild decrease in performance in self-paced time trials of subjects who are not responding (i.e., adapting) to training. The pacing strategy also depends on external factors such as the environment, race situation and the influence of other competitors (Tucker, 2009). Therefore, decreased performance may not be caused only by physiological maladaptation, but may also be a result of stagnation and test variability. In this sense, the army training program does not aim for high performance levels, as in professional athletes; the goal, in this case, is to homogenise the fitness level of young cadets on their first step in an army career. To reduce the differences amongst the 3000-m time trials, all students were subjected to a similar

Figures 5A to 5C show that the army training program was effective in improving the 3000 m time trial for those subjects with higher initial 3000-m times (above ~15 min). However, the training program was less effective in decreasing the 3000-m time trial for subjects who performed below 14 minutes. The scattered points behaviour suggest that the army endurance training program was an insufficient stimulus for students already performing

However, the self-pacing time trial is also the result of a complex, physiological, integrative model of exercise. It has been stated that 'pacing is controlled by the brain, which regulates exercise intensity and alters the adopted pacing strategy to ensure that potentially catastrophic derangements to homeostasis do not occur' (Noakes et al., 2005). This definition holds that during self-paced exercise, when one is able to select an exercise work rate, performance is regulated by a central governor (i.e., brain) to prevent changes in physiological systems that may be harmful during exercise (Noakes et al., 2005; Tucker, 2009). We hypothesize that glutamine and glutamate are related to many of the chronic and acute metabolic aspects related to exercise (topic 1.4 in this chapter) which could interfere with the central governor regulation. In this sense, glutamine and glutamate reference intervals may be used as additional tools for deciding between heavier training loads to avoid detraining/stagnation, or extended recovery to avoid maladaptation. In practice, when the performance decreases and the glutamine, glutamate or Gm/Ga ratio falls within the reference interval limits, then an adjustment in the training load should be considered.

the 3000-m time trial below ~14 min early in the season (Figs. 5A and B).

considered non-responders to training.

training program and load.

For most trainers and sportsmen, the main sign that an athlete has developed NFOR/OTS is sustained poor performance and fatigue. Poor performance and fatigue, however, can also be due to many other factors, such as inadequate training sessions and poor nutrition, respectively, as well as to extraneous factors such as loss of confidence, pressure outside of the sport, and sleep disturbances. The reference intervals of glutamine, glutamate and Gm/Ga ratio presented here may therefore be useful tools to monitor adaptation to training and to thereby identify those athletes who show early signs of OT before prolonged fatigue. In addition, glutamine and glutamate blood concentrations may identify those athletes who are intolerant to a training program versus those who are not responsive to the training program because of insufficient workload.

## **6. Acknowledgements**

The authors thank Ana Maria Marçal Porto and Ismair Teodoro Reis for technical support. This study was financially supported by the Sao Paulo State Research Support Foundation (FAPESP) and Campinas State University Foundation (FUNCAMP). Rodrigo Hohl

Glutamine and Glutamate Reference Intervals as a Clinical

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**3** 

Juliette Hussey

*Ireland* 

**Physical Activity Measures in** 

*F.T.C.D. Discipline of Physiotherapy, School of Medicine, Trinity Centre for Health Sciences, St James's Hospital, Dublin* 

**Children – Which Method to Use?** 

There is increasing evidence supporting the health benefits of physical activity in children both in the immediate term in terms of body composition and in the longer term in the prevention of risk factors for cardiovascular disease, type-2 diabetes osteoporosis and certain cancers (Lee at al, 2000; Mohan et al, 2005; Blair et al, 1999; Pan et al, 1997;

Physical activity is defined as body movement produced by skeletal muscles which results in energy expenditure (Caspersen et al, 1985). It can be difficult to measure physical activity as it is a variable with many dimensions including type, frequency, duration and intensity. The context and location where activity takes place may also be of interest. As a behaviour physical activity is unstable, as habitual levels of activity vary during the day, throughout the week and at different times of the year. Children have less regular patterns of activity than adults and therefore a picture of overall activity may be more difficult to capture. Spontaneous un-planned activity is typical of children, particularly younger children, and may be due to opportunities that present within their environment such as play facilities and other children. Even in older children who do engage in regular planned sporting

The "gold standard" method of measuring energy expenditure as a result of physical activity is by the use of doubly labelled water (DLW). The technique uses stable isotopes of hydrogen and oxygen i.e. deuterium (2H) and oxygen (18O) ingested as water. Oxygen uptake and therefore energy expenditure are then calculated from the rate at which these isotopes are eliminated from the body. The difference between these is the amount of CO2 produced. DLW is not a technique that is generally suitable for use in field studies due to the resources required. Even if cost were not a consideration the information gained pertains to total energy expenditure over a time period e.g. a number of days/ weeks and does not permit the examination of acute patterns of physical activity such as time spent in

The methods that can be used in field and most clinical studies are generally divided into the following; subjective methods such as observation and questionnaires and objective methods such as heart rate monitoring and motion sensors. The advantages and limitations of these methods will be outlined below. Before discussing these methods a synopsis of

Tuomilehto et al, 2001; Colditz et al, 2005; Samad et al, 2005).

activities, there may be a degree of unplanned activity.

specific activities or intensity of specific exercise sessions.

energy expenditure in children will be presented.

**1. Introduction** 


## **Physical Activity Measures in Children – Which Method to Use?**

## Juliette Hussey

*F.T.C.D. Discipline of Physiotherapy, School of Medicine, Trinity Centre for Health Sciences, St James's Hospital, Dublin Ireland* 

### **1. Introduction**

64 An International Perspective on Topics in Sports Medicine and Sports Injury

Wagenmakers, JM. (1998). Muscle amino acid metabolism at rest and during exercise: role in

Walsh, NP., Blannin, AK., Robson, PJ., & Gleeson, M. (1998). Glutamine, exercise and

Hollosky (Ed.), pp. 287-314, Willians and Wilkins, Baltimore.

pp. 177–191.

human physiology and metabolism. In: Exercise and Sport Sciences Reviews, J.O

immune function: links and possible mechanisms. *Sports Med, Vol.* 26, No. 3 (Sep),

There is increasing evidence supporting the health benefits of physical activity in children both in the immediate term in terms of body composition and in the longer term in the prevention of risk factors for cardiovascular disease, type-2 diabetes osteoporosis and certain cancers (Lee at al, 2000; Mohan et al, 2005; Blair et al, 1999; Pan et al, 1997; Tuomilehto et al, 2001; Colditz et al, 2005; Samad et al, 2005).

Physical activity is defined as body movement produced by skeletal muscles which results in energy expenditure (Caspersen et al, 1985). It can be difficult to measure physical activity as it is a variable with many dimensions including type, frequency, duration and intensity. The context and location where activity takes place may also be of interest. As a behaviour physical activity is unstable, as habitual levels of activity vary during the day, throughout the week and at different times of the year. Children have less regular patterns of activity than adults and therefore a picture of overall activity may be more difficult to capture. Spontaneous un-planned activity is typical of children, particularly younger children, and may be due to opportunities that present within their environment such as play facilities and other children. Even in older children who do engage in regular planned sporting activities, there may be a degree of unplanned activity.

The "gold standard" method of measuring energy expenditure as a result of physical activity is by the use of doubly labelled water (DLW). The technique uses stable isotopes of hydrogen and oxygen i.e. deuterium (2H) and oxygen (18O) ingested as water. Oxygen uptake and therefore energy expenditure are then calculated from the rate at which these isotopes are eliminated from the body. The difference between these is the amount of CO2 produced. DLW is not a technique that is generally suitable for use in field studies due to the resources required. Even if cost were not a consideration the information gained pertains to total energy expenditure over a time period e.g. a number of days/ weeks and does not permit the examination of acute patterns of physical activity such as time spent in specific activities or intensity of specific exercise sessions.

The methods that can be used in field and most clinical studies are generally divided into the following; subjective methods such as observation and questionnaires and objective methods such as heart rate monitoring and motion sensors. The advantages and limitations of these methods will be outlined below. Before discussing these methods a synopsis of energy expenditure in children will be presented.

Physical Activity Measures in Children – Which Method to Use? 67

e.g. a school playground. Observers are trained to note behavioural information about the types of activities, the time spent in each activity and the frequency of such. Short time periods may not be reflective of habitual physical activity but observing for longer periods can be tedious and may lead to inaccuracies in reporting information. Recording can also be done by video recording providing a permanent record. In order to obtain a complete picture of physical activity of children a number of time periods such as during school break

Information on the physical surroundings which may influence physical activity levels can be obtained in observational studies, and thus these studies can provide added data which may partly explain the reasons for particular findings e.g. environmental factors such as few public play areas which may explain low levels of activity (Johns & Ha, 1999). While accurate information on the intensity of activity cannot be captured by observation methods they may be useful where classification of activity is all that is required (Chen et al, 2002). Observation methods also allow data to be collected on inactivity as the length of intervals between activities can be determined. With the advent of lightweight activity monitors

Questionnaires and interviews are frequently the method of collecting data on physical activity in studies involving large numbers of subjects where simple, inexpensive methods are required. Questionnaires measuring activity in adults usually include questions on occupational, transport and leisure time activity. As data is collected after its occurrence the procedure does not influence performance. The validity of questionnaires measuring physical activity is difficult to establish due to the lack of a gold standard criterion against which to compare them and the problems with long term recall of activities and the

The use of questionnaires in children requires specific considerations. Below the age of 10-12 years children can only give limited information about their activity patterns. Parents, teachers or other adults may give details of the child's physical activity but this information may be estimated especially for outside activities. Regular planned activity is relatively easy to remember in the short term but so much of childrens' activity is spontaneous, unplanned and of a stop start nature and therefore activity may be difficult to recall. Children can also have difficulties estimating the duration of activities and time frames may need to be provided for the child in terms of *"on the way to school, break time, way home from school, before dinner"* etc. Questions about participation in Physical Education (P.E.), method of transport to and from school and extra-curricular activities may be included to gain information on

The Modifiable Activity Questionnaire for Adolescents (MAQA) assesses physical activity over the previous year (Aaron et al, 1993). It includes questions on the number of times in the previous 14 days the subject engaged in at least 20 minutes of hard and of light exercise. Hours per days spent watching television, videos, playing computer games each day and the number of competitive activities the adolescent participates in is assessed, as is the

Heart rate (HR) monitoring is an objective method of measuring physical activity. HR monitoring can provide details on the time, intensity and frequency of specific activities

times, lunch times and physical education classes may be required.

there may be less need for observation studies in children.

likelihood of overestimation of time and intensity of activity.

energy expended in regular physical activity each week.

**5. Heart rate monitoring** 

childrens' overall activity levels. Providing a list of activities can aid recall.

**4. Questionnaires** 

## **2. Energy expenditure in children**

The largest factor that determines total daily energy expenditure (TDEE) is the basal or resting metabolic rate. It refers to the energy expended during normal cellular and organ functioning during post-absorptive resting conditions and accounts for roughly 60-75% TDEE. The second factor is the thermic effect of feeding and it accounts for approximately 10% of TDEE. The third component is the energy expended during physical activity. This can occur as a result of volitional mechanical work, such as exercise and daily activities, and non-volitional activity, such as fidgeting, spontaneous muscle contractions, and maintaining posture. It is the most modifiable component of TDEE and accounts for 20-30% of TDEE. As the output of physical activity energy expenditure (PAEE) is the most modifiable component of total daily EE, it may elicit the greatest response to intervention and subsequently, produce a beneficial impact on health related issues.

A sum of the estimation of energy expended in activity can be calculated from the data provided in questionnaires. The concept of measuring energy expenditure as a multiple of an individual's basal metabolic rate is used and examples of many activities are presented in the form of tables and published in compendiums. The compendia express EE in METs which are multiples of the resting metabolic rate (RMR).

The concept of denoting energy expenditure as a multiple of an individual's basal metabolic rate (BMR) has been referred to as 1 MET. In 1955 Passmore and Durnin published a review of measurements of human energy expenditure of various activities made by indirect calorimetry. This enabled the calculation of an individual's daily energy expenditure by the total of the metabolic cost of each activity by its duration. Initial work in this area was performed using a human calorimeter where the heat produced by a subject's metabolism was directly related to the temperature change in a contained environment, but more recent studies have used indirect calorimetry where expired air is continuously sampled.

In adults the resting metabolic rate (RMR) is taken as 3.5 mL O2/kg/min. However despite the widespread acceptance of the 1 MET = 3.5 mL O2/kg/min studies that have made such measures in specific cohorts have found different mean values. Byrne et al (2005) found that average resting VO2 was 2.56 +/- 0.4 ml/O2/kg and in this case if the 1 MET of 3.5 was used the resting metabolic rate would have been overestimated by 35%.

The RMR is higher in children and teenagers due to a number of factors including growth and puberty. RMR has been found to be higher than the generic adult value for each age group of children and Tanner stage of pubertal development and significantly higher in younger children (Harrell et al, 2005). Boys have been found to have a higher RMR than girls (Goran et al, 1997). Variation in energy expenditure in typical activity in adolescent girls of approximately 20-25% has been found (Pfeiffer et al, 2006). While a number of studies have found higher RMR in overweight children, the differences are negated when corrected for Fat Mass/Fat Free Mass (Treuth et al, 1998; Molnar & Schutz, 1997; Dietz et al, 1994).

The actual energy cost of adult type activities may also be higher due to the smaller muscle mass in children. Generally it is believed that the adult MET values should be multiplied by the child's RMR for an estimation of EE. Clearly in light of the increasing prevalence of childhood obesity there is a need for further work into the actual energy expenditure due to physical activity in children.

## **3. Observation methods**

Observation methods of determining physical activity are generally used only in documenting workplace activity or in young children who are confined to a physical area e.g. a school playground. Observers are trained to note behavioural information about the types of activities, the time spent in each activity and the frequency of such. Short time periods may not be reflective of habitual physical activity but observing for longer periods can be tedious and may lead to inaccuracies in reporting information. Recording can also be done by video recording providing a permanent record. In order to obtain a complete picture of physical activity of children a number of time periods such as during school break times, lunch times and physical education classes may be required.

Information on the physical surroundings which may influence physical activity levels can be obtained in observational studies, and thus these studies can provide added data which may partly explain the reasons for particular findings e.g. environmental factors such as few public play areas which may explain low levels of activity (Johns & Ha, 1999). While accurate information on the intensity of activity cannot be captured by observation methods they may be useful where classification of activity is all that is required (Chen et al, 2002). Observation methods also allow data to be collected on inactivity as the length of intervals between activities can be determined. With the advent of lightweight activity monitors there may be less need for observation studies in children.

#### **4. Questionnaires**

66 An International Perspective on Topics in Sports Medicine and Sports Injury

The largest factor that determines total daily energy expenditure (TDEE) is the basal or resting metabolic rate. It refers to the energy expended during normal cellular and organ functioning during post-absorptive resting conditions and accounts for roughly 60-75% TDEE. The second factor is the thermic effect of feeding and it accounts for approximately 10% of TDEE. The third component is the energy expended during physical activity. This can occur as a result of volitional mechanical work, such as exercise and daily activities, and non-volitional activity, such as fidgeting, spontaneous muscle contractions, and maintaining posture. It is the most modifiable component of TDEE and accounts for 20-30% of TDEE. As the output of physical activity energy expenditure (PAEE) is the most modifiable component of total daily EE, it may elicit the greatest response to intervention and

A sum of the estimation of energy expended in activity can be calculated from the data provided in questionnaires. The concept of measuring energy expenditure as a multiple of an individual's basal metabolic rate is used and examples of many activities are presented in the form of tables and published in compendiums. The compendia express EE in METs

The concept of denoting energy expenditure as a multiple of an individual's basal metabolic rate (BMR) has been referred to as 1 MET. In 1955 Passmore and Durnin published a review of measurements of human energy expenditure of various activities made by indirect calorimetry. This enabled the calculation of an individual's daily energy expenditure by the total of the metabolic cost of each activity by its duration. Initial work in this area was performed using a human calorimeter where the heat produced by a subject's metabolism was directly related to the temperature change in a contained environment, but more recent

In adults the resting metabolic rate (RMR) is taken as 3.5 mL O2/kg/min. However despite the widespread acceptance of the 1 MET = 3.5 mL O2/kg/min studies that have made such measures in specific cohorts have found different mean values. Byrne et al (2005) found that average resting VO2 was 2.56 +/- 0.4 ml/O2/kg and in this case if the 1 MET of 3.5 was used

The RMR is higher in children and teenagers due to a number of factors including growth and puberty. RMR has been found to be higher than the generic adult value for each age group of children and Tanner stage of pubertal development and significantly higher in younger children (Harrell et al, 2005). Boys have been found to have a higher RMR than girls (Goran et al, 1997). Variation in energy expenditure in typical activity in adolescent girls of approximately 20-25% has been found (Pfeiffer et al, 2006). While a number of studies have found higher RMR in overweight children, the differences are negated when corrected for Fat

The actual energy cost of adult type activities may also be higher due to the smaller muscle mass in children. Generally it is believed that the adult MET values should be multiplied by the child's RMR for an estimation of EE. Clearly in light of the increasing prevalence of childhood obesity there is a need for further work into the actual energy expenditure due to

Observation methods of determining physical activity are generally used only in documenting workplace activity or in young children who are confined to a physical area

studies have used indirect calorimetry where expired air is continuously sampled.

Mass/Fat Free Mass (Treuth et al, 1998; Molnar & Schutz, 1997; Dietz et al, 1994).

subsequently, produce a beneficial impact on health related issues.

the resting metabolic rate would have been overestimated by 35%.

physical activity in children.

**3. Observation methods** 

which are multiples of the resting metabolic rate (RMR).

**2. Energy expenditure in children** 

Questionnaires and interviews are frequently the method of collecting data on physical activity in studies involving large numbers of subjects where simple, inexpensive methods are required. Questionnaires measuring activity in adults usually include questions on occupational, transport and leisure time activity. As data is collected after its occurrence the procedure does not influence performance. The validity of questionnaires measuring physical activity is difficult to establish due to the lack of a gold standard criterion against which to compare them and the problems with long term recall of activities and the likelihood of overestimation of time and intensity of activity.

The use of questionnaires in children requires specific considerations. Below the age of 10-12 years children can only give limited information about their activity patterns. Parents, teachers or other adults may give details of the child's physical activity but this information may be estimated especially for outside activities. Regular planned activity is relatively easy to remember in the short term but so much of childrens' activity is spontaneous, unplanned and of a stop start nature and therefore activity may be difficult to recall. Children can also have difficulties estimating the duration of activities and time frames may need to be provided for the child in terms of *"on the way to school, break time, way home from school, before dinner"* etc. Questions about participation in Physical Education (P.E.), method of transport to and from school and extra-curricular activities may be included to gain information on childrens' overall activity levels. Providing a list of activities can aid recall.

The Modifiable Activity Questionnaire for Adolescents (MAQA) assesses physical activity over the previous year (Aaron et al, 1993). It includes questions on the number of times in the previous 14 days the subject engaged in at least 20 minutes of hard and of light exercise. Hours per days spent watching television, videos, playing computer games each day and the number of competitive activities the adolescent participates in is assessed, as is the energy expended in regular physical activity each week.

#### **5. Heart rate monitoring**

Heart rate (HR) monitoring is an objective method of measuring physical activity. HR monitoring can provide details on the time, intensity and frequency of specific activities

Physical Activity Measures in Children – Which Method to Use? 69

health status as has been demonstrated by Craig et al (2010) in CANPLAY (Canadian Physical Activity Levels Among Youth) where pedometer data on almost 20,000 children was measured. Validity, reliability and accuracy need to be determined for all pedometers used in research. The Walk4Life 2505 has been found to be within 5.3% of actual time across all speeds and was thus recommended for the quantification of physical activity in children ( Beets et al, 2005). However in those with intellectual disability Pitetti et al (2009) found an underestimation of approximately 14% in registered steps and an overestimation of 8.7% in time spent in activity when the Walk4Life was compared to video-recorded activity. Outputs from different pedometers may not be comparable. In addition as stride lengths will vary considerably with different age groups of children data from similar instruments may not be comparable across age groups when distance is the variable of interest. While pedometers may be used in large scale studies due to relatively low cost it is only total ambulatory activity over the measured time period that can be captured. Data on intensity,

duration or frequency of activity bouts within that period cannot be obtained.

right and left hips respectively.

but not to incline.

root of the sum squared of activity counts in each vector.

The assessment of physical activity by accelerometry is based on the measurement of body movement or the dynamic component of activity and accelerometers may be uni-axial, biaxial or tri-axial. Uni-axial accelerometers such as the Caltrac or the Computer Science Application (CSA) incorporate a single, vertical axis piezoelectric bender element which is displaced with movement, and this generates a signal which is proportional to the force of the movement that produced it (Puyau et al. 2002). A study examining the validity of the CSA in children walking and running on a treadmill found the activity counts were strongly correlated with energy expenditure by indirect calorimetry (Trost et al, 1998). In addition to walking and running other activities typical in children such as Nintendo, arts and crafts, aerobic warm up, and Tae Bo were measured by CSA and the Mini-Mitter Actiwatch (MM) (Puyau et al, 2002) and data was compared to by room respiration calorimetry, and heart rate measured by telemetry. Correlations of r=0.78 ± 0.06 and r=0.80±0.05 were found for the MM and r=0.66± 0.08 and r=0.73± 0.07 for the CSA for the

While uni-axial accelerometers can only measure movement in one plane most movements in the saggital and horizontal planes are accompanied by movement in the vertical plane and some would argue that a uni-dimensional (vertical axis) activity monitor may be just as valid as a three dimensional monitor. However as many activities in young children (such as crawling and climbing) may be captured better by tri-axial accelerometers. The Tritrac R3D is a three dimensional motion sensor which measures the acceleration in three planes and integrates values to a vector magnitude. Vector magnitude is calculated as the square

Data from the Tritrac accelerometer has been compared to the energy expenditure measured by indirect calorimetry for treadmill walking and running in 60 young adults who walked and ran on a treadmill at speeds of 3.2, 6.4 and 9.7 km.h-1 (Nichols et al, 1999). The mean differences between energy expenditure measured by indirect calorimetry and that measured by the Tritrac ranged from 0.0082 kcal.kg-1.min-1 at 3.2 km.h-1 to 0.0320kcal.kg1.min1 at 9.7 km.h1 with the Tritrac consistently overestimating EE during horizontal treadmill walking. Overall it was found that the Tritrac accurately distinguished between the various intensities of walking and running on level ground, was highly reliable from day to day and was sensitive to changes in speed of movement

based on the heart rate response to such activity. It is an indirect measure which is based on the linear relationship between heart rate and oxygen uptake, so the relative stress placed on the cardiopulmonary system due to physical activity is assessed. Advances in technology have made it possible to detect and store impulses over a number of weeks prior to being downloaded to a PC. While athletes commonly use heart rate recorders to determine and monitor exercise training zones these instruments can also be used in research and in clinical practice. Heart rates above a percentage of maximum can be identified and the data can be classified into time spent in specific zones for the time measured. Inactivity can be classified from heart rates close to baseline. However if heart rate is elevated for a period of time during inactivity (e.g. due to caffeine), on data analysis it can appear that this was related to activity. Heart rate can also be influenced by emotional stress, ambient temperature, humidity, and drugs and there may be some day to day variation. In addition resting heart rate and heart rate for any given workload is influenced by exercise tolerance. Fitter subjects have higher stroke volumes and hence lower the heart rates for any given workload.

The definition of resting heart rate and its' method of determination has an impact on the apparent level of activity (Logan et al, 2000). The interpretation of physical activity level depends on the threshold used and different thresholds will lead to different overall results. In terms of providing the most accurate results individual fitness testing would need to be performed prior to using heart rate monitoring so that data obtained can be correctly interpreted. Without individual data on exercise tolerance and/or the specifics of the activities being measured, lower heart rate data could be due to less activity in someone unfit or more intense activity for an individual who had a higher fitness level. In those who are less fit the return of heart rate to baseline after activity is slower than for those more fit but in the absence of this information it could appear that a less fit person is active for longer.

In summary heart rate monitoring is a useful method of measuring overall activity in children but interpretation of how active the child is depends on the fitness of the child and the definition of resting heart rate used. In addition heart rate may be influenced by other factors not related to physical activity.

### **6. Motion sensors**

In recent years there has been a move away from other methods as described above to the use of instruments to detect body movement. The selection of motion sensors is ever increasing and ranges from simple pedometers to electronic accelerometers which reflect not only the occurrence of body movement but its intensity and in some instances its location. Pedometers record acceleration and deceleration of the waist in the vertical direction but do not record the intensity of movement. For large studies, where total activity is of interest, the pedometer may be useful and particularly in adults where Sequeia et al (1995) have demonstrated that the pedometer could differentiate between various levels of occupational activity (sitting, standing and moderate effort occupational categories). No differentiation could be made between heavy and moderate work, where heavy work involved much static work such as lifting but in assessing overall activity in large numbers this may not be that important. In adults and children much of the activity accumulated during the day is by walking which can be captured by pedometers.

These monitors have become more available and less expensive. Their use in epidemiological studies is helping to add significantly to our understanding of activity and

based on the heart rate response to such activity. It is an indirect measure which is based on the linear relationship between heart rate and oxygen uptake, so the relative stress placed on the cardiopulmonary system due to physical activity is assessed. Advances in technology have made it possible to detect and store impulses over a number of weeks prior to being downloaded to a PC. While athletes commonly use heart rate recorders to determine and monitor exercise training zones these instruments can also be used in research and in clinical practice. Heart rates above a percentage of maximum can be identified and the data can be classified into time spent in specific zones for the time measured. Inactivity can be classified from heart rates close to baseline. However if heart rate is elevated for a period of time during inactivity (e.g. due to caffeine), on data analysis it can appear that this was related to activity. Heart rate can also be influenced by emotional stress, ambient temperature, humidity, and drugs and there may be some day to day variation. In addition resting heart rate and heart rate for any given workload is influenced by exercise tolerance. Fitter subjects

have higher stroke volumes and hence lower the heart rates for any given workload.

longer.

factors not related to physical activity.

walking which can be captured by pedometers.

**6. Motion sensors** 

The definition of resting heart rate and its' method of determination has an impact on the apparent level of activity (Logan et al, 2000). The interpretation of physical activity level depends on the threshold used and different thresholds will lead to different overall results. In terms of providing the most accurate results individual fitness testing would need to be performed prior to using heart rate monitoring so that data obtained can be correctly interpreted. Without individual data on exercise tolerance and/or the specifics of the activities being measured, lower heart rate data could be due to less activity in someone unfit or more intense activity for an individual who had a higher fitness level. In those who are less fit the return of heart rate to baseline after activity is slower than for those more fit but in the absence of this information it could appear that a less fit person is active for

In summary heart rate monitoring is a useful method of measuring overall activity in children but interpretation of how active the child is depends on the fitness of the child and the definition of resting heart rate used. In addition heart rate may be influenced by other

In recent years there has been a move away from other methods as described above to the use of instruments to detect body movement. The selection of motion sensors is ever increasing and ranges from simple pedometers to electronic accelerometers which reflect not only the occurrence of body movement but its intensity and in some instances its location. Pedometers record acceleration and deceleration of the waist in the vertical direction but do not record the intensity of movement. For large studies, where total activity is of interest, the pedometer may be useful and particularly in adults where Sequeia et al (1995) have demonstrated that the pedometer could differentiate between various levels of occupational activity (sitting, standing and moderate effort occupational categories). No differentiation could be made between heavy and moderate work, where heavy work involved much static work such as lifting but in assessing overall activity in large numbers this may not be that important. In adults and children much of the activity accumulated during the day is by

These monitors have become more available and less expensive. Their use in epidemiological studies is helping to add significantly to our understanding of activity and health status as has been demonstrated by Craig et al (2010) in CANPLAY (Canadian Physical Activity Levels Among Youth) where pedometer data on almost 20,000 children was measured. Validity, reliability and accuracy need to be determined for all pedometers used in research. The Walk4Life 2505 has been found to be within 5.3% of actual time across all speeds and was thus recommended for the quantification of physical activity in children ( Beets et al, 2005). However in those with intellectual disability Pitetti et al (2009) found an underestimation of approximately 14% in registered steps and an overestimation of 8.7% in time spent in activity when the Walk4Life was compared to video-recorded activity. Outputs from different pedometers may not be comparable. In addition as stride lengths will vary considerably with different age groups of children data from similar instruments may not be comparable across age groups when distance is the variable of interest. While pedometers may be used in large scale studies due to relatively low cost it is only total ambulatory activity over the measured time period that can be captured. Data on intensity, duration or frequency of activity bouts within that period cannot be obtained.

The assessment of physical activity by accelerometry is based on the measurement of body movement or the dynamic component of activity and accelerometers may be uni-axial, biaxial or tri-axial. Uni-axial accelerometers such as the Caltrac or the Computer Science Application (CSA) incorporate a single, vertical axis piezoelectric bender element which is displaced with movement, and this generates a signal which is proportional to the force of the movement that produced it (Puyau et al. 2002). A study examining the validity of the CSA in children walking and running on a treadmill found the activity counts were strongly correlated with energy expenditure by indirect calorimetry (Trost et al, 1998). In addition to walking and running other activities typical in children such as Nintendo, arts and crafts, aerobic warm up, and Tae Bo were measured by CSA and the Mini-Mitter Actiwatch (MM) (Puyau et al, 2002) and data was compared to by room respiration calorimetry, and heart rate measured by telemetry. Correlations of r=0.78 ± 0.06 and r=0.80±0.05 were found for the MM and r=0.66± 0.08 and r=0.73± 0.07 for the CSA for the right and left hips respectively.

While uni-axial accelerometers can only measure movement in one plane most movements in the saggital and horizontal planes are accompanied by movement in the vertical plane and some would argue that a uni-dimensional (vertical axis) activity monitor may be just as valid as a three dimensional monitor. However as many activities in young children (such as crawling and climbing) may be captured better by tri-axial accelerometers. The Tritrac R3D is a three dimensional motion sensor which measures the acceleration in three planes and integrates values to a vector magnitude. Vector magnitude is calculated as the square root of the sum squared of activity counts in each vector.

Data from the Tritrac accelerometer has been compared to the energy expenditure measured by indirect calorimetry for treadmill walking and running in 60 young adults who walked and ran on a treadmill at speeds of 3.2, 6.4 and 9.7 km.h-1 (Nichols et al, 1999). The mean differences between energy expenditure measured by indirect calorimetry and that measured by the Tritrac ranged from 0.0082 kcal.kg-1.min-1 at 3.2 km.h-1 to 0.0320kcal.kg1.min1 at 9.7 km.h1 with the Tritrac consistently overestimating EE during horizontal treadmill walking. Overall it was found that the Tritrac accurately distinguished between the various intensities of walking and running on level ground, was highly reliable from day to day and was sensitive to changes in speed of movement but not to incline.

Physical Activity Measures in Children – Which Method to Use? 71

al, 2004). A particular attraction of the IDEEA is the data on the type of activity performed e.g. walking, running. The IDEEA is a portable device and consists of 5 sensors that are attached to the body and a small data collection device (or microcomputer). The basic working principle of the IDEEA is that the sensors are attached to the body in specific areas. One sensor is attached to the sternum (preferably just below the sternal angle that is supposedly perpendicular to the vertical axis of the upper body, its correct alignment is crucial for distinguishing between sitting, reclining and lying down), two sensors are placed on the anterior sides of the upper legs, halfway between the hip and the knee, and the other two sensors are placed on the inferior side of the feet. The IDEEA system monitors the body and limb motions constantly through these sensors and the different combinations of signals from the sensors represent different physical activities, which are coded for as different numbers. The monitor collects 32 samples/second while continuously distinguishing among

The ability of the IDEEA to correctly identify the type of activity and to quantify PA intensity allows for calculations of EE in free-living conditions. The IDEEA device has inbuilt equations that determine the EE in kcal.min-1 or kJ.min-1. Recent work in our laboratory was performed with the objective of examining the validity of the IDEEA in the estimation of energy expenditure during rest, walking and running in 28 young adults against the criterion method of physiological energy expenditure by indirect calorimetry (Oxycon mobile VIASYS). Good correlations in rest and walking were detected (r=0.73, p<0.0001 at rest to r=0.49, p< 001 at 6 km/h). The IDEEA was able to differentiate between inactivity, light, moderate and vigorous activities and can provide a valid estimate of energy expenditure in rest and walking ( Mc Creddin & Hussey, 2009). A particular beneficial feature of the IDEEA is identification of type of activity, gait analysis during walking and

The multisensory monitors the IDEEA and the Sense Wear Pro Armband have also been evaluated in children along with the ActiReg. While it was found that all three needed further development, the IDEEA had the highest ability in assesses energy cost (Arvidsson et al, 2009). The Actireg contains two pairs of sensors worn over the sternum and right thigh. These sensors can determine body position and motion. Along with the data processor the body positions and motion captured are given an "Activity factor" based on a multiple of basal metabolic rate. All three monitors were comparable for resting and sitting but none accurately measured stationary cycling, jumping on a trampoline or playing basketball. The IDEEA was the only one to accurately measure stair walking. In walking and running activities the IDEEA showed a close estimate of EE where the Sense Wear accurately measured slow to normal walking but underestimated higher speeds. In health related research accurate measures of inactivity and light activity may be more important

Global positioning systems are potentially valuable in the assessment of physical activity. The technology permits the identification of location and such data is important in our understanding of physical activity behaviours in children. A Global Position System receiver position is calculated by measuring its distance from a number of GPS satellites. Once switched on the GPS device constantly receives signals from satellites and can calculate the distance from each. In addition to providing a profile of the child's activity patterns the GPS data can be combined with a GIS (Geographical Information Systems) database to provide information on where activity occurs and how the built environment/ transport options

different postures and gaits to identify the type of physical activity.

running and identifying most postures.

than differentiating between more vigorous activity.

The RT3 (Stayhealthy Inc, Monrovia, CA) has followed on from the Tritrac and although smaller it has a similar output to its predecessor. The validity of the RT3 in the assessment of physical activities which included walking on a treadmill, kicking a ball, playing hopscotch and sitting quietly was examined in 10 boys and 10 adult males (Rowlands et al, 2004). The RT3 vector magnitude correlated significantly with oxygen consumption in boys and in men. When compared to oxygen uptake excellent correlations with the RT3 have been found in 7-12 year olds ( Hussey et al, 2009) and 12-14 year olds r-0,96, p< 001 (Sun et al, 2008). The measurements of inactivity, low activity, and moderate activity are very accurate with the RT3. The limits of agreement for vigorous activity are wider but this may not be highly important in measuring overall physical activity where short time periods are spent in vigorous activity each day and the classification of vigorous activity is needed but the absolute measure may not be required. Up to three weeks data can be acquired when the vector magnitude is sampled every minute. Data can be downloaded to a PC and saved in an excel files so data can be presented as required. The data can be manipulated in a number of ways depending on the needs of the project in question.

In our experience one of the most useful methods in the manipulation of the data has been to calculate the mean number of minutes per day spent in different classifications of activity e.g. inactivity, light activity, moderate activity, and vigorous activity. Alternatively the energy expended in activity may be used. However the intensity thresholds used need to be standardised if data is to be compared across groups. Where intensity is defined into categories on specific cut off points there will still be a wide variation in energy expended in subjects depending on where most of the activity within a wide range occurs. Two subjects may be classified as having spent the same amount of time in moderate/vigorous activity in a day yet one may have been at the lower range and the other at the higher and yet the data analysed will be the same. Thresholds for sedentary behaviour also need to be agreed as slight changes will have considerable impact on time spent sedentary per day as most of the day is spent either sedentary or in light activity. Reilly et al (2008) have demonstrated how using the cut off points provided by different researchers on the same data set can lead to significant differences in the time spent either sedentary or in moderate to vigorous activity. There is a real need for consensus on how data is analysed so data can be compared across studies. This needs to be done in children as well as adults and in children age may need to be a factor considered.

In the past decade there has been a substantial increase in the use of portable accelerometers and examples include the BioTrainer Pro and the SenseWear Armband. The former is a biaxial accelerometer that can sample data between 15 seconds and 5 minute intervals and can store up to 112 days of data. The SenseWear Armband is also a biaxial accelerometer with a heart rate receiver and thermocoupler which can measure heat production. The monitor is a wireless armband and is worn on the upper arm in contact with the skin surface. These newer monitors along with the CSA, the Tritrac R3D and the RT3 (King et al, 2004) were evaluated against indirect calorimetry for treadmill walking and running. No significant difference was found between the mean energy expenditure of the activity monitors at all speeds. The SenseWear Armband, Tritrac and RT3 had significant increases in mean EE as the speeds increased.

Another recent accelerometer is the IDEEA which provides an advantage over other acclerometers as it employs a more sophisticated motion-capture system using two dimensional accelerometers which are placed on the thighs, feet and sternum in conjunction with pattern recognition software which allows movement patterns to be detected (Zhang et

The RT3 (Stayhealthy Inc, Monrovia, CA) has followed on from the Tritrac and although smaller it has a similar output to its predecessor. The validity of the RT3 in the assessment of physical activities which included walking on a treadmill, kicking a ball, playing hopscotch and sitting quietly was examined in 10 boys and 10 adult males (Rowlands et al, 2004). The RT3 vector magnitude correlated significantly with oxygen consumption in boys and in men. When compared to oxygen uptake excellent correlations with the RT3 have been found in 7-12 year olds ( Hussey et al, 2009) and 12-14 year olds r-0,96, p< 001 (Sun et al, 2008). The measurements of inactivity, low activity, and moderate activity are very accurate with the RT3. The limits of agreement for vigorous activity are wider but this may not be highly important in measuring overall physical activity where short time periods are spent in vigorous activity each day and the classification of vigorous activity is needed but the absolute measure may not be required. Up to three weeks data can be acquired when the vector magnitude is sampled every minute. Data can be downloaded to a PC and saved in an excel files so data can be presented as required. The data can be manipulated in a number

In our experience one of the most useful methods in the manipulation of the data has been to calculate the mean number of minutes per day spent in different classifications of activity e.g. inactivity, light activity, moderate activity, and vigorous activity. Alternatively the energy expended in activity may be used. However the intensity thresholds used need to be standardised if data is to be compared across groups. Where intensity is defined into categories on specific cut off points there will still be a wide variation in energy expended in subjects depending on where most of the activity within a wide range occurs. Two subjects may be classified as having spent the same amount of time in moderate/vigorous activity in a day yet one may have been at the lower range and the other at the higher and yet the data analysed will be the same. Thresholds for sedentary behaviour also need to be agreed as slight changes will have considerable impact on time spent sedentary per day as most of the day is spent either sedentary or in light activity. Reilly et al (2008) have demonstrated how using the cut off points provided by different researchers on the same data set can lead to significant differences in the time spent either sedentary or in moderate to vigorous activity. There is a real need for consensus on how data is analysed so data can be compared across studies. This needs to be done in children as well as adults and in children age may need to

In the past decade there has been a substantial increase in the use of portable accelerometers and examples include the BioTrainer Pro and the SenseWear Armband. The former is a biaxial accelerometer that can sample data between 15 seconds and 5 minute intervals and can store up to 112 days of data. The SenseWear Armband is also a biaxial accelerometer with a heart rate receiver and thermocoupler which can measure heat production. The monitor is a wireless armband and is worn on the upper arm in contact with the skin surface. These newer monitors along with the CSA, the Tritrac R3D and the RT3 (King et al, 2004) were evaluated against indirect calorimetry for treadmill walking and running. No significant difference was found between the mean energy expenditure of the activity monitors at all speeds. The SenseWear Armband, Tritrac and RT3 had significant increases

Another recent accelerometer is the IDEEA which provides an advantage over other acclerometers as it employs a more sophisticated motion-capture system using two dimensional accelerometers which are placed on the thighs, feet and sternum in conjunction with pattern recognition software which allows movement patterns to be detected (Zhang et

of ways depending on the needs of the project in question.

be a factor considered.

in mean EE as the speeds increased.

al, 2004). A particular attraction of the IDEEA is the data on the type of activity performed e.g. walking, running. The IDEEA is a portable device and consists of 5 sensors that are attached to the body and a small data collection device (or microcomputer). The basic working principle of the IDEEA is that the sensors are attached to the body in specific areas. One sensor is attached to the sternum (preferably just below the sternal angle that is supposedly perpendicular to the vertical axis of the upper body, its correct alignment is crucial for distinguishing between sitting, reclining and lying down), two sensors are placed on the anterior sides of the upper legs, halfway between the hip and the knee, and the other two sensors are placed on the inferior side of the feet. The IDEEA system monitors the body and limb motions constantly through these sensors and the different combinations of signals from the sensors represent different physical activities, which are coded for as different numbers. The monitor collects 32 samples/second while continuously distinguishing among different postures and gaits to identify the type of physical activity.

The ability of the IDEEA to correctly identify the type of activity and to quantify PA intensity allows for calculations of EE in free-living conditions. The IDEEA device has inbuilt equations that determine the EE in kcal.min-1 or kJ.min-1. Recent work in our laboratory was performed with the objective of examining the validity of the IDEEA in the estimation of energy expenditure during rest, walking and running in 28 young adults against the criterion method of physiological energy expenditure by indirect calorimetry (Oxycon mobile VIASYS). Good correlations in rest and walking were detected (r=0.73, p<0.0001 at rest to r=0.49, p< 001 at 6 km/h). The IDEEA was able to differentiate between inactivity, light, moderate and vigorous activities and can provide a valid estimate of energy expenditure in rest and walking ( Mc Creddin & Hussey, 2009). A particular beneficial feature of the IDEEA is identification of type of activity, gait analysis during walking and running and identifying most postures.

The multisensory monitors the IDEEA and the Sense Wear Pro Armband have also been evaluated in children along with the ActiReg. While it was found that all three needed further development, the IDEEA had the highest ability in assesses energy cost (Arvidsson et al, 2009). The Actireg contains two pairs of sensors worn over the sternum and right thigh. These sensors can determine body position and motion. Along with the data processor the body positions and motion captured are given an "Activity factor" based on a multiple of basal metabolic rate. All three monitors were comparable for resting and sitting but none accurately measured stationary cycling, jumping on a trampoline or playing basketball. The IDEEA was the only one to accurately measure stair walking. In walking and running activities the IDEEA showed a close estimate of EE where the Sense Wear accurately measured slow to normal walking but underestimated higher speeds. In health related research accurate measures of inactivity and light activity may be more important than differentiating between more vigorous activity.

Global positioning systems are potentially valuable in the assessment of physical activity. The technology permits the identification of location and such data is important in our understanding of physical activity behaviours in children. A Global Position System receiver position is calculated by measuring its distance from a number of GPS satellites. Once switched on the GPS device constantly receives signals from satellites and can calculate the distance from each. In addition to providing a profile of the child's activity patterns the GPS data can be combined with a GIS (Geographical Information Systems) database to provide information on where activity occurs and how the built environment/ transport options

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may influence activity behaviours (Maddison & Ni Mhurchu, 2009). GPS systems may be combined with accelerometry or other methods to provide richer data. Ideally these should be incorporated into one monitor. A small pilot study that examined how well the combination of GPS and accelerometer data predicted activity modes. Using three variables 91% of observations were correctly classified by the combined methods (Troped et al, 2008) A feasibility study in combining heart rate and GPS data was performed on 39 children during a confined time period (Duncan et al, 2009) and the system was found it to be a promising method for measuring play related energy expenditure. In this instance location, distance, speed and HR data were captured every second using the F500 model.

A limitation to the use of GPS is that it can only be used out doors and even then high buildings and trees may effect the use. The location of the GPS system may effect sitting postures as typically it has been situated on the back in harness/backpack. Increased battery life for devices such as the Garmin is required in order to establish activity patterns in children where at least four days of monitoring is recommended (Trost et al, 2000).

In summary: accelerometers are designed to measure physical movement without impeding activity in free- living situations and can measure periods of inactivity as well as quantity and intensity of movement. The ability of newer accelerometers to store data over a number of weeks permits measurement of habitual activity. Motion sensors are for the most part small unobtrusive devices that have the capacity to store movement data for prolonged time periods. The development of wireless communication to the data collection device could reduce the inconvenience associated with wearing the multiple sensors.

#### **7. Conclusion**

Increased time spent inactive has been cited as one reason for the epidemic of childhood obesity and therefore it is important to be able to obtain valid measures. This review has concentrated on methods of measuring physical activity in both clinical and laboratory settings. Observational studies are time consuming, labour intensive and involve many observers who may require intense training. Their cost may be prohibitive in epidemiological or large scale studies where the use of questionnaires may be more appropriate. While assessing physical activity through the use of questionnaires has some limitations, it is the only feasible approach for epidemiologic investigations. Heart rate monitoring can distinguish between different intensities of activity in children and therefore can be used to determine light, moderate and vigorous activity. However as higher degrees of fitness are associated with lower resting heart rates, fitness may need to be individually assessed before measurement. This would be very difficult in large cohort studies. Accelerometers can measure habitual physical activity and inactivity, are easy to wear and do not interfere or influence movement. An additional benefit of accelerometry is the ability to measure intensity of activity. The ultimate choice of activity measure will depend on the question to be studied, the size of the cohort and the resources available.

#### **8. References**

Aaron D.J.; Kriska, A.M. ; Dearwater, S.R.; Anderson, R.L.; Olsen, T.L.; Cauley, J.A.*,* et al (1993). The epidemiology of leisure physical activity in an adolescent population.

may influence activity behaviours (Maddison & Ni Mhurchu, 2009). GPS systems may be combined with accelerometry or other methods to provide richer data. Ideally these should be incorporated into one monitor. A small pilot study that examined how well the combination of GPS and accelerometer data predicted activity modes. Using three variables 91% of observations were correctly classified by the combined methods (Troped et al, 2008) A feasibility study in combining heart rate and GPS data was performed on 39 children during a confined time period (Duncan et al, 2009) and the system was found it to be a promising method for measuring play related energy expenditure. In this instance location,

A limitation to the use of GPS is that it can only be used out doors and even then high buildings and trees may effect the use. The location of the GPS system may effect sitting postures as typically it has been situated on the back in harness/backpack. Increased battery life for devices such as the Garmin is required in order to establish activity patterns in

In summary: accelerometers are designed to measure physical movement without impeding activity in free- living situations and can measure periods of inactivity as well as quantity and intensity of movement. The ability of newer accelerometers to store data over a number of weeks permits measurement of habitual activity. Motion sensors are for the most part small unobtrusive devices that have the capacity to store movement data for prolonged time periods. The development of wireless communication to the data collection device could

Increased time spent inactive has been cited as one reason for the epidemic of childhood obesity and therefore it is important to be able to obtain valid measures. This review has concentrated on methods of measuring physical activity in both clinical and laboratory settings. Observational studies are time consuming, labour intensive and involve many observers who may require intense training. Their cost may be prohibitive in epidemiological or large scale studies where the use of questionnaires may be more appropriate. While assessing physical activity through the use of questionnaires has some limitations, it is the only feasible approach for epidemiologic investigations. Heart rate monitoring can distinguish between different intensities of activity in children and therefore can be used to determine light, moderate and vigorous activity. However as higher degrees of fitness are associated with lower resting heart rates, fitness may need to be individually assessed before measurement. This would be very difficult in large cohort studies. Accelerometers can measure habitual physical activity and inactivity, are easy to wear and do not interfere or influence movement. An additional benefit of accelerometry is the ability to measure intensity of activity. The ultimate choice of activity measure will depend on the

Aaron D.J.; Kriska, A.M. ; Dearwater, S.R.; Anderson, R.L.; Olsen, T.L.; Cauley, J.A.*,* et al

(1993). The epidemiology of leisure physical activity in an adolescent population.

distance, speed and HR data were captured every second using the F500 model.

children where at least four days of monitoring is recommended (Trost et al, 2000).

reduce the inconvenience associated with wearing the multiple sensors.

question to be studied, the size of the cohort and the resources available.

**7. Conclusion** 

**8. References** 

*Medicine and Science in Sports and Exercise*, Vol. 25, No. 7 (July 1993), pp. 847-53, ISSN 0195-9131


Physical Activity Measures in Children – Which Method to Use? 75

Pitetti, K.H.; Beets, M.W. & Combs, C. (2009). Physical activity levels of children with

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Rowlands, A.V.; Thomas, P.W.M.; Eston, R.G. & Topping O.R. (2004). Validation of the RT3

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Sequeia, M.M.; Rickenbach, M.; Wietlisbach, V.; Tullen, B. & Schutz, Y.(1995). Physical

Sun, D.X.; Schmidt, G.& Teo-Koh, S.M. (2008). Validation of the RT3 accelerometer for

Treuth, M.S.; Figueroa- Colon, R.; Hunter, G.R.; Weinsier, R.L.; Butte, N.F. & Goran,

Troped, P.J.; Oliveira, M.S.; Mathews, C.E.; Cromley, E.K.; Melly, S.J. & Craig, B.A. (2008).

Trost, S.G.; Pate, R.R.; Freedson, P.S.; Sallis, J.F.; & Taylor, W.C. (2000). Using objective

Trost, S.G.; Ward, D.S.; Moorehead, S.M.; Watson, P.D.; Riner, W. & Burke, J.R .(1998).

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R.(2006). Physical activities in adolescent girls: variability in energy expenditure. *American Journal of Preventive Medicine*, Vol, 31, No. 4, ( October 2006), pp. 328-31,


**4** 

*Brazil* 

**Applicability of the Reference Interval and** 

**Biochemical Biomarkers to Sport Science** 

Lázaro Alessandro Soares Nunes, Fernanda Lorenzi Lazarim,

*Laboratory of Instrumentation for Biomechanics, Physical Education Faculty,* 

*Laboratory of Exercise Biochemistry (LABEX), Biology Institute,* 

*State University of Campinas (UNICAMP), Campinas, SP,* 

René Brenzikofer and Denise Vaz Macedo

*State University of Campinas (UNICAMP),* 

**Reference Change Value of Hematological and** 

The training principle states that tissue adaptation depends upon overload applications. The cumulative effect of these programmed breaks in homeostasis through variations in the intensity, duration and frequency of the exercise is higher performance. However, it is important to point out that the positive adaptive response, which is reflected by increased performance, depends upon an adequate recovery time between each training session and

Training sessions and nutrition are highly interrelated. Repeated training sessions typically require a diet that can sustain muscle energy stores to execute the training proposed. The phenotypic alterations that lead to increased performance result from an intense process of protein synthesis that occurs during the recovery period and can last up to 24 h after the exercise session. This process is highly influenced by food ingestion, which offers energy and nutrients that are essential to the process and to the recovery of energy reserves (Hawley et al., 2006). Recent evidence shows that some nutrients can potentiate the protein synthesis pathways that are activated by exercise, influencing the adaptive process and

Professional athletes, for example, soccer players, are submitted to an annual training and competition routine with periods of recovery that are not always properly adjusted to the workload. The main problem is that the current championship schedule generally does not allow the teams to take the minimum time required for appropriate physical preparation because the different stages (competitions and physical, technical and tactical training) overlap. As a consequence, the athletes may suffer an imbalance between the effort put forth and the recovery time during the competitive season, which increases the likelihood that the

Each overload stress results in different degrees of microtrauma in the muscle, connective tissue, and/or bones and joints, which trigger an inflammatory response promoting repair

during training sessions to result in phenotypic alterations (Hohl et al., 2009).

**1. Introduction** 

performance (Hawley et al., 2011).

workload will be excessive for some players.

Zhang, K.; Pi- Sunyer, F.X. & Booger, C.N. (2004). Improving energy expenditure estimation for physical activities. *Medicine and Science in Sports and Exercise,* Vol. 36, No. 5, ( May 2004), pp. 883-9, ISSN 0195-9131

## **Applicability of the Reference Interval and Reference Change Value of Hematological and Biochemical Biomarkers to Sport Science**

Lázaro Alessandro Soares Nunes, Fernanda Lorenzi Lazarim, René Brenzikofer and Denise Vaz Macedo *Laboratory of Exercise Biochemistry (LABEX), Biology Institute, State University of Campinas (UNICAMP), Laboratory of Instrumentation for Biomechanics, Physical Education Faculty, State University of Campinas (UNICAMP), Campinas, SP, Brazil* 

## **1. Introduction**

76 An International Perspective on Topics in Sports Medicine and Sports Injury

Zhang, K.; Pi- Sunyer, F.X. & Booger, C.N. (2004). Improving energy expenditure estimation

May 2004), pp. 883-9, ISSN 0195-9131

for physical activities. *Medicine and Science in Sports and Exercise,* Vol. 36, No. 5, (

The training principle states that tissue adaptation depends upon overload applications. The cumulative effect of these programmed breaks in homeostasis through variations in the intensity, duration and frequency of the exercise is higher performance. However, it is important to point out that the positive adaptive response, which is reflected by increased performance, depends upon an adequate recovery time between each training session and during training sessions to result in phenotypic alterations (Hohl et al., 2009).

Training sessions and nutrition are highly interrelated. Repeated training sessions typically require a diet that can sustain muscle energy stores to execute the training proposed. The phenotypic alterations that lead to increased performance result from an intense process of protein synthesis that occurs during the recovery period and can last up to 24 h after the exercise session. This process is highly influenced by food ingestion, which offers energy and nutrients that are essential to the process and to the recovery of energy reserves (Hawley et al., 2006). Recent evidence shows that some nutrients can potentiate the protein synthesis pathways that are activated by exercise, influencing the adaptive process and performance (Hawley et al., 2011).

Professional athletes, for example, soccer players, are submitted to an annual training and competition routine with periods of recovery that are not always properly adjusted to the workload. The main problem is that the current championship schedule generally does not allow the teams to take the minimum time required for appropriate physical preparation because the different stages (competitions and physical, technical and tactical training) overlap. As a consequence, the athletes may suffer an imbalance between the effort put forth and the recovery time during the competitive season, which increases the likelihood that the workload will be excessive for some players.

Each overload stress results in different degrees of microtrauma in the muscle, connective tissue, and/or bones and joints, which trigger an inflammatory response promoting repair

Applicability of the Reference Interval and Reference Change

Value of Hematological and Biochemical Biomarkers to Sport Science 79

Doubts about the application of CK analysis to the monitoring of the muscular workload in athletes are derived from studies suggesting that this analyte is an unreliable marker for histological muscle lesions (Malm, 2001). Another source of doubt is that the serum CK values measured in individuals exercising to a similar degree showed high variability and a

Studies of subjects performing specific exercises for short, defined periods have shown that the time of CK release into the bloodstream and its clearance from the plasma depends on the training level, type, and intensity as well as the duration of the exercise. Peak serum CK values of approximately twice the baseline levels occur eight hours after strength training (Serrão et al., 2003). After an acute bout of intense plyometric exercise, the serum CK levels reached peak values from 48 through 72 hours of recovery (Chatzinikolaou et al., 2010). Peak serum CK values of approximately sevenfold above the baseline were found 48 h after a soccer game (Fatouros et al., 2010). There are marked differences between the sexes, with lower basal CK values in females than in males. Estrogen levels may be one important factor in maintaining membrane stability post-exercise (Tiidus, 2000). Creatine kinase serum levels

While these studies make important contributions to the understanding of acute responses, they do not provide enough information for longitudinal, seasonal application in actively competing athletes. An important point to be considered is that serum CK activity can arise in the absence of histological lesions as a consequence of changes in the muscle membrane permeability (Manfredi et al., 1991). Thus, monitoring the changes in serum CK may represent an indirect route for monitoring workload effects and a way to prevent sub-clinical damage due to muscle overload. However, to be a useful tool for individual adjustments in the stimulus/recovery ratio during a competitive season, it is necessary to compare individual blood values with population-based reference intervals. With this knowledge, it is possible to individualize training program interventions to adjust overloads or medical/nutritional

One difficulty in assessing the effects of training through blood biomarkers is the lack of appropriate reference intervals obtained from a reference population practicing regular and systematized physical activity or sports modality. To solve this problem, we have recently determined the reference interval for the plasma CK activity of blood samples obtained from 128 professional soccer players at different times during the Brazilian Championship (Lazarim et al., 2009). The upper limits of the 97.5th and 90th percentiles for the CK activity were determined according to the International Federation of Clinical Chemistry (IFCC) rules and were 1.338 U/L (CI = 1191—1639 U/L) and 975 U/L (CI = 810—1090 U/L), respectively. These percentile values were markedly higher than the values previously reported in the literature (< 207 U/L) (Rustad et al., 2004). Taking the upper limit of any percentile as the decision limit, the individual plasma CK activity above the upper reference limit may indicate the transition from adaptive microtrauma to a sub-clinical muscular

In this study, we suggest the 90th percentile (975 U/L) as the upper plasma CK limit for the early detection of muscle overload in competing soccer players (Lazarim et al., 2009). We hypothesized that the same muscle membrane alterations that may increase plasma CK activity also affect the release of growth factors by muscle cells (McNeil & Khakee, 1992), explaining why changes in the plasma CK activity could also reflect muscular adaptation

non-Gaussian distribution (Clarkson & Ebbeling, 1988; Lazarim et al., 2009).

can also be influenced by muscle mass and ethnicity (Eliakim et al., 1995).

programs only when necessary. Prevention thus leads to economy for all.

injury, increasing the potential for histological damage.

and muscular regeneration. These microtraumas are therefore called adaptive microtrauma (AMT). An AMT may be regarded as an initial phase along an injury *continuum*. Conceivably, this injury might progress from the initial benign AMT stage to a subclinical injury in the athlete who is training strenuously and frequently (Smith, 2000). The main challenge for the better adjustment of physical training methodologies is therefore to program a sequence of exercise stimuli that has an ideal relationship between the amount of exercise and the time to recovery between sessions and that leads to increased performance with a lower energy cost.

The longitudinal evaluation of some blood analytes may reveal markers of previously altered situations, which could prevent the amplification of the response before the performance is affected. A variety of studies using different types of exercise protocols have already demonstrated that biomarkers, such as creatine kinase (muscular overload), urea (protein turnover), creatinine (muscular mass), uric acid (the major antioxidant in plasma) and hematological parameters, suffer some modulation by exercise (Sawka et al., 2000; Pattwell et al., 2004; Lac & Maso 2004; Finaud et al., 2006; Peake et al., 2007; Lippi et al., 2008; Lazarim et al., 2009). However, there is currently no consensus about the applicability of these biomarkers as markers of training effects that effectively contribute to reaching and maintaining a better performance.

#### **1.1 Biomarkers of training effects**

#### **1.1.1 Muscular damage**

The muscle tissue may be damaged both directly and indirectly. Direct damage may be due to crush injuries (Brancaccio et al., 2010; Cervellin et al., 2010), but the main force responsible for damage is the mechanical stress that occurs during training sessions (Fielding et al., 1993; Tidball, 2005a). Indirect damage can originate from several sources that reduce membrane permeability (e.g., drugs, toxins, electrolyte alterations, bacterial or viral infections and disorders in carbohydrate metabolism) (Brancaccio et al., 2010).

Muscle damage is related to a disorganization of the myofibrillar structure and a disruption of the Z line, extracellular matrix, basal lamina and sarcolemma, allowing some of the proteins present within the cell to be release into the bloodstream (Sayers & Clarkson, 2003). Among them are creatine kinase (CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and myoglobin. These proteins are blood markers of muscle functional status, and an increase in their serum concentrations or activities may be an index for either muscle damage or muscular adaptation to training (Brancaccio et al., 2008; Lazarim et al., 2009).

The enzyme CK is a globular protein with a molecular mass of 43-45 kDa. It influences the availability of energy to the muscles through the exchange of high-energy phosphate from phosphocreatine (PCr) to ADP (adenosine diphosphate) for fast ATP (adenosine triphosphate) production, as follows:

PCr + ADP + H+ CK ATP + Cr

Five isoforms of the enzyme are present in the skeletal muscle, cardiac muscle and brain: three of them are found in the cytoplasm (CK-MM, CK-MB and CK-BB, respectively), and two isoforms are found in the mitochondria. Because of their differential tissue distribution, they provide different information about tissue damage: CK-MM is a marker for muscle damage, CK-MB is a marker for acute myocardial infarction and CK-BB is a marker for brain damage (Brancaccio et al., 2010).

and muscular regeneration. These microtraumas are therefore called adaptive microtrauma (AMT). An AMT may be regarded as an initial phase along an injury *continuum*. Conceivably, this injury might progress from the initial benign AMT stage to a subclinical injury in the athlete who is training strenuously and frequently (Smith, 2000). The main challenge for the better adjustment of physical training methodologies is therefore to program a sequence of exercise stimuli that has an ideal relationship between the amount of exercise and the time to recovery between sessions and that leads to increased performance with a lower energy cost. The longitudinal evaluation of some blood analytes may reveal markers of previously altered situations, which could prevent the amplification of the response before the performance is affected. A variety of studies using different types of exercise protocols have already demonstrated that biomarkers, such as creatine kinase (muscular overload), urea (protein turnover), creatinine (muscular mass), uric acid (the major antioxidant in plasma) and hematological parameters, suffer some modulation by exercise (Sawka et al., 2000; Pattwell et al., 2004; Lac & Maso 2004; Finaud et al., 2006; Peake et al., 2007; Lippi et al., 2008; Lazarim et al., 2009). However, there is currently no consensus about the applicability of these biomarkers as markers of training effects that effectively contribute to reaching and

The muscle tissue may be damaged both directly and indirectly. Direct damage may be due to crush injuries (Brancaccio et al., 2010; Cervellin et al., 2010), but the main force responsible for damage is the mechanical stress that occurs during training sessions (Fielding et al., 1993; Tidball, 2005a). Indirect damage can originate from several sources that reduce membrane permeability (e.g., drugs, toxins, electrolyte alterations, bacterial or viral

Muscle damage is related to a disorganization of the myofibrillar structure and a disruption of the Z line, extracellular matrix, basal lamina and sarcolemma, allowing some of the proteins present within the cell to be release into the bloodstream (Sayers & Clarkson, 2003). Among them are creatine kinase (CK), lactate dehydrogenase (LDH), aspartate aminotransferase (AST) and myoglobin. These proteins are blood markers of muscle functional status, and an increase in their serum concentrations or activities may be an index for either muscle damage or

The enzyme CK is a globular protein with a molecular mass of 43-45 kDa. It influences the availability of energy to the muscles through the exchange of high-energy phosphate from phosphocreatine (PCr) to ADP (adenosine diphosphate) for fast ATP (adenosine

PCr + ADP + H+ CK ATP + Cr Five isoforms of the enzyme are present in the skeletal muscle, cardiac muscle and brain: three of them are found in the cytoplasm (CK-MM, CK-MB and CK-BB, respectively), and two isoforms are found in the mitochondria. Because of their differential tissue distribution, they provide different information about tissue damage: CK-MM is a marker for muscle damage, CK-MB is a marker for acute myocardial infarction and CK-BB is a marker for brain

infections and disorders in carbohydrate metabolism) (Brancaccio et al., 2010).

muscular adaptation to training (Brancaccio et al., 2008; Lazarim et al., 2009).

maintaining a better performance.

**1.1 Biomarkers of training effects** 

triphosphate) production, as follows:

damage (Brancaccio et al., 2010).

**1.1.1 Muscular damage** 

Doubts about the application of CK analysis to the monitoring of the muscular workload in athletes are derived from studies suggesting that this analyte is an unreliable marker for histological muscle lesions (Malm, 2001). Another source of doubt is that the serum CK values measured in individuals exercising to a similar degree showed high variability and a non-Gaussian distribution (Clarkson & Ebbeling, 1988; Lazarim et al., 2009).

Studies of subjects performing specific exercises for short, defined periods have shown that the time of CK release into the bloodstream and its clearance from the plasma depends on the training level, type, and intensity as well as the duration of the exercise. Peak serum CK values of approximately twice the baseline levels occur eight hours after strength training (Serrão et al., 2003). After an acute bout of intense plyometric exercise, the serum CK levels reached peak values from 48 through 72 hours of recovery (Chatzinikolaou et al., 2010). Peak serum CK values of approximately sevenfold above the baseline were found 48 h after a soccer game (Fatouros et al., 2010). There are marked differences between the sexes, with lower basal CK values in females than in males. Estrogen levels may be one important factor in maintaining membrane stability post-exercise (Tiidus, 2000). Creatine kinase serum levels can also be influenced by muscle mass and ethnicity (Eliakim et al., 1995).

While these studies make important contributions to the understanding of acute responses, they do not provide enough information for longitudinal, seasonal application in actively competing athletes. An important point to be considered is that serum CK activity can arise in the absence of histological lesions as a consequence of changes in the muscle membrane permeability (Manfredi et al., 1991). Thus, monitoring the changes in serum CK may represent an indirect route for monitoring workload effects and a way to prevent sub-clinical damage due to muscle overload. However, to be a useful tool for individual adjustments in the stimulus/recovery ratio during a competitive season, it is necessary to compare individual blood values with population-based reference intervals. With this knowledge, it is possible to individualize training program interventions to adjust overloads or medical/nutritional programs only when necessary. Prevention thus leads to economy for all.

One difficulty in assessing the effects of training through blood biomarkers is the lack of appropriate reference intervals obtained from a reference population practicing regular and systematized physical activity or sports modality. To solve this problem, we have recently determined the reference interval for the plasma CK activity of blood samples obtained from 128 professional soccer players at different times during the Brazilian Championship (Lazarim et al., 2009). The upper limits of the 97.5th and 90th percentiles for the CK activity were determined according to the International Federation of Clinical Chemistry (IFCC) rules and were 1.338 U/L (CI = 1191—1639 U/L) and 975 U/L (CI = 810—1090 U/L), respectively. These percentile values were markedly higher than the values previously reported in the literature (< 207 U/L) (Rustad et al., 2004). Taking the upper limit of any percentile as the decision limit, the individual plasma CK activity above the upper reference limit may indicate the transition from adaptive microtrauma to a sub-clinical muscular injury, increasing the potential for histological damage.

In this study, we suggest the 90th percentile (975 U/L) as the upper plasma CK limit for the early detection of muscle overload in competing soccer players (Lazarim et al., 2009). We hypothesized that the same muscle membrane alterations that may increase plasma CK activity also affect the release of growth factors by muscle cells (McNeil & Khakee, 1992), explaining why changes in the plasma CK activity could also reflect muscular adaptation

Applicability of the Reference Interval and Reference Change

& Hoffman-Goetz, 2000; Steensberg et al., 2000).

pattern (Lira et al., 2009).

hematological parameters.

Value of Hematological and Biochemical Biomarkers to Sport Science 81

that can last for hours (mainly of T and NK cells). The latter effect can lead to a transitory immunosuppressive state that is related to a higher susceptibility for upper respiratory tract infections as an acute effect of exhausting, prolonged exercise (Glesson, 2007; Pedersen et al., 1998). This phenomenon is known as an open window, and it can lead to increased

The literature related to exercise indicates distinct inflammatory responses to both acute and chronic exercise (Catanho da Silva & Macedo, 2011). In general, acute exercise induces a proinflammatory response that is characterized by transient leukocytosis (neutrophilia, monocytosis, and lymphocytosis), followed by a partial cellular immunosuppressive state. After a single bout of physical activity, there is an increase in the number of circulating leukocytes that is related to the intensity and duration of the exercise (Gleeson, 2007). Other substances related to leukocyte function, including inflammatory cytokines and inflammatory acute phase proteins are also increased. These values are generally normalized to basal concentrations within 3-24 hours (Gleeson, 2006). An increase in the serum concentrations of creatine kinase, C-reactive protein and cell adhesion molecules is also observed, in addition to an increase in the secretion of cortisol and cytokines (Pedersen

In contrast, chronic exercise (training) seems to result in a local and systemic imbalance in the anti-inflammatory status as compared to the pro-inflammatory status. This imbalance promotes tissue adaptation and protects the organism against the development of chronic inflammatory diseases and against the deleterious effects of overtraining, a condition in which a systemic and chronic proinflammatory and pro-oxidant state seems to prevail (Petersen & Pedersen, 2005; Catanho da Silva & Macedo, 2011). Some studies have shown an attenuation in the production and secretion of acute phase proteins, especially PCR (Kasapis & Thompson, 2005), greater production and secretion of anti-inflammatory proteins (IL-6) (Petersen & Pedersen, 2005) and improved antioxidant status (Petersen & Pedersen, 2005; Ji, 1999). A possible transient alteration in the production of IL-1β and TNF is dependent upon the exercise type, intensity and duration (Petersen & Pedersen, 2005). Adipose tissue also has been investigated in chronic protocols and has shown the same anti-inflammatory

In general, there is little evidence available to suggest clinical differences between the immune functions of sedentary and exercised subjects. Some studies have reported a lower frequency of upper respiratory tract infections in persons who are moderately active as compared to those with a sedentary lifestyle (Gleeson, 2007). Cross-sectional studies that have compared leukocyte numbers in sedentary control groups with athletes more than 24 hours after their last training session have generally found few differences (Gleeson, 2006). We established reference intervals for hemogram in a large population (n = 357) after four months of systematized endurance training. We followed the criteria established by the International Federation of Clinical Chemistry (IFCC). The outliers were detected and removed before the estimation of the reference interval by Horn's algorithm (Horn et al., 2001). The RefVal program (Solberg, 2004), including practical approaches and formulas recommended by the IFCC, was used to calculate the non-parametric 2.5th and 97.5th percentiles, together with their 90% confidence intervals (CI), using a Bootstrap methodology. Table 1 shows the lower limit (2.5th percentile) and upper limit (97.5th percentile) of the reference intervals and their respective 90% confidence intervals for

susceptibility for infections post-exercise (Rowbottom & Green, 2000; Glesson, 2007).

when the values are lower than the upper limit values. To test this hypothesis, we evaluated a soccer team monthly throughout the Championship. During the five moments of analyses, we detected only six players with plasma CK values that were higher than 975 U/L. These players were asked to decrease their training for 1 week, after which they presented lower CK values. Only one player with a CK value higher than the decision limit (1800 U/L one day before a game) played on the field, and he was unfortunately injured during the game. The CK activity in all of the other players showed a significant decrease over the course of the Championship, and the values became more homogeneous toward the end.

Later, we showed that the 97.5th percentile for a young population with improved performance after four months of systematic endurance training was similar (< 1309 U/L, CI = 882 – 1464 U/L) to that found in soccer players (Nunes & Macedo, 2008). To us, this finding justified the use of blood samples from this physically active population to establish reference population intervals for analytes that respond to exercise stimulus, such as plasma glutamine and glutamate concentrations, which are discussed in another chapter of this book.

#### **1.1.2 Inflammatory response**

The response to AMT is a subsequent inflammation post-exercise, triggering tissue repair and remodeling. The activation of the inflammatory process is both local and systemic and is mediated by different cells and secreted compounds with pro- and anti-inflammatory activities. The objective is to reestablish organ homeostasis after a single bout of exercise or after several exercise sessions. The acute-phase response involves the combined actions of activated leukocytes, cytokines, acute-phase proteins, hormones, and other signaling molecules that control the response to an exercise session and guide the adaptations resulting from training (Gruys et al., 2005).

The leukocytes are the first cells of the immune system to respond to tissue damage (Smith, 2000), and the neutrophils are the first subpopulation to migrate to the damaged site (Tidball, 2005). Neutrophils are produced in the bone marrow and represent 50 to 60% of the total leukocytes in circulation (Toumi & Best, 2003; Tidball, 2005b). Cortisol stimulates their release (Pyne, 1994), and their main function is the removal, by phagocytosis, of undesirable elements that are related to injury. To accomplish this removal, they release proteases to degrade proteins and produce superoxide anions [O2 ●- ] and subsequently other reactive oxygen species (ROS; hydrogen peroxide [H2O2] and hydroxyl radical [OH. ]) through a respiratory burst that is catalyzed by NADPH oxidase and myeloperoxidase (Pyne, 1994; Tidball, 2005b). This action is the starting point for the subsequent response of repair and tissue growth.

The monocytes are the second subpopulation of leukocytes that migrate to the damaged tissue. In the cells, they undergo differentiation and become macrophages (Tidball, 2005b). Recently, it was proposed that the macrophages that invade the lesion site earlier (between 24-48 h) have different functions than do those that appear later (between 48-96 h). The main function of the first group is the removal of damaged tissue, whereas the later group has a more active function in muscular repair and secrete remodeling molecules, such as insulinlike receptor, cytokines and TGF-α, that act in the recruitment and activation of fibroblasts and collagen secretion, contributing to tissue remodeling (Butterfield et al., 2006). Moreover, macrophages signal the activation, proliferation and differentiation of stem satellite cells, an important pathway for tissue remodeling (Pedersen et al., 1998).

The lymphocytes present a biphasic response to training. There is an increase during and immediately after the effort, especially of the Natural Killer (NK) cells, followed by a decrease

when the values are lower than the upper limit values. To test this hypothesis, we evaluated a soccer team monthly throughout the Championship. During the five moments of analyses, we detected only six players with plasma CK values that were higher than 975 U/L. These players were asked to decrease their training for 1 week, after which they presented lower CK values. Only one player with a CK value higher than the decision limit (1800 U/L one day before a game) played on the field, and he was unfortunately injured during the game. The CK activity in all of the other players showed a significant decrease over the course of

Later, we showed that the 97.5th percentile for a young population with improved performance after four months of systematic endurance training was similar (< 1309 U/L, CI = 882 – 1464 U/L) to that found in soccer players (Nunes & Macedo, 2008). To us, this finding justified the use of blood samples from this physically active population to establish reference population intervals for analytes that respond to exercise stimulus, such as plasma glutamine

The response to AMT is a subsequent inflammation post-exercise, triggering tissue repair and remodeling. The activation of the inflammatory process is both local and systemic and is mediated by different cells and secreted compounds with pro- and anti-inflammatory activities. The objective is to reestablish organ homeostasis after a single bout of exercise or after several exercise sessions. The acute-phase response involves the combined actions of activated leukocytes, cytokines, acute-phase proteins, hormones, and other signaling molecules that control the response to an exercise session and guide the adaptations

The leukocytes are the first cells of the immune system to respond to tissue damage (Smith, 2000), and the neutrophils are the first subpopulation to migrate to the damaged site (Tidball, 2005). Neutrophils are produced in the bone marrow and represent 50 to 60% of the total leukocytes in circulation (Toumi & Best, 2003; Tidball, 2005b). Cortisol stimulates their release (Pyne, 1994), and their main function is the removal, by phagocytosis, of undesirable elements that are related to injury. To accomplish this removal, they release proteases to degrade

●-

is the starting point for the subsequent response of repair and tissue growth.

is catalyzed by NADPH oxidase and myeloperoxidase (Pyne, 1994; Tidball, 2005b). This action

The monocytes are the second subpopulation of leukocytes that migrate to the damaged tissue. In the cells, they undergo differentiation and become macrophages (Tidball, 2005b). Recently, it was proposed that the macrophages that invade the lesion site earlier (between 24-48 h) have different functions than do those that appear later (between 48-96 h). The main function of the first group is the removal of damaged tissue, whereas the later group has a more active function in muscular repair and secrete remodeling molecules, such as insulinlike receptor, cytokines and TGF-α, that act in the recruitment and activation of fibroblasts and collagen secretion, contributing to tissue remodeling (Butterfield et al., 2006). Moreover, macrophages signal the activation, proliferation and differentiation of stem satellite cells, an

The lymphocytes present a biphasic response to training. There is an increase during and immediately after the effort, especially of the Natural Killer (NK) cells, followed by a decrease

] and subsequently other reactive oxygen species

]) through a respiratory burst that

the Championship, and the values became more homogeneous toward the end.

and glutamate concentrations, which are discussed in another chapter of this book.

**1.1.2 Inflammatory response** 

resulting from training (Gruys et al., 2005).

proteins and produce superoxide anions [O2

(ROS; hydrogen peroxide [H2O2] and hydroxyl radical [OH.

important pathway for tissue remodeling (Pedersen et al., 1998).

that can last for hours (mainly of T and NK cells). The latter effect can lead to a transitory immunosuppressive state that is related to a higher susceptibility for upper respiratory tract infections as an acute effect of exhausting, prolonged exercise (Glesson, 2007; Pedersen et al., 1998). This phenomenon is known as an open window, and it can lead to increased susceptibility for infections post-exercise (Rowbottom & Green, 2000; Glesson, 2007).

The literature related to exercise indicates distinct inflammatory responses to both acute and chronic exercise (Catanho da Silva & Macedo, 2011). In general, acute exercise induces a proinflammatory response that is characterized by transient leukocytosis (neutrophilia, monocytosis, and lymphocytosis), followed by a partial cellular immunosuppressive state. After a single bout of physical activity, there is an increase in the number of circulating leukocytes that is related to the intensity and duration of the exercise (Gleeson, 2007). Other substances related to leukocyte function, including inflammatory cytokines and inflammatory acute phase proteins are also increased. These values are generally normalized to basal concentrations within 3-24 hours (Gleeson, 2006). An increase in the serum concentrations of creatine kinase, C-reactive protein and cell adhesion molecules is also observed, in addition to an increase in the secretion of cortisol and cytokines (Pedersen & Hoffman-Goetz, 2000; Steensberg et al., 2000).

In contrast, chronic exercise (training) seems to result in a local and systemic imbalance in the anti-inflammatory status as compared to the pro-inflammatory status. This imbalance promotes tissue adaptation and protects the organism against the development of chronic inflammatory diseases and against the deleterious effects of overtraining, a condition in which a systemic and chronic proinflammatory and pro-oxidant state seems to prevail (Petersen & Pedersen, 2005; Catanho da Silva & Macedo, 2011). Some studies have shown an attenuation in the production and secretion of acute phase proteins, especially PCR (Kasapis & Thompson, 2005), greater production and secretion of anti-inflammatory proteins (IL-6) (Petersen & Pedersen, 2005) and improved antioxidant status (Petersen & Pedersen, 2005; Ji, 1999). A possible transient alteration in the production of IL-1β and TNF is dependent upon the exercise type, intensity and duration (Petersen & Pedersen, 2005). Adipose tissue also has been investigated in chronic protocols and has shown the same anti-inflammatory pattern (Lira et al., 2009).

In general, there is little evidence available to suggest clinical differences between the immune functions of sedentary and exercised subjects. Some studies have reported a lower frequency of upper respiratory tract infections in persons who are moderately active as compared to those with a sedentary lifestyle (Gleeson, 2007). Cross-sectional studies that have compared leukocyte numbers in sedentary control groups with athletes more than 24 hours after their last training session have generally found few differences (Gleeson, 2006).

We established reference intervals for hemogram in a large population (n = 357) after four months of systematized endurance training. We followed the criteria established by the International Federation of Clinical Chemistry (IFCC). The outliers were detected and removed before the estimation of the reference interval by Horn's algorithm (Horn et al., 2001). The RefVal program (Solberg, 2004), including practical approaches and formulas recommended by the IFCC, was used to calculate the non-parametric 2.5th and 97.5th percentiles, together with their 90% confidence intervals (CI), using a Bootstrap methodology. Table 1 shows the lower limit (2.5th percentile) and upper limit (97.5th percentile) of the reference intervals and their respective 90% confidence intervals for hematological parameters.

Applicability of the Reference Interval and Reference Change

**1.1.3 Nitrogen compounds** 

subject's training (Finaud et al., 2006).

8.20 mmol/L) (Rustad et al., 2004).

to higher 2,3-diphosphoglycerate concentrations (Smith, 1995).

Value of Hematological and Biochemical Biomarkers to Sport Science 83

endurance training (Weight et al., 1991). This lifespan may lead to the narrow values observed in the red blood cell parameters in our study. The accelerated turnover and increased rate of RBC production in endurance trained subjects can lead to a steady state of a population of younger RBCs, which are more efficient at oxygen transportation due partly

A prolonged, uncontrolled local neutrophil action can damage other cells near the inflammatory site due to increases in ROS production, which compromises the integrity of the muscle cells, contributing to systemic inflammation (Tidball, 2005b). A series of enzymatic and non-enzymatic antioxidants (Ji, 1999) limit the biological activity of ROS. Uric acid is one of the most important non-enzymatic antioxidants in plasma and tissues (Lippi et al., 2008). In addition, the plasma antioxidant system is also composed of other molecules and enzymes, such as ascorbic acid, proteins, vitamin E, bilirubin and peroxidases (Finaud et al., 2006). Athletes and physically active subjects displayed an enhanced antioxidant capacity (Carlsohn et al., 2008) with increased serum concentrations of uric acid (Finaud et al., 2006). It was suggested that urate quantification could be useful for monitoring athletes in training (Youssef et al., 2008). We have found (Nunes et al., 2010) a slightly higher serum uric acid reference interval in athletes (0.24 – 0.49 mmol/L) than in sedentary subjects (0.23 – 0.47 mmol/L) (Rustad et al., 2004). This difference can be explained by the intensity of the

Biomarkers, such as serum creatinine and urea, are also used to monitor the effects of training. The urea is an end product of the degradation of nitrogenous compounds from proteins; it is synthesized in the liver and is excreted by kidneys. The main factors influencing the higher urea serum levels found during the training period may be the increased consumption and protein turnover, the reduced water intake and the incomplete replenishment of glycogen after exercise (Hartmann & Mester, 2000). The serum concentrations of urea have been used as a marker of protein catabolism and were found to stimulate gluconeogenesis during higher training loads. It was proposed that monitoring the serum urea concentrations and the CK activity may indicate an acute impairment in exercise tolerance (Urhausen & Kindermann, 2002; Lehmann et al., 1998). The serum urea level seems to respond to exercise training, and the upper limit of the reference interval observed by Nunes & Macedo (2008) (3.0 – 8.51 mmol/L) in physically active subjects at a well-trained stage is higher compared than that of a non-exercised healthy population (3.20 –

Serum creatinine concentrations have long been the most widely used and commonly accepted biomarker of renal function in clinical medicine (Perrone et al., 1992). Their concentrations can be modified by age, sex, ethnicity, muscle mass and exercise (Banfi et al., 2009). The serum creatinine concentrations found in professional athletes can vary according to their modality, the training load, their aerobic/anaerobic metabolism, the frequency of their competitions, the lengths of their competition and the annual training period (Banfi et al., 2009). The creatinine reference intervals commonly used for sedentary people are 62.0 – 115.0 µmol/L. In physically active individuals, we observed higher values (77.8 – 132.6 µmol/L) (Nunes & Macedo, 2008), which are typically influenced by the higher muscle mass

Our data have shown that the reference intervals were significantly higher in trained subjects than in a non-exercised population, suggesting a training effect on blood analytes,

that is found in exercised populations, as observed by Banfi & Del Fabro (2006).


Table 1. Reference intervals, confidence intervals and outliers obtained from a hemogram of physically active individuals.

We found slightly higher values for counts of leukocytes (4.5x109 – 10.1 x109/L) and neutrophils (1.8 x109 – 6.7 x109cel/L) in our physically active population as compared to sedentary individuals (3.5x109 – 9.8x109/L and 1.4 x109 – 6.6x109/L, respectively) (Kjeldsberg, 1992). Our reference ranges are narrower than the traditional reference intervals for leukocyte counts, mainly due to the homogeneity of the reference population. The 2.5th and 97.5th percentile of the lymphocyte and platelet counts found were similar to those of healthy, nonexercised population values (Lym = 1.2 – 3.5 x 109/L) and (PLT = 145 – 348 x 109/L) (Kjeldsberg, 1992; Nordin et al., 2004), indicating no training effects on these parameters.

The erythrogram is a part of the hemogram that evaluates the red blood cell (RBC) number, volume and hemoglobin content. Erythrograms can be useful to diagnose sports anemia, which can impair the athlete's performance (Sottas et al., 2010, Schumacher et al., 2002). Exercise-induced hemolysis has been reported for more than 50 years (Gilligan et al., 1943). This phenomenon is associated with the destruction of red blood cells (RBC), with higher RBC turnovers in runners as compared to non-trained subjects, although it is commonly observed in other modalities (e.g., swimming, weight lifting, rowing) (Telford et al., 2003). A persistent decrease in the hemoglobin concentration, decreases in indices such as the mean corpuscular volume and the hemoglobin corpuscular volume and an increase in red distribution width (RDW) can also indicate iron deficiency (Zoller & Vogel, 2004). Three subjects presented higher RBC values and lower MCV values, characteristics of thalassemia, while four subjects were detected to have microcytosis (MCV < 80.0 fL and RDW>15%) and hypochromia (HCM=26.0 pg), suggestive of an iron deficiency: these subjects were classified as outliers and were excluded from the calculations of the reference intervals.

The data presented in Table 1 show a more narrow reference interval for RBC, hematocrit and hemoglobin in our population as compared to a sedentary subject's values (RBC = 4.4 – 5.9 x 1012/L; hematocrit = 40 – 50% and hemoglobin 13 – 18 g/dL) (Kjeldsberg, 1992). The mean erythrocyte lifespan in athletes and physically active subjects may be shorter than that in non-exercised subjects, mainly due to the exercise-induced hemolysis that is inherent to endurance training (Weight et al., 1991). This lifespan may lead to the narrow values observed in the red blood cell parameters in our study. The accelerated turnover and increased rate of RBC production in endurance trained subjects can lead to a steady state of a population of younger RBCs, which are more efficient at oxygen transportation due partly to higher 2,3-diphosphoglycerate concentrations (Smith, 1995).

## **1.1.3 Nitrogen compounds**

82 An International Perspective on Topics in Sports Medicine and Sports Injury

**(n)** 

**Outliers (n)** 

**Total Subjects** 

**Interval 90% Confidence Interval Subjects** 

RBC (1012/L) 4.4 - 5.6 4.3 - 4.5 5.6 - 5.7 357 5 352 Ht (%) 39.5 - 48.0 39.0 - 40.2 47.7 - 48.8 357 3 354 Hgb (g/dL) 13.0 - 16.1 12.8 - 13.2 15.9 - 16.3 357 2 352 MCV (fL) 80.9 - 94.9 80.0 - 82.3 94.3 - 95.4 357 6 351 MCH (pg) 26.1 - 31.6 25.7 - 26.5 31.4 - 31.7 357 9 348 RDW (%) 12.1 - 14.3 12.0 - 12.1 14.1 - 14.4 357 16 341 WBC (109/L) 4.5 - 10.1 4.2 - 4.7 9.7 - 10.4 357 12 345 Lym (109/L) 1.2 - 3.3 1.2 - 1.3 3.15 - 3.4 357 4 353 Neut (109/L) 1.8 - 6.7 1.7 – 2.0 6.5 - 7.0 353 10 343 PLT (109/L) 140 – 337 135 – 147 307 – 350 357 4 353 Table 1. Reference intervals, confidence intervals and outliers obtained from a hemogram of

We found slightly higher values for counts of leukocytes (4.5x109 – 10.1 x109/L) and neutrophils (1.8 x109 – 6.7 x109cel/L) in our physically active population as compared to sedentary individuals (3.5x109 – 9.8x109/L and 1.4 x109 – 6.6x109/L, respectively) (Kjeldsberg, 1992). Our reference ranges are narrower than the traditional reference intervals for leukocyte counts, mainly due to the homogeneity of the reference population. The 2.5th and 97.5th percentile of the lymphocyte and platelet counts found were similar to those of healthy, nonexercised population values (Lym = 1.2 – 3.5 x 109/L) and (PLT = 145 – 348 x 109/L) (Kjeldsberg, 1992; Nordin et al., 2004), indicating no training effects on these parameters. The erythrogram is a part of the hemogram that evaluates the red blood cell (RBC) number, volume and hemoglobin content. Erythrograms can be useful to diagnose sports anemia, which can impair the athlete's performance (Sottas et al., 2010, Schumacher et al., 2002). Exercise-induced hemolysis has been reported for more than 50 years (Gilligan et al., 1943). This phenomenon is associated with the destruction of red blood cells (RBC), with higher RBC turnovers in runners as compared to non-trained subjects, although it is commonly observed in other modalities (e.g., swimming, weight lifting, rowing) (Telford et al., 2003). A persistent decrease in the hemoglobin concentration, decreases in indices such as the mean corpuscular volume and the hemoglobin corpuscular volume and an increase in red distribution width (RDW) can also indicate iron deficiency (Zoller & Vogel, 2004). Three subjects presented higher RBC values and lower MCV values, characteristics of thalassemia, while four subjects were detected to have microcytosis (MCV < 80.0 fL and RDW>15%) and hypochromia (HCM=26.0 pg), suggestive of an iron deficiency: these subjects were classified

as outliers and were excluded from the calculations of the reference intervals.

The data presented in Table 1 show a more narrow reference interval for RBC, hematocrit and hemoglobin in our population as compared to a sedentary subject's values (RBC = 4.4 – 5.9 x 1012/L; hematocrit = 40 – 50% and hemoglobin 13 – 18 g/dL) (Kjeldsberg, 1992). The mean erythrocyte lifespan in athletes and physically active subjects may be shorter than that in non-exercised subjects, mainly due to the exercise-induced hemolysis that is inherent to

2.5th – 97.5th 2.5th 97.5th

**Analysis Reference** 

physically active individuals.

A prolonged, uncontrolled local neutrophil action can damage other cells near the inflammatory site due to increases in ROS production, which compromises the integrity of the muscle cells, contributing to systemic inflammation (Tidball, 2005b). A series of enzymatic and non-enzymatic antioxidants (Ji, 1999) limit the biological activity of ROS. Uric acid is one of the most important non-enzymatic antioxidants in plasma and tissues (Lippi et al., 2008). In addition, the plasma antioxidant system is also composed of other molecules and enzymes, such as ascorbic acid, proteins, vitamin E, bilirubin and peroxidases (Finaud et al., 2006).

Athletes and physically active subjects displayed an enhanced antioxidant capacity (Carlsohn et al., 2008) with increased serum concentrations of uric acid (Finaud et al., 2006). It was suggested that urate quantification could be useful for monitoring athletes in training (Youssef et al., 2008). We have found (Nunes et al., 2010) a slightly higher serum uric acid reference interval in athletes (0.24 – 0.49 mmol/L) than in sedentary subjects (0.23 – 0.47 mmol/L) (Rustad et al., 2004). This difference can be explained by the intensity of the subject's training (Finaud et al., 2006).

Biomarkers, such as serum creatinine and urea, are also used to monitor the effects of training. The urea is an end product of the degradation of nitrogenous compounds from proteins; it is synthesized in the liver and is excreted by kidneys. The main factors influencing the higher urea serum levels found during the training period may be the increased consumption and protein turnover, the reduced water intake and the incomplete replenishment of glycogen after exercise (Hartmann & Mester, 2000). The serum concentrations of urea have been used as a marker of protein catabolism and were found to stimulate gluconeogenesis during higher training loads. It was proposed that monitoring the serum urea concentrations and the CK activity may indicate an acute impairment in exercise tolerance (Urhausen & Kindermann, 2002; Lehmann et al., 1998). The serum urea level seems to respond to exercise training, and the upper limit of the reference interval observed by Nunes & Macedo (2008) (3.0 – 8.51 mmol/L) in physically active subjects at a well-trained stage is higher compared than that of a non-exercised healthy population (3.20 – 8.20 mmol/L) (Rustad et al., 2004).

Serum creatinine concentrations have long been the most widely used and commonly accepted biomarker of renal function in clinical medicine (Perrone et al., 1992). Their concentrations can be modified by age, sex, ethnicity, muscle mass and exercise (Banfi et al., 2009). The serum creatinine concentrations found in professional athletes can vary according to their modality, the training load, their aerobic/anaerobic metabolism, the frequency of their competitions, the lengths of their competition and the annual training period (Banfi et al., 2009). The creatinine reference intervals commonly used for sedentary people are 62.0 – 115.0 µmol/L. In physically active individuals, we observed higher values (77.8 – 132.6 µmol/L) (Nunes & Macedo, 2008), which are typically influenced by the higher muscle mass that is found in exercised populations, as observed by Banfi & Del Fabro (2006).

Our data have shown that the reference intervals were significantly higher in trained subjects than in a non-exercised population, suggesting a training effect on blood analytes,

Applicability of the Reference Interval and Reference Change

calculated CVG for these soccer players is 68%.

some estimate of the true value (Fraser, 2001).

(Fraser & Petersen, 1999).

Value of Hematological and Biochemical Biomarkers to Sport Science 85

Another component of biological variation is the coefficient of variation between subjects (CVG), which is obtained via the mean and standard deviation between different subjects. For example, if ten soccer players exhibited mean serum CK values of 390 U/L with a standard deviation of 266 U/L from blood samples collected after one month of training, the

Both CVI and CVG are influenced by age, sex, weight, diet, circadian rhythm, pathologies and physical activity (Ricós et al. 2004), and it is possible to estimate them using data banks that are available in the literature for many analytes in healthy, non-exercised subjects (Ricos et al., 1999). In addition to the biological variation, pre-analytical and analytical variation can also influence the laboratory test results. The pre-analytical variation can be minimized through the adoption of standardized instructions for the patients before sample collection, handling and transportation (Banfi & Dolci, 2003). All analytical measurement techniques (manual or automatic) have some intrinsic sources of variability. This variability cannot be completely eliminated, but it can be minimized by quality laboratory practices and the choice of sound equipment, reagents and methodologies (Fraser, 2001). We can usually

identify two types of analytical variation: random (precision) and systematic (bias).

The precision of one methodology or piece of equipment is measured by replicate analysis of the same sample (a control sample). The precision will be influenced by the analytical conditions. If we use the same equipment, technician, reagent lots and calibration, we will generate smaller variations than if we take replicate results over a long period, varying these components (Fraser, 2001). The precision has a Gaussian distribution, and we can calculate the coefficient of analytical variation (CVA%) from the mean and standard deviation of control sample replicates. The International Organization for Standardization (ISO) defines bias as *the difference between the expectation of the measurement results and the true value of the measured quantity*. In practice, bias is the difference between the results that we obtain and

The analytical variation can be monitored by an internal quality control (IQC) program. The protocol of IQC should include control samples that simulate the matrix of the sample analysis (Westgard, 2004). The statistical analysis of the quality control can be performed through the Levey-Jennings flow chart for each analyte and the correct applications of the Westgard control rules (Westgard, 2004). In addition, the level of performance must be established. The most widely applied term is *quality specifications.* Other terms include *quality goals, quality standards, desirable standards, analytical goals,* and *analytical performance goals* (Fraser, 2001). Considering that all analytes suffer from the influence of biological variation (intra- and inter-individual), one useful quality specification is based on biology (Fraser & Petersen, 1999). In this model, the quality specification has 3 levels for judging imprecision: *desirable performance* is defined as CVA < 0.5 x CVI; *optimum performance* is defined as CVA< 0.25 x CVI and the *minimum performance* is defined as CVA < 0.75 x CVI

The analysis of consecutive samples by comparison with values from a reference population is useful mainly when CVI > CVG (Fraser 2004). However, the majority of analytes that are quantified in the clinical laboratory have CVI < CVG. In this case, the comparison of an individual result with a population-based reference interval might not be useful in monitoring slight effects because some individuals may show significant changes in serial testing that are within the normal limits of a reference population (Petersen et al., 1999). A proposed alternative tool to verify a significant and biologically relevant difference between two consecutive analyses is the reference change value (RCV) or critical difference calculation

such as the hemogram, which were slightly different from those values reported in the literature for a healthy, non-exercised population (Kjeldsberg, 1992; Nordin et al., 2004). In the biochemical parameters, the main differences found were as follows: urea (3.0 – 8.51 mmol/L), creatinine (77.8 – 132.6 µmol/L), uric acid (0.24 – 0.49 mmol/L), creatine kinase (<1309 U/L), aspartate aminotransferase (<62 U/L) and C-reactive protein (<19.8 mg/L), all of which had higher reference intervals in trained subjects as compared to a non-exercised population (Nunes & Macedo, 2008). Now that the lower and upper reference values for the blood analytes of an exercised population are known, it will be possible to use these biomarkers to differentiate between the adaptation and maladaptation states that are induced by training.

#### **1.2 Biological variation and reference change values**

A comparison of individual blood parameter values with the reference intervals obtained from a defined, physically active population has certain limitations, one of which is that the laboratory test results may be influenced by natural fluctuations that are particular to a given analyte: this phenomenon is called biological variation (Fraser, 2004). This biological variation should be assessed in longitudinal studies with serial blood analyses of the same subjects. For example, a determined analyte concentration can change over time within an individual, reflecting the random temporal variation of an analyte in homeostasis that can occur during steady state conditions (Ricos et al., 2004).

The point of biological homeostasis (estimated by the arithmetic mean value, μ) is different for each person, and it is assumed to be constant for a certain period (e.g., months, years). The biological within-subject variation is described by the standard deviation (σ), which is obtained from measurements around the mean value (Petersen, 2005). To facilitate the comparison between individuals and analytes, the coefficient of intra-individual variation (CVI%) is calculated from the following equation:

$$\text{CV}\_{\text{I}} = \text{\text{\textdegree}}/\text{\text{\textdegree}} \times 100\tag{1}$$

The Table 2 shows an example of the biological variation in the serum CK activity from samples that were collected monthly during the training/competition season of six soccer players.


Table 2. Coefficient of intra-individual variation (CVI) related to serum CK activity (U/L) of six male soccer players.

These data show that each athlete has a different point of homeostasis for CK activity (represented here as the individual mean), and this is also true for any other analyte. These individual values span different parts of the reference interval for CK activity (Lazarim et al., 2009).

such as the hemogram, which were slightly different from those values reported in the literature for a healthy, non-exercised population (Kjeldsberg, 1992; Nordin et al., 2004). In the biochemical parameters, the main differences found were as follows: urea (3.0 – 8.51 mmol/L), creatinine (77.8 – 132.6 µmol/L), uric acid (0.24 – 0.49 mmol/L), creatine kinase (<1309 U/L), aspartate aminotransferase (<62 U/L) and C-reactive protein (<19.8 mg/L), all of which had higher reference intervals in trained subjects as compared to a non-exercised population (Nunes & Macedo, 2008). Now that the lower and upper reference values for the blood analytes of an exercised population are known, it will be possible to use these biomarkers to differentiate between the adaptation and maladaptation states that are induced by training.

A comparison of individual blood parameter values with the reference intervals obtained from a defined, physically active population has certain limitations, one of which is that the laboratory test results may be influenced by natural fluctuations that are particular to a given analyte: this phenomenon is called biological variation (Fraser, 2004). This biological variation should be assessed in longitudinal studies with serial blood analyses of the same subjects. For example, a determined analyte concentration can change over time within an individual, reflecting the random temporal variation of an analyte in homeostasis that can

The point of biological homeostasis (estimated by the arithmetic mean value, μ) is different for each person, and it is assumed to be constant for a certain period (e.g., months, years). The biological within-subject variation is described by the standard deviation (σ), which is obtained from measurements around the mean value (Petersen, 2005). To facilitate the comparison between individuals and analytes, the coefficient of intra-individual variation

 CVI = σ/μ x100 (1) The Table 2 shows an example of the biological variation in the serum CK activity from samples that were collected monthly during the training/competition season of six soccer

1 1232 911 1239 1232 1380 1232 173 14 2 424 455 327 347 324 347 60 17 3 218 218 147 247 189 218 38 17 4 1214 1055 667 557 560 667 304 46 5 205 111 230 116 137 137 54 40 6 427 173 200 181 273 200 67 33 Table 2. Coefficient of intra-individual variation (CVI) related to serum CK activity (U/L) of

These data show that each athlete has a different point of homeostasis for CK activity (represented here as the individual mean), and this is also true for any other analyte. These individual values span different parts of the reference interval for CK activity (Lazarim et

**5th result**  **Individual mean (μ)** 

**Standard deviation (σ)** 

**CVI (%)** 

**4th result** 

**1.2 Biological variation and reference change values** 

occur during steady state conditions (Ricos et al., 2004).

(CVI%) is calculated from the following equation:

**2nd result** 

**3rd result** 

players.

**Athletes 1st**

six male soccer players.

al., 2009).

**result** 

Another component of biological variation is the coefficient of variation between subjects (CVG), which is obtained via the mean and standard deviation between different subjects. For example, if ten soccer players exhibited mean serum CK values of 390 U/L with a standard deviation of 266 U/L from blood samples collected after one month of training, the calculated CVG for these soccer players is 68%.

Both CVI and CVG are influenced by age, sex, weight, diet, circadian rhythm, pathologies and physical activity (Ricós et al. 2004), and it is possible to estimate them using data banks that are available in the literature for many analytes in healthy, non-exercised subjects (Ricos et al., 1999). In addition to the biological variation, pre-analytical and analytical variation can also influence the laboratory test results. The pre-analytical variation can be minimized through the adoption of standardized instructions for the patients before sample collection, handling and transportation (Banfi & Dolci, 2003). All analytical measurement techniques (manual or automatic) have some intrinsic sources of variability. This variability cannot be completely eliminated, but it can be minimized by quality laboratory practices and the choice of sound equipment, reagents and methodologies (Fraser, 2001). We can usually identify two types of analytical variation: random (precision) and systematic (bias).

The precision of one methodology or piece of equipment is measured by replicate analysis of the same sample (a control sample). The precision will be influenced by the analytical conditions. If we use the same equipment, technician, reagent lots and calibration, we will generate smaller variations than if we take replicate results over a long period, varying these components (Fraser, 2001). The precision has a Gaussian distribution, and we can calculate the coefficient of analytical variation (CVA%) from the mean and standard deviation of control sample replicates. The International Organization for Standardization (ISO) defines bias as *the difference between the expectation of the measurement results and the true value of the measured quantity*. In practice, bias is the difference between the results that we obtain and some estimate of the true value (Fraser, 2001).

The analytical variation can be monitored by an internal quality control (IQC) program. The protocol of IQC should include control samples that simulate the matrix of the sample analysis (Westgard, 2004). The statistical analysis of the quality control can be performed through the Levey-Jennings flow chart for each analyte and the correct applications of the Westgard control rules (Westgard, 2004). In addition, the level of performance must be established. The most widely applied term is *quality specifications.* Other terms include *quality goals, quality standards, desirable standards, analytical goals,* and *analytical performance goals* (Fraser, 2001). Considering that all analytes suffer from the influence of biological variation (intra- and inter-individual), one useful quality specification is based on biology (Fraser & Petersen, 1999). In this model, the quality specification has 3 levels for judging imprecision: *desirable performance* is defined as CVA < 0.5 x CVI; *optimum performance* is defined as CVA< 0.25 x CVI and the *minimum performance* is defined as CVA < 0.75 x CVI (Fraser & Petersen, 1999).

The analysis of consecutive samples by comparison with values from a reference population is useful mainly when CVI > CVG (Fraser 2004). However, the majority of analytes that are quantified in the clinical laboratory have CVI < CVG. In this case, the comparison of an individual result with a population-based reference interval might not be useful in monitoring slight effects because some individuals may show significant changes in serial testing that are within the normal limits of a reference population (Petersen et al., 1999).

A proposed alternative tool to verify a significant and biologically relevant difference between two consecutive analyses is the reference change value (RCV) or critical difference calculation

Applicability of the Reference Interval and Reference Change

Committee of the University (CAAE: 0200.0.146.000-08).

hemodilution that is induced by exercise (Sawka et al., 2000).

**2.3 Hematological and biochemical analysis** 

reagent lots, standards, or control materials.

**2.4 Statistical analysis** 

used to generate graphs.

months of the competitive season.

**2.2 Collection of blood samples** 

**2. Material and methods** 

**2.1 Subjects** 

Value of Hematological and Biochemical Biomarkers to Sport Science 87

soccer players and their clubs with the physical protection of athletes. Our aim in this study was to verify the applicability of the reference interval described here and the previously determined RCVs (Nunes et al., 2010) for monitoring, through hematological and biochemical analyses, the effects of training/competition on soccer players during five

Fifty-six male soccer players (17-19 years old) participated in this study. The athletes were evaluated during four months that included both training (pre-season) and competitive periods (regional soccer championship for players under 20). The volunteers responded to a questionnaire about their use of medications and complaints of pain and injuries caused by training or competition. Those who were using medications or who were injured were not included in the study. Volunteer subjects were duly informed about the research and signed an informed consent form. This research was approved by the Human Research Ethics

The subjects were longitudinally evaluated through five blood samples that were collected monthly [C1 = pre-competitive period of training; C2 = after 1 month (training); C3 = after 2 months (training and beginning of competitions); C4 = after 3 months (training and competitions); and C5 = after 4 months (mainly competitions)]. The blood samples were collected under standardized conditions: 2.0 mL of total venous blood was collected in vacuum tubes containing EDTA/K3 to determine the hematological parameters, and 8.0 mL of venous blood was collected in tubes with a Vacuette® (Greiner Bio-one) gel separator to obtain serum for the biochemical measurements. The blood samples were collected in the morning after 12 hours of fasting, in a seated position, and they were transported at 4ºC to the laboratory within 30 minutes, centrifuged under refrigeration at 1,800 x g for 10 minutes, were immediately separated, and were protected from light. All blood samples were collected after two days of rest to avoid the effects of hemodynamic variations and the acute

The hematological parameters were measured with a KX-21N Sysmex® analyzer, and the biochemical analyses (CK activity, uric acid, urea and creatinine concentration) were run in an Autolab analyzer (Boehringer) using commercial kits (Wiener Lab, Rosario, Argentina). All analyses were run in parallel with commercial serum and blood controls. To minimize analytical variations, all samples were tested by the same technician without changing

The percent differences between the serial results were calculated for each subject using Microsoft Excel® and were compared with RCV95% to detect significant changes. As the activity of CK had a value distribution that was slightly skewed to the right, we opted to transform the data using natural logarithms (Wu et al., 2009). The Matlab 7.0 software was

(Harris & Yasaka, 1983; Ricos et al., 2004). The RCV defines the percentage of change that must be exceeded given the inherent biological and analytical variation in the test. This tool can increase the sensitivity of serial analyses due to the exclusion of false positive results.

#### **1.3 Calculating reference change values**

The total variation associated with a laboratory test result is the sum of the component variations (pre-analytical, analytical and intra-individual biological variation). The preanalytical variation can be minimized if we standardize the conditions of patient preparation and the procedures for collecting and handling the samples. Therefore, we must consider only the biological and analytical variation when calculating the total variation (CVT) according equation 2 (Fraser & Harris, 1989).

$$\rm{CV}\_{\rm{T}} = (\rm{CV}\_{\rm{A}}^2 + \rm{CV}\_{\rm{I}}^2)^{\rm{V}\_2} \tag{2}$$

The coefficient of analytical variation (CVA) can easily be obtained from the mean and standard deviation of the control sample analytes, and the CVI is available in the data bank for most of the analytes from healthy subjects (Ricos et al., 1999) or physically active subjects (Nunes et al., 2010).

The term RCV was introduced by Harris & Yasaka (1983) and can be calculated by the following equation (3):

$$\text{RCV} = 2^{\aleph\_2} \times \text{Zp} \times (\text{CV}\_{\text{A}}^2 + \text{CV}\_{\text{I}}^2)^{\aleph\_2} \tag{3}$$

Where 21/2 denotes the probability of bidirectional change, and Zp denotes the standard deviation corresponding to the level of statistical significance for the bidirectional change (1.96=95% and 2.58=99%) (Harris & Yasaka; 1983; Fraser & Harris, 1989).

Recently, we have established the respective RCV95% for hemograms and for certain biochemical parameters from individuals who had undergone 4 months of regular and planned physical activity (Nunes el al., 2010). We showed that the RCV values for leukocytes and for all biochemical analytes were elevated as compared to the literature values of sedentary subjects, clearly indicating a training effect on these blood analytes. However, the RCV values for the red blood cell count were slightly lower in physically active than in sedentary individuals (Nunes el al., 2010).

Soccer is the most widely played sport worldwide, and it also has many different cultural, social and economic aspects. The elite soccer player must have good, but not exceptional, all-around physical strength and must be able to effectively respond to the diverse demands of the game. Several studies have determined the pattern of activities performed and the individual distance covered during a game: these values have been used as an indication of the total work performed. It is well accepted that outfield players cover 8-12 km during a game that involves many different activities, with a rapid change in the type or level of activity each 4-6 seconds (Reilly, 1997; Bangsboo, 1994). While the majority of the exercise associated with competitive soccer is at sub-maximal intensities, the intermittent efforts with higher energy demands should not be slighted during a match, and often their successful execution determines the results of a game (Bangsbo et al., 2006).

The trade of elite soccer players produces large sums of money and affects the emotional state of soccer fans all around the world. Great savings can therefore be obtained by elite soccer players and their clubs with the physical protection of athletes. Our aim in this study was to verify the applicability of the reference interval described here and the previously determined RCVs (Nunes et al., 2010) for monitoring, through hematological and biochemical analyses, the effects of training/competition on soccer players during five months of the competitive season.

## **2. Material and methods**

## **2.1 Subjects**

86 An International Perspective on Topics in Sports Medicine and Sports Injury

(Harris & Yasaka, 1983; Ricos et al., 2004). The RCV defines the percentage of change that must be exceeded given the inherent biological and analytical variation in the test. This tool can

The total variation associated with a laboratory test result is the sum of the component variations (pre-analytical, analytical and intra-individual biological variation). The preanalytical variation can be minimized if we standardize the conditions of patient preparation and the procedures for collecting and handling the samples. Therefore, we must consider only the biological and analytical variation when calculating the total variation

The coefficient of analytical variation (CVA) can easily be obtained from the mean and standard deviation of the control sample analytes, and the CVI is available in the data bank for most of the analytes from healthy subjects (Ricos et al., 1999) or physically active subjects

The term RCV was introduced by Harris & Yasaka (1983) and can be calculated by the

Where 21/2 denotes the probability of bidirectional change, and Zp denotes the standard deviation corresponding to the level of statistical significance for the bidirectional change

Recently, we have established the respective RCV95% for hemograms and for certain biochemical parameters from individuals who had undergone 4 months of regular and planned physical activity (Nunes el al., 2010). We showed that the RCV values for leukocytes and for all biochemical analytes were elevated as compared to the literature values of sedentary subjects, clearly indicating a training effect on these blood analytes. However, the RCV values for the red blood cell count were slightly lower in physically

Soccer is the most widely played sport worldwide, and it also has many different cultural, social and economic aspects. The elite soccer player must have good, but not exceptional, all-around physical strength and must be able to effectively respond to the diverse demands of the game. Several studies have determined the pattern of activities performed and the individual distance covered during a game: these values have been used as an indication of the total work performed. It is well accepted that outfield players cover 8-12 km during a game that involves many different activities, with a rapid change in the type or level of activity each 4-6 seconds (Reilly, 1997; Bangsboo, 1994). While the majority of the exercise associated with competitive soccer is at sub-maximal intensities, the intermittent efforts with higher energy demands should not be slighted during a match, and often their successful

The trade of elite soccer players produces large sums of money and affects the emotional state of soccer fans all around the world. Great savings can therefore be obtained by elite

(1.96=95% and 2.58=99%) (Harris & Yasaka; 1983; Fraser & Harris, 1989).

active than in sedentary individuals (Nunes el al., 2010).

execution determines the results of a game (Bangsbo et al., 2006).

<sup>1</sup> 2 2 <sup>2</sup> CV =(CV +CV ) T AI (2)

1 1 2 2 2 2 RCV=2 ×Zp×(CV +CV ) A I (3)

increase the sensitivity of serial analyses due to the exclusion of false positive results.

**1.3 Calculating reference change values** 

(Nunes et al., 2010).

following equation (3):

(CVT) according equation 2 (Fraser & Harris, 1989).

Fifty-six male soccer players (17-19 years old) participated in this study. The athletes were evaluated during four months that included both training (pre-season) and competitive periods (regional soccer championship for players under 20). The volunteers responded to a questionnaire about their use of medications and complaints of pain and injuries caused by training or competition. Those who were using medications or who were injured were not included in the study. Volunteer subjects were duly informed about the research and signed an informed consent form. This research was approved by the Human Research Ethics Committee of the University (CAAE: 0200.0.146.000-08).

## **2.2 Collection of blood samples**

The subjects were longitudinally evaluated through five blood samples that were collected monthly [C1 = pre-competitive period of training; C2 = after 1 month (training); C3 = after 2 months (training and beginning of competitions); C4 = after 3 months (training and competitions); and C5 = after 4 months (mainly competitions)]. The blood samples were collected under standardized conditions: 2.0 mL of total venous blood was collected in vacuum tubes containing EDTA/K3 to determine the hematological parameters, and 8.0 mL of venous blood was collected in tubes with a Vacuette® (Greiner Bio-one) gel separator to obtain serum for the biochemical measurements. The blood samples were collected in the morning after 12 hours of fasting, in a seated position, and they were transported at 4ºC to the laboratory within 30 minutes, centrifuged under refrigeration at 1,800 x g for 10 minutes, were immediately separated, and were protected from light. All blood samples were collected after two days of rest to avoid the effects of hemodynamic variations and the acute hemodilution that is induced by exercise (Sawka et al., 2000).

### **2.3 Hematological and biochemical analysis**

The hematological parameters were measured with a KX-21N Sysmex® analyzer, and the biochemical analyses (CK activity, uric acid, urea and creatinine concentration) were run in an Autolab analyzer (Boehringer) using commercial kits (Wiener Lab, Rosario, Argentina). All analyses were run in parallel with commercial serum and blood controls. To minimize analytical variations, all samples were tested by the same technician without changing reagent lots, standards, or control materials.

#### **2.4 Statistical analysis**

The percent differences between the serial results were calculated for each subject using Microsoft Excel® and were compared with RCV95% to detect significant changes. As the activity of CK had a value distribution that was slightly skewed to the right, we opted to transform the data using natural logarithms (Wu et al., 2009). The Matlab 7.0 software was used to generate graphs.

Applicability of the Reference Interval and Reference Change

analysis. The results are presented in Table 3.

CK (U/L) RCV95% = 119.3% RI = 1309 U/L

Urea (mmol/L) RCV95% = 42.5% RI = 3 – 8.5 mmol/L

Creatinine (μmol/L) RCV95% = 26.8% RI = 77.8 – 132.6 μmol/L

values.

population.

Value of Hematological and Biochemical Biomarkers to Sport Science 89

It is important to point out that the behavior observed in Figure 1A and 1B was the same for all other analytes monitored. Thus, we will show only those athletes who presented at any time point a significant change, based on the RCV values, as compared to the previous

**current - previous (∆)** 

19 942 – 289 (653) +226.0

8 6.2– 3.7 (2.5) +68.2 14 4.7 – 3.2 (1.5) +47.4 19 7.7 – 5.0 (2.7) +53.3 23 4.4 – 2.7 (1.7) +62.5 25 3.8 – 2.6 (1.2) +43.8 27 5.3 – 3.5 (1.8) +52.4 29 4.9 – 3.4 (1.5) +45 32 6.4 – 3.7 (2.7) +72.7

3 4.9 – 2.9 (2) +70.6 32 3.3 – 6.3 (- 3) -47.4 38 5.8 – 4.0 (1.8) +45.8 42 3.8 – 2.6 (1.2) +43.8

4 113 – 87. 6 (25.4) +29.3 7 95.4 - 74.2 (21.2) +28.6 15 93.7 – 70.7 (23) +32.5 42 95.4 – 73.4 (22) +30.1 44 121.0 – 88.0 (33) +37

C3 35 535 – 223 (312) +139.9 C4 14 951 – 345 (606) +175.7

C3 9 4.2 – 4.34 (1.3) +47.1

**Percentage change (%)** 

**Analyte Time point Players Measured values** 

C2

C4

C3

subjects; ∆ = absolute difference between 2 consecutive analyses.

RI= reference interval for physically active subjects; RCV = reference change value for physically active

Table 3. Soccer players and their biochemical analytes with significant change based on RCV

One player at C3 and two at C4 presented CK values above 119.3%. The serum urea concentrations were significantly increased in eight subjects after the first month of training (C2). Five athletes showed creatinine values that were significantly increased at C3. Note that all of the altered analytes were inside of the reference intervals for the physically active

The Table 4 presents the results of the hematological parameters of those athletes that showed significant changes, based on the RCV values, as compared to the previous analysis.

## **3. Results**

Figure 1 presents, as an example, the CK values analyzed in comparison to both the upper reference limit and the RCV established from a physically active population (Nunes & Macedo, 2008; Nunes et al., 2010). Thus, Figure 1A presents the CK values (mean, minimum and maximum) for each soccer player at five time points in comparison with the reference upper limit (97.5th percentile - <1.309 U/L), and Figure 1B presents the percentage change between successive pairs of serial results of the five time points in comparison to the RCV (119.8%).

Fig. 1. (A.) CK values (mean, minimum and maximum) for each soccer player at five time points. The dotted horizontal lines indicate the reference interval (97.5% upper limit = 1309 U/L) from a physically active population. (B.) CK percentage change between successive pairs of serial results of the soccer players during the training/competition season. The dotted horizontal lines indicate the RCV 95% for CK = 119.3%.

We can observe that the five consecutive results for all individuals were within the reference upper values (Figure 1A). However, the comparison of the serial analytes to the RCV showed a significant increase of 119.8% in three players (Figure 1B), even though the serial results were within the upper reference limit.

Figure 1 presents, as an example, the CK values analyzed in comparison to both the upper reference limit and the RCV established from a physically active population (Nunes & Macedo, 2008; Nunes et al., 2010). Thus, Figure 1A presents the CK values (mean, minimum and maximum) for each soccer player at five time points in comparison with the reference upper limit (97.5th percentile - <1.309 U/L), and Figure 1B presents the percentage change between successive pairs of serial results of the five time points in comparison to the RCV (119.8%).

Creatine Kinase

<sup>0</sup> <sup>10</sup> <sup>20</sup> <sup>30</sup> <sup>40</sup> <sup>50</sup> <sup>60</sup> <sup>3</sup>

Subjects

A**.** 

Creatine kinase

C2 C3 C4 C5

B. Fig. 1. (A.) CK values (mean, minimum and maximum) for each soccer player at five time points. The dotted horizontal lines indicate the reference interval (97.5% upper limit = 1309 U/L) from a physically active population. (B.) CK percentage change between successive pairs of serial results of the soccer players during the training/competition season. The

We can observe that the five consecutive results for all individuals were within the reference upper values (Figure 1A). However, the comparison of the serial analytes to the RCV showed a significant increase of 119.8% in three players (Figure 1B), even though the serial

4


results were within the upper reference limit.

dotted horizontal lines indicate the RCV 95% for CK = 119.3%.



0

100

Percentage Change

200

300

5

6

Creatine Kinase Activity (log-transformed)

7

8

**3. Results** 


It is important to point out that the behavior observed in Figure 1A and 1B was the same for all other analytes monitored. Thus, we will show only those athletes who presented at any time point a significant change, based on the RCV values, as compared to the previous analysis. The results are presented in Table 3.


RI= reference interval for physically active subjects; RCV = reference change value for physically active subjects; ∆ = absolute difference between 2 consecutive analyses.

Table 3. Soccer players and their biochemical analytes with significant change based on RCV values.

One player at C3 and two at C4 presented CK values above 119.3%. The serum urea concentrations were significantly increased in eight subjects after the first month of training (C2). Five athletes showed creatinine values that were significantly increased at C3. Note that all of the altered analytes were inside of the reference intervals for the physically active population.

The Table 4 presents the results of the hematological parameters of those athletes that showed significant changes, based on the RCV values, as compared to the previous analysis.

Applicability of the Reference Interval and Reference Change

0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5 0.55

> -40 -30 -20 -10 0 10 20 30 40

indicate the RCV95% for uric acid = 35.0%

Percentage change

Uric Acid (mmol/L)

rule was the serum uric acid concentrations, as shown in Figure 2.

Value of Hematological and Biochemical Biomarkers to Sport Science 91

Nine athletes were found to have elevated leukocyte counts, mainly neutrophils, at one or more time points. We found seven athletes with hemoglobin values that were significantly increased at C3. It is important to point out that all variations in these hematological parameters were within the reference intervals shown here (Table 1). The exception for this

Uric Acid

<sup>0</sup> <sup>10</sup> <sup>20</sup> <sup>30</sup> <sup>40</sup> <sup>50</sup> <sup>60</sup> 0.1

Subjects A.

Uric Acid

C2 C3 C4 C5

B. Fig. 2. (A.) Uric acid concentration (mean, minimum and maximum) for each soccer player at five time points. The dotted horizontal lines indicate the reference interval (2.5% lower limit = 0.24 mmol/L and 97.5% upper limit = 0.49 mmol/L) from a physically active population. (B.) Uric acid percentage change between successive pairs of serial results for the soccer players during a season of training/competition. The dotted horizontal lines

While there were no significant changes in the serial results during the season (Figure 2B),

three players were determined to be below the lower reference interval (Figure 2A).


RI= reference interval for physically active subjects; RCV = reference change value; ∆ = absolute difference between 2 consecutive analyses.

Table 4. Soccer players and their hematological parameters with significant changes based on RCV values.

**current - previous (∆)** 

4 6.2 – 4.0 (2.2) +55

15 7.2 – 4.9 (2.3) +46.9 44 9.3 – 5.3 (4.0) +75.5

20 9.8 – 6.6 (3.2) +48.5 47 8.1 – 4.8 (3.3) +68.8 53 7.1 – 4.5 (2.6) +57.8

1 5.1 – 2.3 (2.8) +119.5 4 4.5 – 2.4 (2.1) +84.9 23 4.0 – 2.4 (1.6) +69.7

14 6.9 – 3.7 (3.2) +86.4 15 4.8 – 2.5 (2.3) +92.9 44 6.4 – 3.0 (3.4) +113.8

42 3.9 – 2.2 (1.7) +79.6

20 7.9 – 3.7 (4.2) +112.2 47 4.8 – 1.9 (2.9) +150.8 53 4.9 – 2.4 (2.5) +104.6

7 15.2 – 14.0 (1.2) +8.6 15 15.7 – 14.5 (1.2) +8.3 17 15.3 – 13.3 (2.0) +15 23 14.8 – 13.2 (1.6) +12.1 39 16.5 – 14.7 (1.8) +12.2 42 13.8 – 12.5 (1.3) +10.4 43 15.6 – 13.7 (1.9) +13.9

7 5.3 – 4.8 (0.5) +10 17 5 – 4.3 (0.7) +15.9 20 4.3 – 4.8 (-0.5) -9.8 23 5.1 – 4.6 (-0.5) +10.3 39 5.4 – 4.9 (0.5) +10.4 42 5.1 – 4.6 (0.5) +10.4 43 5.3 – 4.7 (0.6) +14.1

C2 1 7.8 – 4.5 (3.3) +73.3

C3 14 9.6 – 6.5 (3.1) +47.7

C4 19 10.1 – 5.4 (4.7) +105.6

C4 19 8.6 – 3.0 (5.6) +182.7

C2 23 13.2 – 14.4 (-1.2) -8.3

C4 23 13.6 – 14.8 (-1.2) -8.1

C4 20 5.0 – 4.3 (0.7) +10.3

**Percentage change (%)** 

**Analyte Time point Players Measured values** 

C5

C2

C3

C5

C3

C3

RI= reference interval for physically active subjects; RCV = reference change value; ∆ = absolute

Table 4. Soccer players and their hematological parameters with significant changes based

WBC (109/L) RCV95% = 43.9% RI = 4.5 – 10.1 109/L

Neutrophils (109/L) RCV95% = 65.3% RI = 1.8 – 6.7 109/L

Hemoglobin (g/dL) RCV95% = 8% RI = 13 – 16 g/dL

RBC (1012/L) RCV95% = 8.3% RI = 4.4 – 5.6 1012/L

on RCV values.

difference between 2 consecutive analyses.

Nine athletes were found to have elevated leukocyte counts, mainly neutrophils, at one or more time points. We found seven athletes with hemoglobin values that were significantly increased at C3. It is important to point out that all variations in these hematological parameters were within the reference intervals shown here (Table 1). The exception for this rule was the serum uric acid concentrations, as shown in Figure 2.

Fig. 2. (A.) Uric acid concentration (mean, minimum and maximum) for each soccer player at five time points. The dotted horizontal lines indicate the reference interval (2.5% lower limit = 0.24 mmol/L and 97.5% upper limit = 0.49 mmol/L) from a physically active population. (B.) Uric acid percentage change between successive pairs of serial results for the soccer players during a season of training/competition. The dotted horizontal lines indicate the RCV95% for uric acid = 35.0%

While there were no significant changes in the serial results during the season (Figure 2B), three players were determined to be below the lower reference interval (Figure 2A).

Applicability of the Reference Interval and Reference Change

copper, folic acid and complex B vitamins (Lukaski, 2004).

**5. Conclusions** 

**6. Acknowledgments** 

respectively.

**7. References** 

pp.675-678, ISSN 0306-3674.

pp.331-337, ISSN 0112-1642

Value of Hematological and Biochemical Biomarkers to Sport Science 93

beginning of the competitions. In addition to creatinine, the hemoglobin concentrations increased significantly at C3, mainly in the athletes with levels near the lower limit of the population-based reference interval, indicating a positive training adaptation that is related to O2 transport, such as increasing the plasma volume and the erythrocyte number (Sawka et al., 2000; Convertino, 2007). Only athlete 23 must be followed more carefully after C4 due to a significant decrease in the Hb concentration as compared to C3. This decrease can also be influenced by nutritional status, such as an inadequate intake of minerals like iron, zinc,

In this study we have also shown the need for a comparison of results with reference intervals that were established from a physically active population. For example, all of the players exhibited serum urate concentrations that were within the RCV values (Figure 2B), but three athletes exhibited values below the 2.5% percentile (Figure 2A). As uric acid is one of the most important plasma antioxidants (Lippi et al., 2008) this result could indicate a lower capacity of defense against ROS. The nutritionist could individually improve the antioxidant content of the diet, offering to these athletes more vegetables and fruits rich in this nutrient.

The data presented here point to the Reference Interval and the RCV as important tools for the correct interpretation of the results proceeding from blood analyses related to the monitoring of athletes' training. Advances in analytical technology, such as proteomic analysis, may present new information concerning the athlete's protein and metabolic profile. These methods must also have their reference intervals and RCVs determined for effective for sport science application, providing coaches and interdisciplinary teams with the ability to make individual, punctual interventions that could make a difference in the

The authors thank the voluntary research subjects, Cristian Ramirez (Paulinia Futebol Clube coach) and the Commander of the Military Army, who permitted this study. We would also like to thank Ana Porto, Mauro Pascoa, Madla Adami dos Passos and Lucas Samuel Tessuti for their assessment of the given technique. This study was supported by the University Foundation (Funcamp-Conv927.7–BIO0100). Lázaro Alessandro S. Nunes and Fernanda Lorenzi Lazarim received scholarships from Brazilian agencies CNPq and CAPES,

Banfi, G. & Del Fabro, M. (2006). Relation between serum creatinine and body mass index in

Banfi, G. & Dolci, A. (2003). Preanalytical phase of sport biochemistry and haematology. J *Sports Med Phys Fitness,* Vol.43, No.2 (Jun), pp.223-230, ISSN 0022-4707 Banfi, G., Del Fabbro, M. & Lippi, G. (2009). Serum creatinine concentration and creatinine-

elite athletes of different sport disciplines. *Br J Sports Med,* Vol.40, No.8 (Aug),

based estimation of glomerular filtration rate in athletes. *Sports Med,* Vol.39, No.4,

This simple intervention may increase plasma antioxidant levels (Brevik et al., 2004).

continuous adaptive processes of all athletes throughout the season.

## **4. Discussion**

This study is the first to test the applicability of reference intervals and the RCV values obtained from physically active individuals (Nunes & Macedo, 2008; Nunes et al., 2010) in longitudinally monitoring the effects of training/competition on soccer players.

The players analyzed in this study were from the under-20 category. During the competition season (14 weeks), the games occurred on Saturday mornings. All players, including the outfield players, were submitted to a daily game-based training, where modified games are played on reduced pitch areas, often using adapted rules and involving a smaller number of players than in traditional soccer games. Different small-sided game designs were used during the season to improve specific physical capacities (e.g., sprint, speed, aerobic) (Hill-Haas et al., 2011). An important observation emerging from our data is that all of the soccer players supported this type of training very well for the entire season, including the competitions schedule, as the majority of the alterations shown here were within the reference interval for all of the analytes.

On the other hand, the comparison of serial analyses with RCV values increased the sensitivity and specificity of some analytes as biomarkers of individual training effects. From 56 players, only 17 and 15 exhibited significant alterations in a biochemical analyte or hemogram parameter, respectively, at any time point (Table 3 and 4). Knowledge of the RCV values permits an individual response to those subjects who exceed the percentage of alteration at one or more blood analyses as compared to previous analyses; these subjects can undergo a closer follow up daily or weekly, contributing to the individual adjustment of the training intensity, improvement of nutritional interventions and prevention of stress overload.

For example, the athletes 14 and 19 (Table 3 and 4) were submitted to the same training load and competitions and showed significant increases in the levels of their CK, leukocytes and neutrophils after three months. Although only the neutrophils showed changes that were above the reference interval, this set of changes may be related to a higher inflammatory response and muscle damage after this period of training and competitions, which can lead to an acute deterioration in performance (Ispirlidis et al., 2008). Note that athlete 35 at C3 also exhibited a ΔCK that was higher than the RCV (119.8%), but no other parameters were altered. Additionally, the absolute value was lower than that for athletes 14 and 19. It is likely that the higher ΔCK for this player merely indicated a higher participation in the training schedule. This information can therefore be useful to help the coach plan for adequate recovery time for just those athletes, especially if they are competing players. This information can also be useful to the nutritionist in individually adjusting some nutrients to improve the recovery rate between training sections and games, preserving these athletes.

An interesting point observed here is that after the first month of training, 8 athletes showed significant changes in their serum urea when compared to RCV values. This result may indicate a continuous catabolic state at the beginning of competitions (Hartmann & Mester, 2000). It is not unusual that soccer players present with an inadequate ingestion of carbohydrates because of a low energy intake or a high fat and protein intake (Garrido et al., 2007). As this macronutrient is important in preserving muscle mass, an inadequate ingestion can lead some athletes to a catabolic state that can impair their performance if not corrected. Thus, these 8 athletes could undergo a specific nutritional intervention.

The time point (C3) showed a higher number of positive training adaptations in the majority of the players. The serum creatinine values were significantly higher than the RCV values for five players, which could indicate a muscular mass gain in these subjects at the beginning of the competitions. In addition to creatinine, the hemoglobin concentrations increased significantly at C3, mainly in the athletes with levels near the lower limit of the population-based reference interval, indicating a positive training adaptation that is related to O2 transport, such as increasing the plasma volume and the erythrocyte number (Sawka et al., 2000; Convertino, 2007). Only athlete 23 must be followed more carefully after C4 due to a significant decrease in the Hb concentration as compared to C3. This decrease can also be influenced by nutritional status, such as an inadequate intake of minerals like iron, zinc, copper, folic acid and complex B vitamins (Lukaski, 2004).

In this study we have also shown the need for a comparison of results with reference intervals that were established from a physically active population. For example, all of the players exhibited serum urate concentrations that were within the RCV values (Figure 2B), but three athletes exhibited values below the 2.5% percentile (Figure 2A). As uric acid is one of the most important plasma antioxidants (Lippi et al., 2008) this result could indicate a lower capacity of defense against ROS. The nutritionist could individually improve the antioxidant content of the diet, offering to these athletes more vegetables and fruits rich in this nutrient. This simple intervention may increase plasma antioxidant levels (Brevik et al., 2004).

## **5. Conclusions**

92 An International Perspective on Topics in Sports Medicine and Sports Injury

This study is the first to test the applicability of reference intervals and the RCV values obtained from physically active individuals (Nunes & Macedo, 2008; Nunes et al., 2010) in

The players analyzed in this study were from the under-20 category. During the competition season (14 weeks), the games occurred on Saturday mornings. All players, including the outfield players, were submitted to a daily game-based training, where modified games are played on reduced pitch areas, often using adapted rules and involving a smaller number of players than in traditional soccer games. Different small-sided game designs were used during the season to improve specific physical capacities (e.g., sprint, speed, aerobic) (Hill-Haas et al., 2011). An important observation emerging from our data is that all of the soccer players supported this type of training very well for the entire season, including the competitions schedule, as the majority of the alterations shown here were within the

On the other hand, the comparison of serial analyses with RCV values increased the sensitivity and specificity of some analytes as biomarkers of individual training effects. From 56 players, only 17 and 15 exhibited significant alterations in a biochemical analyte or hemogram parameter, respectively, at any time point (Table 3 and 4). Knowledge of the RCV values permits an individual response to those subjects who exceed the percentage of alteration at one or more blood analyses as compared to previous analyses; these subjects can undergo a closer follow up daily or weekly, contributing to the individual adjustment of the training intensity, improvement of nutritional interventions and prevention of stress

For example, the athletes 14 and 19 (Table 3 and 4) were submitted to the same training load and competitions and showed significant increases in the levels of their CK, leukocytes and neutrophils after three months. Although only the neutrophils showed changes that were above the reference interval, this set of changes may be related to a higher inflammatory response and muscle damage after this period of training and competitions, which can lead to an acute deterioration in performance (Ispirlidis et al., 2008). Note that athlete 35 at C3 also exhibited a ΔCK that was higher than the RCV (119.8%), but no other parameters were altered. Additionally, the absolute value was lower than that for athletes 14 and 19. It is likely that the higher ΔCK for this player merely indicated a higher participation in the training schedule. This information can therefore be useful to help the coach plan for adequate recovery time for just those athletes, especially if they are competing players. This information can also be useful to the nutritionist in individually adjusting some nutrients to improve the recovery rate between training sections and games, preserving these athletes. An interesting point observed here is that after the first month of training, 8 athletes showed significant changes in their serum urea when compared to RCV values. This result may indicate a continuous catabolic state at the beginning of competitions (Hartmann & Mester, 2000). It is not unusual that soccer players present with an inadequate ingestion of carbohydrates because of a low energy intake or a high fat and protein intake (Garrido et al., 2007). As this macronutrient is important in preserving muscle mass, an inadequate ingestion can lead some athletes to a catabolic state that can impair their performance if not

corrected. Thus, these 8 athletes could undergo a specific nutritional intervention.

The time point (C3) showed a higher number of positive training adaptations in the majority of the players. The serum creatinine values were significantly higher than the RCV values for five players, which could indicate a muscular mass gain in these subjects at the

longitudinally monitoring the effects of training/competition on soccer players.

**4. Discussion** 

overload.

reference interval for all of the analytes.

The data presented here point to the Reference Interval and the RCV as important tools for the correct interpretation of the results proceeding from blood analyses related to the monitoring of athletes' training. Advances in analytical technology, such as proteomic analysis, may present new information concerning the athlete's protein and metabolic profile. These methods must also have their reference intervals and RCVs determined for effective for sport science application, providing coaches and interdisciplinary teams with the ability to make individual, punctual interventions that could make a difference in the continuous adaptive processes of all athletes throughout the season.

## **6. Acknowledgments**

The authors thank the voluntary research subjects, Cristian Ramirez (Paulinia Futebol Clube coach) and the Commander of the Military Army, who permitted this study. We would also like to thank Ana Porto, Mauro Pascoa, Madla Adami dos Passos and Lucas Samuel Tessuti for their assessment of the given technique. This study was supported by the University Foundation (Funcamp-Conv927.7–BIO0100). Lázaro Alessandro S. Nunes and Fernanda Lorenzi Lazarim received scholarships from Brazilian agencies CNPq and CAPES, respectively.

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**5** 

*USA* 

Paul M. Vanderburgh

*University of Dayton, Dayton, OH* 

**Body Mass Bias in Exercise Physiology** 

Body mass bias in the field of exercise physiology has been the subject of increased focus over the past twenty years. This is based primarily on the fact that key widely held assumptions about the relationships between body mass and human performance have been challenged by theory and empirical data. The result for how we express certain variables of physical fitness has been generally two-fold: a systematic and meaningful body mass bias against larger, not fatter, individuals and spurious attribution of the effect of body mass on

Physical educators, the military services, law enforcement agencies, and conditioning coaches have readily used fitness tests comprised of events that involve body mass as the primary resistance. Common tests include pushups, situps, and timed distance runs. Virtually none of these tests takes into account one's body mass in scoring because a common assumption has been that larger people have more muscle to move the heavier mass. In short, these factors are assumed to "wash out" any body mass bias. Empirical research has shown, however, that these types of tests impose a substantial and predictable bias against larger, not just fatter, body mass (Crowder & Yunker, 1996; Harman & Frykman, 1992; Jaric et al., 2005; Markovic & Jaric, 2004; Vanderburgh et al., 1995). Similarly, because maximal oxygen consumption (VO2max, in L/min) and maximal strength increase with body mass, a common convention to compare individuals is to divide VO2max or strength measures by body mass. Expressing these human performance indices as simple ratios this way has been scrutinized given that the numerator does not change at the same rate as the denominator (Astrand & Rodahl, 1986; Heil, 1997). Again, the result is not only a body mass bias against larger individuals but, in the case of inferential research, improper

In certain physically demanding occupations, especially the military, body mass bias has substantive implications. Work physiologists have determined that despite body mass bias in the common military physical fitness tests, the larger service members were often better performers of the physically demanding occupational tasks (Bilzon et al., 2002; Lyons et al., 2005; Rayson et al., 2000). That is, they could carry more, more easily evacuate casualties, and better engage in heavy materiel handling. Yet, the smaller personnel were achieving better scores on the physical fitness tests, the results of which have significant promotion and advancement implications (Vanderburgh & Mahar , 1995; Crowder & Yunker, 1996). This chapter chronicles the fundamentals and applications of body mass bias in fitness and exercise physiology, to include the theory and empirical data used to evaluate it. It also

accounting for the effects of body mass on outcome variables.

**1. Introduction** 

key dependent variables.


## **Body Mass Bias in Exercise Physiology**

Paul M. Vanderburgh

*University of Dayton, Dayton, OH USA* 

#### **1. Introduction**

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Body mass bias in the field of exercise physiology has been the subject of increased focus over the past twenty years. This is based primarily on the fact that key widely held assumptions about the relationships between body mass and human performance have been challenged by theory and empirical data. The result for how we express certain variables of physical fitness has been generally two-fold: a systematic and meaningful body mass bias against larger, not fatter, individuals and spurious attribution of the effect of body mass on key dependent variables.

Physical educators, the military services, law enforcement agencies, and conditioning coaches have readily used fitness tests comprised of events that involve body mass as the primary resistance. Common tests include pushups, situps, and timed distance runs. Virtually none of these tests takes into account one's body mass in scoring because a common assumption has been that larger people have more muscle to move the heavier mass. In short, these factors are assumed to "wash out" any body mass bias. Empirical research has shown, however, that these types of tests impose a substantial and predictable bias against larger, not just fatter, body mass (Crowder & Yunker, 1996; Harman & Frykman, 1992; Jaric et al., 2005; Markovic & Jaric, 2004; Vanderburgh et al., 1995). Similarly, because maximal oxygen consumption (VO2max, in L/min) and maximal strength increase with body mass, a common convention to compare individuals is to divide VO2max or strength measures by body mass. Expressing these human performance indices as simple ratios this way has been scrutinized given that the numerator does not change at the same rate as the denominator (Astrand & Rodahl, 1986; Heil, 1997). Again, the result is not only a body mass bias against larger individuals but, in the case of inferential research, improper accounting for the effects of body mass on outcome variables.

In certain physically demanding occupations, especially the military, body mass bias has substantive implications. Work physiologists have determined that despite body mass bias in the common military physical fitness tests, the larger service members were often better performers of the physically demanding occupational tasks (Bilzon et al., 2002; Lyons et al., 2005; Rayson et al., 2000). That is, they could carry more, more easily evacuate casualties, and better engage in heavy materiel handling. Yet, the smaller personnel were achieving better scores on the physical fitness tests, the results of which have significant promotion and advancement implications (Vanderburgh & Mahar , 1995; Crowder & Yunker, 1996).

This chapter chronicles the fundamentals and applications of body mass bias in fitness and exercise physiology, to include the theory and empirical data used to evaluate it. It also

Body Mass Bias in Exercise Physiology 101

Table 1, if length increases by 1.25 (a 25% change), then CSA changes by 1.252 and weight changes by 1.253. This is because length is considered a one-dimensional variable, area is two-dimensional, and weight (just like volume) is three-dimensional. In terms of biological significance, one can think of the human body just like the block in the present example. It has its own body mass and its CSA is considered to be muscle cross sectional area, one of the prime determinants of strength. Therefore, the 95% increase in weight shown in Table 1 is accompanied by only a 56.25% increase in strength. This human performance scaling concept is critically important because it challenges the common assumption that an X% larger person should be X% stronger. Empirical evidence supports the fallacy of this assumption. Also of critical importance is the notion that "larger" assumes an exact scale

**PRE POST Multiplier / % Increase Calculation** 

**Length** 4' 5' 1.25 / 25% (1.25)1 = 1.25 **CSA** 4 ft2 6.25 ft2 1.5625 / 56.25% (1.25)2 = 1.5625 **Weight** 16 lbs 31.25 lbs 1.9531 / 95.31% (1.25)3 = 1.9531 Table 1. Calculations of Fig. 1 changes in dimensions due to scale model increase in size

Allometry, a term often associated with biological scaling, is the relationship between the size of an organism and the size of any of its parts, such as muscle or blood vessel CSA, limb length, eyeball radius, etc. Allometry provides the theoretical bases upon which empirical findings can be compared. In the case of Fig. 1 and its analogous application to the human body, strength (S) does not change proportionally to body mass (M). If it did, it would also increase by 95.3%. Instead, its 56.25% increase can be explained by the allometric relationship:

 S α M2/3 (1) This is derived from the fact that weight is a three-dimensional and CSA a two-dimensional variable (Astrand & Rodahl, 1986; Jaric, 2002). From Eq. 1 we can substitute the delta, or

Indeed, 1.5625 (the change in CSA, or strength) = 1.95312/3 (the change in mass). This exponent of 2/3, often called an allometric exponent, tells us that the proper way to express muscle strength to allow for comparisons between individuals of different body mass is: S/M2/3. This is because both sides change at the same rate – a necessary condition for expressing ratios in physiology (Astrand & Rodahl, 1986; Vanderburgh, 1998). Though unconventional, this index should show zero correlation with body mass in a large sample of subjects. It is also very useful in understanding how other variables, such as fitness test

Since blood vessel CSA is also a two-dimensional variable, and oxygen delivery is associated with blood flow, then maximal oxygen uptake, VO2max, would be subject to a

VO2max α ΔM2/3 (3)

scores, or maximal oxygen uptake, change with body size changes.

ΔS α ΔM2/3 (2)

model, not larger because he/she is fatter or taller.

**3. Allometric scaling in fitness tests** 

change on both sides such that:

similar relationship:

explains the real world implications of body mass bias in the military services, and how to mitigate its undesirable or unintended effects.

## **2. Body mass bias and biological scaling**

Body mass bias is simply the notion that larger individuals have an unfair advantage over smaller, or vice-versa, in measures of exercise performance. It is more formally defined as the correlation between a raw score (e.g., maximal weight lifted, pushups repetitions, oxygen uptake) and body mass. A non-zero correlation indicates the presence of bias; a correlation not different from zero indicates the absence of such bias. Some biases are rather intuitive. Maximal grip strength, bench press or absolute work rate on a cycle ergometer are measures that would give larger individuals an advantage since each is largely dependent on muscle yet body mass is not the source of the resistance. As a result, a sport like powerlifting employs body weight classes and maximal power is often expressed relative to body mass. Less intuitive, perhaps, are measures that advantage smaller individuals. These include those that measure the capacity to move one's body mass in exercises such as pushups or distance running. The less-than-intuitive quality is based on the common assumption that larger individuals have more muscle to move body mass so there should be no particular advantage to the smaller. A closer look, however, at laws of biological scaling and their application to military physical fitness data make a compelling case that the common fitness test events of pushups, situps and distance running, and even VO2max expressed per unit of body mass, impose a bias against larger, not just fatter, personnel (Vanderburgh, 2007, 2008).

Perhaps an easy way to think of this is through the analogy of a 2 x 2 x 4 solid rectangular block (Fig. 1) of constant density. Its cross-sectional area (CSA) would be 2 x 2 or 4 sq ft and, with a density of 1.0, its weight would be 16 lbs. Imagine that the block (PRE) turned into an identical scale-model block of the same density, but with sides 25%, or 1.25 times longer (the POST block). Simple geometric principles dictate, then, that CSA would be 2.5 x 2.5, or 5.25 sq ft. This represents a 56.25% increase in CSA. Similarly, the new weight of 31.25 lbs (2.5 x 2.5 x 5 = 31.25 lbs) is a 95.3% increase.

Fig. 1. Comparison of change in length, cross-sectional area (CSA) and weight

Scaling theory helps us understand why these dimensions change at different rates and biological scaling principles elucidate the relevance to human performance. As shown in

explains the real world implications of body mass bias in the military services, and how to

Body mass bias is simply the notion that larger individuals have an unfair advantage over smaller, or vice-versa, in measures of exercise performance. It is more formally defined as the correlation between a raw score (e.g., maximal weight lifted, pushups repetitions, oxygen uptake) and body mass. A non-zero correlation indicates the presence of bias; a correlation not different from zero indicates the absence of such bias. Some biases are rather intuitive. Maximal grip strength, bench press or absolute work rate on a cycle ergometer are measures that would give larger individuals an advantage since each is largely dependent on muscle yet body mass is not the source of the resistance. As a result, a sport like powerlifting employs body weight classes and maximal power is often expressed relative to body mass. Less intuitive, perhaps, are measures that advantage smaller individuals. These include those that measure the capacity to move one's body mass in exercises such as pushups or distance running. The less-than-intuitive quality is based on the common assumption that larger individuals have more muscle to move body mass so there should be no particular advantage to the smaller. A closer look, however, at laws of biological scaling and their application to military physical fitness data make a compelling case that the common fitness test events of pushups, situps and distance running, and even VO2max expressed per unit of body mass, impose a bias against larger, not just fatter, personnel

Perhaps an easy way to think of this is through the analogy of a 2 x 2 x 4 solid rectangular block (Fig. 1) of constant density. Its cross-sectional area (CSA) would be 2 x 2 or 4 sq ft and, with a density of 1.0, its weight would be 16 lbs. Imagine that the block (PRE) turned into an identical scale-model block of the same density, but with sides 25%, or 1.25 times longer (the POST block). Simple geometric principles dictate, then, that CSA would be 2.5 x 2.5, or 5.25 sq ft. This represents a 56.25% increase in CSA. Similarly, the new weight of 31.25 lbs (2.5 x

Fig. 1. Comparison of change in length, cross-sectional area (CSA) and weight

Scaling theory helps us understand why these dimensions change at different rates and biological scaling principles elucidate the relevance to human performance. As shown in

mitigate its undesirable or unintended effects.

(Vanderburgh, 2007, 2008).

2.5 x 5 = 31.25 lbs) is a 95.3% increase.

**2. Body mass bias and biological scaling** 

Table 1, if length increases by 1.25 (a 25% change), then CSA changes by 1.252 and weight changes by 1.253. This is because length is considered a one-dimensional variable, area is two-dimensional, and weight (just like volume) is three-dimensional. In terms of biological significance, one can think of the human body just like the block in the present example. It has its own body mass and its CSA is considered to be muscle cross sectional area, one of the prime determinants of strength. Therefore, the 95% increase in weight shown in Table 1 is accompanied by only a 56.25% increase in strength. This human performance scaling concept is critically important because it challenges the common assumption that an X% larger person should be X% stronger. Empirical evidence supports the fallacy of this assumption. Also of critical importance is the notion that "larger" assumes an exact scale model, not larger because he/she is fatter or taller.


Table 1. Calculations of Fig. 1 changes in dimensions due to scale model increase in size

#### **3. Allometric scaling in fitness tests**

Allometry, a term often associated with biological scaling, is the relationship between the size of an organism and the size of any of its parts, such as muscle or blood vessel CSA, limb length, eyeball radius, etc. Allometry provides the theoretical bases upon which empirical findings can be compared. In the case of Fig. 1 and its analogous application to the human body, strength (S) does not change proportionally to body mass (M). If it did, it would also increase by 95.3%. Instead, its 56.25% increase can be explained by the allometric relationship:

$$\mathbf{S} \mathbf{a} \, \mathbf{M}^{2/3} \tag{1}$$

This is derived from the fact that weight is a three-dimensional and CSA a two-dimensional variable (Astrand & Rodahl, 1986; Jaric, 2002). From Eq. 1 we can substitute the delta, or change on both sides such that:

$$
\Delta \mathbf{S} \text{ } \mathbf{a} \,\Delta \mathbf{M}^{2/3} \tag{2}
$$

Indeed, 1.5625 (the change in CSA, or strength) = 1.95312/3 (the change in mass). This exponent of 2/3, often called an allometric exponent, tells us that the proper way to express muscle strength to allow for comparisons between individuals of different body mass is: S/M2/3. This is because both sides change at the same rate – a necessary condition for expressing ratios in physiology (Astrand & Rodahl, 1986; Vanderburgh, 1998). Though unconventional, this index should show zero correlation with body mass in a large sample of subjects. It is also very useful in understanding how other variables, such as fitness test scores, or maximal oxygen uptake, change with body size changes.

Since blood vessel CSA is also a two-dimensional variable, and oxygen delivery is associated with blood flow, then maximal oxygen uptake, VO2max, would be subject to a similar relationship:

$$\text{VO}\_{2\text{max}} \text{ or } \Delta \text{M}^{2/3} \tag{3}$$

Body Mass Bias in Exercise Physiology 103

where a and b are constants. This is an allometric, not linear relationship. For linear regression, the terms must be in the form of y = mx + b. A log transformation of both sides,

Fig. 2. World powerlifting records by body mass (as of May, 2011,

http://records.powerlifting.org/world)

lnTOT = (b)lnM + ln(a) (7)

then, yields the following:

and the following index: VO2max/M2/3. Once again, this is an unconventional index but allows for comparisons of maximal aerobic capacity between individuals of different body mass such that body mass bias is zero. This index has been validated this index in a large sample of 230 women and 210 men (Heil, 1997).

The effects of body mass changes on strength and VO2max can be used to derive appropriate scaling indices for events like distance runs (DR) and maximal pushups (PU) or situps (SU) repetitions. One explanation (Jaric et al., 2002b) for its derivation is that the ability to move one's body mass is directly proportional to strength (which is directly proportional to M2/3) and indirectly proportional to body mass (M1). Therefore, since PU or SU α M2/3/M1, then:

$$\mathbf{PU} \text{ or } \mathbf{SU} \text{ or } \mathbf{M^{\downarrow 1/3}} \tag{4}$$

Interestingly, the ratio scaling that results, PU/M-1/3, is equivalent to PU. M1/3 (same for SU). For the distance run (DR), the scaling index is derived as follows (Vanderburgh & Mahar, Vanderburgh & Crowder, 2006; Vanderburgh & Laubach, 2007): Since distance run time is indirectly proportional to VO2max, expressed per unit of body mass (Nevill et al., 1992), and VO2max in L/min ( i.e., no adjustment for body mass) is directly proportional to M2/3 (Eq. 3), then DR α M1/M2/3, or

$$\mathbf{DR} \,\mathbf{a} \,\mathbf{M}^{1/3} \tag{5}$$

This means that DR time goes up as M goes up. Since low score wins in run time, the DR expression would be DR/M1/3. This index has been empirically validated as well (Crecilius et al., 2008; Crowder & Yunker, 1996; Vanderburgh et al., in press).

#### **4. Empirical validation of allometric modeling in fitness tests**

"Empirically validated" in these cases indicates that researchers have tested the hypotheses of Eqns. 1,3-5 in reasonably large samples to determine the actual body mass exponent and compared it with the theoretical. For purposes of illustration, this can be done using real data from the sport of competitive powerlifting and the methods described in more detail by Vanderburgh (1998). Powerlifting, comprised of maximal one-repetition lifts in the squat (SQ), bench press (BP), deadlift (DL), and total (TOT) of all three, is a good choice for examining body mass exponents for several reasons. First, it is a sport of primarily muscular strength, not power, hand-eye coordination, or even complex cognition, all of which could be confounders in examining the relationship between performance and body mass. Second, at the elite level (not counting the super heavyweight division, which has no weight limit), all competitors are very lean, thus eliminating body fat as a confounder. Third, the two primary determinants of performance are strength and body mass. Therefore, a sample of powerlifting world record holders would be heterogeneous in body mass and weight lifted – almost nothing else.

The determination of the empirical exponent is done using linear regression, but on the logarithmic transformations of M and SQ, BP, DL and TOT. The procedure starts with Eq. 1, S α Mb, but with SQ, BP, DL and TOT replacing S. Scatterplots of current male world record holders (as of May, 2011, http://records.powerlifting.org/world) for these four events are shown in Fig. 2. Note that the exponent is the unknown, as the purpose of empirical testing is to determine the actual M exponent for that sample. For purposes of illustrations, TOT will be chosen: TOT α Mb. This really means that the best-fit curve of a scatterplot of TOT vs. M will conform to the following equation:

$$\text{TOT} = \text{aMb} \tag{6}$$

and the following index: VO2max/M2/3. Once again, this is an unconventional index but allows for comparisons of maximal aerobic capacity between individuals of different body mass such that body mass bias is zero. This index has been validated this index in a large

The effects of body mass changes on strength and VO2max can be used to derive appropriate scaling indices for events like distance runs (DR) and maximal pushups (PU) or situps (SU) repetitions. One explanation (Jaric et al., 2002b) for its derivation is that the ability to move one's body mass is directly proportional to strength (which is directly proportional to M2/3) and indirectly proportional to body mass (M1). Therefore, since PU or SU α M2/3/M1, then:

For the distance run (DR), the scaling index is derived as follows (Vanderburgh & Mahar, Vanderburgh & Crowder, 2006; Vanderburgh & Laubach, 2007): Since distance run time is indirectly proportional to VO2max, expressed per unit of body mass (Nevill et al., 1992), and VO2max in L/min ( i.e., no adjustment for body mass) is directly proportional to M2/3 (Eq. 3),

 DR α M1/3 (5) This means that DR time goes up as M goes up. Since low score wins in run time, the DR expression would be DR/M1/3. This index has been empirically validated as well (Crecilius

"Empirically validated" in these cases indicates that researchers have tested the hypotheses of Eqns. 1,3-5 in reasonably large samples to determine the actual body mass exponent and compared it with the theoretical. For purposes of illustration, this can be done using real data from the sport of competitive powerlifting and the methods described in more detail by Vanderburgh (1998). Powerlifting, comprised of maximal one-repetition lifts in the squat (SQ), bench press (BP), deadlift (DL), and total (TOT) of all three, is a good choice for examining body mass exponents for several reasons. First, it is a sport of primarily muscular strength, not power, hand-eye coordination, or even complex cognition, all of which could be confounders in examining the relationship between performance and body mass. Second, at the elite level (not counting the super heavyweight division, which has no weight limit), all competitors are very lean, thus eliminating body fat as a confounder. Third, the two primary determinants of performance are strength and body mass. Therefore, a sample of powerlifting world record

holders would be heterogeneous in body mass and weight lifted – almost nothing else.

M will conform to the following equation:

The determination of the empirical exponent is done using linear regression, but on the logarithmic transformations of M and SQ, BP, DL and TOT. The procedure starts with Eq. 1, S α Mb, but with SQ, BP, DL and TOT replacing S. Scatterplots of current male world record holders (as of May, 2011, http://records.powerlifting.org/world) for these four events are shown in Fig. 2. Note that the exponent is the unknown, as the purpose of empirical testing is to determine the actual M exponent for that sample. For purposes of illustrations, TOT will be chosen: TOT α Mb. This really means that the best-fit curve of a scatterplot of TOT vs.

Interestingly, the ratio scaling that results, PU/M-1/3, is equivalent to PU.

et al., 2008; Crowder & Yunker, 1996; Vanderburgh et al., in press).

**4. Empirical validation of allometric modeling in fitness tests** 

PU or SU α M-1/3 (4)

TOT = aMb (6)

M1/3 (same for SU).

sample of 230 women and 210 men (Heil, 1997).

then DR α M1/M2/3, or

where a and b are constants. This is an allometric, not linear relationship. For linear regression, the terms must be in the form of y = mx + b. A log transformation of both sides, then, yields the following:

Fig. 2. World powerlifting records by body mass (as of May, 2011, http://records.powerlifting.org/world)

Body Mass Bias in Exercise Physiology 105

found, researchers are faced with offering reasonable explanations without the statistical control to back such claims. The best research samples, then, are those in which the subjects are heterogeneous in the dependent variable (performance score) and body mass but

Some studies have indicated deviation from theoretical for the body mass exponent for expressing VO2max. Batterham et al. (1999) found that, in a sample of 1314 adult men, although the body mass exponent was 0.65, the fat-free mass exponent was not different from 1.0. Since body fat is essentially metabolically inert, and fat-free mass is the body compartment largely responsible for generating oxygen consumption, then body composition was a confounder in leading to the spurious conclusion that the 0.65 body mass exponent matched the theoretically expected value of 2/3. Similarly, Vanderburgh et al. (1996a), in a sample of 94 adult women, determined that while ratio scaling of VO2max

Another interesting but quite common dilemma is assuming validity when the empirically derived exponent matches the theoretical, as is the case with the BP and TOT events. Indeed, these findings could be the result of confounding effects on the exponent such that the primary reason it "made the 95% CI" is that certain confounders, such as those listed above, happened to push the exponent value into the theoretical range. In other words, one can

As is the case with inferential statistics, however, one should always consider the totality of research evidence from multiple samples from different populations using appropriate statistical control to evaluate the overall trend for allometry's effects on human performance. Based on two extensive reviews (Jaric et al., 2005; Vanderburgh, 2008), one can make a compelling case that use of the exponents as described in Table 3 is appropriate for

**BP**

Vanderburgh, 2000 Markovic & Jaric, 2004 Vanderburgh & Dooman, 2000

Dooman &

These indices can be used to compare individual fitness scores among people of varying body weights. The results of some hypothetical examples can be illuminating. Consider two individuals, Ellen and June, with the fitness scores as shown in Table 4. Scaled scores are calculated using the indices from Table 3, maintaining the appropriate units. Clearly, body mass influences scoring with the allometric indices, which make a proper adjustment for its

M1/3 BP/M2/3 DR/M1/3

**(1RM) DR (time)** 

2000

press

Crowder & Yunker,

Vanderburgh et al., in

Nevill et al., 1992 Vanderburgh & Mahar, 1995

homogeneous in the potential confounders such as effort and body composition.

penalized heavier women, the penalty was due to the body fatness.

fitness testing, especially for adult men.

**PU (max reps)**

Crowder & Yunker, 2000 Markovic & Jaric, 2004 Vanderburgh et al., in press

PU = Pushups, SU = Situps, DR = Distance run time Table 3. Allometric indices for common fitness tests

**5. The utility of allometry in fitness testing** 

M1/3 SU.

**Exponent** PU.

**References** 

never be quite sure how the empirical did or did not match the theoretical.

**SU (max reps)**

Markovic & Jaric, 2004

Now, lnTOT becomes "y," b becomes "m," and ln(a) becomes "b" in the linear equation. Regressing lnTOT on lnM will yield not only the value of b but its confidence interval (CI) as well. This is quite important since scatterplots of human performance always deviate from the best-fit curve and CIs give an indication of probability that the population's true exponent lies within it. Linear regression of the log-transformed terms for the TOT event yields an exponent of 0.58 with a 95% confidence interval of 0.48 to 0.67. Within this confidence interval (CI), an M exponent would yield zero body mass bias for TOT. Note that 2/3, or 0.667, is within (but barely) the CI. Table 2 shows the actual body mass exponents for the four different powerlifting events, along with the 95% CI ranges.


Table 2. Body mass exponents for world record powerlifting performances (Fig.2)

This table illustrates a number of key points in evaluating empirical data for allometric scaling. First, among the world's elite, a small number of subjects (N = 10 in this case) can show the characteristic curvilinear allometric relationship between body mass and performance. Each event indicates, as body mass increases, the expected smaller and smaller increase in performance. Said differently, the relationships are clearly not linear. Second, since neither is in the CI ranges, the exponents of 0 or 1 impose a body mass bias. While an exponent of 0 yielding such bias is expected since that would be analogous to no weight classes, the exponent of 1 doing the same might be surprising. These data show that, in any of the events, dividing the performance by body mass (e.g., SQ/M1) would also yield body mass bias. This actually conforms to the laws of biological scaling since the exponent of 1 is too large. In other words, the index SQ/M makes too much of an adjustment for M, thus penalizing larger competitors.

Third, and perhaps surprisingly, not all the exponents' CIs contain the expected value of 2/3. There are many reasons why this can, and often does happen in allometry research. With a small sample size, one case can influence the magnitude of the exponent. In the TOT, for example, removing the heaviest competitor changes the exponent from 0.58 to 0.63, the latter for which the 2/3 exponent easily fits within the 95% CI range, not barely as with all the competitors. In the case of world record holders, as others have conjectured for world class powerlifting events (Dooman & Vanderburgh, 2000; Vanderburgh & Batterham, 1999; Vanderburgh & Dooman, 2000), there are more competitors worldwide in the middle weight classes and fewer at the extremes. This would suggest that, adjusted for body mass differences, the world's best in the middle weight classes would be better than the best from the lightest and heaviest classes, thus "bumping" up the middle of the curve. This would affect goodnessof-fit with an allometric model and alter the exponent value away from the theoretical. One other worthwhile explanation in the present data is that both the squat and deadlift exercises, unlike the bench press, lift not only the barbells but a substantial percentage of the body mass as well. Since moving body mass is disadvantageous for larger individuals, this would lessen the slope of the best-fit curve for the scatterplots such as those in Fig. 2, the result would be a smaller exponent. Indeed, Table 2 indicates that the smallest exponents are for squat and deadlift. In large samples of non-world-class subjects, there are many other confounders such as body composition, effort, biomechanics, etc. Often, when deviation from theoretical is

Now, lnTOT becomes "y," b becomes "m," and ln(a) becomes "b" in the linear equation. Regressing lnTOT on lnM will yield not only the value of b but its confidence interval (CI) as well. This is quite important since scatterplots of human performance always deviate from the best-fit curve and CIs give an indication of probability that the population's true exponent lies within it. Linear regression of the log-transformed terms for the TOT event yields an exponent of 0.58 with a 95% confidence interval of 0.48 to 0.67. Within this confidence interval (CI), an M exponent would yield zero body mass bias for TOT. Note that 2/3, or 0.667, is within (but barely) the CI. Table 2 shows the actual body mass exponents for

**Exponent** 0.50 0.65 0.48 0.58 **95% CI** 0.42 – 0.59 0.49 – 0.80 0.37 – 0.60 0.48 – 0.67

This table illustrates a number of key points in evaluating empirical data for allometric scaling. First, among the world's elite, a small number of subjects (N = 10 in this case) can show the characteristic curvilinear allometric relationship between body mass and performance. Each event indicates, as body mass increases, the expected smaller and smaller increase in performance. Said differently, the relationships are clearly not linear. Second, since neither is in the CI ranges, the exponents of 0 or 1 impose a body mass bias. While an exponent of 0 yielding such bias is expected since that would be analogous to no weight classes, the exponent of 1 doing the same might be surprising. These data show that, in any of the events, dividing the performance by body mass (e.g., SQ/M1) would also yield body mass bias. This actually conforms to the laws of biological scaling since the exponent of 1 is too large. In other words, the index SQ/M makes too much of an adjustment for M, thus

Third, and perhaps surprisingly, not all the exponents' CIs contain the expected value of 2/3. There are many reasons why this can, and often does happen in allometry research. With a small sample size, one case can influence the magnitude of the exponent. In the TOT, for example, removing the heaviest competitor changes the exponent from 0.58 to 0.63, the latter for which the 2/3 exponent easily fits within the 95% CI range, not barely as with all the competitors. In the case of world record holders, as others have conjectured for world class powerlifting events (Dooman & Vanderburgh, 2000; Vanderburgh & Batterham, 1999; Vanderburgh & Dooman, 2000), there are more competitors worldwide in the middle weight classes and fewer at the extremes. This would suggest that, adjusted for body mass differences, the world's best in the middle weight classes would be better than the best from the lightest and heaviest classes, thus "bumping" up the middle of the curve. This would affect goodnessof-fit with an allometric model and alter the exponent value away from the theoretical. One other worthwhile explanation in the present data is that both the squat and deadlift exercises, unlike the bench press, lift not only the barbells but a substantial percentage of the body mass as well. Since moving body mass is disadvantageous for larger individuals, this would lessen the slope of the best-fit curve for the scatterplots such as those in Fig. 2, the result would be a smaller exponent. Indeed, Table 2 indicates that the smallest exponents are for squat and deadlift. In large samples of non-world-class subjects, there are many other confounders such as body composition, effort, biomechanics, etc. Often, when deviation from theoretical is

Table 2. Body mass exponents for world record powerlifting performances (Fig.2)

**SQ BP DL TOT** 

the four different powerlifting events, along with the 95% CI ranges.

penalizing larger competitors.

found, researchers are faced with offering reasonable explanations without the statistical control to back such claims. The best research samples, then, are those in which the subjects are heterogeneous in the dependent variable (performance score) and body mass but homogeneous in the potential confounders such as effort and body composition.

Some studies have indicated deviation from theoretical for the body mass exponent for expressing VO2max. Batterham et al. (1999) found that, in a sample of 1314 adult men, although the body mass exponent was 0.65, the fat-free mass exponent was not different from 1.0. Since body fat is essentially metabolically inert, and fat-free mass is the body compartment largely responsible for generating oxygen consumption, then body composition was a confounder in leading to the spurious conclusion that the 0.65 body mass exponent matched the theoretically expected value of 2/3. Similarly, Vanderburgh et al. (1996a), in a sample of 94 adult women, determined that while ratio scaling of VO2max penalized heavier women, the penalty was due to the body fatness.

Another interesting but quite common dilemma is assuming validity when the empirically derived exponent matches the theoretical, as is the case with the BP and TOT events. Indeed, these findings could be the result of confounding effects on the exponent such that the primary reason it "made the 95% CI" is that certain confounders, such as those listed above, happened to push the exponent value into the theoretical range. In other words, one can never be quite sure how the empirical did or did not match the theoretical.

As is the case with inferential statistics, however, one should always consider the totality of research evidence from multiple samples from different populations using appropriate statistical control to evaluate the overall trend for allometry's effects on human performance. Based on two extensive reviews (Jaric et al., 2005; Vanderburgh, 2008), one can make a compelling case that use of the exponents as described in Table 3 is appropriate for fitness testing, especially for adult men.


PU = Pushups, SU = Situps, DR = Distance run time

Table 3. Allometric indices for common fitness tests

## **5. The utility of allometry in fitness testing**

These indices can be used to compare individual fitness scores among people of varying body weights. The results of some hypothetical examples can be illuminating. Consider two individuals, Ellen and June, with the fitness scores as shown in Table 4. Scaled scores are calculated using the indices from Table 3, maintaining the appropriate units. Clearly, body mass influences scoring with the allometric indices, which make a proper adjustment for its

Body Mass Bias in Exercise Physiology 107

In some sports, such as wrestling and power lifting, weight classes are the main method of accounting for body weight differences. One of the challenges of such a convention is that there are often few competitors in the extremes of weight and many in the middle weights. This imposes a body weight bias against the middle weight competitors who must compete against many more athletes to win or place. Recent empirical evidence suggests that allometric scaling is a technique that can be used to eliminate all weight classes for each gender and determine the best overall lifter when properly adjusting for body weight differences (Vanderburgh & Batterham, 1999). In competitive rowing, there are typically only two weight classes for each gender, light and heavy. Vanderburgh et al. (1996) developed and validated an allometric index that allows all rowers of any size within each gender, to be compared to each other while properly factoring out the influence of body mass and age. Their results suggest that rowing time multiplied by stature (or M1/3) was the

One of the main critiques of using allometric scaling in fitness testing is that giving credit for weight in the scoring is to also give credit for excess body fat. In other words, some perceive that such scoring gives advantages for being fatter. Indeed, one's denominator of M1/3 for the distance run score computed from the DR/M1/3 calculation would be larger for a fatter individual, thus leading to a lower (and better score, since low score wins). What is not often considered, however, is the effect of the excess fatness on the numerator – in this case, the distance run time. Vanderburgh and colleagues have modeled the effects of adding additional fat weight on the resultant distance run time for the 5 km and 2-mile runs and have shown that, in all cases, adding fat weight leads to a worse scaled score (Crecilius et al., 2008; Vanderburgh & Laubach, 2007). Recently, they tested this empirically, by adding external weight to the DR and PU events for college-age men. Results indicated that the addition of 16 kg of external weight led to 38% worse scaled PU scores and 12% worse two-

As shown in Table 4, allometrically scaled scores yield strange units and currency. That is,

elsewhere and the magnitude of 3.50 not being as readily evaluated as 65 kg would be. Another problem with these scores is that they require a calculator to compute. In short,

Other solutions have been proposed. The first, correction factors, are dimensionless numbers based on body weight, which are multiplied by the raw score to produce an adjusted in the same units and more easily interpretable (Vanderburgh, 2007). For example, from Table 4, Ellen's and June's correction factors would, for PU, be: 1.0 and 1.17, respectively. This is calculated based on a body mass standard - a baseline from which all ratios are computed, the individual's body mass, and the particular event. For a standard weight of 50 kg, for example, and June's body mass of 80 kg, and the PU, her correction factor would be (80 kg/50 kg)1/3, or 1.170" (in both cases). Multiplying her raw score of 34 pushups by 1.211 yields an adjusted score of 39.78 pushups. Using the same methodology,

M-0.67, quite

M-0.67, being unlike those encountered

optimal index to remove body mass bias among these competitive rowers.

**6. Body fatness and allometry in fitness testing** 

mile run scaled scores (Vanderburgh et al., in press).

difficult to interpret. This is likely due to the units, kg.

**7. Practical techniques for using allometry in fitness testing** 

one may find the interpretation of a scaled score for a 65 kg bench press, 3.50 kg.

though arguably proper and fair, allometrically scaled scores are not practical.

influence. One can interpret allometric comparisons as follows, using a Table 4 example: "Without consideration of the influence of body mass on pushups, Ellen performed 11.8% more repetitions. Considering the influence of body mass, however, June scored 4.6% better." In the case of the bench press, June's performance was 47.7% better than Ellen's for raw score but only 8.0% better when making a proper adjustment for body mass.

On a more significant scale, the effect of body mass bias among military personnel has been quantified (Vanderburgh & Crowder, 2006). For example, a 90 kg man performing at the same physiological level as a 60 kg man who achieved a maximum score on the U.S.


Table 4. Hypothetical example of the effects of allometry in fitness testing

Army Physical Fitness Test of pushups, situps, and two-mile run, would actually receive a 15% lower score (256 vs. 300 points). In this case, "same physiological level" is the expected performance of a 90 kg man who is an exact scale model of the 60 kg man. Similarly, for a 45 kg vs. 75 kg woman, the U.S. Navy's test levies a 20% penalty. In the case of the Armed Services, there are actually two very real undesirable consequences for the larger service members. First, advancement and promotion is influenced by the fitness test scores. For the two women above, the heavier who scored physiologically equivalent 240 points compared to the lighter's 300 max points, is at a substantial promotion disadvantage, all other factors being equal. Second, occupational physiology findings suggest that larger military personnel tend to be better performers of physically demanding military tasks (Bilzon et al., 2001; Bilzon et al., 2002, Harman & Frykman, 1992; Harman et al., 2008; Lyons et al., 2005; Rayson et al., 2000). These include: heavy materiel handling, load carriage, and casualty evacuation - tasks that involve not only moving one's own weight but additional external weight. As a result, and because physical fitness test performance is linked with promotions and advancement within the services, the current physical fitness tests of the U.S. Army, Air Force, and Navy penalize the very populations that perform physically demanding military tasks better.

Body mass bias also occurs in competitive sports, most notably distance running which attracts over two million competitors in race distances of five kilometers or greater each year in the United States. The implications can be substantial. For example, a 68 kg woman's 50:00 10 km race time would be physiologically equivalent to a 50 kg woman's 45:07 10 km time. Accordingly, body weight handicap models for distance runs have been developed and validated to account for body weight differences in determining race performance (Vanderburgh & Laubach, 2007). Despite the additional credit for larger body mass, these models have also been determined to be disadvantageous for those whose larger mass is due to excess fat mass, an important health-related finding (Vanderburgh et al., in press).

influence. One can interpret allometric comparisons as follows, using a Table 4 example: "Without consideration of the influence of body mass on pushups, Ellen performed 11.8% more repetitions. Considering the influence of body mass, however, June scored 4.6% better." In the case of the bench press, June's performance was 47.7% better than Ellen's for

On a more significant scale, the effect of body mass bias among military personnel has been quantified (Vanderburgh & Crowder, 2006). For example, a 90 kg man performing at the

**Ellen June Comparison** 

M1/3 June by 4.6%

M-0.67 June by 8.0%

M-1/3 June by 4.4 %

Raw 38 reps 34 reps Ellen by 11.8%

Raw 44 kg 65 kg June by 47.7%

Raw 13:24 15:00 Ellen by 10.7%

M1/3 146.5 reps.

M-0.67 3.50 kg.

M-1/3 3.48 min.

Army Physical Fitness Test of pushups, situps, and two-mile run, would actually receive a 15% lower score (256 vs. 300 points). In this case, "same physiological level" is the expected performance of a 90 kg man who is an exact scale model of the 60 kg man. Similarly, for a 45 kg vs. 75 kg woman, the U.S. Navy's test levies a 20% penalty. In the case of the Armed Services, there are actually two very real undesirable consequences for the larger service members. First, advancement and promotion is influenced by the fitness test scores. For the two women above, the heavier who scored physiologically equivalent 240 points compared to the lighter's 300 max points, is at a substantial promotion disadvantage, all other factors being equal. Second, occupational physiology findings suggest that larger military personnel tend to be better performers of physically demanding military tasks (Bilzon et al., 2001; Bilzon et al., 2002, Harman & Frykman, 1992; Harman et al., 2008; Lyons et al., 2005; Rayson et al., 2000). These include: heavy materiel handling, load carriage, and casualty evacuation - tasks that involve not only moving one's own weight but additional external weight. As a result, and because physical fitness test performance is linked with promotions and advancement within the services, the current physical fitness tests of the U.S. Army, Air Force, and Navy penalize

raw score but only 8.0% better when making a proper adjustment for body mass.

same physiological level as a 60 kg man who achieved a maximum score on the U.S.

**Body Mass** -- 50 kg 80 kg --

Table 4. Hypothetical example of the effects of allometry in fitness testing

the very populations that perform physically demanding military tasks better.

Body mass bias also occurs in competitive sports, most notably distance running which attracts over two million competitors in race distances of five kilometers or greater each year in the United States. The implications can be substantial. For example, a 68 kg woman's 50:00 10 km race time would be physiologically equivalent to a 50 kg woman's 45:07 10 km time. Accordingly, body weight handicap models for distance runs have been developed and validated to account for body weight differences in determining race performance (Vanderburgh & Laubach, 2007). Despite the additional credit for larger body mass, these models have also been determined to be disadvantageous for those whose larger mass is due to excess fat mass, an important health-related finding (Vanderburgh et al., in press).

Scaled 140.0 reps.

Scaled 3.24 kg.

Scaled 3.64 min.

**Pushups** 

**2-Mile Run** 

**Bench Press** 

In some sports, such as wrestling and power lifting, weight classes are the main method of accounting for body weight differences. One of the challenges of such a convention is that there are often few competitors in the extremes of weight and many in the middle weights. This imposes a body weight bias against the middle weight competitors who must compete against many more athletes to win or place. Recent empirical evidence suggests that allometric scaling is a technique that can be used to eliminate all weight classes for each gender and determine the best overall lifter when properly adjusting for body weight differences (Vanderburgh & Batterham, 1999). In competitive rowing, there are typically only two weight classes for each gender, light and heavy. Vanderburgh et al. (1996) developed and validated an allometric index that allows all rowers of any size within each gender, to be compared to each other while properly factoring out the influence of body mass and age. Their results suggest that rowing time multiplied by stature (or M1/3) was the optimal index to remove body mass bias among these competitive rowers.

## **6. Body fatness and allometry in fitness testing**

One of the main critiques of using allometric scaling in fitness testing is that giving credit for weight in the scoring is to also give credit for excess body fat. In other words, some perceive that such scoring gives advantages for being fatter. Indeed, one's denominator of M1/3 for the distance run score computed from the DR/M1/3 calculation would be larger for a fatter individual, thus leading to a lower (and better score, since low score wins). What is not often considered, however, is the effect of the excess fatness on the numerator – in this case, the distance run time. Vanderburgh and colleagues have modeled the effects of adding additional fat weight on the resultant distance run time for the 5 km and 2-mile runs and have shown that, in all cases, adding fat weight leads to a worse scaled score (Crecilius et al., 2008; Vanderburgh & Laubach, 2007). Recently, they tested this empirically, by adding external weight to the DR and PU events for college-age men. Results indicated that the addition of 16 kg of external weight led to 38% worse scaled PU scores and 12% worse twomile run scaled scores (Vanderburgh et al., in press).

#### **7. Practical techniques for using allometry in fitness testing**

As shown in Table 4, allometrically scaled scores yield strange units and currency. That is, one may find the interpretation of a scaled score for a 65 kg bench press, 3.50 kg. M-0.67, quite difficult to interpret. This is likely due to the units, kg. M-0.67, being unlike those encountered elsewhere and the magnitude of 3.50 not being as readily evaluated as 65 kg would be. Another problem with these scores is that they require a calculator to compute. In short, though arguably proper and fair, allometrically scaled scores are not practical.

Other solutions have been proposed. The first, correction factors, are dimensionless numbers based on body weight, which are multiplied by the raw score to produce an adjusted in the same units and more easily interpretable (Vanderburgh, 2007). For example, from Table 4, Ellen's and June's correction factors would, for PU, be: 1.0 and 1.17, respectively. This is calculated based on a body mass standard - a baseline from which all ratios are computed, the individual's body mass, and the particular event. For a standard weight of 50 kg, for example, and June's body mass of 80 kg, and the PU, her correction factor would be (80 kg/50 kg)1/3, or 1.170" (in both cases). Multiplying her raw score of 34 pushups by 1.211 yields an adjusted score of 39.78 pushups. Using the same methodology,

Body Mass Bias in Exercise Physiology 109

A third solution is the use of a balanced fitness test – one that imposes no body mass advantage. They can be single or multi-event. A backpack run is an example of a singleevent balanced test, in which neither larger nor smaller personnel are disadvantaged. This test, which would require all military personnel to run a given distance with a standardweight backpack, has been mathematically modeled and shown to eliminate body mass bias in men (Vanderburgh & Flanagan, 2000). This has occupational relevance since the standard backpack load for service members is typically the same, regardless of one's body mass. He/she must carry that load often over some considerable distance in arduous terrain. The advantage to the larger body mass of the standard weight for everyone is counterbalanced by the disadvantage of moving one's body mass. This backpack run test, along with a backpack pushups test, using the same standard weight of 16 kg, has been validated for college-age men as being free of body mass bias (Vanderburgh, in press). These types of tests, then, require no special calculations and produce raw scores that are fair and occupationally relevant. They do, however, pose a mass testing challenge in terms of the

One multi-event test that purports to be balanced is the popular "Pump & Run,"which entails a distance run and bench press event. One's final score is equal to the distance run time (in sec) minus 30 sec times each repetition of the bench press. The weight lifted, however, is based on a percentage of one's body mass. As Vanderburgh & Laubach (2008) determined empirically for 74 female and 343 male competitors of one event, the body mass bias against larger competitors was substantial, largely because both events imposed the penalty. The bench press was actually analogous to the pushup exercise which also lifts a percentage of one's weight. The researchers proposed a correction factor table but also recommended the study of a standard weight lifted for all competitors, not one based on body mass. This case study is probably the best single example of the non-intuitive nature of body mass bias; race officials were trying to level the playing field with the two events but

Body mass bias is a real phenomenon in fitness testing which is based on the fundamental notion that the ability to move one's weight is not directly proportional to one's body mass. This bias, especially in large-scale testing such as the military, leads to not only an advantage for smaller service members, but a disadvantage against those who perform the physically demanding occupational tasks of the military better – the larger service members. Allometric scaling and its derivative technique of correction factors can be used to erase such biases but, while these are mathematically appropriate and valid, they impose logistical and non-intuitive challenges. Such scoring is also useful in determining the best overall performer in sports such as distance running, powerlifting, and even indoor rowing. Evidence suggests that, although allometric scaling grants a credit for being heavier, if the increased body mass is fat mass, then the resulting scaled score is worse. This is because the detriment in raw score performance is of greater magnitude than the credit granted. Balanced fitness tests like the backpack run and pushups tests, which impose no body mass bias, have been shown to be intuitive, useful and occupationally relevant but are not without their mass testing challenges with regard to equipment needed. Most importantly, exercise scientists who can exercise some level of fluency in the principles of biological scaling and allometry as they apply to fitness testing

the result had just as much body mass bias as the distance run alone.

extra equipment needed.

**8. Conclusion** 

Ellen's adjusted score would be 38 x 1.0 = 39.4 pushups and June's adjusted performance is exactly the same 4.6% better than Ellen using the correction factors.

Simple tables can be constructed to determine correction factors without calculators and the multiplication of raw score by correction factor can be done with pencil-and-paper. Table 5 illustrates an example from Vanderburgh (2007), applied to the distance run events of United States armed forces fitness tests. In this case, the weight standards were selected as 120 lbs. and 150 lbs. for women and men, respectively. These values were specifically chosen to allow no credit below these body weights. Though the selection of the standard is arbitrary, it must be used consistently once chosen. Also, correction factors can be less than one as well. Nonetheless, correction factors still impose a logistical challenge in developing tables for each event and their use is not intuitively obvious to users with very little exercise science background.


Table 5. Correction Factors for Distance Runs, Pushups, or Situps Tests. A 186 lb man with an actual score two-mile run time of 15:05, for example, would go to the "180" column and down to the row corresponding to "6" to yield the correction factor of 0.93. Since low score wins, this number would be multiplied by the actual time of 905 sec to yield an adjusted score of 841.7 sec or 14:02. For a 172 lb woman with 32 pushups, and high score wins, one would divide her raw score by the 0.90 correction factor to yield an adjusted score of 35.6 pushups (from Vanderburgh, 2007).

Ellen's adjusted score would be 38 x 1.0 = 39.4 pushups and June's adjusted performance is

Simple tables can be constructed to determine correction factors without calculators and the multiplication of raw score by correction factor can be done with pencil-and-paper. Table 5 illustrates an example from Vanderburgh (2007), applied to the distance run events of United States armed forces fitness tests. In this case, the weight standards were selected as 120 lbs. and 150 lbs. for women and men, respectively. These values were specifically chosen to allow no credit below these body weights. Though the selection of the standard is arbitrary, it must be used consistently once chosen. Also, correction factors can be less than one as well. Nonetheless, correction factors still impose a logistical challenge in developing tables for each event and their use is not intuitively obvious to users with very little exercise

Women 120 130 140 150 160 170 180 190 200 0 1.00 0.99 0.96 0.94 0.92 0.90 0.89 0.87 0.85 1 1.00 0.98 0.96 0.94 0.92 0.90 0.88 0.87 0.85 2 1.00 0.98 0.96 0.94 0.92 0.90 0.88 0.87 0.85 3 1.00 0.98 0.96 0.93 0.92 0.90 0.88 0.87 0.85 4 1.00 0.98 0.95 0.93 0.91 0.90 0.88 0.86 0.85 5 1.00 0.97 0.95 0.93 0.91 0.89 0.88 0.86 0.85 6 1.00 0.97 0.95 0.93 0.91 0.89 0.88 0.86 0.85 7 0.99 0.97 0.95 0.93 0.91 0.89 0.87 0.86 0.85 8 0.99 0.97 0.95 0.92 0.91 0.89 0.87 0.86 0.84 9 0.99 0.97 0.94 0.92 0.90 0.89 0.87 0.86 0.84

Men 150 160 170 180 190 200 210 220 230 240 250 0 1.00 0.98 0.96 0.94 0.92 0.91 0.89 0.88 0.87 0.85 0.84 1 1.00 0.98 0.96 0.94 0.92 0.91 0.89 0.88 0.87 0.85 0.84 2 1.00 0.97 0.96 0.94 0.92 0.91 0.89 0.88 0.86 0.85 0.84 3 0.99 0.97 0.95 0.94 0.92 0.90 0.89 0.88 0.86 0.85 0.84 4 0.99 0.97 0.95 0.93 0.92 0.90 0.89 0.87 0.86 0.85 0.84 5 0.99 0.97 0.95 0.93 0.92 0.90 0.89 0.87 0.86 0.85 0.84 6 0.99 0.97 0.95 0.93 0.91 0.90 0.89 0.87 0.86 0.85 0.84 7 0.98 0.96 0.95 0.93 0.91 0.90 0.88 0.87 0.86 0.85 0.84 8 0.98 0.96 0.94 0.93 0.91 0.90 0.88 0.87 0.86 0.85 0.83 9 0.98 0.96 0.94 0.93 0.91 0.90 0.88 0.87 0.86 0.84 0.83 Table 5. Correction Factors for Distance Runs, Pushups, or Situps Tests. A 186 lb man with an actual score two-mile run time of 15:05, for example, would go to the "180" column and down to the row corresponding to "6" to yield the correction factor of 0.93. Since low score wins, this number would be multiplied by the actual time of 905 sec to yield an adjusted score of 841.7 sec or 14:02. For a 172 lb woman with 32 pushups, and high score wins, one would divide her raw score by the 0.90 correction factor to yield an adjusted score of 35.6

exactly the same 4.6% better than Ellen using the correction factors.

science background.

pushups (from Vanderburgh, 2007).

A third solution is the use of a balanced fitness test – one that imposes no body mass advantage. They can be single or multi-event. A backpack run is an example of a singleevent balanced test, in which neither larger nor smaller personnel are disadvantaged. This test, which would require all military personnel to run a given distance with a standardweight backpack, has been mathematically modeled and shown to eliminate body mass bias in men (Vanderburgh & Flanagan, 2000). This has occupational relevance since the standard backpack load for service members is typically the same, regardless of one's body mass. He/she must carry that load often over some considerable distance in arduous terrain. The advantage to the larger body mass of the standard weight for everyone is counterbalanced by the disadvantage of moving one's body mass. This backpack run test, along with a backpack pushups test, using the same standard weight of 16 kg, has been validated for college-age men as being free of body mass bias (Vanderburgh, in press). These types of tests, then, require no special calculations and produce raw scores that are fair and occupationally relevant. They do, however, pose a mass testing challenge in terms of the extra equipment needed.

One multi-event test that purports to be balanced is the popular "Pump & Run,"which entails a distance run and bench press event. One's final score is equal to the distance run time (in sec) minus 30 sec times each repetition of the bench press. The weight lifted, however, is based on a percentage of one's body mass. As Vanderburgh & Laubach (2008) determined empirically for 74 female and 343 male competitors of one event, the body mass bias against larger competitors was substantial, largely because both events imposed the penalty. The bench press was actually analogous to the pushup exercise which also lifts a percentage of one's weight. The researchers proposed a correction factor table but also recommended the study of a standard weight lifted for all competitors, not one based on body mass. This case study is probably the best single example of the non-intuitive nature of body mass bias; race officials were trying to level the playing field with the two events but the result had just as much body mass bias as the distance run alone.

#### **8. Conclusion**

Body mass bias is a real phenomenon in fitness testing which is based on the fundamental notion that the ability to move one's weight is not directly proportional to one's body mass. This bias, especially in large-scale testing such as the military, leads to not only an advantage for smaller service members, but a disadvantage against those who perform the physically demanding occupational tasks of the military better – the larger service members. Allometric scaling and its derivative technique of correction factors can be used to erase such biases but, while these are mathematically appropriate and valid, they impose logistical and non-intuitive challenges. Such scoring is also useful in determining the best overall performer in sports such as distance running, powerlifting, and even indoor rowing. Evidence suggests that, although allometric scaling grants a credit for being heavier, if the increased body mass is fat mass, then the resulting scaled score is worse. This is because the detriment in raw score performance is of greater magnitude than the credit granted. Balanced fitness tests like the backpack run and pushups tests, which impose no body mass bias, have been shown to be intuitive, useful and occupationally relevant but are not without their mass testing challenges with regard to equipment needed. Most importantly, exercise scientists who can exercise some level of fluency in the principles of biological scaling and allometry as they apply to fitness testing

Body Mass Bias in Exercise Physiology 111

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Vol.55, No.5., pp. 380-384, ISSN 0962-7480

139-149, ISSN 1439-6319

0014-0139

9131

No.2, pp. 110-17, ISSN 1439-6319

Vol.66, No.1, pp.80-84, ISSN 0270-1367

Vol.28, No.9, pp. 1204-8, ISSN 0195-9131

No.2, pp. 67-70, ISSN 1064-8011

will be able to best interpret human performance scores not only sport, but in occupational fitness and health-related fitness as well.

## **9. Acknowledgment**

I would like to acknowledge the following:


#### **10. References**


will be able to best interpret human performance scores not only sport, but in

 My colleagues at the United States Military Academy at West Point, also my alma mater, where physical fitness is highly valued and excellence is a habit; your energy

 The many soldiers and USMA/ROTC cadets of the United States Army who served as subjects in our research; your compliance and cooperation were top-shelf in every way and your efforts were always maximal, a necessary condition for this type of research. My colleagues in the field of exercise physiology, especially Dr. Todd Crowder, Dr. Alan Batterham, Dr. Lloyd Laubach, and Dr. Tom Rowland; your friendship, enthusiasm, and collegiality were not only the key ingredients for productivity but for

The graduate students with whom I've had the pleasure to work; I hope you learned as

Astrand, P. & Rodahl, K. (1986). *Textbook of Work Physiology*. McGraw Hill ISBN 0-7360-0140-

Batterham, A.; Vanderburgh, P.; Mahar, M. & Jackson, A. (1999). Modeling the influence of

Bilzon, J.; Allsopp, A. & Tipton, M. (2001). Assessment of physical fitness for occupations

Bilzon, J; Scarpello, E.; Bilzon, E. & Allsop, A. (2002) Generic task-related occupational

Crecelius, A.; Vanderburgh, P. & Laubach, L. (2008). Contributions of body fat and effort in

Crowder, T. & Yunker, C. (1986). Scaling of push-up, sit-up and two-mile run performances

Dooman C. & Vanderburgh, P. (2000). Allometric modeling of the bench press and squat:

Harman, E. & Frykman, P. (1992). The relationship of body size and composition to the

*Physical Performance.* National Academy Press ISBN 0-309-0458, pp.105-18 Harman, E.; Gutekunst, D.; Frykman, P.; Sharp, M.; Nindl, B.; Alemany, J. & Mello, R.

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body size on VO2peak: Effects of model choice and body composition. *Journal of* 

encompassing load-carriage tasks. *Occupational Medicine.* Vol.51, No.5, pp. 357-361,

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performance of physically demanding military tasks. In: *Body Composition and* 

(2008). Prediction of Simulated Battlefield Physical Performance from Field-

occupational fitness and health-related fitness as well.

I would like to acknowledge the following:

and passion were contagious.

having fun along the way.

**10. References** 

much from our collaborations as I did.

9, New York, pp.399-405,

ISSN 0962-7480

510, ISSN 0962-7480

**9. Acknowledgment** 


**6** 

*Greece* 

**Eccentric Exercise, Muscle** 

**Damage and Oxidative Stress** 

*1Department of Physical Education and Sport Science,* 

*2Institute of Human Performance and Rehabilitation, Center for Research and Technology –Thessaly, Trikala, 3Department of Physical Education and Sport Science,* 

*Democritus University of Thrace, Komotini,* 

*University of Thessaly, Trikala,* 

Athanasios Z. Jamurtas1,2 and Ioannis G. Fatouros2,3

Participation in exercise has been linked with positive results on the cardiovascular system, metabolism, musculature etc. Some of these benefits are linked with reductions in blood pressure, increases in resting energy expenditure, changes in lipid profile, reductions in fat mass, increases in fat free mass etc. (Evans, 1999). There are different types of exercise that someone can participate in, i.e. walking, running, lifting weights, participating in organized sports which involve different muscular contractions. Isometric, concentric and eccentric muscle actions are the main muscular contractions involved in all exercise activities. Eccentric muscular contraction is this type of contraction where the length of the muscle is increased while tension is developed. Unaccustomed eccentric exercise has been linked with greater muscle damage compared to isometric or concentric muscular contractions. However this phenomenon is temporary and perturbations in functional and biochemical indices are back to normal within a week from the initiation of the trauma. Furthermore, the damage works as a protective mechanism since data indicates that the muscle damage is attenuated when a subsequent exercise bout of the same intensity is performed even a few months later. Eventhough eccentric exercise leads to greater muscle damage, recent data indicates that eccentrically induced muscle damage is related with positive changes in lipid profile that are evident for a few days following the initial event. Furthermore, the limited data from eccentric training studies indicate that this type of exercise is linked with positive

changes in strength as well as in the metabolic profile of the exercise participant.

Oxidative stress indicates a condition where the cellular production of pro-oxidant molecules exceeds the ability of the antioxidant system to reduce reactive oxygen or nitrogen species (RONS). There are several studies that indicate that oxidative stress is evident following muscle damaging exercise. Its role has been related to cleaning the debris from the damaged tissue and providing the means for biochemical adaptations that lead to a stronger and more resistant to muscle damage muscular tissue. This phenomenon is transient and existing evidence suggests that oxidative stress indices are attenuated when a

**1. Introduction** 


## **Eccentric Exercise, Muscle Damage and Oxidative Stress**

Athanasios Z. Jamurtas1,2 and Ioannis G. Fatouros2,3

*1Department of Physical Education and Sport Science, University of Thessaly, Trikala, 2Institute of Human Performance and Rehabilitation, Center for Research and Technology –Thessaly, Trikala, 3Department of Physical Education and Sport Science, Democritus University of Thrace, Komotini, Greece* 

## **1. Introduction**

112 An International Perspective on Topics in Sports Medicine and Sports Injury

Vanderburgh, P. & Dooman C. (2000). Considering body mass differences, who are the

Vanderburgh, P. & Flanagan, S. (2000). The Backpack Run Test: A model for a fair and

Vanderburgh, P. & Crowder, T. (2006). Body weight penalties in the physical fitness tests of

Vanderburgh, P. & Laubach, L. (2007). Derivation of an age and weight handicap for the 5K

Vanderburgh, P. (2007). Correction factors for body mass bias in military physical fitness

Vanderburgh, P. & Laubach, L. (2008). Body mass bias in a competition of muscle strength

Vanderburgh, P. (2008). Occupational relevance and body mass bias of in military physical

Vanderburgh, P.; Mickley, N.; Anloague, P. & Lucius K. Load carriage distance run and

tests. *Military Medicine,* Vol.172, No.7, pp. 738-742, ISSN 0026-4075

pp. 197-201, ISSN 0195-9131

pp.418-21, ISSN 0026-4075

ISSN ISSN 1091-367X

375-382, ISSN 1064-8011

(in press), ISSN 0026-4075

ISSN 0195-9131

0026-4075

world's strongest women? *Medicine and Science in Sports and Exercise,* Vol.32, No.1,

occupationally relevant military fitness test. *Military Medicine,* Vol.165, No.5,

the Army, Air Force, and Navy. *Military Medicine,* Vol.171, No.8, pp. 753-756, ISSN

run. *Measurement in Physical Education and Exercise Science*, Vol.11, No.1, pp. 49-59,

and aerobic power. *Journal of Strength and Conditioning Research,* Vol.22, No.2, pp.

fitness tests. *Medicine and Science in Sports and Exercise,* Vol.40, No.8, pp. 1538-1545,

pushups tests: No body mass bias and occupationally relevant. *Military Medicine,*

Participation in exercise has been linked with positive results on the cardiovascular system, metabolism, musculature etc. Some of these benefits are linked with reductions in blood pressure, increases in resting energy expenditure, changes in lipid profile, reductions in fat mass, increases in fat free mass etc. (Evans, 1999). There are different types of exercise that someone can participate in, i.e. walking, running, lifting weights, participating in organized sports which involve different muscular contractions. Isometric, concentric and eccentric muscle actions are the main muscular contractions involved in all exercise activities. Eccentric muscular contraction is this type of contraction where the length of the muscle is increased while tension is developed. Unaccustomed eccentric exercise has been linked with greater muscle damage compared to isometric or concentric muscular contractions. However this phenomenon is temporary and perturbations in functional and biochemical indices are back to normal within a week from the initiation of the trauma. Furthermore, the damage works as a protective mechanism since data indicates that the muscle damage is attenuated when a subsequent exercise bout of the same intensity is performed even a few months later. Eventhough eccentric exercise leads to greater muscle damage, recent data indicates that eccentrically induced muscle damage is related with positive changes in lipid profile that are evident for a few days following the initial event. Furthermore, the limited data from eccentric training studies indicate that this type of exercise is linked with positive changes in strength as well as in the metabolic profile of the exercise participant.

Oxidative stress indicates a condition where the cellular production of pro-oxidant molecules exceeds the ability of the antioxidant system to reduce reactive oxygen or nitrogen species (RONS). There are several studies that indicate that oxidative stress is evident following muscle damaging exercise. Its role has been related to cleaning the debris from the damaged tissue and providing the means for biochemical adaptations that lead to a stronger and more resistant to muscle damage muscular tissue. This phenomenon is transient and existing evidence suggests that oxidative stress indices are attenuated when a

Eccentric Exercise, Muscle Damage and Oxidative Stress 115

During eccentric exercise, force is generated when muscle fibers are lengthened. It is well documented that intense, unaccustomed eccentric exercise is associated with muscle damage (Clarkson et al. 1992). Evidence of damage includes morphological changes with ultrastructural damage to muscle fibers usually seen with microscopy Friden 1984), decrements in muscle force and the involved joint's range of motion (ROM) (Nosaka & Newton 2002), deterioration of running economy (Paschalis et al. 2005; 2008; Chen et al. 2007; 2009), alterations in position sense and reaction angle (Paschalis et al. 2007; 2010) elevated plasma proteins such as creatine kinase (CK) and myoglobin (Nosaka & Clarkson 1995; Jamurtas et al. 2000), elevation in inammatory by products (Fatouros et al. 2010; McIntyre et al. 2001), connective tissue damage (Tofas et al. 2008), large increases in blood and muscle oxidative stress (Paschalis et al. 2007; Theodorou et al. 2010; 2011) and delayed onset of muscle soreness (Cheung et al. 2003). Eccentric exercises have been shown to produce the greatest amount of delayed onset muscle soreness (DOMS) and larger elevations of plasma CK compared to concentric or isometric exercises (Ebbeling & Clarkson

Eccentric exercise results in greater muscle damage because fewer muscle fibers are recruited to exert a given amount of force as compared to concentric contractions. Since the force–velocity relationship indicates that each individual muscle fiber can exert a larger force while being stretched than it can while being shortened (Hill, 1938) and fewer fibers are activated during eccentric contractions, larger forces per muscle ber are developed during eccentric actions thereby resulting in greater damage. Furthermore, during an eccentric contraction, some sarcomeres in muscle fibers are more resistant to stretching than others forcing weaker sarcomeres to absorb more stretch. With repeated eccentric contractions, the weaker sarcomeres first and then the stronger sarcomeres are overstretched. If the latter fail to withstand the stretching force during the relaxation phase, damage may occur. If the damage spreads to adjacent fibers, disruption of the membrane of the sarcoplasmic reticulum or sarcolemma may be seen. In that case intracellular Ca++ concentration increases leading to additional degradation of muscle fibers due to activation of calcium dependent proteolytic enzymes, such as the calpain mediated proteases, resulting

in neutrophil infiltration to the injured site (Raj et al. 1998; Proske & Allen 2005).

Histological changes are evident following an intense bout of eccentric exercise and data indicates that approximately one third of the muscle fibers obtained from individuals who performed 300 maximal voluntary eccentric contractions of the knee extensors show intense myofibrillar disruptions, myofillament disorganization and loss of Z line integrity (Raastad et al. 2010). Changes in functional measurements are considered to be the best tool for quantifying muscle damage and monitoring exercise-induced muscle damage (Warren et al. 1999). Following eccentrically induced muscle damage, functional measurements (Maximal Voluntary Contractions, eccentric peak torque, jumping performance) demonstrate a marked deterioration reaching their lowest values approximately 72 hours post exercise and return to normal values within 7 days of recovery (Miyama & Nosaka 2004; Nikolaidis et al. 2008). Following eccentrically induced muscle damage, changes in running economy appear to depend on the intensity of exercise used to asses this parameter. Reports indicate no changes in running economy following eccentric exercise when a moderate intensity exercise is used to assess running economy (Paschalis et al. 2005; 2008; Chen et al. 2009) whereas others report significant perturbations when higher intensities are used (Braun & Dutto 2003; Chen

**4. Eccentric exercise and muscle damage** 

1989; Jamurtas et al. 2000).

subsequent exercise bout is performed a few weeks after the initial damaging protocol. Finally, eccentric exercise has been linked with health benefits that are evident either after an acute bout of exercise or following a training protocol.

This review highlights muscle damage and oxidative stress adaptations as well as the health benefits associated with acute and chronic eccentric exercise.

## **2. Benefits from exercise**

Numerous experimental and epidemiological studies have documented a large number of health benefits derived from systematic engagement with cardiovascular exercise training such as enhanced physiologic, metabolic, and psychologic adaptations, as well as reduced risk for development of many chronic diseases and premature mortality (USA, 1996; Kesaniemi et al., 2001). It is well-established that physical activity and/or chronic exercise prevent the occurrence of adverse cardiac events, decreases the incidence of hypertension, atherosclerosis, stroke, osteoporosis, obesity, insulin resistance, type 2 diabetes, cancer, and depression, causes body weight and fat loss, and delays mortality (USA, 1996; Kesaniemi et al., 2001; ACSM, 2006; Feskanich et al., 2002; Leitzmann et al., 1999; Sahi et al., 1998; Rockville, 1995). Large-scale studies have also demonstrated that systematic exercise or switching from being sedentary to a more physically active life-style reduces disease rates and premature mortality (Hein et al., 1994; Paffenbarger et al., 1993; Blair et al., 1995; Erikssen et al., 1998). In fact, exercise-induced health benefits occur even at old ages (Paffenbarger et al., 1993).

Resistance exercise training (a form of which is eccentric training) also induces substantial health benefits, especially to older individuals. Strength training increases not only muscular strength and power but also improves bone mineral density as well as cardiovascular and psychological function (Ay and Yurtkuran, 2005; Takeshima et al., 2002; Wang et al., 2007). Although, the response to resistance exercise training is individualized, there is a consensus that a properly designed and supervised program should improve not only muscular fitness parameters (strength, muscular endurance, power, balance, speed) but the quality of life as well (Evans, 1999). These adaptations are particularly evident in the elderly. Resistance exercise training has been repeatedly shown to increase muscular strength and muscle mass which in turn improve the functional status (coordination, balance etc.) and limit sarcopenia in the aged (Evans, 1999; Fatouros et al., 2006; Fiatarone et al., 1993; Frontera et al., 1991; ACSM, 1998).

## **3. Eccentric exercise**

Daily movement involves different muscular contractions. Concentric type of contractions occurs when a muscle is activated and shortens. In contrast, eccentric contraction occurs when a skeletal muscle lengthens while it produces force. Both types of contractions occur during the day and the best example to differentiate between the two types of contraction is the ascending or descending the stairs. During the ascending of stairs leg muscles are working concentrically while during the descending of the stairs leg muscles are working eccentrically.

Eccentric exercise has been used as a means to develop muscle strength and size (Dudley et al. 1991). However, eccentric exercise training has been used lately as a novel rehabilitation modality in order to improve several conditions, i.e. tendinopathy, following anterior cruciate ligament reconstruction e.t.c. (Gerber et al. 2009) The health benefits derived from eccentric exercise training on metabolism will be discussed in a subsequent section.

subsequent exercise bout is performed a few weeks after the initial damaging protocol. Finally, eccentric exercise has been linked with health benefits that are evident either after

This review highlights muscle damage and oxidative stress adaptations as well as the health

Numerous experimental and epidemiological studies have documented a large number of health benefits derived from systematic engagement with cardiovascular exercise training such as enhanced physiologic, metabolic, and psychologic adaptations, as well as reduced risk for development of many chronic diseases and premature mortality (USA, 1996; Kesaniemi et al., 2001). It is well-established that physical activity and/or chronic exercise prevent the occurrence of adverse cardiac events, decreases the incidence of hypertension, atherosclerosis, stroke, osteoporosis, obesity, insulin resistance, type 2 diabetes, cancer, and depression, causes body weight and fat loss, and delays mortality (USA, 1996; Kesaniemi et al., 2001; ACSM, 2006; Feskanich et al., 2002; Leitzmann et al., 1999; Sahi et al., 1998; Rockville, 1995). Large-scale studies have also demonstrated that systematic exercise or switching from being sedentary to a more physically active life-style reduces disease rates and premature mortality (Hein et al., 1994; Paffenbarger et al., 1993; Blair et al., 1995; Erikssen et al., 1998). In fact, exercise-induced

Resistance exercise training (a form of which is eccentric training) also induces substantial health benefits, especially to older individuals. Strength training increases not only muscular strength and power but also improves bone mineral density as well as cardiovascular and psychological function (Ay and Yurtkuran, 2005; Takeshima et al., 2002; Wang et al., 2007). Although, the response to resistance exercise training is individualized, there is a consensus that a properly designed and supervised program should improve not only muscular fitness parameters (strength, muscular endurance, power, balance, speed) but the quality of life as well (Evans, 1999). These adaptations are particularly evident in the elderly. Resistance exercise training has been repeatedly shown to increase muscular strength and muscle mass which in turn improve the functional status (coordination, balance etc.) and limit sarcopenia in the aged (Evans, 1999; Fatouros et al., 2006; Fiatarone et

Daily movement involves different muscular contractions. Concentric type of contractions occurs when a muscle is activated and shortens. In contrast, eccentric contraction occurs when a skeletal muscle lengthens while it produces force. Both types of contractions occur during the day and the best example to differentiate between the two types of contraction is the ascending or descending the stairs. During the ascending of stairs leg muscles are working concentrically

Eccentric exercise has been used as a means to develop muscle strength and size (Dudley et al. 1991). However, eccentric exercise training has been used lately as a novel rehabilitation modality in order to improve several conditions, i.e. tendinopathy, following anterior cruciate ligament reconstruction e.t.c. (Gerber et al. 2009) The health benefits derived from

while during the descending of the stairs leg muscles are working eccentrically.

eccentric exercise training on metabolism will be discussed in a subsequent section.

an acute bout of exercise or following a training protocol.

**2. Benefits from exercise** 

benefits associated with acute and chronic eccentric exercise.

health benefits occur even at old ages (Paffenbarger et al., 1993).

al., 1993; Frontera et al., 1991; ACSM, 1998).

**3. Eccentric exercise** 

### **4. Eccentric exercise and muscle damage**

During eccentric exercise, force is generated when muscle fibers are lengthened. It is well documented that intense, unaccustomed eccentric exercise is associated with muscle damage (Clarkson et al. 1992). Evidence of damage includes morphological changes with ultrastructural damage to muscle fibers usually seen with microscopy Friden 1984), decrements in muscle force and the involved joint's range of motion (ROM) (Nosaka & Newton 2002), deterioration of running economy (Paschalis et al. 2005; 2008; Chen et al. 2007; 2009), alterations in position sense and reaction angle (Paschalis et al. 2007; 2010) elevated plasma proteins such as creatine kinase (CK) and myoglobin (Nosaka & Clarkson 1995; Jamurtas et al. 2000), elevation in inammatory by products (Fatouros et al. 2010; McIntyre et al. 2001), connective tissue damage (Tofas et al. 2008), large increases in blood and muscle oxidative stress (Paschalis et al. 2007; Theodorou et al. 2010; 2011) and delayed onset of muscle soreness (Cheung et al. 2003). Eccentric exercises have been shown to produce the greatest amount of delayed onset muscle soreness (DOMS) and larger elevations of plasma CK compared to concentric or isometric exercises (Ebbeling & Clarkson 1989; Jamurtas et al. 2000).

Eccentric exercise results in greater muscle damage because fewer muscle fibers are recruited to exert a given amount of force as compared to concentric contractions. Since the force–velocity relationship indicates that each individual muscle fiber can exert a larger force while being stretched than it can while being shortened (Hill, 1938) and fewer fibers are activated during eccentric contractions, larger forces per muscle ber are developed during eccentric actions thereby resulting in greater damage. Furthermore, during an eccentric contraction, some sarcomeres in muscle fibers are more resistant to stretching than others forcing weaker sarcomeres to absorb more stretch. With repeated eccentric contractions, the weaker sarcomeres first and then the stronger sarcomeres are overstretched. If the latter fail to withstand the stretching force during the relaxation phase, damage may occur. If the damage spreads to adjacent fibers, disruption of the membrane of the sarcoplasmic reticulum or sarcolemma may be seen. In that case intracellular Ca++ concentration increases leading to additional degradation of muscle fibers due to activation of calcium dependent proteolytic enzymes, such as the calpain mediated proteases, resulting in neutrophil infiltration to the injured site (Raj et al. 1998; Proske & Allen 2005).

Histological changes are evident following an intense bout of eccentric exercise and data indicates that approximately one third of the muscle fibers obtained from individuals who performed 300 maximal voluntary eccentric contractions of the knee extensors show intense myofibrillar disruptions, myofillament disorganization and loss of Z line integrity (Raastad et al. 2010). Changes in functional measurements are considered to be the best tool for quantifying muscle damage and monitoring exercise-induced muscle damage (Warren et al. 1999). Following eccentrically induced muscle damage, functional measurements (Maximal Voluntary Contractions, eccentric peak torque, jumping performance) demonstrate a marked deterioration reaching their lowest values approximately 72 hours post exercise and return to normal values within 7 days of recovery (Miyama & Nosaka 2004; Nikolaidis et al. 2008).

Following eccentrically induced muscle damage, changes in running economy appear to depend on the intensity of exercise used to asses this parameter. Reports indicate no changes in running economy following eccentric exercise when a moderate intensity exercise is used to assess running economy (Paschalis et al. 2005; 2008; Chen et al. 2009) whereas others report significant perturbations when higher intensities are used (Braun & Dutto 2003; Chen

Eccentric Exercise, Muscle Damage and Oxidative Stress 117

molecules have a singlet electron in the outer membrane and often are called free radicals. RONS occur as a consequence of normal cellular metabolism and have an effect on important biological processes such as gene expression (Pendyala & Natarajan 2010), signal transduction (Santos et al. 2011) and posttranslational modifications (Radak et al. 2011). Therefore, it appears that low levels of RONS are important for normal physiological function and homeostasis. RONS seem to be increased under conditions of psychological and physical stress (Sen et al. 1994). Evidence also indicates that enhanced production of RONS can lead to

Due to short half-life of RONS an indication of their presence is monitored by the measurement of by-products resulting from damage of various macromolecules such as proteins, lipids and nucleic acids. Oxidative damage to proteins involves the oxidation of amino acids and the most often utilized index of protein oxidation is protein carbonyls (Vincent & Taylor 2006). Lipid peroxidation markers include lipid hydroperoxides, conjugated dienes, malondialdehyde (MDA), thiobarbituric acid reactive substances (TBARS), and isoprostanes, with the level of F2-isoprostanes in blood or urine to be widely regarded as the reference marker for the assessment of oxidative stress (Nikolaidis et al. 2011). Significant alterations to normal physiological function can be induced due to elevated lipid peroxidation and loss of membrane fluidity and cytosolic membranes are examples of this modification. Strand breaks and single base modifications of DNA are examples of RONS associated DNA damage. 8-hydroxy-2΄-deoxyguanosine (8-OHdG) represents the most frequently marker used to assess DNA damage (Vincent & Taylor 2006). RONS are quenched through molecules that are called antioxidants. The main purpose of these molecules is to delay or prevent oxidative stress and damage. Antioxidants donate one of their electrons in order to reduce the formed oxidizing agent. Antioxidants are separated into the ones that have enzymatic activity and those with non-enzymatic activity. The main enzymatic antioxidants include superoxide dismutase, glutathione peroxidase and catalase. Non enzymatic compounds include vitamins (e.g. vitamin C, vitamin E), proteins (e.g.

cardiovascular diseases and cancer due to chronic inflammation (Halliwell B, 1993).

such as peroxynitrite (ONOO-

). These

) and nonradical derivatives of NO.

ferritin, transferrin, ceruloplasmin) or peptides (e.g. glutathione).

between muscle damage and oxidative stress.

**6. Eccentric exercise, oxidative stress and muscle damage** 

It was stated in a previous section that eccentric exercise leads to DOMS. At the same time numerous evidence suggests that eccentrically induced muscle damage appears concurrently with changes in oxidative stress. Paschalis et al. had 10 healthy females with no previous history of eccentric training perform five sets of 15 eccentric maximal voluntary contractions of the knee extensors and assessed indices of muscle function and muscle damage (isokinetic peak torque, ROM, CK) as well as indices of oxidative stress and the antioxidant system (glutathione, TBARS, protein carbonyls, catalase, uric acid, total antioxidant capacity) (Paschalis et al. 2007). The results showed that eccentric exercise resulted in significant loss of torque, decreases in ROM and elevation in CK concentration for several days following the exercise bout. These changes coincided with marked elevations of selected oxidative stress indices manifested in a uniform and prolonged pattern. Oxidative stress indices peaked at 48 hours of recovery and remained significantly elevated for 72 hours post exercise (Paschalis et al. 2007). The authors also report a moderate relationship between muscle damage and oxidative stress indices that may indicate a link

oxide (NO.

et al. 2007; 2009). For example, Chen et al. reported significant changes in running economy during level running when the intensity of exercise was set at 80% and 90% of VO2max but not at 70% VO2max (Chen et al. 2009). When submaximal intensities (55% and 75% of VO2max) were used to assess changes in running economy following eccentrically induced muscle damage it was found that running economy indicators remained unaffected throughout recovery (24-96 hours post exercise) (Paschalis et al. 2005; 2008). Perhaps there is an impairment in the fast twitch fibers, which are the ones that are mainly affected by intense eccentric exercise thereby leading to changes in running economy and kinematic measures (Paschalis et al. 2007; Tsatalas et al. 2010).

As it was indicated earlier, eccentric exercise may cause a disruption to the plasma membrane of a muscle fiber. Disruption of the sarcolemma results in the release of intracellular proteins (CK, myoglobin) into circulation. The time frame of entry of various muscle proteins into the circulation following sarcolemma damage may depend on the size of the protein. For instance, there is a difference in the peak between CK and myoglobin (Nosaka, 2011). Small proteins such as myoglobin (the molecular weight of myoglobin is 18 kD) enter the circulation through capillaries whereas large proteins such as CK (the molecular weight of CK is 80 kD) enter the circulation via the lymph (Lindena et al. 1979).

One of the main characteristics of the unaccustomed eccentric exercise is the development of muscle soreness. Muscle soreness needs to be differentiated between the temporary soreness and DOMS. Temporary soreness is usually felt during the final stages of fatiguing exercise and is a product of metabolic waste accumulation (Friden J 1984). DOMS is characterized by a sensation of dull, aching pain that is usually felt during movement or palpation of the affected muscle (Clarkson et al. 1992). DOMS appears 24 hours after exercise and peaks 48- 72 hours post-exercise. DOMS subsides and dissipates slowly and does not fully disappear until 7-10 days after exercise. The delayed response of DOMS seems to be related to an initial insult to the muscle due to mechanical reasons and this insult sets off a chain of events that leads to more damage while regeneration processes are also activated. Inflammatory responses to eccentric exercise play a role in the degeneration and regeneration of the damaged muscle (Peake et al. 2005). Following the initial insult, neutrophils are released into the circulation and enter the damaged muscle tissue within several hours (Beaton et al. 2002). Natural killer cells and lymphocytes are also released into the circulation during and after eccentric exercise. Macrophages and proinflammatory cytokines are produced in the muscle within 24 hours and can be present for several days following exercise. These responses are important for the acute phase response of the immune system and the removal of the damaged muscle tissue. Reactive oxygen and nitrogen species (RONS), such as superoxide produced by neutrophils and nitric oxide generated by macrophages, contribute to muscle damage (Close et al. 2005). The role of RONS on muscle damage will be discussed in a following section.

#### **5. Oxidative stress**

Oxidative stress may be defined as a condition in which cellular production of prooxidants exceeds the physiological ability of the system to quench reactive species. It is an imbalance between the production of reactive oxygen and nitrogen (RONS) species and antioxidant defense mechanisms. When the imbalance is in favor of RONS it can lead to biomolecular damage (Sies, 1991). RONS include several molecules such as superoxide (O2 .-), hydroxyl radical (. OH) and nonradical derivatives of oxygen such as hydrogen peroxide (H2O2), nitric

et al. 2007; 2009). For example, Chen et al. reported significant changes in running economy during level running when the intensity of exercise was set at 80% and 90% of VO2max but not at 70% VO2max (Chen et al. 2009). When submaximal intensities (55% and 75% of VO2max) were used to assess changes in running economy following eccentrically induced muscle damage it was found that running economy indicators remained unaffected throughout recovery (24-96 hours post exercise) (Paschalis et al. 2005; 2008). Perhaps there is an impairment in the fast twitch fibers, which are the ones that are mainly affected by intense eccentric exercise thereby leading to changes in running economy and kinematic

As it was indicated earlier, eccentric exercise may cause a disruption to the plasma membrane of a muscle fiber. Disruption of the sarcolemma results in the release of intracellular proteins (CK, myoglobin) into circulation. The time frame of entry of various muscle proteins into the circulation following sarcolemma damage may depend on the size of the protein. For instance, there is a difference in the peak between CK and myoglobin (Nosaka, 2011). Small proteins such as myoglobin (the molecular weight of myoglobin is 18 kD) enter the circulation through capillaries whereas large proteins such as CK (the molecular weight of CK is 80 kD) enter the circulation via the lymph (Lindena et al. 1979). One of the main characteristics of the unaccustomed eccentric exercise is the development of muscle soreness. Muscle soreness needs to be differentiated between the temporary soreness and DOMS. Temporary soreness is usually felt during the final stages of fatiguing exercise and is a product of metabolic waste accumulation (Friden J 1984). DOMS is characterized by a sensation of dull, aching pain that is usually felt during movement or palpation of the affected muscle (Clarkson et al. 1992). DOMS appears 24 hours after exercise and peaks 48- 72 hours post-exercise. DOMS subsides and dissipates slowly and does not fully disappear until 7-10 days after exercise. The delayed response of DOMS seems to be related to an initial insult to the muscle due to mechanical reasons and this insult sets off a chain of events that leads to more damage while regeneration processes are also activated. Inflammatory responses to eccentric exercise play a role in the degeneration and regeneration of the damaged muscle (Peake et al. 2005). Following the initial insult, neutrophils are released into the circulation and enter the damaged muscle tissue within several hours (Beaton et al. 2002). Natural killer cells and lymphocytes are also released into the circulation during and after eccentric exercise. Macrophages and proinflammatory cytokines are produced in the muscle within 24 hours and can be present for several days following exercise. These responses are important for the acute phase response of the immune system and the removal of the damaged muscle tissue. Reactive oxygen and nitrogen species (RONS), such as superoxide produced by neutrophils and nitric oxide generated by macrophages, contribute to muscle damage (Close et al. 2005). The role of

measures (Paschalis et al. 2007; Tsatalas et al. 2010).

RONS on muscle damage will be discussed in a following section.

Oxidative stress may be defined as a condition in which cellular production of prooxidants exceeds the physiological ability of the system to quench reactive species. It is an imbalance between the production of reactive oxygen and nitrogen (RONS) species and antioxidant defense mechanisms. When the imbalance is in favor of RONS it can lead to biomolecular

OH) and nonradical derivatives of oxygen such as hydrogen peroxide (H2O2), nitric

.-), hydroxyl

damage (Sies, 1991). RONS include several molecules such as superoxide (O2

**5. Oxidative stress** 

radical (.

oxide (NO. ) and nonradical derivatives of NO. such as peroxynitrite (ONOO- ). These molecules have a singlet electron in the outer membrane and often are called free radicals. RONS occur as a consequence of normal cellular metabolism and have an effect on important biological processes such as gene expression (Pendyala & Natarajan 2010), signal transduction (Santos et al. 2011) and posttranslational modifications (Radak et al. 2011). Therefore, it appears that low levels of RONS are important for normal physiological function and homeostasis. RONS seem to be increased under conditions of psychological and physical stress (Sen et al. 1994). Evidence also indicates that enhanced production of RONS can lead to cardiovascular diseases and cancer due to chronic inflammation (Halliwell B, 1993).

Due to short half-life of RONS an indication of their presence is monitored by the measurement of by-products resulting from damage of various macromolecules such as proteins, lipids and nucleic acids. Oxidative damage to proteins involves the oxidation of amino acids and the most often utilized index of protein oxidation is protein carbonyls (Vincent & Taylor 2006). Lipid peroxidation markers include lipid hydroperoxides, conjugated dienes, malondialdehyde (MDA), thiobarbituric acid reactive substances (TBARS), and isoprostanes, with the level of F2-isoprostanes in blood or urine to be widely regarded as the reference marker for the assessment of oxidative stress (Nikolaidis et al. 2011). Significant alterations to normal physiological function can be induced due to elevated lipid peroxidation and loss of membrane fluidity and cytosolic membranes are examples of this modification. Strand breaks and single base modifications of DNA are examples of RONS associated DNA damage. 8-hydroxy-2΄-deoxyguanosine (8-OHdG) represents the most frequently marker used to assess DNA damage (Vincent & Taylor 2006). RONS are quenched through molecules that are called antioxidants. The main purpose of these molecules is to delay or prevent oxidative stress and damage. Antioxidants donate one of their electrons in order to reduce the formed oxidizing agent. Antioxidants are separated into the ones that have enzymatic activity and those with non-enzymatic activity. The main enzymatic antioxidants include superoxide dismutase, glutathione peroxidase and catalase. Non enzymatic compounds include vitamins (e.g. vitamin C, vitamin E), proteins (e.g. ferritin, transferrin, ceruloplasmin) or peptides (e.g. glutathione).

#### **6. Eccentric exercise, oxidative stress and muscle damage**

It was stated in a previous section that eccentric exercise leads to DOMS. At the same time numerous evidence suggests that eccentrically induced muscle damage appears concurrently with changes in oxidative stress. Paschalis et al. had 10 healthy females with no previous history of eccentric training perform five sets of 15 eccentric maximal voluntary contractions of the knee extensors and assessed indices of muscle function and muscle damage (isokinetic peak torque, ROM, CK) as well as indices of oxidative stress and the antioxidant system (glutathione, TBARS, protein carbonyls, catalase, uric acid, total antioxidant capacity) (Paschalis et al. 2007). The results showed that eccentric exercise resulted in significant loss of torque, decreases in ROM and elevation in CK concentration for several days following the exercise bout. These changes coincided with marked elevations of selected oxidative stress indices manifested in a uniform and prolonged pattern. Oxidative stress indices peaked at 48 hours of recovery and remained significantly elevated for 72 hours post exercise (Paschalis et al. 2007). The authors also report a moderate relationship between muscle damage and oxidative stress indices that may indicate a link between muscle damage and oxidative stress.

Eccentric Exercise, Muscle Damage and Oxidative Stress 119

muscle damaging exercise where the disturbances in oxidative stress indices are not uniform and return towards baseline within hours after the end of exercise (Michailidis et al. 2007). Direct comparisons of different studies are difficult. It has to be stated here that only human studies were presented in this section. A point of consideration relates to the mode of exercise used to cause muscle damage and the muscle groups used to perform the exercise. Eccentric exercise on an isokinetic device and downhill running are primarily the two main modes of exercise used to induce muscle damage following which oxidative stress markers were assessed. These two modes differ considerably since eccentric exercise on an isokinetic device isolates the muscle group used to perform the exercise whereas downhill running integrates the action of multiple muscle groups besides quadriceps in order to perform the exercise. In addition, the aerobic component of downhill running might be an additional confounding factor in causing oxidative stress (through electron leakage from mitochondrial

Another point of consideration that relates to eccentrically induced muscle damage is the cause of the enhanced appearance of RONS following eccentric exercise. Muscle mitochondria (through electron leakage in the electron transport chain) could be one source of RONS during or shortly after muscle damaging exercise. However, that mild elevation of RONS production is unlikely to contribute to the delayed increased oxidative stress response that appears hours or days following exercise. Ischemia-reperfusion could be another cause of RONS elevation during exercise. It is well-known from cardiac physiology that reperfusion in cardiac tissue following angioplasty operations leads to elevated myocardium damage that is partly attributed to elevated RONS production (Zhao et al. 2000). Blood flow redistribution during exercise is a well-known adaptation in exercise physiology and describes the vasodilation of the vascular system of the active muscle and the vasoconstriction of the vasculature of the non-active muscle tissue. The hypoxic nonactive tissue receives a greater quantity of blood after exercise and enhanced formation of RONS is possible due to the xanthine oxidase mechanism (Finaud et al. 2006; Veskoukis et al. 2008). This mechanism of RONS production seems also unlikely to account for the increased oxidative stress that appears days following eccentrically-induced muscle damage. Oxidation of hemoglobin and myoglobin during exercise can also cause RONS formation (Finaud et al. 2006) but this mechanism is also unlikely to be responsible for the

Inflammatory responses to eccentric exercise play a major role in the degeneration and regeneration of the damaged muscle (Peake et al. 2005). Following the initial insult neutrophils are released into the circulation and enter the damaged muscle tissue within several hours (Beaton et al. 2002). Therefore, infiltrating white blood cells into skeletal muscle may be another source of RONS production following the insult caused to skeletal muscle due to eccentric exercise. Indeed activated neutrophils and other phagocytic cells are a major cause of RONS production leading to tissue damage and if remain unchecked can destroy adjacent healthy tissue (Close et al. 2005). Therefore, RONS can assist in repairing damaged tissue via phagocytosis and white blood cell respiratory burst activity. Production of RONS when muscle damage is present may serve a secondary role which is not other than the induction of the antioxidant activity of the damaged tissue in order to prevent harm when a subsequent exercise session is performed. Figure 1 illustrates a simplistic approach to the events that takes place following an eccentrically induced muscle damage exercise session. In brief, eccentric exercise due to mechanical stress causes injury to the sarcolemma and muscle damage is induced. The inflammatory

delayed response of oxidative stress when muscle damage is present.

respiration and other mechanisms).

Nikolaidis et al. assessed also oxidative stress following eccentric exercise in healthy females and found significant perturbations in all assessed indices (Nikolaidis et al. 2007). Subjects had to perform five sets of 15 eccentric maximal voluntary contractions of the knee flexors and indices of muscle function and muscle damage (isokinetic peak torque, ROM, CK) and indices of oxidative stress and the antioxidant system (glutathione, TBARS, protein carbonyls, catalase, uric acid, total antioxidant capacity) were assessed before the exercise session and 1, 2, 3, 4 and 7 days post exercise. Eccentric exercise caused muscle damage and uniformly modified the levels of the selected oxidative stress indices in the blood. Oxidative stress indices peaked at 72 hours and returned toward baseline after 7 days post exercise (Nikolaidis et al. 2008).

Results from another study from our laboratory (Theodorou et al. 2010) performed in healthy males provided similar results with the aforementioned studies. In Thedorou et al. study, nine healthy males performed five sets of 15 eccentric maximal voluntary contractions of the knee extensors and indices of muscle function and muscle damage and oxidative stress were also assessed. Indices of oxidative stress in this study were assessed in plasma and erythrocyte lysate before, as well as 1, 2, 3, 4, and 5 days post-exercise in order to determine whether there was a different response between the two blood compartments (plasma and red blood cells) as well. The results showed that eccentric exercise markedly increased muscle damage, oxidative stress and hemolysis indices that peaked at 2 and 3 days post exercise (Theodorou et al. 2010).

Silva et al. reported significant elevation in lipid peroxidation (TBARS) and protein carbonylation indices following eccentric exercise (Silva et al. 2010). Subjects performed three sets of eccentric exercise of the elbow exors at an intensity of 80% of maximum repetition until exhaustion. A point of interest is the significant elevated TBARS and protein carbonyls for 7 days after the eccentric exercise which is in contrast with previous reports indicating a return of oxidative stress indices at baseline levels within five to seven days post exercise (Nikolaidis et al. 2007; Theodorou et al. 2010). Goldfarb et al. reported significant elevations in protein carbonyls and MDA up to 72 hours post exercise in subjects that performed four sets of 12 maximal repetitions of eccentric actions at an angular velocity of 20o.s-1, with 60 s of rest between sets using their nondominant arm elbow flexors (Goldfarb et al. 2011). Eccentric exercise resulted also in significant changes in force and muscle damage indices. Other reports also indicate significant elevations in indices of oxidative stress (i.e. protein carbonyls) following eccentric resistance exercise (arm elbow flexors) performed by humans (Goldfarb et al. 2005; Lee et al. 2002) or downhill running (Close et al. 2004; 2005; 2006).

Eventhough the previously reported studies suggest that eccentrically induced muscle damage is accompanied with changes in oxidative stress indices for some days after exercise there are reports indicating no changes in oxidative stress following eccentric exercise. Kerksick et al. showed no changes in lipid peroxidation (F2-isoprostanes) and superoxide dismutase following eccentric exercise (10 sets of 10 repetitions at an isokinetic eccentric speed of 60o.s-1 on an isokinetic dynamometer) that caused muscle damage (Kerksick et al. 2010). In Goldfarb et al. study no changes in lipid hydroperoxides and glutathione levels were observed (Goldfarb et al. 2011). Saxton et al. also did not find significant changes in oxidative stress measures immediately post and two days following exercise of the forearm flexors (Saxton et al. 1994).

Taken collectively, the results from the aforementioned studies suggest that muscle damaging exercise seems to increase lipid peroxidation and protein oxidation in blood of humans. These results also indicate that disturbances in indices of blood oxidative stress may persist for several days following muscle-damaging exercise. This response is different compared to non-

Nikolaidis et al. assessed also oxidative stress following eccentric exercise in healthy females and found significant perturbations in all assessed indices (Nikolaidis et al. 2007). Subjects had to perform five sets of 15 eccentric maximal voluntary contractions of the knee flexors and indices of muscle function and muscle damage (isokinetic peak torque, ROM, CK) and indices of oxidative stress and the antioxidant system (glutathione, TBARS, protein carbonyls, catalase, uric acid, total antioxidant capacity) were assessed before the exercise session and 1, 2, 3, 4 and 7 days post exercise. Eccentric exercise caused muscle damage and uniformly modified the levels of the selected oxidative stress indices in the blood. Oxidative stress indices peaked at 72 hours and returned toward baseline after 7 days post exercise

Results from another study from our laboratory (Theodorou et al. 2010) performed in healthy males provided similar results with the aforementioned studies. In Thedorou et al. study, nine healthy males performed five sets of 15 eccentric maximal voluntary contractions of the knee extensors and indices of muscle function and muscle damage and oxidative stress were also assessed. Indices of oxidative stress in this study were assessed in plasma and erythrocyte lysate before, as well as 1, 2, 3, 4, and 5 days post-exercise in order to determine whether there was a different response between the two blood compartments (plasma and red blood cells) as well. The results showed that eccentric exercise markedly increased muscle damage, oxidative stress and hemolysis indices that peaked at 2 and 3

Silva et al. reported significant elevation in lipid peroxidation (TBARS) and protein carbonylation indices following eccentric exercise (Silva et al. 2010). Subjects performed three sets of eccentric exercise of the elbow exors at an intensity of 80% of maximum repetition until exhaustion. A point of interest is the significant elevated TBARS and protein carbonyls for 7 days after the eccentric exercise which is in contrast with previous reports indicating a return of oxidative stress indices at baseline levels within five to seven days post exercise (Nikolaidis et al. 2007; Theodorou et al. 2010). Goldfarb et al. reported significant elevations in protein carbonyls and MDA up to 72 hours post exercise in subjects that performed four sets of 12 maximal repetitions of eccentric actions at an angular velocity of 20o.s-1, with 60 s of rest between sets using their nondominant arm elbow flexors (Goldfarb et al. 2011). Eccentric exercise resulted also in significant changes in force and muscle damage indices. Other reports also indicate significant elevations in indices of oxidative stress (i.e. protein carbonyls) following eccentric resistance exercise (arm elbow flexors) performed by humans (Goldfarb et

Eventhough the previously reported studies suggest that eccentrically induced muscle damage is accompanied with changes in oxidative stress indices for some days after exercise there are reports indicating no changes in oxidative stress following eccentric exercise. Kerksick et al. showed no changes in lipid peroxidation (F2-isoprostanes) and superoxide dismutase following eccentric exercise (10 sets of 10 repetitions at an isokinetic eccentric speed of 60o.s-1 on an isokinetic dynamometer) that caused muscle damage (Kerksick et al. 2010). In Goldfarb et al. study no changes in lipid hydroperoxides and glutathione levels were observed (Goldfarb et al. 2011). Saxton et al. also did not find significant changes in oxidative stress measures immediately post and two days following exercise of the forearm flexors (Saxton et al. 1994). Taken collectively, the results from the aforementioned studies suggest that muscle damaging exercise seems to increase lipid peroxidation and protein oxidation in blood of humans. These results also indicate that disturbances in indices of blood oxidative stress may persist for several days following muscle-damaging exercise. This response is different compared to non-

al. 2005; Lee et al. 2002) or downhill running (Close et al. 2004; 2005; 2006).

(Nikolaidis et al. 2008).

days post exercise (Theodorou et al. 2010).

muscle damaging exercise where the disturbances in oxidative stress indices are not uniform and return towards baseline within hours after the end of exercise (Michailidis et al. 2007).

Direct comparisons of different studies are difficult. It has to be stated here that only human studies were presented in this section. A point of consideration relates to the mode of exercise used to cause muscle damage and the muscle groups used to perform the exercise. Eccentric exercise on an isokinetic device and downhill running are primarily the two main modes of exercise used to induce muscle damage following which oxidative stress markers were assessed. These two modes differ considerably since eccentric exercise on an isokinetic device isolates the muscle group used to perform the exercise whereas downhill running integrates the action of multiple muscle groups besides quadriceps in order to perform the exercise. In addition, the aerobic component of downhill running might be an additional confounding factor in causing oxidative stress (through electron leakage from mitochondrial respiration and other mechanisms).

Another point of consideration that relates to eccentrically induced muscle damage is the cause of the enhanced appearance of RONS following eccentric exercise. Muscle mitochondria (through electron leakage in the electron transport chain) could be one source of RONS during or shortly after muscle damaging exercise. However, that mild elevation of RONS production is unlikely to contribute to the delayed increased oxidative stress response that appears hours or days following exercise. Ischemia-reperfusion could be another cause of RONS elevation during exercise. It is well-known from cardiac physiology that reperfusion in cardiac tissue following angioplasty operations leads to elevated myocardium damage that is partly attributed to elevated RONS production (Zhao et al. 2000). Blood flow redistribution during exercise is a well-known adaptation in exercise physiology and describes the vasodilation of the vascular system of the active muscle and the vasoconstriction of the vasculature of the non-active muscle tissue. The hypoxic nonactive tissue receives a greater quantity of blood after exercise and enhanced formation of RONS is possible due to the xanthine oxidase mechanism (Finaud et al. 2006; Veskoukis et al. 2008). This mechanism of RONS production seems also unlikely to account for the increased oxidative stress that appears days following eccentrically-induced muscle damage. Oxidation of hemoglobin and myoglobin during exercise can also cause RONS formation (Finaud et al. 2006) but this mechanism is also unlikely to be responsible for the delayed response of oxidative stress when muscle damage is present.

Inflammatory responses to eccentric exercise play a major role in the degeneration and regeneration of the damaged muscle (Peake et al. 2005). Following the initial insult neutrophils are released into the circulation and enter the damaged muscle tissue within several hours (Beaton et al. 2002). Therefore, infiltrating white blood cells into skeletal muscle may be another source of RONS production following the insult caused to skeletal muscle due to eccentric exercise. Indeed activated neutrophils and other phagocytic cells are a major cause of RONS production leading to tissue damage and if remain unchecked can destroy adjacent healthy tissue (Close et al. 2005). Therefore, RONS can assist in repairing damaged tissue via phagocytosis and white blood cell respiratory burst activity. Production of RONS when muscle damage is present may serve a secondary role which is not other than the induction of the antioxidant activity of the damaged tissue in order to prevent harm when a subsequent exercise session is performed. Figure 1 illustrates a simplistic approach to the events that takes place following an eccentrically induced muscle damage exercise session. In brief, eccentric exercise due to mechanical stress causes injury to the sarcolemma and muscle damage is induced. The inflammatory

Eccentric Exercise, Muscle Damage and Oxidative Stress 121

be the basis for the repeated bout effect. Results from human data are needed in order to

Participation in exercise has been linked with positive results on the cardiovascular system, metabolism, musculature etc. Some of these benefits are linked with reductions in blood pressure, increases in resting energy expenditure, changes in lipid profile, reductions in fat mass, increases in fat free mass etc. The majority of the studies that examined the positive effects of exercise have used either endurance exercise or resistance exercise with both types

It has been mentioned previously that pure eccentric contractions lead to increased muscle damage and soreness levels. This phenomenon is transient, lasts a few days and can make the muscle more resistant to further damage when a repeated bout is performed. However, due to appearance of muscle soreness and the associated changes in muscle function eccentric exercise was viewed as the "bad guy" in the exercise physiology area. Besides the negative effects on muscular function eccentrically induced muscle damage was associated with impaired insulin action (Kirwan et al. 1992) and impaired muscle glycogen resynthesis (O'reilly et al. 1987). The transient augmented insulin responses to hyperglycaemia resulting from muscle damaging exercise seems to serve a dual purpose, i.e. to maintain glucose homeostasis and provide an anabolic environment for the damaged muscle, at least in

Eccentric exercise has been used as means of training for several pathological conditions showing positive results in patients with Parkinson disease (Dibble et al. 2006), older cancer survivors (LaStayo et al. 2010), patients undergone anterior cruciate ligament reconstruction (Gerber et al. 2007) etc. Lately there has been an attempt to elucidate the acute and chronic effects of eccentric exercise on metabolism. Eventhough previous reports have attempted to examine the acute effects of muscle damaging exercise on blood lipids only total cholesterol was used as an index of blood lipid profile in these studies (Smith et al. 1994; Shahbazpour et al. 2004). In a study that was performed in our laboratory, the acute effects of muscle damaging exercise on time-course changes of blood lipid profile and the effect of the repeated bout on blood lipids were assessed (Nikolaidis et al. 2007). Twelve healthy females participated in this study. They performed two isokinetic eccentric exercise sessions (five sets of 15 eccentric maximal voluntary contractions at an angular velocity of 60o.s-1) during the luteal phase. The two exercise sessions were separated by 24-30 days, depending on the duration of their menstrual cycle. Markers of the lipid profile and muscle damage indices were assessed before, immediately, 1, 2, 3, 4, and 7 days after exercise. The results revealed that eccentric exercise uniformly modified the levels of the lipids and lipoproteins (triglycerides, total cholesterol, HDL and LDL). The repeated bout effect affected the assessed variables in a way that the response of lipids and lipoproteins were higher after the first session of exercise compared to those induced by the second identical session performed 4 weeks later. Those changes in the blood peaked at 2 to 4 days post exercise (Nikolaidis et al. 2007). Beneficial changes in lipid profile after eccentric exercise in overweight and lean women were observed in another study performed in our laboratory (Paschalis et al.

substantiate the results obtained from the animal studies.

of exercise showing beneficial effects on health (Booth et al. 2000).

muscles predominately composed of fast twitch fibres (Flucke et al. 2001).

**8. Health benefits from eccentric exercise** 

processes that take place lead to enhanced production of RONS which serve a dual purpose: first, they clean the debris and repair the damaged tissue and secondly upregulate several transcription factors that lead to increased antioxidant activity of the remaining healthy muscle fibers that become more resistant to muscle damage when a bout of similar exercise is performed.

#### **7. Eccentric exercise, oxidative stress and the repeated bout effect**

As it has been mentioned earlier unaccustomed eccentric exercise results in muscle damage. Furthermore, it was eluded that the initial injury to muscle tissue leads to changes in its structure that make it more resistant to a subsequent bout of exercise. This process is referred in the literature as the "repeated bout effect". Numerous studies have shown an attenuation of indices related to muscle damage due to the repeated bout effect (Chen et al. 2007; McHugh et al. 2003). In regards to oxidative stress similar changes (i.e. attenuation) to indices of muscle damage have been observed. Nikolaidis et al. had 12 females perform two sessions of eccentric exercise, separated by three weeks, and assessed muscle damage and oxidative stress indices prior to exercise and 1, 2, 3, 4, and 7 days after exercise (Nikolaidis et al. 2007). The two exercise sessions were identical in intensity and duration and consisted of five sets of 15 maximum eccentric voluntary contractions of the knee flexors. The first exercise bout changed significantly all muscle damage and oxidative stress indices indicating that severe muscle damage and increased oxidative stress had occur. Nevertheless, the second exercise bout resulted in significant attenuation in the perturbations of muscle damage and oxidative stress indices. Assessment of the increase or decrease area under the curve for the oxidative stress indices revealed a 1.8-6.1-fold less change in oxidative stress compared to the changes induced by the first bout (Nikolaidis et al. 2007). One possible explanation for the reduced oxidative stress following the second bout of exercise relates to the less muscle damage and less invasion of white blood cells in the damaged tissue. Data from animal work supports this idea since no significant changes in the concentration of ED1+ and ED2+ macrophages were found after a second bout of lengthening contractions (Lapointe et al. 2002) and treatment with diclofenac, a widely used non-steroidal anti-inflammatory drug (NSAID), affected in parallel the concentration of macrophage subpopulations and the adaptive response after the second bout of exercise (Lapointe et al. 2002). Therefore, inflammation plays a significant role in repair or strengthening of the muscle and might

processes that take place lead to enhanced production of RONS which serve a dual purpose: first, they clean the debris and repair the damaged tissue and secondly upregulate several transcription factors that lead to increased antioxidant activity of the remaining healthy muscle fibers that become more resistant to muscle damage when a

**7. Eccentric exercise, oxidative stress and the repeated bout effect** 

As it has been mentioned earlier unaccustomed eccentric exercise results in muscle damage. Furthermore, it was eluded that the initial injury to muscle tissue leads to changes in its structure that make it more resistant to a subsequent bout of exercise. This process is referred in the literature as the "repeated bout effect". Numerous studies have shown an attenuation of indices related to muscle damage due to the repeated bout effect (Chen et al. 2007; McHugh et al. 2003). In regards to oxidative stress similar changes (i.e. attenuation) to indices of muscle damage have been observed. Nikolaidis et al. had 12 females perform two sessions of eccentric exercise, separated by three weeks, and assessed muscle damage and oxidative stress indices prior to exercise and 1, 2, 3, 4, and 7 days after exercise (Nikolaidis et al. 2007). The two exercise sessions were identical in intensity and duration and consisted of five sets of 15 maximum eccentric voluntary contractions of the knee flexors. The first exercise bout changed significantly all muscle damage and oxidative stress indices indicating that severe muscle damage and increased oxidative stress had occur. Nevertheless, the second exercise bout resulted in significant attenuation in the perturbations of muscle damage and oxidative stress indices. Assessment of the increase or decrease area under the curve for the oxidative stress indices revealed a 1.8-6.1-fold less change in oxidative stress compared to the changes induced by the first bout (Nikolaidis et al. 2007). One possible explanation for the reduced oxidative stress following the second bout of exercise relates to the less muscle damage and less invasion of white blood cells in the damaged tissue. Data from animal work supports this idea since no significant changes in the concentration of ED1+ and ED2+ macrophages were found after a second bout of lengthening contractions (Lapointe et al. 2002) and treatment with diclofenac, a widely used non-steroidal anti-inflammatory drug (NSAID), affected in parallel the concentration of macrophage subpopulations and the adaptive response after the second bout of exercise (Lapointe et al. 2002). Therefore, inflammation plays a significant role in repair or strengthening of the muscle and might

bout of similar exercise is performed.

be the basis for the repeated bout effect. Results from human data are needed in order to substantiate the results obtained from the animal studies.

#### **8. Health benefits from eccentric exercise**

Participation in exercise has been linked with positive results on the cardiovascular system, metabolism, musculature etc. Some of these benefits are linked with reductions in blood pressure, increases in resting energy expenditure, changes in lipid profile, reductions in fat mass, increases in fat free mass etc. The majority of the studies that examined the positive effects of exercise have used either endurance exercise or resistance exercise with both types of exercise showing beneficial effects on health (Booth et al. 2000).

It has been mentioned previously that pure eccentric contractions lead to increased muscle damage and soreness levels. This phenomenon is transient, lasts a few days and can make the muscle more resistant to further damage when a repeated bout is performed. However, due to appearance of muscle soreness and the associated changes in muscle function eccentric exercise was viewed as the "bad guy" in the exercise physiology area. Besides the negative effects on muscular function eccentrically induced muscle damage was associated with impaired insulin action (Kirwan et al. 1992) and impaired muscle glycogen resynthesis (O'reilly et al. 1987). The transient augmented insulin responses to hyperglycaemia resulting from muscle damaging exercise seems to serve a dual purpose, i.e. to maintain glucose homeostasis and provide an anabolic environment for the damaged muscle, at least in muscles predominately composed of fast twitch fibres (Flucke et al. 2001).

Eccentric exercise has been used as means of training for several pathological conditions showing positive results in patients with Parkinson disease (Dibble et al. 2006), older cancer survivors (LaStayo et al. 2010), patients undergone anterior cruciate ligament reconstruction (Gerber et al. 2007) etc. Lately there has been an attempt to elucidate the acute and chronic effects of eccentric exercise on metabolism. Eventhough previous reports have attempted to examine the acute effects of muscle damaging exercise on blood lipids only total cholesterol was used as an index of blood lipid profile in these studies (Smith et al. 1994; Shahbazpour et al. 2004). In a study that was performed in our laboratory, the acute effects of muscle damaging exercise on time-course changes of blood lipid profile and the effect of the repeated bout on blood lipids were assessed (Nikolaidis et al. 2007). Twelve healthy females participated in this study. They performed two isokinetic eccentric exercise sessions (five sets of 15 eccentric maximal voluntary contractions at an angular velocity of 60o.s-1) during the luteal phase. The two exercise sessions were separated by 24-30 days, depending on the duration of their menstrual cycle. Markers of the lipid profile and muscle damage indices were assessed before, immediately, 1, 2, 3, 4, and 7 days after exercise. The results revealed that eccentric exercise uniformly modified the levels of the lipids and lipoproteins (triglycerides, total cholesterol, HDL and LDL). The repeated bout effect affected the assessed variables in a way that the response of lipids and lipoproteins were higher after the first session of exercise compared to those induced by the second identical session performed 4 weeks later. Those changes in the blood peaked at 2 to 4 days post exercise (Nikolaidis et al. 2007). Beneficial changes in lipid profile after eccentric exercise in overweight and lean women were observed in another study performed in our laboratory (Paschalis et al.

Eccentric Exercise, Muscle Damage and Oxidative Stress 123

Latest research indicates that systematic eccentric exercise can lead to positive changes in physical capabilities, improved rehabilitation and health outcome measures. It has been also reported that unaccustomed eccentric exercise produces greater muscle damage and pain which subsides in a few days. Therefore, it is of great importance to develop exercise programs that incorporate eccentric actions that minimize muscle damage and the associated pain discomfort. Initial low intensity, short duration and progression are key elements in designing eccentric exercise programs. Rate of perceived exertion (RPE) is an important element that could be used in the exercise program. An example of how the aforementioned elements could be appropriately used is presented in an elegant study by Lastayo where older cancer survivors participated in an eccentric exercise intervention study (Lastayo et al. 2011). Subjects begun the intervention program participating in an exercise regimen of low intensity (7, very very light on an RPE scale) and short duration exercise (3-5 minutes per session) that progressed to a higher intensity (11-13, fairly light to somewhat hard on an RPE scale) and longer duration (16-20 minutes) after 12 weeks which was the duration of the exercise program. The program proved to be efficacious since increases in muscle size, strength and power along with improved mobility were noted. Another area where eccentric training could be applied is rehabilitation. Program designing in this area should also follow the same principles as the ones outlined previously. Lorenz & Reiman have outlined the role of eccentric training in various injuries in the athletic field and the reader is encouraged to read their review (Lorenz & Reiman 2011). In conclusion, eccentric training could be an effective means of increasing performance and lead to better health. Incorporation of basic principles in exercise program development (load, volume, intensity, frequency and progression) is essential in order to avoid unwanted outcomes (i.e.

Unaccustomed eccentric exercise can cause muscle damage that is evident by morphological changes of the muscle fiber, reductions in physical performance, elevation in inflammatory products and muscle soreness. These responses are significantly attenuated when a second exercise bout of the same intensity is implemented, a phenomenon known as the repeated bout effect. Oxidative stress indices follow the same pattern of response as the one previously mentioned. Significant perturbations in oxidative stress are evident following an eccentrically induced muscle damage exercise protocol that are attenuated due to the repeated bout effect. Elevation in oxidative stress seems to be related with cleaning the debris from damaged muscle fibers and upregulating the antioxidant activity of healthy fibers making them more resistant to muscle damage. Finally, strong evidence indicates that

[1] American College of Sports Medicine. Guidelines for Exercise Testing and Prescription,

7th ed., Whaley MH (ed.). Lippincott Williams & Wilkins: Baltimore, Maryland,

eccentric training can induce health-promoting effects.

**9. Eccentric exercise programming** 

muscle damage).

**10. Conclusion** 

**11. References** 

2006.

2010b). Subjects performed again five sets of 15 maximal voluntary contractions and energy expenditure, respiratory quotient (RQ), muscle damage and lipid and lipoprotein (triglycerides, total cholesterol, HDL and LDL) changes were assessed prior to, immediately after, 12, 24, 48 and 72 hours post exercise. The results revealed increased energy expenditure at all time-points following exercise, lower RQ at 24 hours post exercise andsignificant changes in muscle damage indices and lipids and lipoproteins at 24, 48 and 72 hours post exercise. These changes were exacerbated in the overweight group probably due to the higher muscle damage that was induced by the eccentric exercise protocol in this group of participants (Paschalis et al. 2010b). Similar results with the aforementioned studies were obtained from another study where significant lower triacylglycerol total area under the curve by approximately 12% and significantly elevated insulin incremental area under the curve, indicating transient insulin resistance, were also found 16 hours and 40 hours following an acute bout of eccentric exercise (Pafili et al. 2009).

Eccentric training seems also to improve muscle performance and diminishes the reductions in muscle performance, the elevation in muscle damage indices and the oxidative stress responses (Theodorou et al. 2011). The chronic effects of eccentric training on metabolism have been examined and show promising results in regards to this type of training. Drexel et al. had two groups of subjects hiking either upwards or downwards, three times a week, and assessed metabolic and inflammatory indices (Drexel et al. 2008). The results showed a reduction in total cholesterol (4.1%), LDL (8.4%), apolipoprotein B/apolipoprotein A1 ratio (10·9%*),* homeostasis model assessment of insulin resistance (26·2%) and C-reactive protein (30.0%) in the eccentric group. These results indicate favorable metabolic and anti-inflammatory results following this type of exercise. Paschalis et al. examined also the effects of a weekly bout of eccentric versus concentric exercise on health parameters of healthy women (Paschalis et al. 2011). Subjects performed an isokinetic eccentric or concentric exercise protocol once per week for eight weeks. Subjects had to complete five sets of 15 concentric or eccentric maximum voluntary contractions in each of their lower limbs with a 2-min rest between sets. The results showed that eccentric training improved the resting levels of blood lipid profile. More specifically, triglygerides, total cholesterol (TC), LDL and TC/HDL ratio decreased by 12.8%, 8.8%, 16.4% and 17%, respectively whereas HDL levels increased by 9.3%. No changes in apolipoprotein A1, apolipoprotein B and lipoprotein (α) were seen. Eccentric training resulted also in significant reductions in glucose, insulin, HOMA and glycosylated hemoglobin levels (Paschalis et al. 2011). However, Marcus et al. did not find any significant changes in insulin sensitivity in overweight or obese postmenopausal women with impaired glucose tolerance (Marcus et al. 2009). Subjects performed three exercise sessions per week for 12 weeks. The exercise was performed on a high-force eccentric ergometer and ranged from 5 minutes in the beginning to 30 minutes at the end of training. Eventhough the eccentric training resulted in significant positive changes on body composition, strength, and physical function no significant changes were found in insulin sensitivity following a hyperinsulinemic-euglycemic clamp test. The different modes of exercise (isolated isokinetic eccentric exercise vs. aerobic type eccentric exercise on an ergometer) might account for the difference in the results obtained in the latter study compared to the former studies.

## **9. Eccentric exercise programming**

122 An International Perspective on Topics in Sports Medicine and Sports Injury

2010b). Subjects performed again five sets of 15 maximal voluntary contractions and energy expenditure, respiratory quotient (RQ), muscle damage and lipid and lipoprotein (triglycerides, total cholesterol, HDL and LDL) changes were assessed prior to, immediately after, 12, 24, 48 and 72 hours post exercise. The results revealed increased energy expenditure at all time-points following exercise, lower RQ at 24 hours post exercise andsignificant changes in muscle damage indices and lipids and lipoproteins at 24, 48 and 72 hours post exercise. These changes were exacerbated in the overweight group probably due to the higher muscle damage that was induced by the eccentric exercise protocol in this group of participants (Paschalis et al. 2010b). Similar results with the aforementioned studies were obtained from another study where significant lower triacylglycerol total area under the curve by approximately 12% and significantly elevated insulin incremental area under the curve, indicating transient insulin resistance, were also found 16 hours and 40 hours following an acute bout of eccentric exercise (Pafili et al.

Eccentric training seems also to improve muscle performance and diminishes the reductions in muscle performance, the elevation in muscle damage indices and the oxidative stress responses (Theodorou et al. 2011). The chronic effects of eccentric training on metabolism have been examined and show promising results in regards to this type of training. Drexel et al. had two groups of subjects hiking either upwards or downwards, three times a week, and assessed metabolic and inflammatory indices (Drexel et al. 2008). The results showed a reduction in total cholesterol (4.1%), LDL (8.4%), apolipoprotein B/apolipoprotein A1 ratio (10·9%*),* homeostasis model assessment of insulin resistance (26·2%) and C-reactive protein (30.0%) in the eccentric group. These results indicate favorable metabolic and anti-inflammatory results following this type of exercise. Paschalis et al. examined also the effects of a weekly bout of eccentric versus concentric exercise on health parameters of healthy women (Paschalis et al. 2011). Subjects performed an isokinetic eccentric or concentric exercise protocol once per week for eight weeks. Subjects had to complete five sets of 15 concentric or eccentric maximum voluntary contractions in each of their lower limbs with a 2-min rest between sets. The results showed that eccentric training improved the resting levels of blood lipid profile. More specifically, triglygerides, total cholesterol (TC), LDL and TC/HDL ratio decreased by 12.8%, 8.8%, 16.4% and 17%, respectively whereas HDL levels increased by 9.3%. No changes in apolipoprotein A1, apolipoprotein B and lipoprotein (α) were seen. Eccentric training resulted also in significant reductions in glucose, insulin, HOMA and glycosylated hemoglobin levels (Paschalis et al. 2011). However, Marcus et al. did not find any significant changes in insulin sensitivity in overweight or obese postmenopausal women with impaired glucose tolerance (Marcus et al. 2009). Subjects performed three exercise sessions per week for 12 weeks. The exercise was performed on a high-force eccentric ergometer and ranged from 5 minutes in the beginning to 30 minutes at the end of training. Eventhough the eccentric training resulted in significant positive changes on body composition, strength, and physical function no significant changes were found in insulin sensitivity following a hyperinsulinemic-euglycemic clamp test. The different modes of exercise (isolated isokinetic eccentric exercise vs. aerobic type eccentric exercise on an ergometer) might account for the difference in the results obtained in the latter

2009).

study compared to the former studies.

Latest research indicates that systematic eccentric exercise can lead to positive changes in physical capabilities, improved rehabilitation and health outcome measures. It has been also reported that unaccustomed eccentric exercise produces greater muscle damage and pain which subsides in a few days. Therefore, it is of great importance to develop exercise programs that incorporate eccentric actions that minimize muscle damage and the associated pain discomfort. Initial low intensity, short duration and progression are key elements in designing eccentric exercise programs. Rate of perceived exertion (RPE) is an important element that could be used in the exercise program. An example of how the aforementioned elements could be appropriately used is presented in an elegant study by Lastayo where older cancer survivors participated in an eccentric exercise intervention study (Lastayo et al. 2011). Subjects begun the intervention program participating in an exercise regimen of low intensity (7, very very light on an RPE scale) and short duration exercise (3-5 minutes per session) that progressed to a higher intensity (11-13, fairly light to somewhat hard on an RPE scale) and longer duration (16-20 minutes) after 12 weeks which was the duration of the exercise program. The program proved to be efficacious since increases in muscle size, strength and power along with improved mobility were noted. Another area where eccentric training could be applied is rehabilitation. Program designing in this area should also follow the same principles as the ones outlined previously. Lorenz & Reiman have outlined the role of eccentric training in various injuries in the athletic field and the reader is encouraged to read their review (Lorenz & Reiman 2011). In conclusion, eccentric training could be an effective means of increasing performance and lead to better health. Incorporation of basic principles in exercise program development (load, volume, intensity, frequency and progression) is essential in order to avoid unwanted outcomes (i.e. muscle damage).

## **10. Conclusion**

Unaccustomed eccentric exercise can cause muscle damage that is evident by morphological changes of the muscle fiber, reductions in physical performance, elevation in inflammatory products and muscle soreness. These responses are significantly attenuated when a second exercise bout of the same intensity is implemented, a phenomenon known as the repeated bout effect. Oxidative stress indices follow the same pattern of response as the one previously mentioned. Significant perturbations in oxidative stress are evident following an eccentrically induced muscle damage exercise protocol that are attenuated due to the repeated bout effect. Elevation in oxidative stress seems to be related with cleaning the debris from damaged muscle fibers and upregulating the antioxidant activity of healthy fibers making them more resistant to muscle damage. Finally, strong evidence indicates that eccentric training can induce health-promoting effects.

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**7** 

*USA* 

**Aging in Women Athletes** 

*VA Research Service, Department of Medicine, Division of Gerontology and Geriatric Medicine, University of Maryland School of Medicine, Baltimore VA Medical Center Geriatric Research,* 

*Education and Clinical Center (GRECC), VA Maryland Health Care System, Baltimore,* 

Monica C. Serra, Shawna L. McMillin and Alice S. Ryan\*

Since instating Title IX of the Education Amendments of 1972, there has been a significant increase in sports participation and athletic opportunities among women (1). While it is still more common for younger than older women to engage in athletic competition, the participation of older women is growing, with over 50 countries sponsoring master athletes events (2). While aging is associated with a decrease in metabolic and physiologic function, competitive athletic women may experience more gradual declines. These declines can be slowed further, by combining adequate dietary intake with proper exercise training. Therefore, this chapter will 1) discuss how aging influences physiologic and metabolic adaptations of highly trained women athletes and 2) explore how nutrition recommendations may change with exercise and the possible benefit of supplementation of

Many master athletes are capable of performances equal to those of non-elite young athletes (3). Nevertheless, age-related alteration to functional and physiological capacities are inescapable and as a result these age-related alteration lead to a decline in performances. It is widely accepted that aerobic capacity decreases with age. The rate of decline in maximal oxygen consumption (VO2max) varies between 5-9% per decade starting at the age of ~35 years in healthy sedentary adults (4, 5, 6). Several studies report a greater rate of decline with age in endurance-trained men and women (7, 8). Running performances decrease in a curvilinear fashion with the greatest decline after 60 years of age with women demonstrating a threefold greater decrease in performance compared to men (8, 9). Marcell et al. (10) provided evidence that a decline in VO2max is the best predictor of age-related changes in endurance performances in female athletes. Elite endurance performances are attributed to three primary

**1. Introduction** 

micronutrients to improve athletic performance.

**2.1 Endurance performance** 

 \*

Corresponding Author

**2. Aging and physiological adaptations of women athletes** 

determinants: aerobic capacity, lactate threshold, and exercise economy.

[92] Lorenz D, Reiman M. The role and implementation of eccentric training in athletic rehabilitation: tendinopathy, hamstring strains, and acl reconstruction. *Int J Sports Phys Ther.* 6(1):27-44, 2011.

## **Aging in Women Athletes**

Monica C. Serra, Shawna L. McMillin and Alice S. Ryan\*

*VA Research Service, Department of Medicine, Division of Gerontology and Geriatric Medicine, University of Maryland School of Medicine, Baltimore VA Medical Center Geriatric Research, Education and Clinical Center (GRECC), VA Maryland Health Care System, Baltimore, USA* 

## **1. Introduction**

130 An International Perspective on Topics in Sports Medicine and Sports Injury

[92] Lorenz D, Reiman M. The role and implementation of eccentric training in athletic

*Phys Ther.* 6(1):27-44, 2011.

rehabilitation: tendinopathy, hamstring strains, and acl reconstruction. *Int J Sports* 

Since instating Title IX of the Education Amendments of 1972, there has been a significant increase in sports participation and athletic opportunities among women (1). While it is still more common for younger than older women to engage in athletic competition, the participation of older women is growing, with over 50 countries sponsoring master athletes events (2). While aging is associated with a decrease in metabolic and physiologic function, competitive athletic women may experience more gradual declines. These declines can be slowed further, by combining adequate dietary intake with proper exercise training. Therefore, this chapter will 1) discuss how aging influences physiologic and metabolic adaptations of highly trained women athletes and 2) explore how nutrition recommendations may change with exercise and the possible benefit of supplementation of micronutrients to improve athletic performance.

## **2. Aging and physiological adaptations of women athletes**

## **2.1 Endurance performance**

Many master athletes are capable of performances equal to those of non-elite young athletes (3). Nevertheless, age-related alteration to functional and physiological capacities are inescapable and as a result these age-related alteration lead to a decline in performances. It is widely accepted that aerobic capacity decreases with age. The rate of decline in maximal oxygen consumption (VO2max) varies between 5-9% per decade starting at the age of ~35 years in healthy sedentary adults (4, 5, 6). Several studies report a greater rate of decline with age in endurance-trained men and women (7, 8). Running performances decrease in a curvilinear fashion with the greatest decline after 60 years of age with women demonstrating a threefold greater decrease in performance compared to men (8, 9). Marcell et al. (10) provided evidence that a decline in VO2max is the best predictor of age-related changes in endurance performances in female athletes. Elite endurance performances are attributed to three primary determinants: aerobic capacity, lactate threshold, and exercise economy.

 \* Corresponding Author

Aging in Women Athletes 133

stroke volume are cardiac preload, left-ventricular end-diastolic volume, and myocardial contractility. Blood volume plays an important role in stroke volume and decreases with normal aging in healthy sedentary females. However, total blood volume is maintained in older endurance trained female athletes (16). Master athletes demonstrate a larger left ventricular mass and left ventricular end-diastolic volume compared to healthy sedentary adults (17). Given the benefits of habitual endurance training, it is uncertain how advanced age alters stroke volume which would consequently result in a similar decrease in VO2max in aging athletes compared to sedentary women. Peripheral adaptations with aging have also been suggested to contribute to reductions in VO2max through changes in both oxygen delivery and utilization to active skeletal muscles (9). Arteriole-venous oxygen difference decreases slightly with age in trained athletes (5). It has been observed that enzyme activity and capillarization (expressed per muscle fiber) of skeletal muscle are preserved in older male athletes (18). Though muscle characteristics have not been examined in older female athletes, the reduced VO2max per kilogram muscle in female athletes is similar to male athletes (19). Therefore, it is likely that the age-associated reductions in VO2max are a result

Changes in body weight/composition may be a second mechanism for the decline in performance with age in athletes. Regardless of age, a decrease in lean body mass and an increase in percent body fat may contribute to a decrease in VO2max (6, 19). Endurance trained women did not demonstrate the expected relationship between changes in body composition and age-related changes in VO2max (4). Male endurance runners who maintained their lean body mass also maintained their relative VO2max, whereas the female runners who maintained their relative VO2max had the greatest decrease in lean body mass (4). This finding suggests that other factors, including the maintenance of training and/or estrogen rather than body composition have a greater affect on the female age-related decline of VO2max (4). The training stimulus may also play a role in the performance decline with age. With advanced age there seems to be a reduction in overall exercise "stimulus" (i.e. intensity, duration, and frequency) (5, 9, 14). VO2max is positively associated with training volume and as such, the age-related decrease in VO2max is associated with a reduction in training volume (14). However, female endurance athletes between the ages 34-78 years who maintained or increased their training volume with age, exhibited a similar change in VO2max compared to healthy sedentary adults (14). Training stimulus appears to be a key determinant in the decline in aerobic capacity with age. Whether the decline in training is a

result of the aging-process, injury, time, or motivation, has yet to be determined.

it will be interesting to see whether the gender difference is maintained in the future.

**3. Aging and metabolic adaptations in women athletes** 

**3.1 Body composition** 

Despite the health benefits achieved through a lifetime of participating in physical activity, it seems that diminished performances are an inevitable aspect of aging. The exact mechanism(s) for the reduction in performance with age has yet to be determined. The finding that women demonstrate a greater rate of decline in performances compared to men could be a result of fewer women participating in competitive events as they age (3). However, given that more women have been encouraged to participate in sporting events since the induction of Title IX,

Normal aging results in significant changes in body composition with increases in abdominal fat and losses of muscle mass. The increase in obesity alters the risks for type 2

of oxygen delivery and/or muscle mass.

#### **2.2 Aerobic capacity**

A high VO2max is an identifiable marker for a successful endurance athlete. Observed VO2max in elite male endurance athletes can measure between 75 and 85 ml/kg/min; whereas; VO2max is approximately 10% lower in elite women athletes (11). VO2max is higher in athletes at any age than sedentary women (8, 7, 11). Endurance performance and aerobic capacity are strongly related across varying age groups of competitive athletes (9). Aerobic capacity, as measured by VO2max is determined by cardiac output and arteriole-venous oxygen difference (12). Both cardiac output and arteriole-venous oxygen difference decrease with age in endurance athletes (5). Cardiac output is the product of heart rate and stroke volume and accounts for approximately 50% of oxygen consumption during exercise (12). Heart rate is the primary factor for increases in cardiac output during exercise; whereas stroke volume peaks at ~50% of max exercise then levels off or slightly decreases (12). The age-related loss in maximal heart rate is between 0.5-1 beat per year (13). Several studies have exhibited that habitual exercise status has no effect on the age-associated reductions in maximal heart rate (7, 14, 4). With maximal heart rates similar between athletes and non-athletes, the principle difference in cardiac output is stroke volume (11). Ogawa et al. (5) observed a greater rate of decline in stroke volume in female athletes compared to that observed in sedentary controls. The decrease in maximal heart rate, stroke volume, and arteriole-venous oxygen difference contributes to the decline in master athletes' endurance performances.

#### **2.3 Lactate threshold**

Lactate threshold is the fraction of VO2max where there is a significant increase in blood lactate accumulation (12). Lactate threshold is a primary factor in determining endurance performances of both men and women (11). In sedentary subjects there is typically a rise in blood lactate concentration to ~60% VO2max. In trained athletes this value can be 75– 90% of VO2max (14). A study by Evans et al. (7), showed that lactate threshold as a percentage of VO2max did not change with age in female distance runners. This evidence coupled with similar findings in male distance runners (15) suggests that a reduction in VO2max rather than a reduction in lactate threshold contribute the most to the decline in performance with age.

#### **2.4 Exercise economy**

Exercise economy is the oxygen cost of an endurance performance at a given velocity and can vary up to ~ 30-40% among individuals (11). Exercise economy is a predictor of performance in a population with similar VO2max (11). Results in male runners suggest that exercise economy does not change with age in highly trained endurance runners (15). Older female runners have demonstrated a slight change in economy at submax speeds and yet displayed no relationship between age and economy at a 10K race pace (7). Therefore, exercise economy is unlikely to contribute to the age-related decline in endurance performances.

#### **2.5 Physiological and training mechanisms for aging declines in VO2 max**

Both central (cardiac output and blood volume) and peripheral (muscle mass and oxygen delivery/utilization) factors contribute to the high VO2max demonstrated in elite athletes (11). At present, it is still unclear as to the exact cause(s) of the age-related decrease in VO2max in master athletes compared to young athletes. Stroke volume is responsible for the higher cardiac output in athletes versus healthy sedentary individuals (11). Determinates of

A high VO2max is an identifiable marker for a successful endurance athlete. Observed VO2max in elite male endurance athletes can measure between 75 and 85 ml/kg/min; whereas; VO2max is approximately 10% lower in elite women athletes (11). VO2max is higher in athletes at any age than sedentary women (8, 7, 11). Endurance performance and aerobic capacity are strongly related across varying age groups of competitive athletes (9). Aerobic capacity, as measured by VO2max is determined by cardiac output and arteriole-venous oxygen difference (12). Both cardiac output and arteriole-venous oxygen difference decrease with age in endurance athletes (5). Cardiac output is the product of heart rate and stroke volume and accounts for approximately 50% of oxygen consumption during exercise (12). Heart rate is the primary factor for increases in cardiac output during exercise; whereas stroke volume peaks at ~50% of max exercise then levels off or slightly decreases (12). The age-related loss in maximal heart rate is between 0.5-1 beat per year (13). Several studies have exhibited that habitual exercise status has no effect on the age-associated reductions in maximal heart rate (7, 14, 4). With maximal heart rates similar between athletes and non-athletes, the principle difference in cardiac output is stroke volume (11). Ogawa et al. (5) observed a greater rate of decline in stroke volume in female athletes compared to that observed in sedentary controls. The decrease in maximal heart rate, stroke volume, and arteriole-venous oxygen

difference contributes to the decline in master athletes' endurance performances.

Lactate threshold is the fraction of VO2max where there is a significant increase in blood lactate accumulation (12). Lactate threshold is a primary factor in determining endurance performances of both men and women (11). In sedentary subjects there is typically a rise in blood lactate concentration to ~60% VO2max. In trained athletes this value can be 75– 90% of VO2max (14). A study by Evans et al. (7), showed that lactate threshold as a percentage of VO2max did not change with age in female distance runners. This evidence coupled with similar findings in male distance runners (15) suggests that a reduction in VO2max rather than a reduction in lactate threshold contribute the most to the decline in

Exercise economy is the oxygen cost of an endurance performance at a given velocity and can vary up to ~ 30-40% among individuals (11). Exercise economy is a predictor of performance in a population with similar VO2max (11). Results in male runners suggest that exercise economy does not change with age in highly trained endurance runners (15). Older female runners have demonstrated a slight change in economy at submax speeds and yet displayed no relationship between age and economy at a 10K race pace (7). Therefore, exercise economy

Both central (cardiac output and blood volume) and peripheral (muscle mass and oxygen delivery/utilization) factors contribute to the high VO2max demonstrated in elite athletes (11). At present, it is still unclear as to the exact cause(s) of the age-related decrease in VO2max in master athletes compared to young athletes. Stroke volume is responsible for the higher cardiac output in athletes versus healthy sedentary individuals (11). Determinates of

is unlikely to contribute to the age-related decline in endurance performances.

**2.5 Physiological and training mechanisms for aging declines in VO2 max** 

**2.2 Aerobic capacity** 

**2.3 Lactate threshold** 

performance with age.

**2.4 Exercise economy** 

stroke volume are cardiac preload, left-ventricular end-diastolic volume, and myocardial contractility. Blood volume plays an important role in stroke volume and decreases with normal aging in healthy sedentary females. However, total blood volume is maintained in older endurance trained female athletes (16). Master athletes demonstrate a larger left ventricular mass and left ventricular end-diastolic volume compared to healthy sedentary adults (17). Given the benefits of habitual endurance training, it is uncertain how advanced age alters stroke volume which would consequently result in a similar decrease in VO2max in aging athletes compared to sedentary women. Peripheral adaptations with aging have also been suggested to contribute to reductions in VO2max through changes in both oxygen delivery and utilization to active skeletal muscles (9). Arteriole-venous oxygen difference decreases slightly with age in trained athletes (5). It has been observed that enzyme activity and capillarization (expressed per muscle fiber) of skeletal muscle are preserved in older male athletes (18). Though muscle characteristics have not been examined in older female athletes, the reduced VO2max per kilogram muscle in female athletes is similar to male athletes (19). Therefore, it is likely that the age-associated reductions in VO2max are a result of oxygen delivery and/or muscle mass.

Changes in body weight/composition may be a second mechanism for the decline in performance with age in athletes. Regardless of age, a decrease in lean body mass and an increase in percent body fat may contribute to a decrease in VO2max (6, 19). Endurance trained women did not demonstrate the expected relationship between changes in body composition and age-related changes in VO2max (4). Male endurance runners who maintained their lean body mass also maintained their relative VO2max, whereas the female runners who maintained their relative VO2max had the greatest decrease in lean body mass (4). This finding suggests that other factors, including the maintenance of training and/or estrogen rather than body composition have a greater affect on the female age-related decline of VO2max (4).

The training stimulus may also play a role in the performance decline with age. With advanced age there seems to be a reduction in overall exercise "stimulus" (i.e. intensity, duration, and frequency) (5, 9, 14). VO2max is positively associated with training volume and as such, the age-related decrease in VO2max is associated with a reduction in training volume (14). However, female endurance athletes between the ages 34-78 years who maintained or increased their training volume with age, exhibited a similar change in VO2max compared to healthy sedentary adults (14). Training stimulus appears to be a key determinant in the decline in aerobic capacity with age. Whether the decline in training is a result of the aging-process, injury, time, or motivation, has yet to be determined.

Despite the health benefits achieved through a lifetime of participating in physical activity, it seems that diminished performances are an inevitable aspect of aging. The exact mechanism(s) for the reduction in performance with age has yet to be determined. The finding that women demonstrate a greater rate of decline in performances compared to men could be a result of fewer women participating in competitive events as they age (3). However, given that more women have been encouraged to participate in sporting events since the induction of Title IX, it will be interesting to see whether the gender difference is maintained in the future.

#### **3. Aging and metabolic adaptations in women athletes**

#### **3.1 Body composition**

Normal aging results in significant changes in body composition with increases in abdominal fat and losses of muscle mass. The increase in obesity alters the risks for type 2

Aging in Women Athletes 135

women. In comparison to sedentary controls, the athletes had lower % body fat, fat mass, waist circumference and trunk fat. Their results suggest that women who engage in vigorous exercise have a much smaller increase in total adiposity with advancing age (21). Two more studies (7, 22) provide some contrast as to whether age-related changes in body fat occur in women athletes. When female runners are divided into three age groups (e.g. 23-35, 37-47, 49-56 years), percent body fat by hydrostatic weighing did not differ by age and averaged 15, 14, and 18%, respectively (7). Across a continuum of age (40-77 years), body fat measured by hydrostatic weighing increased with age in women athletes, the majority of whom (90%) competed in running events (22). Thus, athletes have less total and central body fat than sedentary women (21, 23) and the vigorous training of master athletes may

Menstrual dysfunction in athletes could potentially alter body composition in young women. Young rowers with menstrual disorders have less subcutaneous and visceral fat by MRI compared to young controls (23). We are unaware of any studies in women athletes in the perimenopausal state. Further investigation is necessary to investigate whether the changes in hormonal status as women athletes age and go through menopause, influence

There are two studies examining glucose metabolism in women athletes (24, 25) with one in older women athletes. We utilized a sequential clamp procedure which allowed the assessment of both ß-cell sensitivity to glucose and peripheral tissue sensitivity to insulin in a single session in young, middle-aged and older female athletes (25). Plasma insulin responses during the hyperglycemic clamp were reduced in older athletes vs. older controls and ß-cell sensitivity was maintained across the age span. First and second phase insulin response was positively correlated with body fat and negatively with VO2max suggesting that high levels of training and low body fat in women athletes across the age span predict insulin action. Rates of utilization (Rd) of glucose during the euglycemic portion of the clamp were significantly higher in athletes than controls and were not different across the age groups of athletes (Figure 2). Although some studies show a difference in insulin clearance rate with age (26), we showed that insulin clearance rate was similar across the age of 18 to 70 years in women athletes. Thus, older sedentary women had a 70% greater firstphase and 103% greater second-phase insulin response during hyperglycemia than the athletes. Moreover, older athletes utilized on average 31% more glucose than similarly aged sedentary women, suggesting an increase in insulin sensitivity due to the effects of training. Investigators have examined the relationships between insulin sensitivity, body composition, fitness, and muscle and metabolic predictors. In younger women athletes (age 29 yrs), insulin sensitivity determined by the frequently sampled intravenous glucose tolerance test (FSIGT) was weakly correlated with VO2max and proportion of type 1 muscle fibers but not with percent body fat, fasting respiratory exchange ratio (RER) or RER during exercise, energy intake, macronutrient composition, and muscle triglyceride and glycogen content (24). In women athletes aged 18-69 years, we showed that percent body fat is associated with firstphase insulin release, whereas visceral fat and total body percent fat predict second-phase insulin release during hyperglycemic clamps (25). In addition, glucose uptake during the last hour of a hyperinsulinemic-euglycemic clamp was positively associated with FFM and VO2max, negatively associated with total fat mass, visceral fat, and subcutaneous abdominal

prevent an increase in total adiposity (7, 21).

body composition.

**3.2 Glucose metabolism** 

diabetes, cardiovascular disease, and hypertension, whereas the decline in fat-free mass (FFM) may alter energy expenditure and resting metabolic rate. It is interesting to question whether the increase in visceral fat and decrease in FFM can be prevented in women athletes. Our study of highly trained competitive women athletes aged 18 – 69 years indicates that percent body fat by DXA (dual energy x-ray absorptiometry) was lowest in 30 – 39 year old women athletes (~16% body fat) but was not different in 18 – 69 yr, 40 – 49 yr, and >50 yr old athletes (20). Total body fat was low and averaged 21 – 23% in these groups and considerably lower than normal BMI age-matched controls who were approximately 30 - 36% fat. To address whether central fat was different with age in women athletes, we measured visceral fat and subcutaneous fat by CT (computed tomography) scans. Visceral fat was significantly lower in the youngest athletes (18 – 29 yrs vs. 30 – 39 yrs) and significantly lower in the middle-aged than older athletes (Figure 1). Thus, despite the finding that athletes prevented gains in total body fat with aging, visceral fat increased with age in women athletes. However, putting the central obesity in context, it is remarkable that the oldest athletes have similar visceral fat and lower subcutaneous abdominal fat than normal BMI control women who were one-third their age. Lastly, FFM was not significantly different among the women athlete groups suggesting that muscle mass was maintained with aging and may be, in part, explained by the competitive training of these women.

Fig. 1. Visceral adipose tissue (VAT) of women athletes and controls. Values are means ± SE. \* P< 0.01

There are only a few other studies besides our own that have examined body composition in older women athletes. In agreement with our study, FFM did not differ between pre- and post-menopausal women runners (21). However, in contrast to our results, postmenopausal women athletes had higher % body fat and fat mass than the premenopausal athletes (21) but these differences were modest. More specifically, the difference was less than half that of the comparison between the healthy sedentary premenopausal and postmenopausal

diabetes, cardiovascular disease, and hypertension, whereas the decline in fat-free mass (FFM) may alter energy expenditure and resting metabolic rate. It is interesting to question whether the increase in visceral fat and decrease in FFM can be prevented in women athletes. Our study of highly trained competitive women athletes aged 18 – 69 years indicates that percent body fat by DXA (dual energy x-ray absorptiometry) was lowest in 30 – 39 year old women athletes (~16% body fat) but was not different in 18 – 69 yr, 40 – 49 yr, and >50 yr old athletes (20). Total body fat was low and averaged 21 – 23% in these groups and considerably lower than normal BMI age-matched controls who were approximately 30 - 36% fat. To address whether central fat was different with age in women athletes, we measured visceral fat and subcutaneous fat by CT (computed tomography) scans. Visceral fat was significantly lower in the youngest athletes (18 – 29 yrs vs. 30 – 39 yrs) and significantly lower in the middle-aged than older athletes (Figure 1). Thus, despite the finding that athletes prevented gains in total body fat with aging, visceral fat increased with age in women athletes. However, putting the central obesity in context, it is remarkable that the oldest athletes have similar visceral fat and lower subcutaneous abdominal fat than normal BMI control women who were one-third their age. Lastly, FFM was not significantly different among the women athlete groups suggesting that muscle mass was maintained with aging and may be, in part, explained by the competitive training of these women.

Fig. 1. Visceral adipose tissue (VAT) of women athletes and controls. Values are means ± SE.

There are only a few other studies besides our own that have examined body composition in older women athletes. In agreement with our study, FFM did not differ between pre- and post-menopausal women runners (21). However, in contrast to our results, postmenopausal women athletes had higher % body fat and fat mass than the premenopausal athletes (21) but these differences were modest. More specifically, the difference was less than half that of the comparison between the healthy sedentary premenopausal and postmenopausal

\* P< 0.01

women. In comparison to sedentary controls, the athletes had lower % body fat, fat mass, waist circumference and trunk fat. Their results suggest that women who engage in vigorous exercise have a much smaller increase in total adiposity with advancing age (21). Two more studies (7, 22) provide some contrast as to whether age-related changes in body fat occur in women athletes. When female runners are divided into three age groups (e.g. 23-35, 37-47, 49-56 years), percent body fat by hydrostatic weighing did not differ by age and averaged 15, 14, and 18%, respectively (7). Across a continuum of age (40-77 years), body fat measured by hydrostatic weighing increased with age in women athletes, the majority of whom (90%) competed in running events (22). Thus, athletes have less total and central body fat than sedentary women (21, 23) and the vigorous training of master athletes may prevent an increase in total adiposity (7, 21).

Menstrual dysfunction in athletes could potentially alter body composition in young women. Young rowers with menstrual disorders have less subcutaneous and visceral fat by MRI compared to young controls (23). We are unaware of any studies in women athletes in the perimenopausal state. Further investigation is necessary to investigate whether the changes in hormonal status as women athletes age and go through menopause, influence body composition.

#### **3.2 Glucose metabolism**

There are two studies examining glucose metabolism in women athletes (24, 25) with one in older women athletes. We utilized a sequential clamp procedure which allowed the assessment of both ß-cell sensitivity to glucose and peripheral tissue sensitivity to insulin in a single session in young, middle-aged and older female athletes (25). Plasma insulin responses during the hyperglycemic clamp were reduced in older athletes vs. older controls and ß-cell sensitivity was maintained across the age span. First and second phase insulin response was positively correlated with body fat and negatively with VO2max suggesting that high levels of training and low body fat in women athletes across the age span predict insulin action. Rates of utilization (Rd) of glucose during the euglycemic portion of the clamp were significantly higher in athletes than controls and were not different across the age groups of athletes (Figure 2). Although some studies show a difference in insulin clearance rate with age (26), we showed that insulin clearance rate was similar across the age of 18 to 70 years in women athletes. Thus, older sedentary women had a 70% greater firstphase and 103% greater second-phase insulin response during hyperglycemia than the athletes. Moreover, older athletes utilized on average 31% more glucose than similarly aged sedentary women, suggesting an increase in insulin sensitivity due to the effects of training. Investigators have examined the relationships between insulin sensitivity, body composition, fitness, and muscle and metabolic predictors. In younger women athletes (age 29 yrs), insulin sensitivity determined by the frequently sampled intravenous glucose tolerance test (FSIGT) was weakly correlated with VO2max and proportion of type 1 muscle fibers but not with percent body fat, fasting respiratory exchange ratio (RER) or RER during exercise, energy intake, macronutrient composition, and muscle triglyceride and glycogen content (24). In

women athletes aged 18-69 years, we showed that percent body fat is associated with firstphase insulin release, whereas visceral fat and total body percent fat predict second-phase insulin release during hyperglycemic clamps (25). In addition, glucose uptake during the last hour of a hyperinsulinemic-euglycemic clamp was positively associated with FFM and VO2max, negatively associated with total fat mass, visceral fat, and subcutaneous abdominal

Aging in Women Athletes 137

who exercise at greater levels have significantly greater increases in HDL-C which in turn

Other cardiovascular risk and metabolic parameters have been examined in older athletes. Women athletes (n=94) between 13 and 77 years of age showed some cardiovascular risk factors, including hypertension that were prevalent in athletes over the age of 35 (31). In a small sample of women master athletes (n=6), coronary artery calcium which is linked to endothelial dysfunction (32) was not significantly different than age-matched sedentary women (28). In another study that also contained only six females, older endurance trained athletes with pre-hypertension had lower arterial stiffness than sedentary controls and longer travel time of pressure waves (33). In addition, the greater augmented pressure in the athletes which disappeared after controlling for resting heart rate may have contributed to the lack of difference in carotid SBP (systolic blood pressure) and carotid intima-media thickness. The authors suggest that the vascular stiffening with pre-hypertension can be modified by chronic exercise training but that chronic training is unable to compensate for

Most athletes strive to achieve energy balance where energy intake = energy expenditure during exercise training. Energy expenditure (EE) consists of 3 components: basal metabolic rate, thermic effect of activity, and the thermic effect of food. These generally account for 60- 70%, 25-35%, and 5-10%, respectively, of total daily energy expenditure, but can be greatly

Typically, energy requirements decline with age; however, debate exists whether these declines are due only to decreases in physical activity patterns or if there is also an accompanying decline in basal metabolic rate. This information is difficult to obtain because environmental factors, such as work schedules and family obligations, often make maintaining vigorous intensity training difficult for older athletes. However, in older adults, matched for exercise volume, compared to younger adults, RMR is not different (34). This one study would suggest that the decline in RMR does not occur in older adults who maintain their exercise volume. Lean mass is the greatest determinant of basal metabolic rate, accounting for up to 75-80% of energy expenditure. In our study of women athletes, age and FFM were independent predictors of the decline in RMR where the oldest athletes expended approximated 965 kJ/day less than the youngest athletes (20). In middle aged women with similar BMI and fat-free mass, habitual exercisers (9 hours per week of physical activity for 10 or more years) have greater RMR than their sedentary counterparts (35). In women athletes, decreased energy intake can result in declines in body weight, muscle mass and bone density, as well as increased menstrual dysfunction, fatigue, injury and illness. Maintaining or gaining body weight is often difficult for athletes performing large volumes of physical activity. A popular trend is for athletes to consume only extra protein which may promote greater WL, (weight loss) by increasing EE through thermogenesis (36). Ideally, extra energy should come from a combination of all three macronutrients. Caloric intake recommendations are often based upon prediction equations, which multiply a predicted resting metabolic rate by a physical activity factor, and the athlete's goal to

age-associated increases in pressure from wave reflections (33).

**4. Nutrition recommendations in women athletes** 

altered by the type, intensity, and duration of exercise.

reduced their risk for CVD (30).

**4.1 Energy and macronutrients** 

maintain, gain, or lose weight.

fat (25). Thus, greater physical fitness and muscle mass and lower total and abdominal fat contribute to the enhanced tissue sensitivity observed in female athletes.

Fig. 2. Rate of utilization (Rd) of glucose during the 3-step clamp in 40- to 50 yr-old athletes and controls. Values are means ± SE. \* P< 0.005

#### **3.3 Cardiovascular risk factors**

Lipid profiles are generally better in endurance trained athletes than sedentary individuals (27, 28). What occurs with aging in athletes with respect to lipid levels? We showed that total cholesterol, LDL-C (low density lipoprotein cholesterol) and triglyceride levels increased with age in women athletes (27). These relationships persisted even after adjusting for age-related declines in VO2max and increases in visceral fat. HDL-C (high density lipoprotein cholesterol) was higher in athletes than controls and LDL-C was lower in athletes than sedentary women. Regarding the lipoprotein subfractions, we also demonstrated that LDL3-C (larger LDL-C subfraction) was lower in athletes than untrained women and there was a tendency for a higher HDL5-C (the largest HDL-C subfraction) which would suggest a protective effect. Middle-aged women (n =147) who were grouped into active ex-athletes, sedentary ex-athletes, recreational exercisers, and non-exercisers did not differ in TG (triglycerides) and HDL-C (29). In another study, HDL-C was higher in master athletes than older sedentary women but LDL-C did not differ (28). These lipid differences suggest that women athletes would have a lower risk of coronary heart disease.

Intensity or the level of exercise may influence lipoprotein lipid levels. Williams (30) utilized a national survey of ~1800 female recreational runners to examine the dose-response relationship between exercise levels and HDL-C and CVD risk factors. The women were divided into groups based on weekly running mileage and were on average 40 years of age. Lipid levels were obtained from medical records. The results of the survey indicated that women who ran more than 64 km/week had significantly higher HDL-C levels than women who ran less than 48 km/week. Further analysis revealed that plasma HDL-C was 0.133 mg/dl higher for every additional kilometer run per week. The results suggest that women

fat (25). Thus, greater physical fitness and muscle mass and lower total and abdominal fat

Fig. 2. Rate of utilization (Rd) of glucose during the 3-step clamp in 40- to 50 yr-old athletes

Lipid profiles are generally better in endurance trained athletes than sedentary individuals (27, 28). What occurs with aging in athletes with respect to lipid levels? We showed that total cholesterol, LDL-C (low density lipoprotein cholesterol) and triglyceride levels increased with age in women athletes (27). These relationships persisted even after adjusting for age-related declines in VO2max and increases in visceral fat. HDL-C (high density lipoprotein cholesterol) was higher in athletes than controls and LDL-C was lower in athletes than sedentary women. Regarding the lipoprotein subfractions, we also demonstrated that LDL3-C (larger LDL-C subfraction) was lower in athletes than untrained women and there was a tendency for a higher HDL5-C (the largest HDL-C subfraction) which would suggest a protective effect. Middle-aged women (n =147) who were grouped into active ex-athletes, sedentary ex-athletes, recreational exercisers, and non-exercisers did not differ in TG (triglycerides) and HDL-C (29). In another study, HDL-C was higher in master athletes than older sedentary women but LDL-C did not differ (28). These lipid differences suggest that women athletes would

Intensity or the level of exercise may influence lipoprotein lipid levels. Williams (30) utilized a national survey of ~1800 female recreational runners to examine the dose-response relationship between exercise levels and HDL-C and CVD risk factors. The women were divided into groups based on weekly running mileage and were on average 40 years of age. Lipid levels were obtained from medical records. The results of the survey indicated that women who ran more than 64 km/week had significantly higher HDL-C levels than women who ran less than 48 km/week. Further analysis revealed that plasma HDL-C was 0.133 mg/dl higher for every additional kilometer run per week. The results suggest that women

and controls. Values are means ± SE. \* P< 0.005

have a lower risk of coronary heart disease.

**3.3 Cardiovascular risk factors** 

contribute to the enhanced tissue sensitivity observed in female athletes.

who exercise at greater levels have significantly greater increases in HDL-C which in turn reduced their risk for CVD (30).

Other cardiovascular risk and metabolic parameters have been examined in older athletes. Women athletes (n=94) between 13 and 77 years of age showed some cardiovascular risk factors, including hypertension that were prevalent in athletes over the age of 35 (31). In a small sample of women master athletes (n=6), coronary artery calcium which is linked to endothelial dysfunction (32) was not significantly different than age-matched sedentary women (28). In another study that also contained only six females, older endurance trained athletes with pre-hypertension had lower arterial stiffness than sedentary controls and longer travel time of pressure waves (33). In addition, the greater augmented pressure in the athletes which disappeared after controlling for resting heart rate may have contributed to the lack of difference in carotid SBP (systolic blood pressure) and carotid intima-media thickness. The authors suggest that the vascular stiffening with pre-hypertension can be modified by chronic exercise training but that chronic training is unable to compensate for age-associated increases in pressure from wave reflections (33).

#### **4. Nutrition recommendations in women athletes**

#### **4.1 Energy and macronutrients**

Most athletes strive to achieve energy balance where energy intake = energy expenditure during exercise training. Energy expenditure (EE) consists of 3 components: basal metabolic rate, thermic effect of activity, and the thermic effect of food. These generally account for 60- 70%, 25-35%, and 5-10%, respectively, of total daily energy expenditure, but can be greatly altered by the type, intensity, and duration of exercise.

Typically, energy requirements decline with age; however, debate exists whether these declines are due only to decreases in physical activity patterns or if there is also an accompanying decline in basal metabolic rate. This information is difficult to obtain because environmental factors, such as work schedules and family obligations, often make maintaining vigorous intensity training difficult for older athletes. However, in older adults, matched for exercise volume, compared to younger adults, RMR is not different (34). This one study would suggest that the decline in RMR does not occur in older adults who maintain their exercise volume. Lean mass is the greatest determinant of basal metabolic rate, accounting for up to 75-80% of energy expenditure. In our study of women athletes, age and FFM were independent predictors of the decline in RMR where the oldest athletes expended approximated 965 kJ/day less than the youngest athletes (20). In middle aged women with similar BMI and fat-free mass, habitual exercisers (9 hours per week of physical activity for 10 or more years) have greater RMR than their sedentary counterparts (35).

In women athletes, decreased energy intake can result in declines in body weight, muscle mass and bone density, as well as increased menstrual dysfunction, fatigue, injury and illness. Maintaining or gaining body weight is often difficult for athletes performing large volumes of physical activity. A popular trend is for athletes to consume only extra protein which may promote greater WL, (weight loss) by increasing EE through thermogenesis (36). Ideally, extra energy should come from a combination of all three macronutrients. Caloric intake recommendations are often based upon prediction equations, which multiply a predicted resting metabolic rate by a physical activity factor, and the athlete's goal to maintain, gain, or lose weight.

Aging in Women Athletes 139

including B12, folate, riboflavin, pyridoxine, and magnesium (41). Additionally, the Dietary Reference Intakes (DRIs) acknowledge a decreased need for iron in older women (http://www.iom.edu/Activities/Nutrition/ SummaryDRIs.pdf). Because specific recommendation regarding micronutrient intake for older women have not been established, women athletes should consume at least the recommended dietary allowance (RDA) for all micronutrients to avoid nutrient deficiencies. In female master athletes partaking in nutritional supplementation, the supplemented group had significantly greater intakes of calcium, magnesium, vitamin C, and vitamin E than non supplemented women, indicating that female master athletes may rely on supplements to assist achieving micronutrient intake goals (40). If women consume a variety of foods in their diets and meet caloric requirements, vitamin and mineral supplementation typically is not necessary. Women greater than 60 years of age may want to consider a synthetic form of vitamin D and B12 because of altered absorption and nutrient action occurring with age. If a nutrientbalanced diet is not consumed, athletes should consider taking a multivitamin and mineral supplement. Too little data exists to recommend micronutrient supplementation above the

Free radicals produce oxidative damage during aging, as well as following strenuous exercise. During an intense endurance competition, master athletes experience elevations in reactive oxygen metabolites and biological antioxidant potentials, which continue at least 48 hours after completion of competition (43). Antioxidant supplementation may improve athletic performance, recovery time, and overall health by reducing oxidative damage. In endurance trained master athletes supplemented with antioxidants 21 days prior to intense cycling, antioxidant supplementation resulted in improved cycling efficiency (44). Unfortunately, most over the counter antioxidant supplements are not regulated by the FDA, and are not subject to thorough safety and effectiveness tests. One should heed caution not to consume vitamin intakes beyond the recommended upper limit (i.e. 2,000 mg

The injury rate for master athletes is higher than younger athletes, making a balanced dietary intake especially important to support tissue healing (45). Ensuring adequate protein intake is important during all phases of tissue repair. Insufficient protein intake can inhibit wound healing and increase inflammation (46). It appears that several amino acids, including leucine, arginine, and glutamine, play a role in tissue repair mainly through amelioration of muscle atrophy (47) and/or stimulation of collagen formation (48). Current recommendations do not include supplementing with a specific amino acid as limited research exists research exists. While it is possible to consume all essential amino acids from plant based sources, it is easier to consume the essential amino acids from animal based protein sources. Omega-3 fatty acids modulate inflammation, resulting in reduced wound healing time (49). Unless the athlete encounters excessive inflammation following an injury, supplementation is not necessary. A diet high in omega-3 rich foods, such as salmon, walnuts, and flaxseeds would be effective. Several micronutrients also act to enhance tissue healing. For example, vitamin A is required for epithelial and bone formation, cellular differentiation, and immune function, vitamin C for collagen formation, proper immune function, and as a tissue antioxidant. Vitamin E is the major lipid-soluble antioxidant in the skin (50). Although not enough information exists to warrant supplementation to promote wound healing above RDA recommendations, nutritional intake should be assessed to

RDA to improve athletic performance.

for vitamin C and 1,000 mg for vitamin E).

ensure recommended dietary intake of all micronutrients.

While numerous studies exist examining the macronutrient requirement of athletes, a variety of variables (i.e. sport type, training status) affect nutritional requirements, resulting in broad recommendations. Current recommendations for a trained women include 45-65% (5-7 g/kg/d for general training, 7-10 g/kg/d for endurance athletes, and 11+g/kg/d for ultraendurance athletes) of energy from carbohydrates, 20-35% (~1 g/d) from fat, for general training, and 10-35% (1.2-1.4 g/kg/d for endurance trained and 1.6-1.7 g/kg/d for strength trained athletes) from protein (37). For all athletes, carbohydrates are recommended to make up the majority of energy intake, with an emphasis on whole grains, fruits, and vegetables. A diet high in carbohydrates typically results in adequate total protein intake, but may be lacking some of the essential amino acids, as well as intake of essential fatty acids and fat soluble vitamins and minerals.

Meal timing and nutrient composition recommendations surrounding athletic competition recommendations are based upon substrate utilization. Exercise intensity and duration drive these recommendations. For lower intensity activities (performed at ~25% of VO2max), circulating fat provides the majority of energy during exercise. At moderate intensity (performed at ~65% VO2max), fat oxidation contributes less and energy is mainly supplied from intramuscular stores of fat and glycogen. During high intensity exercise (performed at ~85% VO2max), glycogen is the major energy source. At lower intensities where fat oxidation is providing the dominate source of energy, exercise can be sustained for up to a few hours; however, as intensity increases and requirements switch to glycogen, the ability to perform physical activities decline without carbohydrate repletion. Few studies have examined how macronutrient needs of women are altered by age; therefore, current macronutrient recommendations are similar between older and younger women athletes. During endurance based activities, depletion of plasma and muscle glycogen results in reduced exercise performance and fatigue. Prior to endurance exercise, it is recommended that 1 g/kg of carbohydrates be consumed for each hour prior to exercise (i.e. 1 g/kg if 1 hour prior and 4 g/kg if 4 hours prior) (37). Also, the meal should be low in fat and fiber and moderate in protein to facilitate gastric emptying and minimize gastrointestinal distress. During exercise, 30-60 g should be consumed every hour. If longer than 90 minutes, 6-20 g of protein should also be consumed during exercise and 1.5 g/kg carbohydrates with a small amount of protein immediately following exercise, with an additional 1.5 g/kg of carbohydrates consumed 2 hours later (37). Ensuring adequate fat intake during aerobic training is important since fat oxidation results in sparing of glycogen. Very low fat diets reduce intramuscular fat stores, impeding endurance. For strength based activities, protein intake has been suggested to maximize muscle synthesis by enhance amino acid uptake into skeletal muscle, providing substrate for hypertrophy if consumed immediate after the strength training bout. However, protein intake greater than 1.7-1.8 g/kg/d results in oxidation of the excess amino acids and is not incorporation into greater muscle mass, even when coupled with vigorous resistance training (38).

#### **4.2 Micronutrients**

Exercise and micronutrient activity work synergistically to ensure maximal performance of the body; therefore, if micronutrient deficiencies exist, there is a subsequent risk for declines in metabolic and physical function. Studies of dietary intake in women endurance athletes shown low intakes of calcium, vitamin D, vitamin E and zinc (39, 40). However, numerous other nutrients should be monitored for insufficient intake in women master athletes,

While numerous studies exist examining the macronutrient requirement of athletes, a variety of variables (i.e. sport type, training status) affect nutritional requirements, resulting in broad recommendations. Current recommendations for a trained women include 45-65% (5-7 g/kg/d for general training, 7-10 g/kg/d for endurance athletes, and 11+g/kg/d for ultraendurance athletes) of energy from carbohydrates, 20-35% (~1 g/d) from fat, for general training, and 10-35% (1.2-1.4 g/kg/d for endurance trained and 1.6-1.7 g/kg/d for strength trained athletes) from protein (37). For all athletes, carbohydrates are recommended to make up the majority of energy intake, with an emphasis on whole grains, fruits, and vegetables. A diet high in carbohydrates typically results in adequate total protein intake, but may be lacking some of the essential amino acids, as well as intake of

Meal timing and nutrient composition recommendations surrounding athletic competition recommendations are based upon substrate utilization. Exercise intensity and duration drive these recommendations. For lower intensity activities (performed at ~25% of VO2max), circulating fat provides the majority of energy during exercise. At moderate intensity (performed at ~65% VO2max), fat oxidation contributes less and energy is mainly supplied from intramuscular stores of fat and glycogen. During high intensity exercise (performed at ~85% VO2max), glycogen is the major energy source. At lower intensities where fat oxidation is providing the dominate source of energy, exercise can be sustained for up to a few hours; however, as intensity increases and requirements switch to glycogen, the ability to perform physical activities decline without carbohydrate repletion. Few studies have examined how macronutrient needs of women are altered by age; therefore, current macronutrient recommendations are similar between older and younger women athletes. During endurance based activities, depletion of plasma and muscle glycogen results in reduced exercise performance and fatigue. Prior to endurance exercise, it is recommended that 1 g/kg of carbohydrates be consumed for each hour prior to exercise (i.e. 1 g/kg if 1 hour prior and 4 g/kg if 4 hours prior) (37). Also, the meal should be low in fat and fiber and moderate in protein to facilitate gastric emptying and minimize gastrointestinal distress. During exercise, 30-60 g should be consumed every hour. If longer than 90 minutes, 6-20 g of protein should also be consumed during exercise and 1.5 g/kg carbohydrates with a small amount of protein immediately following exercise, with an additional 1.5 g/kg of carbohydrates consumed 2 hours later (37). Ensuring adequate fat intake during aerobic training is important since fat oxidation results in sparing of glycogen. Very low fat diets reduce intramuscular fat stores, impeding endurance. For strength based activities, protein intake has been suggested to maximize muscle synthesis by enhance amino acid uptake into skeletal muscle, providing substrate for hypertrophy if consumed immediate after the strength training bout. However, protein intake greater than 1.7-1.8 g/kg/d results in oxidation of the excess amino acids and is not incorporation into greater muscle mass, even

Exercise and micronutrient activity work synergistically to ensure maximal performance of the body; therefore, if micronutrient deficiencies exist, there is a subsequent risk for declines in metabolic and physical function. Studies of dietary intake in women endurance athletes shown low intakes of calcium, vitamin D, vitamin E and zinc (39, 40). However, numerous other nutrients should be monitored for insufficient intake in women master athletes,

essential fatty acids and fat soluble vitamins and minerals.

when coupled with vigorous resistance training (38).

**4.2 Micronutrients** 

including B12, folate, riboflavin, pyridoxine, and magnesium (41). Additionally, the Dietary Reference Intakes (DRIs) acknowledge a decreased need for iron in older women (http://www.iom.edu/Activities/Nutrition/ SummaryDRIs.pdf). Because specific recommendation regarding micronutrient intake for older women have not been established, women athletes should consume at least the recommended dietary allowance (RDA) for all micronutrients to avoid nutrient deficiencies. In female master athletes partaking in nutritional supplementation, the supplemented group had significantly greater intakes of calcium, magnesium, vitamin C, and vitamin E than non supplemented women, indicating that female master athletes may rely on supplements to assist achieving micronutrient intake goals (40). If women consume a variety of foods in their diets and meet caloric requirements, vitamin and mineral supplementation typically is not necessary. Women greater than 60 years of age may want to consider a synthetic form of vitamin D and B12 because of altered absorption and nutrient action occurring with age. If a nutrientbalanced diet is not consumed, athletes should consider taking a multivitamin and mineral supplement. Too little data exists to recommend micronutrient supplementation above the RDA to improve athletic performance.

Free radicals produce oxidative damage during aging, as well as following strenuous exercise. During an intense endurance competition, master athletes experience elevations in reactive oxygen metabolites and biological antioxidant potentials, which continue at least 48 hours after completion of competition (43). Antioxidant supplementation may improve athletic performance, recovery time, and overall health by reducing oxidative damage. In endurance trained master athletes supplemented with antioxidants 21 days prior to intense cycling, antioxidant supplementation resulted in improved cycling efficiency (44). Unfortunately, most over the counter antioxidant supplements are not regulated by the FDA, and are not subject to thorough safety and effectiveness tests. One should heed caution not to consume vitamin intakes beyond the recommended upper limit (i.e. 2,000 mg for vitamin C and 1,000 mg for vitamin E).

The injury rate for master athletes is higher than younger athletes, making a balanced dietary intake especially important to support tissue healing (45). Ensuring adequate protein intake is important during all phases of tissue repair. Insufficient protein intake can inhibit wound healing and increase inflammation (46). It appears that several amino acids, including leucine, arginine, and glutamine, play a role in tissue repair mainly through amelioration of muscle atrophy (47) and/or stimulation of collagen formation (48). Current recommendations do not include supplementing with a specific amino acid as limited research exists research exists. While it is possible to consume all essential amino acids from plant based sources, it is easier to consume the essential amino acids from animal based protein sources. Omega-3 fatty acids modulate inflammation, resulting in reduced wound healing time (49). Unless the athlete encounters excessive inflammation following an injury, supplementation is not necessary. A diet high in omega-3 rich foods, such as salmon, walnuts, and flaxseeds would be effective. Several micronutrients also act to enhance tissue healing. For example, vitamin A is required for epithelial and bone formation, cellular differentiation, and immune function, vitamin C for collagen formation, proper immune function, and as a tissue antioxidant. Vitamin E is the major lipid-soluble antioxidant in the skin (50). Although not enough information exists to warrant supplementation to promote wound healing above RDA recommendations, nutritional intake should be assessed to ensure recommended dietary intake of all micronutrients.

Aging in Women Athletes 141

mode of exercise, stage of training, and recovery time, as well as the intensity, duration, and frequency of each exercise session. Athletes may want to consider taking a multivitamin and mineral supplement, pay attention to fluid requirements and consume a nutrient-

This research was supported by VA Research Career Scientist Award, Veterans Affairs Merit Award, the Baltimore Veterans Affairs Medical Center Geriatric Research, Education and Clinical Center (GRECC), T32-AG-00219, NIH grant RO1 AG030075, NORC of Maryland (DK072488), and the University of Maryland Claude D. Pepper Center (P30-AG-

[1] Kaestner R, Xin, X 2010 Title IX, girls' sports participation, and adult female physical

[2] Rosenbloom C, Bahns M 2006 What can we learn about diet and physical activity from

[3] Ransdall L VJ, and Huberty J 2009 Masters athletes: an analysis of running, swimming, and cycling performance by age and gender. J Exerc Sci Fit 7:S61-S73 [4] Hawkins S, Wiswell R 2003 Rate and mechanism of maximal oxygen consumption decline with aging: implications for exercise training. Sports Med 33:877-888 [5] Ogawa T, Spina RJ, Martin WH, 3rd, Kohrt WM, Schechtman KB, Holloszy JO, Ehsani

[6] Pollock ML, Foster C, Knapp D, Rod JL, Schmidt DH 1987 Effect of age and training on

[7] Evans SL, Davy KP, Stevenson ET, Seals DR 1995 Physiological determinants of 10-km

[8] Tanaka H, Desouza CA, Jones PP, Stevenson ET, Davy KP, Seals DR 1997 Greater rate of

[9] Tanaka H, Seals DR 2008 Endurance exercise performance in Masters athletes: age-

[11] Joyner MJ, Coyle EF 2008 Endurance exercise performance: the physiology of

[12] Brooks G, Fahey, T., and Baldwin, K 2005 Exercise Physiology: Human Bioienergetics

AA 1992 Effects of aging, sex, and physical training on cardiovascular responses to

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performance in highly trained female runners of different ages. J Appl Physiol

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associated changes and underlying physiological mechanisms. J Physiol 586:55-63 [10] Marcell TJ, Hawkins SA, Tarpenning KM, Hyslop DM, Wiswell RA 2003 Longitudinal

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balanced diet.

12583).

**7. References** 

731

78:1931-1941

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champions. J Physiol 586:35-44

**6. Acknowledgements** 

## **4.3 Fluid**

Dehydration can have serious health consequences to all athletes, but older athletes are more susceptible than younger ones. During periods of heat stress, older individuals typically respond with attenuated sweat gland output, decreased skin blood flow, reduced cardiac outputs, and smaller distribution of blood flow from the splanchnic and renal circulation (51). Kenney et al. (52) compared the effects of fluid restriction while exercising under different environmental stimuli in older versus younger women. They found that the percent decrease in sweat rate and plasma volume is greater in older versus younger women, indicating that older women have a greater propensity to develop dehydration associated with lack of fluid replacement. Additionally, older individuals are more likely to have altered thirst and kidney function placing them at increased risk for consequences of dehydration. However, if older women athletes are well conditioned and acclimatized to exercising in warm environments, a tolerance to heat stress can be developed. Athletes should drink 16 oz of fluid 30-40 minutes prior to exercise to ensure enough time to optimize hydration status and excrete excess fluid (37). During exercise, athletes should attempt to match their sweat rate with fluids following the guideline to consume 6-12 fl oz every 15-20 minutes (37). Sports drinks containing 6% to 8% carbohydrates and electrolytes are recommended for events lasting greater than 1 hour (41). One needs to compare post exercise weight to pre exercise weight and replace 16-24 fl oz of a fluid for every 0.5 kg of weight lost during exercise. This should supply ample fluid for rehydration following exercise (41). Additionally, consuming foods with high water content will aid rehydration following exercise.

Prior to and during exercise, nutrition intake should be aimed at maintaining hydration, while providing carbohydrates to maintain blood glucose concentrations during exercise. After exercise, meals should provide adequate fluids, electrolytes, energy, protein, and carbohydrates to replace nutrients lost during exercise and promote recovery. More research is needed before nutritional supplementation to improve performance, promote tissue healing, and optimize aging are recommended to women master athletes. However, encouraging a varied diet with balanced energy intake will help to ensure adequate macroand micronutrient intakes.

## **5. Summary**

Competitive athletic women may experience successful aging. Older trained women athletes can have a 30-50% higher VO2max than sedentary women but may have a greater age-related decline per decade than the normal population. Factors such as a decrease in cardiac output due to a decrease in maximal heart rate and stroke volume, altered pulmonary function, changes in arteriole compliance, and a decrease and change in skeletal muscle fibers may play a role in the age associated decrease in aerobic capacity in the normal population as well as in athletes. Women athletes also confer a favorable body composition coincident with enhanced glucose and lipid metabolism. Highly trained women athletes maintain a low percentage of total and central body fat compared to healthy sedentary women. The reduced body fat and maintenance of muscle mass may contribute to enhanced glucose uptake and insulin action observed in highly trained women athletes. Proper nutrition is essential for maximizing athletic performance and general health in older women athletes. Specific needs are highly individualized and depend upon the athlete's

Dehydration can have serious health consequences to all athletes, but older athletes are more susceptible than younger ones. During periods of heat stress, older individuals typically respond with attenuated sweat gland output, decreased skin blood flow, reduced cardiac outputs, and smaller distribution of blood flow from the splanchnic and renal circulation (51). Kenney et al. (52) compared the effects of fluid restriction while exercising under different environmental stimuli in older versus younger women. They found that the percent decrease in sweat rate and plasma volume is greater in older versus younger women, indicating that older women have a greater propensity to develop dehydration associated with lack of fluid replacement. Additionally, older individuals are more likely to have altered thirst and kidney function placing them at increased risk for consequences of dehydration. However, if older women athletes are well conditioned and acclimatized to exercising in warm environments, a tolerance to heat stress can be developed. Athletes should drink 16 oz of fluid 30-40 minutes prior to exercise to ensure enough time to optimize hydration status and excrete excess fluid (37). During exercise, athletes should attempt to match their sweat rate with fluids following the guideline to consume 6-12 fl oz every 15-20 minutes (37). Sports drinks containing 6% to 8% carbohydrates and electrolytes are recommended for events lasting greater than 1 hour (41). One needs to compare post exercise weight to pre exercise weight and replace 16-24 fl oz of a fluid for every 0.5 kg of weight lost during exercise. This should supply ample fluid for rehydration following exercise (41). Additionally, consuming foods with high water content will aid rehydration

Prior to and during exercise, nutrition intake should be aimed at maintaining hydration, while providing carbohydrates to maintain blood glucose concentrations during exercise. After exercise, meals should provide adequate fluids, electrolytes, energy, protein, and carbohydrates to replace nutrients lost during exercise and promote recovery. More research is needed before nutritional supplementation to improve performance, promote tissue healing, and optimize aging are recommended to women master athletes. However, encouraging a varied diet with balanced energy intake will help to ensure adequate macro-

Competitive athletic women may experience successful aging. Older trained women athletes can have a 30-50% higher VO2max than sedentary women but may have a greater age-related decline per decade than the normal population. Factors such as a decrease in cardiac output due to a decrease in maximal heart rate and stroke volume, altered pulmonary function, changes in arteriole compliance, and a decrease and change in skeletal muscle fibers may play a role in the age associated decrease in aerobic capacity in the normal population as well as in athletes. Women athletes also confer a favorable body composition coincident with enhanced glucose and lipid metabolism. Highly trained women athletes maintain a low percentage of total and central body fat compared to healthy sedentary women. The reduced body fat and maintenance of muscle mass may contribute to enhanced glucose uptake and insulin action observed in highly trained women athletes. Proper nutrition is essential for maximizing athletic performance and general health in older women athletes. Specific needs are highly individualized and depend upon the athlete's

**4.3 Fluid** 

following exercise.

and micronutrient intakes.

**5. Summary** 

mode of exercise, stage of training, and recovery time, as well as the intensity, duration, and frequency of each exercise session. Athletes may want to consider taking a multivitamin and mineral supplement, pay attention to fluid requirements and consume a nutrientbalanced diet.

### **6. Acknowledgements**

This research was supported by VA Research Career Scientist Award, Veterans Affairs Merit Award, the Baltimore Veterans Affairs Medical Center Geriatric Research, Education and Clinical Center (GRECC), T32-AG-00219, NIH grant RO1 AG030075, NORC of Maryland (DK072488), and the University of Maryland Claude D. Pepper Center (P30-AG-12583).

## **7. References**


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**8** 

Baruch Wolach

*Israel* 

**Exercise and the Immune** 

*The Sackler School of Medicine, Tel Aviv University,* 

**System – Focusing on the Effect** 

**of Exercise on Neutrophil Functions** 

A relationship between intense exercise, leukocytosis and susceptibility to illness was already reported at the beginning of the past century (1-3). Today there is a consensus among researchers and clinicians that exercise have effects on various aspects of the immune function (4). The complexity of the underlying mechanisms and the clinical implications and directions need continuous evaluation. Investigators face challenges associated with immune measures and the interpretation of their changes. They should bear in mind that there is inter-individual variability of the exercise capacity, recovery, stress tolerance and immunocompetence. Short exposure to exercise could promote beneficial and apropriate physiological response of the immune system, while heavy exertion could be detrimental to health. In recent years, the development of advanced laboratory techniques contributed to enrich our knowledge and deepened the understanding of the mechanisms underlying the immune system in sports medicine. The development of fluorescent antibodies techniques allow identifying cell sub-types and receptors. Molecular technology and new cytokine methods of identification have permitted the detection of humoral factors present in the body at low concentrations, for short periods of time and to study the effect of

Studies on recreational and elite athletes should be systematic and well controlled in order

The immune response can be divided into innate, natural-non-adaptive immunity and acquired-adaptive immunity. Innate immunity is the first response to physical or chemical foreign agents and it occurs naturally and immediately, providing the first line of defense in early stages of the infection. The innate immunity is comprised of phagocyte cells, natural killer cells, soluble factors as the complement and acute phase proteins, as well as the mucosal immune responses. The acquired immunity occurs after an adaptive, specific response to a pathogen and involves the antigen-antibody response. It includes B and T

to formulate evidence-based guidelines to preserve a balanced immune function.

**1. Introduction** 

exercise on gene expression profiles (5,6).

lymphocytes and the immunoglobulines (7).

**2. The immune system** 


## **Exercise and the Immune System – Focusing on the Effect of Exercise on Neutrophil Functions**

Baruch Wolach *The Sackler School of Medicine, Tel Aviv University, Israel* 

## **1. Introduction**

144 An International Perspective on Topics in Sports Medicine and Sports Injury

[44] Louis J, Hausswirth C, Bieuzen F, Brisswalter J 2010 Vitamin and mineral

[45] McKean KA, Manson NA, Stanish WD 2006 Musculoskeletal injury in the masters

[46] Demling RH 2009 Nutrition, anabolism, and the wound healing process: an overview.

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[50] MacKay D, Miller AL 2003 Nutritional support for wound healing. Altern Med Rev

[51] Kenney WL, Munce TA 2003 Invited review: aging and human temperature regulation.

[52] Kenney WL, Anderson RK 1988 Responses of older and younger women to exercise in dry and humid heat without fluid replacement. Med Sci Sports Exerc 20:155-160

athletes. Appl Physiol Nutr Metab 35:251-260

wound healing. Wound Repair Regen 16:337-345

runners. Clin J Sport Med 16:149-154

Eplasty 9:e9

Pract 25:61-68

8:359-377

J Appl Physiol 95:2598-2603

2:43-53

supplementation effect on muscular activity and cycling efficiency in master

A relationship between intense exercise, leukocytosis and susceptibility to illness was already reported at the beginning of the past century (1-3). Today there is a consensus among researchers and clinicians that exercise have effects on various aspects of the immune function (4). The complexity of the underlying mechanisms and the clinical implications and directions need continuous evaluation. Investigators face challenges associated with immune measures and the interpretation of their changes. They should bear in mind that there is inter-individual variability of the exercise capacity, recovery, stress tolerance and immunocompetence. Short exposure to exercise could promote beneficial and apropriate physiological response of the immune system, while heavy exertion could be detrimental to health. In recent years, the development of advanced laboratory techniques contributed to enrich our knowledge and deepened the understanding of the mechanisms underlying the immune system in sports medicine. The development of fluorescent antibodies techniques allow identifying cell sub-types and receptors. Molecular technology and new cytokine methods of identification have permitted the detection of humoral factors present in the body at low concentrations, for short periods of time and to study the effect of exercise on gene expression profiles (5,6).

Studies on recreational and elite athletes should be systematic and well controlled in order to formulate evidence-based guidelines to preserve a balanced immune function.

#### **2. The immune system**

The immune response can be divided into innate, natural-non-adaptive immunity and acquired-adaptive immunity. Innate immunity is the first response to physical or chemical foreign agents and it occurs naturally and immediately, providing the first line of defense in early stages of the infection. The innate immunity is comprised of phagocyte cells, natural killer cells, soluble factors as the complement and acute phase proteins, as well as the mucosal immune responses. The acquired immunity occurs after an adaptive, specific response to a pathogen and involves the antigen-antibody response. It includes B and T lymphocytes and the immunoglobulines (7).

Exercise and the Immune System – Focusing on the Effect of Exercise on Neutrophil Functions 147

competitive events infections are less common (7). Frequent illness has been associated with the overtraining syndrome in athletes (17-19). During heavy exertion could be an immune suppression that creates an 'open window' of decreased host protection. Bacteria or viruses may gain a foothold, increasing the risk of subclinical and clinical infections (17, 20). In team sports or in other sports where participants are in close physical contact before, during or after the sporting event, both the infected individual and the fellow sportsmen may become infected. Some infections may appear in clusters in the sports setting, such as gastroenteritis, herpes simplex, meningitis, viral hepatitis, skin infections, tonsillo-pharyngitis (21,22). A large number of viruses and bacteria can give rise of myocarditis that can be aggravated by

There is consistent data suggesting that male endurance athletes may develop after 1 to 2 wk period increased rates of Upper Respiratory Tract Infection (URTI), following marathon or ultramarathon race events (16,23,24). URTI appears to be the most common minor viral infection in athletes. The current consensus is that the cause of URTI in athletes is uncertain (4). There is today disagreement whether 'sore throats', frequently reported by athletes, are caused by infections or are a reflexion of other inflammatory stimuli mimicking URTI (25,26). Cytokines play an important role in modulating the immune function, inducing changes that increase the risk of infection or the appearance of inflammatory symptoms (27). The physician diagnosis of URTI is based on clinical symptoms and signs, rather than by determining the infectious etiology. In few studies the pathogen was identified as the usual respiratory pathogens associated with URTI in the general population **(**4**).** The salivary IgA concentrations and secretion rates have been shown to be significantly decreased in athletes with prolong high intensity exercise (28,29). We could hypothesize that their immunity is reduced with an increase tendency to develop URTI. Other markers of infection as antimicrobial proteins in saliva (-amylase, lactoferrin, and lysozyme) have been identified (26,30). Further, viral infections as URTI may lead to a debilitating state and an unexplained deterioration in athletic performance. Viral infections could run a protracted course of easy fatigability, myalgia and lethargy for weeks or even months (31). Additionally, it seems that athletes are more susceptible to

Infections of non-viral origin, as bacterial pneumonia, mycoplasma and Chlamydia myocarditis, sinusitis, etc., although uncommonly reported in athletes, could also develop following intense exercise (2,15,33). Athletes could aggravate the course of the disease

Neutrophils comprise the majority of circulating leukocytes and represent the early body's response in the battle against bacterial and fungal infections. Multi-factorial elements could be involved in the neutrophil behavior and in the immune responses to exercise, as neuro-endocrine mediators (36), corticosteroid release, interleukin production (37) and oxy-reduction processes associated with free radical production (38). Most studies show that of all subsets of circulating leukocytes, mainly neutrophils and lymphocytes, increase dramatically during exercise (39,40). The magnitude is related to the exercise intensity and duration, being more persistent with intense, prolonged exercise (40, 41). Neutrophil count may exhibit a biphasic response, characterized by an initial small increase, followed by a decline to resting values 30-60 minutes after the cessation of exercise. A delayed larger increase in neutrophil numbers could be observed

physical exertion (15).

develop Infectious Mononucleosis (32).

during incubation periods of infections (34,35).

In the innate immunity phagocytes can recognize and act immediately against the foreign agent without prior exposure, while the adaptive immunity is characterized by a specific response to the infectious agent, becoming fully activated after a lag period. The innate mucosal defenses are the first line of defense against pathogens present at the mucosal surfaces. The 'Common Mucosal Immune System' is a network of organized structures that protect the oral cavity, the respiratory, thegastrointestinal and the urogenital systems. The major effector function of this system is the secretory IgA (8,9)**.** 

The adaptive immunity involves the action of specialized immune cells, as are the lymphocytes, which generate antibodies against specific microorganisms, killing them directly or activating other cells through the secretion of cytokines. This adaptive response generates memory which is the basis of the preventive immunization. Both systems of immunity, the innate and the acquired, work synergistically and are essential for an optimal function of the immune response. Phagocytes play an important role in the initiation of the adaptive response by presenting antigens and secreting cytokines that stimulate cells of the adaptive system.

Neutrophils (55-65% of blood leukocytes) and monocytes (5-10%) play an important role in innate immunity and provide a major defense system against microorganisms. They act as the first line of defense against infectious agents and are involve in the muscle tissue inflammatory response to exercise-induced injury (10). The multi-step phagocytic process is activated in response to invasion of foreign microorganisms and includes the rolling and adherence of neutrophils to the blood vessel endothelium, the diapedesis and chemotaxis towards the invading organism, the ingestion, degranulation and the oxidative burst, ending with killing of the pathogen (11). Experienced and well equipped laboratories in this specific field, have established the normal range values, based on a large number of subjects examined, in health and disease. Today it is possible to assess the different steps of the phagocytic process and to detect dysfunctions at each level (11-13).

#### **3. Exercise and the immune response – Clinical implications**

The potential influence of exercise on the immune system could be beneficial, detrimental or neutral. The immune response depends on the type of the particular exercise, its intensity, volume and duration. While mild or moderate exercise was shown to be beneficial, acute intense or prolonged exercise elicits depression of several aspects of the immune response. The fitness level of the performers could also exert influence in their immunological response. It seems that there is a combination of physiological and psychological factors, known to exert their influences on the immune system. Appropriate interpretation of the immune response is vital for determining the clinical directions and the integral training program for each athlete.

The physical activity could affect one or all three arms of the immune system, the humoral, the phagocytic and the cellular arm. Eventually, dysfunction of one or more arms of the immune system could lead to the outburst of an infection. In general, the etiology of infections is usually of bacterial origin when the humoral or phagocytic arm is affected, while viral–parasitic infections are usually originated when the cellular arm is involved. Excessive, prolonged training and major competitions have been long considered factors affecting the susceptibility to infections in athletes (14-16), however, in shorter and less

In the innate immunity phagocytes can recognize and act immediately against the foreign agent without prior exposure, while the adaptive immunity is characterized by a specific response to the infectious agent, becoming fully activated after a lag period. The innate mucosal defenses are the first line of defense against pathogens present at the mucosal surfaces. The 'Common Mucosal Immune System' is a network of organized structures that protect the oral cavity, the respiratory, thegastrointestinal and the urogenital systems. The

The adaptive immunity involves the action of specialized immune cells, as are the lymphocytes, which generate antibodies against specific microorganisms, killing them directly or activating other cells through the secretion of cytokines. This adaptive response generates memory which is the basis of the preventive immunization. Both systems of immunity, the innate and the acquired, work synergistically and are essential for an optimal function of the immune response. Phagocytes play an important role in the initiation of the adaptive response by presenting antigens and secreting cytokines that stimulate cells of the

Neutrophils (55-65% of blood leukocytes) and monocytes (5-10%) play an important role in innate immunity and provide a major defense system against microorganisms. They act as the first line of defense against infectious agents and are involve in the muscle tissue inflammatory response to exercise-induced injury (10). The multi-step phagocytic process is activated in response to invasion of foreign microorganisms and includes the rolling and adherence of neutrophils to the blood vessel endothelium, the diapedesis and chemotaxis towards the invading organism, the ingestion, degranulation and the oxidative burst, ending with killing of the pathogen (11). Experienced and well equipped laboratories in this specific field, have established the normal range values, based on a large number of subjects examined, in health and disease. Today it is possible to assess the different steps of the phagocytic process and to detect dysfunctions at each level (11-13).

The potential influence of exercise on the immune system could be beneficial, detrimental or neutral. The immune response depends on the type of the particular exercise, its intensity, volume and duration. While mild or moderate exercise was shown to be beneficial, acute intense or prolonged exercise elicits depression of several aspects of the immune response. The fitness level of the performers could also exert influence in their immunological response. It seems that there is a combination of physiological and psychological factors, known to exert their influences on the immune system. Appropriate interpretation of the immune response is vital for determining the clinical directions and the integral training

The physical activity could affect one or all three arms of the immune system, the humoral, the phagocytic and the cellular arm. Eventually, dysfunction of one or more arms of the immune system could lead to the outburst of an infection. In general, the etiology of infections is usually of bacterial origin when the humoral or phagocytic arm is affected, while viral–parasitic infections are usually originated when the cellular arm is involved. Excessive, prolonged training and major competitions have been long considered factors affecting the susceptibility to infections in athletes (14-16), however, in shorter and less

**3. Exercise and the immune response – Clinical implications** 

major effector function of this system is the secretory IgA (8,9)**.** 

adaptive system.

program for each athlete.

competitive events infections are less common (7). Frequent illness has been associated with the overtraining syndrome in athletes (17-19). During heavy exertion could be an immune suppression that creates an 'open window' of decreased host protection. Bacteria or viruses may gain a foothold, increasing the risk of subclinical and clinical infections (17, 20). In team sports or in other sports where participants are in close physical contact before, during or after the sporting event, both the infected individual and the fellow sportsmen may become infected. Some infections may appear in clusters in the sports setting, such as gastroenteritis, herpes simplex, meningitis, viral hepatitis, skin infections, tonsillo-pharyngitis (21,22). A large number of viruses and bacteria can give rise of myocarditis that can be aggravated by physical exertion (15).

There is consistent data suggesting that male endurance athletes may develop after 1 to 2 wk period increased rates of Upper Respiratory Tract Infection (URTI), following marathon or ultramarathon race events (16,23,24). URTI appears to be the most common minor viral infection in athletes. The current consensus is that the cause of URTI in athletes is uncertain (4). There is today disagreement whether 'sore throats', frequently reported by athletes, are caused by infections or are a reflexion of other inflammatory stimuli mimicking URTI (25,26). Cytokines play an important role in modulating the immune function, inducing changes that increase the risk of infection or the appearance of inflammatory symptoms (27). The physician diagnosis of URTI is based on clinical symptoms and signs, rather than by determining the infectious etiology. In few studies the pathogen was identified as the usual respiratory pathogens associated with URTI in the general population **(**4**).** The salivary IgA concentrations and secretion rates have been shown to be significantly decreased in athletes with prolong high intensity exercise (28,29). We could hypothesize that their immunity is reduced with an increase tendency to develop URTI. Other markers of infection as antimicrobial proteins in saliva (-amylase, lactoferrin, and lysozyme) have been identified (26,30). Further, viral infections as URTI may lead to a debilitating state and an unexplained deterioration in athletic performance. Viral infections could run a protracted course of easy fatigability, myalgia and lethargy for weeks or even months (31). Additionally, it seems that athletes are more susceptible to develop Infectious Mononucleosis (32).

Infections of non-viral origin, as bacterial pneumonia, mycoplasma and Chlamydia myocarditis, sinusitis, etc., although uncommonly reported in athletes, could also develop following intense exercise (2,15,33). Athletes could aggravate the course of the disease during incubation periods of infections (34,35).

Neutrophils comprise the majority of circulating leukocytes and represent the early body's response in the battle against bacterial and fungal infections. Multi-factorial elements could be involved in the neutrophil behavior and in the immune responses to exercise, as neuro-endocrine mediators (36), corticosteroid release, interleukin production (37) and oxy-reduction processes associated with free radical production (38). Most studies show that of all subsets of circulating leukocytes, mainly neutrophils and lymphocytes, increase dramatically during exercise (39,40). The magnitude is related to the exercise intensity and duration, being more persistent with intense, prolonged exercise (40, 41). Neutrophil count may exhibit a biphasic response, characterized by an initial small increase, followed by a decline to resting values 30-60 minutes after the cessation of exercise. A delayed larger increase in neutrophil numbers could be observed

Exercise and the Immune System – Focusing on the Effect of Exercise on Neutrophil Functions 149

functions as chemotaxis, oxidative burst, and bactericidal activity were unaffected immediately post-exercise, however neutrophil chemotactic activity was found significantly

decreased 24 h after the cessation of the exercise (Table 3) (47).

\* significantly different from pre-exercise value in both groups
