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## **Meet the editor**

Prof. El Maghraoui was born in 1969, and is currently a Professor of Rheumatology at the Rabat Medicine and Pharmacy University. He is also the head of the Rheumatology Department, Military Hospital Mohammed V, Rabat, Morocco, and the vice president of the Moroccan Society of Rheumatology. He graduated from the Rabat Medicine and Pharmacy University, and received di-

plomas in Podology from the Paris V University, in Muscular Ultrasound from the Paris VI University, and in Methodology of Clinical Research from the Bordeaux University. Prof. El Maghraoui has authored more than 90 PubMed indexed articles, and has published a book titled L'ostéoporose in 2010. He is also the founder, designer and Webmaster of www.rhumato. info, a website for public health professionals, dedicated to rheumatology.

Contents

**Preface IX** 

**Part 1 Bone Mineral Density 1** 

Abdellah El Maghraoui

Abdellah El Maghraoui

**Part 2 Body Composition 43** 

Chapter 1 **Interpreting a DXA Scan in Clinical Practice 3** 

Chapter 3 **Bone Loss and Seasonal Variation in Serial DXA** 

Chapter 4 **The Validity of Body Composition Measurement Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 45** 

Kazuko Ishikawa-Takata and Mitsuru Higuchi

Chapter 7 **Internal Design of the Dry Human Ulna by DXA 97**  S. Aguado-Henche, A. Bosch-Martín, P. Spottorno-Rubio and R. Rodríguez-Torres

**of Morphological Asymmetry in Athletes 59** 

Chiyoko Usui, Motoko Taguchi,

**of Central Nervous System 75** 

Chapter 5 **Dxa as a Tool for the Assessment** 

Magdalena Krzykała

Chapter 6 **Body Composition in Disabilities** 

Yannis Dionyssiotis

**Part 3 Miscellaneous 95** 

Chapter 2 **Monitoring DXA Measurement in Clinical Practice 19** 

**Densitometry – A Population-Based Study 29**  Joonas Sirola, Toni Rikkonen, Risto Honkanen,

Jukka S. Jurvelin, Marjo Tuppurainen and Heikki Kröger

### Contents

#### **Preface XI**


#### **Part 2 Body Composition 43**


#### **Part 3 Miscellaneous 95**

	- P. Spottorno-Rubio and R. Rodríguez-Torres

Chapter 9 **Dietary Protein and Bone Health 121**  Anne Blais, Emilien Rouy and Daniel Tomé

### Preface

Dual-energy x-ray absorptiometry (DXA) is now recognized as the reference method to measure bone mineral density (BMD) with acceptable accuracy errors, good precision and reproducibility. The World Health Organization (WHO) has established DXA as the best densitometric technique for assessing BMD in postmenopausal women and has based the definitions of osteopenia and osteoporosis on its results. DXA allows accurate diagnosis of osteoporosis, estimation of fracture risk and monitoring of patients undergoing treatment. Correct performance of BMD measurements using DXA requires rigorous attention to detail in positioning and analysis. Performing DXA studies incorrectly can lead to major mistakes in diagnosis and therapy. Measurement error must be considered when evaluating serial assessments. A clear understanding of the interpretation of serial measurements and the statistical principles impacting upon their interpretation is necessary to determine whether a change is real and not simply random fluctuation. Moreover, it is important to keep in mind that fracture-protection benefit may be realized before BMD gains are detected. Physicians interested in osteoporosis management, even if not directly involved in the performance and interpretation of DXA, should be familiar with the principles of DXA to minimize serious errors and allow proper use of bone densitometry.

Additional features of DXA include measurement of BMD at multiple skeletal sites, vertebral fracture assessment (also called vertebral morphometry) using evaluation of vertebral heights with a special software to determine vertebral body dimensions, body composition assessment including bone mineral content, fat mass and lean soft tissue mass of the whole body and the segments.

This book contains reviews and original studies about DXA and many different uses in clinical practice (diagnosis of osteoporosis, monitoring of BMD measurement) and in medical research in several situations (e.g. assessment of morphological asymmetry in athletes, estimation of resting energy expenditure, consequences of dietary protein or disabilities of the central nervous system on BMD and body composition, assessment of vertebral strength and vertebral fracture risk, or study of dry bones such as the ulna). It will certainly help the researchers and/or physicians

#### X Preface

using DXA to improve their knowledge of the wide range of potential uses that this technique allows.

> **Prof. A. El Maghraoui**  Rheumatology Department, Military Hospital Mohammed V, Rabat, Morocco

X Preface

technique allows.

using DXA to improve their knowledge of the wide range of potential uses that this

**Prof. A. El Maghraoui**  Rheumatology Department, Military Hospital Mohammed V,

> Rabat, Morocco

**Part 1** 

**Bone Mineral Density** 

**Part 1** 

**Bone Mineral Density** 

**1** 

*Morocco* 

Abdellah El Maghraoui

**Interpreting a DXA Scan in Clinical Practice** 

Osteoporosis is a metabolic bone disorder characterized by low bone mass and microarchitectural deterioration, with a subsequent increase in bone fragility and susceptibility to fracture. Dual-energy x-ray absorptiometry (DXA) is recognized as the reference method to measure bone mineral density (BMD) with acceptable accuracy errors and good precision and reproducibility(Blake and Fogelman 2007). The World Health Organization (WHO) has established DXA as the best densitometric technique for assessing BMD in postmenopausal women and based the definitions of osteopenia and osteoporosis on its results (table 1)(Kanis 1994; Kanis, Borgstrom et al. 2005). DXA allows accurate diagnosis of osteoporosis, estimation of fracture risk, and monitoring of patients undergoing treatment. Additional features of DXA include measurement of BMD at multiple skeletal sites, safety of performance, short investigation time, and ease of use(Hans, Downs et al. 2006; Lewiecki, Binkley et al. 2006). A DXA measurement can be completed in about 5 minutes with minimal radiation exposure (about one tenth that of a standard chest x-ray for

> *Diagnosis T-score*  Normal >–1.0 Osteopenia <–1.0, >–2.5 Osteoporosis <–2.5

Severe osteoporosis <–2.5 plus fragility fractures

As with many other diagnostic examinations, DXA scans should be critically assessed by the interpreting physician and densitometrist for abnormalities that may affect BMD measurements. In clinical practice, recognition of diverse artifacts and disease processes that may influence BMD results can be of major importance in the optimal interpretation of DXA scans(Roux 1998). Physicians not directly involved in the performance and interpretation of DXA should be familiar enough to detect common positioning and scanning problems, to know what should appear on a report, what questions to ask if the necessary information is not on the report, how to apply the results in patient management, and when to do and how

to interpret a second measurement to monitor treatment(Watts 2004).

**1. Introduction** 

a quick hips and spine exam).

Table 1. WHO Osteoporosis Classification

**2. Principle of DXA scanning** 

*Rheumatology Department, Military Hospital Mohammed V, Rabat,* 

### **Interpreting a DXA Scan in Clinical Practice**

Abdellah El Maghraoui

*Rheumatology Department, Military Hospital Mohammed V, Rabat, Morocco* 

#### **1. Introduction**

Osteoporosis is a metabolic bone disorder characterized by low bone mass and microarchitectural deterioration, with a subsequent increase in bone fragility and susceptibility to fracture. Dual-energy x-ray absorptiometry (DXA) is recognized as the reference method to measure bone mineral density (BMD) with acceptable accuracy errors and good precision and reproducibility(Blake and Fogelman 2007). The World Health Organization (WHO) has established DXA as the best densitometric technique for assessing BMD in postmenopausal women and based the definitions of osteopenia and osteoporosis on its results (table 1)(Kanis 1994; Kanis, Borgstrom et al. 2005). DXA allows accurate diagnosis of osteoporosis, estimation of fracture risk, and monitoring of patients undergoing treatment. Additional features of DXA include measurement of BMD at multiple skeletal sites, safety of performance, short investigation time, and ease of use(Hans, Downs et al. 2006; Lewiecki, Binkley et al. 2006). A DXA measurement can be completed in about 5 minutes with minimal radiation exposure (about one tenth that of a standard chest x-ray for a quick hips and spine exam).


Table 1. WHO Osteoporosis Classification

#### **2. Principle of DXA scanning**

As with many other diagnostic examinations, DXA scans should be critically assessed by the interpreting physician and densitometrist for abnormalities that may affect BMD measurements. In clinical practice, recognition of diverse artifacts and disease processes that may influence BMD results can be of major importance in the optimal interpretation of DXA scans(Roux 1998). Physicians not directly involved in the performance and interpretation of DXA should be familiar enough to detect common positioning and scanning problems, to know what should appear on a report, what questions to ask if the necessary information is not on the report, how to apply the results in patient management, and when to do and how to interpret a second measurement to monitor treatment(Watts 2004).

Interpreting a DXA Scan in Clinical Practice 5

worldwide. Thus, the WHO diagnostic criteria for osteoporosis define osteoporosis in terms of

The T-score is calculated using the formula: (patient's BMD - young normal mean)/SD of young normal. For example, if a patient has a BMD of 0.700 g/cm2, the young normal mean is 1.000 g/cm2, and the young normal standard deviation is 0.100 g/cm2, then this patient's T-score would be (0.700 - 1.000)/0.100, or –0.300/0.100, or –3.0(Watts 2004). A T-score of 0 is equal to the young normal mean value, -1.0 is 1 SD low, -2.0 is 2 SD low, etc. Although the WHO classification was not intended to be applied to individual patients, it works well to define ''normal'' (T-score –1.0 and above) and ''osteoporosis'' (T-score –2.5 and below). Several large studies have shown an unacceptably high risk of fracture in post-menopausal women who have T-scores of –2.5 and below. Thus, this threshold is the cornerstone of the patient's assessment. For the therapeutic decisions, however, other risk factors are

In addition to the T-scores, DXA reports also provide Z-scores, which are calculated similarly to the T-score, except that the patient's BMD is compared with an age-matched (and race- and gender-matched) mean, and the result expressed as a standard deviation score(Watts 2004). In premenopausal women, a low Z-score (below -2.0) indicates that bone

Most official groups recommend screening healthy women for osteoporosis at age 65, and testing higher-risk women earlier(Baddoura, Awada et al. 2006). In Europe the recommendations are to screen for risk factors of osteoporosis and to perform BMD measurement in women with such risks. The International Society for Clinical Densitometry (ISCD) recommends screening men without risk factors for osteoporosis at age 70, and screening higher-risk men earlier. Risk factors include dementia, poor health, recent falls, prolonged immobilization, smoking, alcohol abuse, low body weight, history of fragility fracture in a first-degree relative, estrogen deficiency at an early age (<45 years), and steroid use for more than 3 months. Of course, BMD testing is an appropriate tool in the evaluation of patients who have diseases (e.g. hyperthyroidism, hyperparathyroidism, celiac disease, etc.) or use medications (e.g. glucocorticoids, GnRH agonists, aromatase inhibitors etc.) that might cause bone loss. Another indication is radiographic evidence of ''osteopenia'' or a

Recently, many epidemiological studies have validated risk assessment indices for osteoporosis in women. The purpose of the risk assessment indices is not to diagnose osteoporosis or low BMD, but to identify women who are more likely to have low BMD (Hillier, Stone et al. 2007). Such indices, while not identifying all cases of osteoporosis, increase the efficiency of BMD measurement by focusing on subjects who are at increased risk (Cadarette, Jaglal et al. 2000; Gnudi and Sitta 2005; Salaffi, Silveri et al. 2005). The easiest to use in clinical practice is certainly the Osteoporosis Self-assessment Tool (OST). The calculated risk index is based on self-reported age and weight: [(weight in kilograms – age in years) × 0.2, truncated to an integer]. It was developed and validated in several studies in Asian and White women(Richy, Ethgen et al. 2004; El Maghraoui, Guerboub et al. 2007; El Maghraoui, Habbassi

et al. 2007) and men (Adler, Tran et al. 2003; Ghazi, Mounach et al. 2007).

density is lower than expected and should trigger a search for an underlying cause.

a T-score below −2.5 and osteopenia when T-score is between -2.5 and -1.

considered such as prevalent fractures, age and low body mass index.

**3. Who should have a DXA measurement?** 

vertebral fracture).

Several different types of DXA systems are available, but they all operate on similar principles. A radiation source is aimed at a radiation detector placed directly opposite the site to be measured. The patient is placed on a table in the path of the radiation beam. The source/detector assembly is then scanned across the measurement region. The attenuation of the radiation beam is determined and is related to the BMD (Blake and Fogelman 2002; Blake and Fogelman 2003).

Because DXA scanners use two X-ray energies in the presence of three types of tissue (bone mineral, lean tissue and adipose tissue), there are considerable errors arising from the inhomogeneous distribution of adipose tissue in the human body(Tothill and Avenell 1994) (which can be studied either through cadaver studies(Svendsen, Hassager et al. 1995), CT imaging to delineate the distribution of adipose tissue external to bone(Kuiper, van Kuijk et al. 1996; Lee, Wren et al. 2007) or MRI to measure the percentage of marrow fat inside bone(Griffith, Yeung et al. 2006)). These studies suggest BMD measurement errors of around 5 to 8%.

DXA technology can measure virtually any skeletal site, but clinical use has been concentrated on the lumbar spine, proximal femur, forearm, and total body (Hans, Downs et al. 2006). DXA systems are available as either full table systems (capable of multiple skeletal measurements, including the spine and hip) or as peripheral systems (limited to measuring the peripheral skeleton). Because of their versatility, and the ability to measure the skeletal sites of greatest clinical interest, full table DXA systems are the current clinical choice for osteoporosis assessment. Peripheral DXA systems, portable and less expensive than full table systems, are more frequently used as screening and early risk assessment tools; they cannot be used for treatments follow-up. Spine and proximal femur scans represent the majority of the clinical measurements performed using DXA. Most full table DXA systems are able to perform additional scans, including lateral spine BMD measurements, body composition study, assessment of vertebral fractures, measurements of children and infants, assessment of bone around prosthetic implants, small-animal studies and measurements of excised bone specimens. However, for children measurement, the exam should be undertaken by clinicians skilled in interpretation of scans in children in centers that have an adapted paediatric software.

Early DXA systems used a pencil beam geometry and a single detector, which was scanned across the measurement region. Modern full table DXA scanners use a fan-beam source and multiple detectors, which are swept across the measurement region. Fan beam provides the advantage of decreased scan times compared to single-beam systems, but these machines typically cost more because of the need for multiple X-ray detectors. Fan-beam systems use either a single-view or multiview mode to image the skeleton (Lewiecki and Borges 2006).

In clinical practice, BMD measurements are widely used to diagnose osteoporosis and measurement in bone mass are commonly used as a surrogate for fracture risk (Price, Walters et al. 2003). BMD is the measured parameter, and allows the calculation of the bone mineral content (BMC) in grams and the two-dimensional projected area in cm2 of the bone(s) being measured; thus the units of BMD are g/cm2. The BMD values (in g/cm2) are not used for diagnosing osteoporosis. Instead, a working group of the WHO proposed to define osteoporosis on the basis of the T-score (which is the difference between the measured BMD and the mean value of young adults, expressed in standard deviations (SD) for a normal population of the same gender and ethnicity)(Watts 2004). Despite its limitations; this definition, which concerns only postmenopausal women and men over 50, is currently applied

Several different types of DXA systems are available, but they all operate on similar principles. A radiation source is aimed at a radiation detector placed directly opposite the site to be measured. The patient is placed on a table in the path of the radiation beam. The source/detector assembly is then scanned across the measurement region. The attenuation of the radiation beam is determined and is related to the BMD (Blake and Fogelman 2002;

Because DXA scanners use two X-ray energies in the presence of three types of tissue (bone mineral, lean tissue and adipose tissue), there are considerable errors arising from the inhomogeneous distribution of adipose tissue in the human body(Tothill and Avenell 1994) (which can be studied either through cadaver studies(Svendsen, Hassager et al. 1995), CT imaging to delineate the distribution of adipose tissue external to bone(Kuiper, van Kuijk et al. 1996; Lee, Wren et al. 2007) or MRI to measure the percentage of marrow fat inside bone(Griffith, Yeung et al. 2006)). These studies suggest BMD measurement errors of around

DXA technology can measure virtually any skeletal site, but clinical use has been concentrated on the lumbar spine, proximal femur, forearm, and total body (Hans, Downs et al. 2006). DXA systems are available as either full table systems (capable of multiple skeletal measurements, including the spine and hip) or as peripheral systems (limited to measuring the peripheral skeleton). Because of their versatility, and the ability to measure the skeletal sites of greatest clinical interest, full table DXA systems are the current clinical choice for osteoporosis assessment. Peripheral DXA systems, portable and less expensive than full table systems, are more frequently used as screening and early risk assessment tools; they cannot be used for treatments follow-up. Spine and proximal femur scans represent the majority of the clinical measurements performed using DXA. Most full table DXA systems are able to perform additional scans, including lateral spine BMD measurements, body composition study, assessment of vertebral fractures, measurements of children and infants, assessment of bone around prosthetic implants, small-animal studies and measurements of excised bone specimens. However, for children measurement, the exam should be undertaken by clinicians skilled in interpretation of scans in children in centers that have an

Early DXA systems used a pencil beam geometry and a single detector, which was scanned across the measurement region. Modern full table DXA scanners use a fan-beam source and multiple detectors, which are swept across the measurement region. Fan beam provides the advantage of decreased scan times compared to single-beam systems, but these machines typically cost more because of the need for multiple X-ray detectors. Fan-beam systems use either a single-view or multiview mode to image the skeleton (Lewiecki and Borges 2006). In clinical practice, BMD measurements are widely used to diagnose osteoporosis and measurement in bone mass are commonly used as a surrogate for fracture risk (Price, Walters et al. 2003). BMD is the measured parameter, and allows the calculation of the bone mineral content (BMC) in grams and the two-dimensional projected area in cm2 of the bone(s) being measured; thus the units of BMD are g/cm2. The BMD values (in g/cm2) are not used for diagnosing osteoporosis. Instead, a working group of the WHO proposed to define osteoporosis on the basis of the T-score (which is the difference between the measured BMD and the mean value of young adults, expressed in standard deviations (SD) for a normal population of the same gender and ethnicity)(Watts 2004). Despite its limitations; this definition, which concerns only postmenopausal women and men over 50, is currently applied

Blake and Fogelman 2003).

adapted paediatric software.

5 to 8%.

worldwide. Thus, the WHO diagnostic criteria for osteoporosis define osteoporosis in terms of a T-score below −2.5 and osteopenia when T-score is between -2.5 and -1.

The T-score is calculated using the formula: (patient's BMD - young normal mean)/SD of young normal. For example, if a patient has a BMD of 0.700 g/cm2, the young normal mean is 1.000 g/cm2, and the young normal standard deviation is 0.100 g/cm2, then this patient's T-score would be (0.700 - 1.000)/0.100, or –0.300/0.100, or –3.0(Watts 2004). A T-score of 0 is equal to the young normal mean value, -1.0 is 1 SD low, -2.0 is 2 SD low, etc. Although the WHO classification was not intended to be applied to individual patients, it works well to define ''normal'' (T-score –1.0 and above) and ''osteoporosis'' (T-score –2.5 and below). Several large studies have shown an unacceptably high risk of fracture in post-menopausal women who have T-scores of –2.5 and below. Thus, this threshold is the cornerstone of the patient's assessment. For the therapeutic decisions, however, other risk factors are considered such as prevalent fractures, age and low body mass index.

In addition to the T-scores, DXA reports also provide Z-scores, which are calculated similarly to the T-score, except that the patient's BMD is compared with an age-matched (and race- and gender-matched) mean, and the result expressed as a standard deviation score(Watts 2004). In premenopausal women, a low Z-score (below -2.0) indicates that bone density is lower than expected and should trigger a search for an underlying cause.

#### **3. Who should have a DXA measurement?**

Most official groups recommend screening healthy women for osteoporosis at age 65, and testing higher-risk women earlier(Baddoura, Awada et al. 2006). In Europe the recommendations are to screen for risk factors of osteoporosis and to perform BMD measurement in women with such risks. The International Society for Clinical Densitometry (ISCD) recommends screening men without risk factors for osteoporosis at age 70, and screening higher-risk men earlier. Risk factors include dementia, poor health, recent falls, prolonged immobilization, smoking, alcohol abuse, low body weight, history of fragility fracture in a first-degree relative, estrogen deficiency at an early age (<45 years), and steroid use for more than 3 months. Of course, BMD testing is an appropriate tool in the evaluation of patients who have diseases (e.g. hyperthyroidism, hyperparathyroidism, celiac disease, etc.) or use medications (e.g. glucocorticoids, GnRH agonists, aromatase inhibitors etc.) that might cause bone loss. Another indication is radiographic evidence of ''osteopenia'' or a vertebral fracture).

Recently, many epidemiological studies have validated risk assessment indices for osteoporosis in women. The purpose of the risk assessment indices is not to diagnose osteoporosis or low BMD, but to identify women who are more likely to have low BMD (Hillier, Stone et al. 2007). Such indices, while not identifying all cases of osteoporosis, increase the efficiency of BMD measurement by focusing on subjects who are at increased risk (Cadarette, Jaglal et al. 2000; Gnudi and Sitta 2005; Salaffi, Silveri et al. 2005). The easiest to use in clinical practice is certainly the Osteoporosis Self-assessment Tool (OST). The calculated risk index is based on self-reported age and weight: [(weight in kilograms – age in years) × 0.2, truncated to an integer]. It was developed and validated in several studies in Asian and White women(Richy, Ethgen et al. 2004; El Maghraoui, Guerboub et al. 2007; El Maghraoui, Habbassi et al. 2007) and men (Adler, Tran et al. 2003; Ghazi, Mounach et al. 2007).

Interpreting a DXA Scan in Clinical Practice 7

by the use of positioning devices. Internal rotation may be improved by having the patient flex the foot before doing the internal rotation, and then relaxing the foot after the strap is in place. This amount of internal rotation presents the long axis of the femoral neck perpendicular to the X-ray beam, providing the greatest area and the lowest bone mineral content (and the lowest BMD), and is confirmed on the scan by seeing little or none of the lesser trochanter (Fig. 1b)(Lekamwasam and Lenora 2003; 2004). If the desired amount of internal rotation cannot be achieved, as is often the case in patients with hip arthritis or short femoral necks, the technologist should place the patient comfortably in a position that is likely to be reproducible

(a)

The software marks regions of interest in the spine and hip, but the technologist can and should make adjustments if needed. The spine region of interest consists of the L1 through L4 vertebrae (Fig. 1a). Correct placement of the top and bottom of the spine ''box'' is critical.

Fig. 1. Correct positioning and analysis of the L1–L4 spine (a) and the proximal femur

(b) (c)

(Lunar (b) and Hologic (c)).

**5.2 DXA scan analysis** 

in a subsequent scan (Hamdy, Kiebzak et al. 2006; Lewiecki, Binkley et al. 2006).

#### **4. Site of measurement of BMD**

The ISCD recommends obtaining BMD measurements of the posteroanterior spine and hip(Leib, Binkley et al. 2006). The lateral spine and Ward's triangle region of the hip should not be used for diagnosis, because these sites overestimate osteoporosis and results can be false-positive. Evidence suggests that the femur (neck or total hip) is the optimum site for predicting the risk of hip fracture and the spine is the optimum site for monitoring response to treatment. Thus, many authors recommend hip measure alone for the fracture risk assessment(Kanis, Johnell et al. 2000; Kanis, Oden et al. 2001; Kanis 2002; Johnell, Kanis et al. 2005; Kanis, Seeman et al. 2005; Arabi, Baddoura et al. 2007). In very obese patients, those with primary hyperparathyroidism, or those in whom the hip or the spine, or both, cannot be measured or interpreted, BMD may be measured in the forearm, using a 33% radius on the nondominant forearm.

#### **5. Interpreting a DXA scan**

The most important informations to check are the correct identification of the patient, his date of birth and also the sex and ethnicity which are mandatory to calculate T-scores. Sex is used by all manufacturers to calculate T-scores (i.e. T-scores for women are calculated using a female normative database, while T-scores for men are calculated using a male normative database). Although all manufacturers use race in calculating Z-scores, there is inconsistency in the way race is handled when calculating T-scores. Norland and Hologic are using race in calculating T-scores (i.e. T-scores for Caucasians are calculated using a Caucasian normative database, T-scores for Blacks are calculated using a normative database for Blacks); however, GE Lunar and recent Hologic machines use the database for young-normal Caucasians to calculate T-scores, regardless of the race of the subject. The ISCD recommends the latter approach for use in North America (Baim, Wilson et al. 2005) because using race-adjusted T-scores results in a similar prevalence of ''osteoporosis'' in every racial group, despite the fact that age-specific fracture rates can be very different.

#### **5.1 Positioning**

The main purpose of the DXA scan image is to check if the patient is positioned correctly, something that the technologist must determine before the patient leaves the testing centre. Positioning should also be doublechecked by the clinician who interprets the test(Roux 1998). There is many available resources for BMD technologists and physicians training, such as ISCD or International Osteoporosis Foundation (IOF) courses.

A scan with correct positioning of the spine is shown in Fig. 1a: the patient is straight on the table (spine is straight on the image), not rotated (spinous processes are centered), and centered in the field (roughly equal soft tissue fields on either side of the spine). Patients with scoliosis cannot be positioned with the spine straight on the table; moreover with severe scoliosis degenerative changes can occur that invalidate the spine measurement. The scan should extend up sufficiently far to include part of the lowest vertebra with ribs (which is usually T12) and low enough to show the pelvic brim (which is usually the level of the L4–L5 interspace). Most testing centers will elevate the patient's knees with a foam block (hip at a 90° angle to the spine) to try to partially flatten the normal lumbar lordosis. For proper positioning of the hip, the patient should have the femur straight on the table (shaft parallel to the edge of the picture), with 15–25° of internal rotation, which can be achieved

The ISCD recommends obtaining BMD measurements of the posteroanterior spine and hip(Leib, Binkley et al. 2006). The lateral spine and Ward's triangle region of the hip should not be used for diagnosis, because these sites overestimate osteoporosis and results can be false-positive. Evidence suggests that the femur (neck or total hip) is the optimum site for predicting the risk of hip fracture and the spine is the optimum site for monitoring response to treatment. Thus, many authors recommend hip measure alone for the fracture risk assessment(Kanis, Johnell et al. 2000; Kanis, Oden et al. 2001; Kanis 2002; Johnell, Kanis et al. 2005; Kanis, Seeman et al. 2005; Arabi, Baddoura et al. 2007). In very obese patients, those with primary hyperparathyroidism, or those in whom the hip or the spine, or both, cannot be measured or interpreted, BMD may be measured in the forearm, using a 33% radius on

The most important informations to check are the correct identification of the patient, his date of birth and also the sex and ethnicity which are mandatory to calculate T-scores. Sex is used by all manufacturers to calculate T-scores (i.e. T-scores for women are calculated using a female normative database, while T-scores for men are calculated using a male normative database). Although all manufacturers use race in calculating Z-scores, there is inconsistency in the way race is handled when calculating T-scores. Norland and Hologic are using race in calculating T-scores (i.e. T-scores for Caucasians are calculated using a Caucasian normative database, T-scores for Blacks are calculated using a normative database for Blacks); however, GE Lunar and recent Hologic machines use the database for young-normal Caucasians to calculate T-scores, regardless of the race of the subject. The ISCD recommends the latter approach for use in North America (Baim, Wilson et al. 2005) because using race-adjusted T-scores results in a similar prevalence of ''osteoporosis'' in every racial group, despite the fact that age-specific fracture rates can be very different.

The main purpose of the DXA scan image is to check if the patient is positioned correctly, something that the technologist must determine before the patient leaves the testing centre. Positioning should also be doublechecked by the clinician who interprets the test(Roux 1998). There is many available resources for BMD technologists and physicians training,

A scan with correct positioning of the spine is shown in Fig. 1a: the patient is straight on the table (spine is straight on the image), not rotated (spinous processes are centered), and centered in the field (roughly equal soft tissue fields on either side of the spine). Patients with scoliosis cannot be positioned with the spine straight on the table; moreover with severe scoliosis degenerative changes can occur that invalidate the spine measurement. The scan should extend up sufficiently far to include part of the lowest vertebra with ribs (which is usually T12) and low enough to show the pelvic brim (which is usually the level of the L4–L5 interspace). Most testing centers will elevate the patient's knees with a foam block (hip at a 90° angle to the spine) to try to partially flatten the normal lumbar lordosis. For proper positioning of the hip, the patient should have the femur straight on the table (shaft parallel to the edge of the picture), with 15–25° of internal rotation, which can be achieved

such as ISCD or International Osteoporosis Foundation (IOF) courses.

**4. Site of measurement of BMD** 

the nondominant forearm.

**5.1 Positioning** 

**5. Interpreting a DXA scan** 

by the use of positioning devices. Internal rotation may be improved by having the patient flex the foot before doing the internal rotation, and then relaxing the foot after the strap is in place. This amount of internal rotation presents the long axis of the femoral neck perpendicular to the X-ray beam, providing the greatest area and the lowest bone mineral content (and the lowest BMD), and is confirmed on the scan by seeing little or none of the lesser trochanter (Fig. 1b)(Lekamwasam and Lenora 2003; 2004). If the desired amount of internal rotation cannot be achieved, as is often the case in patients with hip arthritis or short femoral necks, the technologist should place the patient comfortably in a position that is likely to be reproducible in a subsequent scan (Hamdy, Kiebzak et al. 2006; Lewiecki, Binkley et al. 2006).

(a)

Fig. 1. Correct positioning and analysis of the L1–L4 spine (a) and the proximal femur (Lunar (b) and Hologic (c)).

#### **5.2 DXA scan analysis**

The software marks regions of interest in the spine and hip, but the technologist can and should make adjustments if needed. The spine region of interest consists of the L1 through L4 vertebrae (Fig. 1a). Correct placement of the top and bottom of the spine ''box'' is critical.

Interpreting a DXA Scan in Clinical Practice 9

(e) Fig. 2. Examples among some common spine scanning problems: (a) The spine is too close to the right side of the image (b) Vertebral levels are mis-identified (c) Metal button over L4 (d)

(a) (b)

(c) (d)

Scoliosis, and osteophyte at L3–L4 (e) Laminectomy

The intervertebral lines can be moved or angled, if necessary. There must be sufficient soft tissue on both sides of the spine; otherwise BMD will be under estimated. The hip regions of interest include the femoral neck, trochanter, and total hip (Fig. 1b). Ward's region and the intertrochanteric region are not relevant (and can be deleted from the results reports. The default hip analysis includes a midline that must be placed correctly for the other sites to be identified correctly. The preferred position for the rectangular femoral neck box differs for the different manufacturers. For GE Lunar, the femoral neck box is located by the analysis program at the narrowest and lowest density section of the neck; typically this will be about half way between the femoral head and the trochanter (Fig. 1b). For Hologic the box is on the distal part of the femoral neck (Fig. 1c). This induces a large difference among these 2 measurements, because of a gradient of BMD all along the femoral neck (the proximal being the highest, the distal being the lowest). Thus careful checking of the femoral neck box is mandatory.

The image should be evaluated for artifacts (e.g. surgical clips, navel rings, barium sulphate, metal from zipper, coin, clip, or other metallic object) or local structural change (e.g. osteophytes, syndesmophytes, compression fractures, aortic calcification). Almost all artifacts and local structural change will spuriously elevate BMD(El Maghraoui 2004). This is especially true for spinal degenerative change, which can elevate spine BMD by 2, 3, or more T-score. In the spine, absent bone (laminectomy or spina bifida) or vertebral rotation (idiopathic scoliosis) will spuriously lower BMD. All evaluable vertebrae should be used, but vertebrae that are affected by local structural change should be deleted from the analysis. Most agree that decisions can be based on two vertebrae; the use of a single vertebra is not recommended. If all vertebrae are affected, the spine should be reported as ''invalid,'' with no BMD or T-score results given. Figure 2 and 3 show examples from common spine and hips scanning problems.

Finally, physicians must keep in mind to actively look for secondary osteoporosis in front of low BMD value, either by thorough history taking or with biochemical studies before stating about post menopausal osteoporosis.

#### **6. Vertebral fracture assessment (VFA)**

For assessing vertebral heights (also called vertebral morphometry), a special software is used to determine vertebral body dimensions. The computer (with the help of the technologist) places points on the superior and inferior endplates of each vertebra. The vertebral heights are calculated and compared to each other as well as to the expected normal dimensions. With the advent of higher-resolution DXA systems, visual assessment of fractures is also possible from DXA-based lateral spine images (Figure 4). In this situation, the DXA system essentially functions as a digital X-ray imaging device. Visual assessment is performed from a computer monitor or high-resolution printout. To optimize the assessment, the use of high-definition dual-energy images has been recommended (Rea, Li et al. 2000; Chapurlat, Duboeuf et al. 2006; Olenginski, Newman et al. 2006). Using a DXA system for assessing vertebral fracture status has several advantages. The evaluation of spine fractures can be performed without a conventional lateral spine X-ray. This can be done at the same time and at the same place as the BMD measurement, with much less radiation than a conventional spine X-ray. Moreover, VFA is a technology for diagnosing vertebral fractures that may alter diagnostic classification, improve fracture risk stratification,

The intervertebral lines can be moved or angled, if necessary. There must be sufficient soft tissue on both sides of the spine; otherwise BMD will be under estimated. The hip regions of interest include the femoral neck, trochanter, and total hip (Fig. 1b). Ward's region and the intertrochanteric region are not relevant (and can be deleted from the results reports. The default hip analysis includes a midline that must be placed correctly for the other sites to be identified correctly. The preferred position for the rectangular femoral neck box differs for the different manufacturers. For GE Lunar, the femoral neck box is located by the analysis program at the narrowest and lowest density section of the neck; typically this will be about half way between the femoral head and the trochanter (Fig. 1b). For Hologic the box is on the distal part of the femoral neck (Fig. 1c). This induces a large difference among these 2 measurements, because of a gradient of BMD all along the femoral neck (the proximal being the highest, the distal being the lowest). Thus careful checking of the femoral neck box is

The image should be evaluated for artifacts (e.g. surgical clips, navel rings, barium sulphate, metal from zipper, coin, clip, or other metallic object) or local structural change (e.g. osteophytes, syndesmophytes, compression fractures, aortic calcification). Almost all artifacts and local structural change will spuriously elevate BMD(El Maghraoui 2004). This is especially true for spinal degenerative change, which can elevate spine BMD by 2, 3, or more T-score. In the spine, absent bone (laminectomy or spina bifida) or vertebral rotation (idiopathic scoliosis) will spuriously lower BMD. All evaluable vertebrae should be used, but vertebrae that are affected by local structural change should be deleted from the analysis. Most agree that decisions can be based on two vertebrae; the use of a single vertebra is not recommended. If all vertebrae are affected, the spine should be reported as ''invalid,'' with no BMD or T-score results given. Figure 2 and 3 show examples from

Finally, physicians must keep in mind to actively look for secondary osteoporosis in front of low BMD value, either by thorough history taking or with biochemical studies before stating

For assessing vertebral heights (also called vertebral morphometry), a special software is used to determine vertebral body dimensions. The computer (with the help of the technologist) places points on the superior and inferior endplates of each vertebra. The vertebral heights are calculated and compared to each other as well as to the expected normal dimensions. With the advent of higher-resolution DXA systems, visual assessment of fractures is also possible from DXA-based lateral spine images (Figure 4). In this situation, the DXA system essentially functions as a digital X-ray imaging device. Visual assessment is performed from a computer monitor or high-resolution printout. To optimize the assessment, the use of high-definition dual-energy images has been recommended (Rea, Li et al. 2000; Chapurlat, Duboeuf et al. 2006; Olenginski, Newman et al. 2006). Using a DXA system for assessing vertebral fracture status has several advantages. The evaluation of spine fractures can be performed without a conventional lateral spine X-ray. This can be done at the same time and at the same place as the BMD measurement, with much less radiation than a conventional spine X-ray. Moreover, VFA is a technology for diagnosing vertebral fractures that may alter diagnostic classification, improve fracture risk stratification,

mandatory.

common spine and hips scanning problems.

**6. Vertebral fracture assessment (VFA)** 

about post menopausal osteoporosis.

(a) (b)

(c) (d)

(e)

Fig. 2. Examples among some common spine scanning problems: (a) The spine is too close to the right side of the image (b) Vertebral levels are mis-identified (c) Metal button over L4 (d) Scoliosis, and osteophyte at L3–L4 (e) Laminectomy

Interpreting a DXA Scan in Clinical Practice 11

and identify patients likely to benefit from pharmacological therapy who otherwise might not be treated(Olenginski, Newman et al. 2006; Roux, Fechtenbaum et al. 2007). Despite the apparent advantages, the future of VFA using DXA remains unclear. Skeletal radiologists have criticized the technique for being insensitive and inaccurate for detecting vertebral fractures in particular at the upper thoracic spine. A DXA image is of lower resolution than a conventional X-ray and might fail to identify other potential problems or diseases that would be apparent on a spine film. However, VFA allows ruling out vertebral fracture at levels where vertebral fracture is most common, i.e. the lumbar and the mid and lower thoracic levels, and the pencil beam mode of assessment eliminates parallax errors in viewing the vertebral body, which can sometimes make a normal vertebral body appear to have been compressed in a routine spine x-ray(Duboeuf, Bauer et al. 2005; Jacobs-Kosmin,

Sandorfi et al. 2005; Chapurlat, Duboeuf et al. 2006; Damiano, Kolta et al. 2006).

Fig. 4. Vertebral fracture assessment from a dual x-ray absorptiometry image of the spine.

fractures or spinal radiographs could benefit from a VFA.

At this time, DXA devices are not generally accepted as a surrogate for spinal X-rays, though they may provide a useful screening tool in higher-risk patients when spinal X-rays are unavailable. For example, individuals over 65, subjects reporting significant height loss or patients on long term glucocorticoid therapy who have not had previous vertebral

(e)

Fig. 3. Examples among some common hip scanning problems: (a) The scan did not go far enough laterally and part of the femoral head is missing. (b) The femur is adducted (c) The femur is abducted (d) Suboptimal internal rotation (too much of the lesser trochanter is showing) (e) Abnormal bone (history of hip fracture and osteosynthesis)

(e) Fig. 3. Examples among some common hip scanning problems: (a) The scan did not go far enough laterally and part of the femoral head is missing. (b) The femur is adducted (c) The femur is abducted (d) Suboptimal internal rotation (too much of the lesser trochanter is

showing) (e) Abnormal bone (history of hip fracture and osteosynthesis)

(a) (b)

(c) (d)

and identify patients likely to benefit from pharmacological therapy who otherwise might not be treated(Olenginski, Newman et al. 2006; Roux, Fechtenbaum et al. 2007). Despite the apparent advantages, the future of VFA using DXA remains unclear. Skeletal radiologists have criticized the technique for being insensitive and inaccurate for detecting vertebral fractures in particular at the upper thoracic spine. A DXA image is of lower resolution than a conventional X-ray and might fail to identify other potential problems or diseases that would be apparent on a spine film. However, VFA allows ruling out vertebral fracture at levels where vertebral fracture is most common, i.e. the lumbar and the mid and lower thoracic levels, and the pencil beam mode of assessment eliminates parallax errors in viewing the vertebral body, which can sometimes make a normal vertebral body appear to have been compressed in a routine spine x-ray(Duboeuf, Bauer et al. 2005; Jacobs-Kosmin, Sandorfi et al. 2005; Chapurlat, Duboeuf et al. 2006; Damiano, Kolta et al. 2006).

Fig. 4. Vertebral fracture assessment from a dual x-ray absorptiometry image of the spine.

At this time, DXA devices are not generally accepted as a surrogate for spinal X-rays, though they may provide a useful screening tool in higher-risk patients when spinal X-rays are unavailable. For example, individuals over 65, subjects reporting significant height loss or patients on long term glucocorticoid therapy who have not had previous vertebral fractures or spinal radiographs could benefit from a VFA.

Interpreting a DXA Scan in Clinical Practice 13

3. Anatomic discordance is owing to differences in the composition of bone envelopes tested. An example is the difference in T-scores found for the posteroanterior lumbar

4. Artifactual discordance occurs when dense synthetic manmade substances are within the field of ROI of the test: e.g. barium sulphate, metal from zipper, coin, clip, or other

5. And finally, technical discordance occurs because of device errors, technician variability, patients' movements, and variation due to other unpredictable sources. With respect to positioning error, some studies showed that either excessive internal or external rotation of the femur during test acquisition resulted in a BMD difference of as much as 10% compared with correct positioning. We demonstrated in a previous study that DXA in vivo reproducibility is two-fold better in the hips than the spine especially when measuring both hips(El Maghraoui, Do Santos Zounon et al. 2005). Finally, technical discordance can occur due to the normative reference data used by the device software to analyze the test(Liao, Wu et al. 2003; McMahon, Nightingale et al. 2004; Lewiecki, Binkley et al. 2006). This type of discordance occurs when the average BMD of the normative group used to calculate the T-score is significantly different from the

spine and the supine lateral lumbar spine in the same patient.

average value found for the whole population.

**7.2 Consequences of T-score discordance on osteoporosis management** 

The high prevalence of T-score discordance could induce some problems for the physicians in decision-making regarding these patients. In general, high prevalence of discordance between lumbar spine and hip T-scores suggests some defects in the cut-off values for definition of osteoporosis and osteopenia proposed with the WHO. The inconsistencies in the diagnostic classification of osteoporosis between skeletal sites lend credence to the notion that BMD should be used as only one of the factors in making therapeutic decisions when evaluating patients with osteoporosis. An international team convened by the WHO is trying to develop a globally applicable measure of absolute fracture risk based upon multiple risk factors including BMD. This could silence much of the controversy regarding the choice of reference data for T-score calculation and usefulness of relatively arbitrary densitometric categorizations. However, one can speculate that discordance in individual fracture risk estimation with this new absolute fracture risk will still be observed as it will be

Correct performance of BMD measurements using DXA requires rigorous attention to detail in positioning and analysis. When DXA studies are performed incorrectly, it can lead to major mistakes in diagnosis and therapy. Measurement error must be considered when evaluating serial assessments. A clear understanding of the interpretation of serial measurements and the statistical principles impacting upon their interpretation is necessary

lumbar osteoporosis.

metallic object.

based on different sites BMD.

**8. Conclusion** 

Maghraoui 2004; Khan, Hanley et al. 2006). This will lead to higher prevalence of

#### **7. Concordance between measurement sites**

It is recommended to measure the PA lumbar spine and proximal femur and classifying the patient based on the lowest T-score from three sites (lumbar spine, femoral neck, and total hip). Although the BMDs at different anatomic regions are correlated, the agreement between sites is low when it comes to classifying individual subjects as osteoporotic or not. Thus, T-score discordance between the lumbar spine and hip testing sites is a commonly observed phenomenon in densitometery. T-score discordance is the observation that the T-score of an individual patient varies from one key measurement site to another.

#### **7.1 Prevalence and risk factors of T-score discordance**

Various studies have analyzed the prevalence and impact of T-score discordance on the management of osteoporosis(Faulkner, von Stetten et al. 1999; Woodson 2000; O'Gradaigh, Debiram et al. 2003; Moayyeri, Soltani et al. 2005). Only two studies focused on risk factors of this commonly observed discordance (Moayyeri, Soltani et al. 2005; El Maghraoui, Mouinga Abayi et al. 2007; El Maghraoui, Mouinga Abayi et al. 2007). Five different causes for occurrence of discordance between the spine and the hip sites have been described(Woodson 2000).


It is recommended to measure the PA lumbar spine and proximal femur and classifying the patient based on the lowest T-score from three sites (lumbar spine, femoral neck, and total hip). Although the BMDs at different anatomic regions are correlated, the agreement between sites is low when it comes to classifying individual subjects as osteoporotic or not. Thus, T-score discordance between the lumbar spine and hip testing sites is a commonly observed phenomenon in densitometery. T-score discordance is the observation that the T-score of an individual patient varies from one key measurement

Various studies have analyzed the prevalence and impact of T-score discordance on the management of osteoporosis(Faulkner, von Stetten et al. 1999; Woodson 2000; O'Gradaigh, Debiram et al. 2003; Moayyeri, Soltani et al. 2005). Only two studies focused on risk factors of this commonly observed discordance (Moayyeri, Soltani et al. 2005; El Maghraoui, Mouinga Abayi et al. 2007; El Maghraoui, Mouinga Abayi et al. 2007). Five different causes for occurrence of discordance between the spine and the hip sites have been

1. Physiologic discordance is related to the skeleton's natural adaptive reaction to normal external and internal factors and forces. Mechanical strain especially related to weight bearing plays a key role in this kind of discordance. An example of this type of discordance is the difference observed between the dominant and nondominant total hip(Hamdy, Kiebzak et al. 2006). The explanation is that weight bearing can cause rise in bone density especially in the hip and femur regions. Moreover, the spine and hips usually start out with different T-scores (the spine is said to reach peak at least 5 yrs before the hip)(Blank, Malone et al. 2006). And finally, bone loss observed with age in an individual may be more rapid and important in trabecular than cortical bone is another explanation(Agarwal and Camacho 2006). Trabecular bones (typical of lumbar area) are known to have a more rapid rate of deprivation in early post-menopausal state in comparison to cortical bone (typical of

2. The second type of discordance described as pathophysiologic discordance is seen secondary to a disease. Common examples observed in the elderly include vertebral osteophytosis, vertebral end plate and facet sclerosis, osteochondrosis, and aortic calcification(Bolotin 2001; Theodorou and Theodorou 2002). Another important cause in younger patients is ankylosing spondylitis syndesmophytes(El Maghraoui, Borderie et al. 1999; Maillefert, Aho et al. 2001; El Maghraoui 2004; El Maghraoui 2004; El Maghraoui, Do Santos Zounon et al. 2005). The abnormal calcium deposition within the field of the DXA region of interest (ROI) leads to the falsely elevated spine T-score. A second subtype is a true discordance resulting from a more decreased BMD in the lumbar spine than the hips. Indeed, most of the aetiologies of the secondary osteoporosis (such as glucocorticoid excess, hyperthyroidism, malabsorption, liver disease, rheumatoid arthritis) first affect spinal column(El

**7. Concordance between measurement sites** 

**7.1 Prevalence and risk factors of T-score discordance** 

site to another.

described(Woodson 2000).

proximal femur).

Maghraoui 2004; Khan, Hanley et al. 2006). This will lead to higher prevalence of lumbar osteoporosis.


#### **7.2 Consequences of T-score discordance on osteoporosis management**

The high prevalence of T-score discordance could induce some problems for the physicians in decision-making regarding these patients. In general, high prevalence of discordance between lumbar spine and hip T-scores suggests some defects in the cut-off values for definition of osteoporosis and osteopenia proposed with the WHO. The inconsistencies in the diagnostic classification of osteoporosis between skeletal sites lend credence to the notion that BMD should be used as only one of the factors in making therapeutic decisions when evaluating patients with osteoporosis. An international team convened by the WHO is trying to develop a globally applicable measure of absolute fracture risk based upon multiple risk factors including BMD. This could silence much of the controversy regarding the choice of reference data for T-score calculation and usefulness of relatively arbitrary densitometric categorizations. However, one can speculate that discordance in individual fracture risk estimation with this new absolute fracture risk will still be observed as it will be based on different sites BMD.

#### **8. Conclusion**

Correct performance of BMD measurements using DXA requires rigorous attention to detail in positioning and analysis. When DXA studies are performed incorrectly, it can lead to major mistakes in diagnosis and therapy. Measurement error must be considered when evaluating serial assessments. A clear understanding of the interpretation of serial measurements and the statistical principles impacting upon their interpretation is necessary

Interpreting a DXA Scan in Clinical Practice 15

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#### **9. Abbreviations**

BMC: bone mineral content BMD: bone mineral density CV: coefficient of variation DXA: dual-energy x-ray absorptiometry IOF: international osteoporosis foundation ISCD: international society for clinical densitometry LSC: least significant change OST: osteoporosis self-assessment tool PE: precision error ROI: region of interest SD: standard deviation SDD: smallest detectable difference VFA: vertebral fracture assessment

WHO: world health organization

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ISCD Official Positions." *J Clin Densitom* 9(1): 15-21.

fractures." *Arch Intern Med* 167(2): 155-60.

density." *J Clin Densitom* 8(3): 267-72.

*Bone Miner Res* 20(7): 1185-94.

*Osteoporos Int* 4(6): 368-81.

359(9321): 1929-36.

33(11): 2319-21.

581-9.

women from bone mineral density measurement: an Italian study." *J Clin Densitom*

perfusion indexes in women with varying bone density: MR evaluation." *Radiology*

measurement and prediction of fractures in older women: the study of osteoporotic

fracture assessment: associations with clinical characteristics and bone mineral

postmenopausal osteoporosis: synopsis of a WHO report. WHO Study Group."

in Moroccan men." *Clin Rheumatol*.

8(2): 199-205.

241(3): 831-8.


**2** 

*Morocco* 

**Monitoring DXA Measurement** 

*Rheumatology Department, Military Hospital Mohammed V, Rabat,* 

Osteoporosis is a worldwide major public health problem. Bone densitometry has become the "gold standard" in its diagnosis and treatment evaluation(El Maghraoui and Roux 2008). With its advantages of high precision, short scan times, low radiation dose, and stable calibration, dual-energy x-ray absorptiometry (DXA) has been established by the World Health Organization (WHO) as the technique of reference for assessing bone mineral density (BMD) in postmenopausal women and based the definitions of osteopenia and osteoporosis on its results. Recently, efficient therapeutic options for treatment of osteoporosis have been developed which create possibilities of effective intervention. Therefore, screening for and treatment of osteoporosis are widely practised in postmenopausal women and in people with an increased risk of osteoporosis because of underlying diseases (e.g. chronic rheumatic diseases especially when treated by corticosteroids)(Phillipov, Seaborn et al. 2001). Moreover, BMD measurement is needed to select patients for osteoporosis treatment, as there is no proof that drugs for osteoporosis (other than hormone replacement therapy [HRT]) are beneficial in women with clinical risk

It has also become more and more common to perform a second DXA measurement to monitor BMD status or the effect of therapeutic intervention. When a second measurement is performed on a patient, the clinician needs to distinguish between a true change in BMD and a random fluctuation related to variability in the measurement procedure. The reproducibility of DXA measurements is claimed to be good. Such variability is due to multiple causes, such as device errors, technician variability, patients' movements, and variation due to other unpredictable sources (Nguyen, Sambrook et al. 1997; Lodder, Lems

The precision error is usually expressed as the coefficient of variation (CV), which is the ratio of the standard deviation (SD) to the mean of the measurements, although several other statistics to express reproducibility exist such as the smallest detectable difference (SDD) or the least significant change (LSC). The SDD represents a cut-off that can be measured in an individual and is usually considered more useful than the CV in clinical practice (Fuleihan,

**1. Introduction** 

et al. 2004).

factors for fractures but normal BMD values.

Testa et al. 1995; Ravaud, Reny et al. 1999).

 **in Clinical Practice** 

Abdellah El Maghraoui


### **Monitoring DXA Measurement in Clinical Practice**

Abdellah El Maghraoui *Rheumatology Department, Military Hospital Mohammed V, Rabat, Morocco* 

#### **1. Introduction**

18 Dual Energy X-Ray Absorptiometry

Watts, N. B. (2004). "Fundamentals and pitfalls of bone densitometry using dual-energy X-

Woodson, G. (2000). "Dual X-ray absorptiometry T-score concordance and discordance between the hip and spine measurement sites." *J Clin Densitom* 3(4): 319-24.

ray absorptiometry (DXA)." *Osteoporos Int* 15(11): 847-54.

Osteoporosis is a worldwide major public health problem. Bone densitometry has become the "gold standard" in its diagnosis and treatment evaluation(El Maghraoui and Roux 2008). With its advantages of high precision, short scan times, low radiation dose, and stable calibration, dual-energy x-ray absorptiometry (DXA) has been established by the World Health Organization (WHO) as the technique of reference for assessing bone mineral density (BMD) in postmenopausal women and based the definitions of osteopenia and osteoporosis on its results. Recently, efficient therapeutic options for treatment of osteoporosis have been developed which create possibilities of effective intervention. Therefore, screening for and treatment of osteoporosis are widely practised in postmenopausal women and in people with an increased risk of osteoporosis because of underlying diseases (e.g. chronic rheumatic diseases especially when treated by corticosteroids)(Phillipov, Seaborn et al. 2001). Moreover, BMD measurement is needed to select patients for osteoporosis treatment, as there is no proof that drugs for osteoporosis (other than hormone replacement therapy [HRT]) are beneficial in women with clinical risk factors for fractures but normal BMD values.

It has also become more and more common to perform a second DXA measurement to monitor BMD status or the effect of therapeutic intervention. When a second measurement is performed on a patient, the clinician needs to distinguish between a true change in BMD and a random fluctuation related to variability in the measurement procedure. The reproducibility of DXA measurements is claimed to be good. Such variability is due to multiple causes, such as device errors, technician variability, patients' movements, and variation due to other unpredictable sources (Nguyen, Sambrook et al. 1997; Lodder, Lems et al. 2004).

The precision error is usually expressed as the coefficient of variation (CV), which is the ratio of the standard deviation (SD) to the mean of the measurements, although several other statistics to express reproducibility exist such as the smallest detectable difference (SDD) or the least significant change (LSC). The SDD represents a cut-off that can be measured in an individual and is usually considered more useful than the CV in clinical practice (Fuleihan, Testa et al. 1995; Ravaud, Reny et al. 1999).

Monitoring DXA Measurement in Clinical Practice 21

two observations for each subject, the standard deviation of the differences (SDdiff) estimates the within variability of the measurements. Most disagreements between measurements are expected to be between limits called ''limits of agreement'' defined as dz(1-a/2) SDdiff where d is the mean difference between the pairs of measurements and z(1-a/2) is the 100(1-a/2)th centile of the normal distribution. The value d is an estimate of the mean systematic bias of measurement 1 to measurement 2. d is expected to be 0 because a true change in BMD is not assumed to occur during the interval between the two BMD measurements. Defining a to be 5%, the limits of agreement are +1.96SDdiff and -1.96SDdiff. Thus, about twice the standard deviation (SD) of the difference scores gives the 95% limits of agreement for the two measurements by the machine. A test is considered to be capable of detecting a difference, in

**3. Clinical implications of bone mineral density reproducibility measurement**  In clinical practice, two absolute values (g/cm2) have to be compared, rather than two percentages (T-scores). When serial measurements are obtained in a patient, only changes greater than the LSC (in %) or the SDD (in g/cm2) can be ascribed to treatment effects.

We studied recently the in vivo short term variability of BMD measurement by DXA in three groups of subjects with a wide range of BMD values: healthy young volunteers, postmenopausal women and patients with chronic rheumatic diseases (most of them taking corticosteroids). In all studied subjects, reproducibility expressed by different means was good and independent from clinical and BMD status. Thus, the clinician interpreting a repeated DXA scan of a subject should be aware that a BMD change exceeding the LSC is significant, in our centre arising from a BMD change of at least 3.56% at the total hip and 5.60% at the spine. Expressed as SDD, a BMD change should exceed 0.02 g/cm2 at the total hip and 0.04 g/cm2 at the spine before it can be considered a significant change(El Maghraoui, Do Santos Zounon et al. 2005). Indeed, it has become usual to perform repeated DXA measurement: in postmenopausal women to monitor efficacy of treatment and in patients with chronic rheumatic diseases where high prevalence of bone loss has been demonstrated (Maillefert, Aho et al. 2001; Johnson, Petkov et al. 2005) especially when long term corticosteroid therapy is used. In the reports published, variability is usually expressed as CV and the figures for short term variability are lower than the ones we found [7-9]. However, two studies showed variability data more in line with our results. In Ravaud et al.(Ravaud, Reny et al. 1999) study, two samples of healthy (n=70) and elderly (n=57) postmenopausal women showed a CV (%) of 0.9 and 1.8, respectively, at the spine, and of 0.9 and 2.3, respectively, at the total hip. Eastell showed an LSC (%) of 5.4 at the lumbar spine and 8 at the total hip, respectively, in osteoporotic postmenopausal women (Eastell 1996). It has been suggested that the varying results of reproducibility studies might be explained by the ''population'' investigated; a phantom and healthy young subjects are likely to show more favourable variability than postmenopausal women, possibly in part because of easier positioning for measurement(Gluer, Blake et al. 1995). However, our study failed to show better variability, expressed as CV (%), in young healthy volunteers (El Maghraoui, Do Santos Zounon et al. 2005). Another reason advocated was that osteoarthritis in postmenopausal women may contribute to poorer variability than found in healthy young subjects. The SDD values found in our study were comparable to the figures presented by Ravaud et al. (Ravaud, Reny et al. 1999). In the first group of postmenopausal

absolute units, of at least the magnitude of the limits of agreement.

Smaller changes may be related to measurement error.

#### **2. Methods of bone mineral density reproducibility measurement**

Precision errors are evaluated by performing repeated scans on a representative set of individuals to characterize the reproducibility of the technique. Most published studies examine the short-term precision error, based on repeated measurements of each subject performed over a time period of no more than 2 weeks. Over such a short period, no true change in BMD is expected.

#### **2.1 The coefficient of variation (CV)**

The CV, the most commonly presented measure for BMD variability, is the SD corrected for the mean of paired measurements. CV, expressed as a percentage, is calculated as CV (%) = (√((∑(a-b)2)/2n))/((Ma+Mb)/2)x100 where a and b are the first and the second measurement, Ma and Mb are the mean values for the two groups, and n is the number of paired observations.

Reproducibility is far better for BMD measurement than for most laboratory tests. Reproducibility expressed by the CV is usually 1–2% at the spine on anteroposterior images and 2–3% at the proximal femur in individuals with normal BMD values; the difference between the two sites is ascribable to greater difficulties with repositioning and examining the femur, as compared to the spine. However, these data obtained under nearly experimental conditions may not apply to everyday clinical practice. Reproducibility depends heavily on quality assurance factors, including tests to control the quality and performance of the machine, as well as the experience of the operator. Assessment of machine performance requires daily scanning of a phantom (which may be anthropomorphic or not), followed by calculation of the in vitro coefficient of variation (CV), which serves to evaluate short-term and longterm performance and to detect drift in measurement accuracy. These in vitro data, however, do not necessarily reflect in vivo reproducibility, which should be evaluated at each measurement centre. Measurements are obtained either three times in each of 15 patients or twice in each of 30 patients, and the CV (m/*r*) is calculated from the mean (m) and standard deviation *(r)* of these repeated measurements. The CV is expressed as a percentage and depends on mean BMD values(Phillipov, Seaborn et al. 2001). The standard deviation reflects measurement error, which is a characteristic of machine performance and is independent from the value measured.

#### **2.2 The least significant change (LSC)**

For two point measurements in time, a BMD change exceeding 2√2 times the precision error (PE) of a technique is considered a significant change (with 95% confidence): the corresponding change criterion has been termed "least significant change" or LSC. LSC = 2.8 x PE; where PE is the largest precision error of the technique used (or more easily the CV expressed in percentage). This smallest change that is considered statistically significant is also expressed in percentage(Gluer 1999).

#### **2.3 The smallest detectable difference (SDD)**

The measurement error can be calculated using Bland and Altman's 95% limits of agreement method(Bland and Altman 1986). Precision expressed by this method gives an absolute and metric estimate of random measurement error, also called SDD. In this case, where there are

Precision errors are evaluated by performing repeated scans on a representative set of individuals to characterize the reproducibility of the technique. Most published studies examine the short-term precision error, based on repeated measurements of each subject performed over a time period of no more than 2 weeks. Over such a short period, no true

The CV, the most commonly presented measure for BMD variability, is the SD corrected for the mean of paired measurements. CV, expressed as a percentage, is calculated as CV (%) = (√((∑(a-b)2)/2n))/((Ma+Mb)/2)x100 where a and b are the first and the second measurement, Ma and Mb are the mean values for the two groups, and n is the number of paired

Reproducibility is far better for BMD measurement than for most laboratory tests. Reproducibility expressed by the CV is usually 1–2% at the spine on anteroposterior images and 2–3% at the proximal femur in individuals with normal BMD values; the difference between the two sites is ascribable to greater difficulties with repositioning and examining the femur, as compared to the spine. However, these data obtained under nearly experimental conditions may not apply to everyday clinical practice. Reproducibility depends heavily on quality assurance factors, including tests to control the quality and performance of the machine, as well as the experience of the operator. Assessment of machine performance requires daily scanning of a phantom (which may be anthropomorphic or not), followed by calculation of the in vitro coefficient of variation (CV), which serves to evaluate short-term and longterm performance and to detect drift in measurement accuracy. These in vitro data, however, do not necessarily reflect in vivo reproducibility, which should be evaluated at each measurement centre. Measurements are obtained either three times in each of 15 patients or twice in each of 30 patients, and the CV (m/*r*) is calculated from the mean (m) and standard deviation *(r)* of these repeated measurements. The CV is expressed as a percentage and depends on mean BMD values(Phillipov, Seaborn et al. 2001). The standard deviation reflects measurement error, which is a characteristic of machine performance and is independent from

For two point measurements in time, a BMD change exceeding 2√2 times the precision error (PE) of a technique is considered a significant change (with 95% confidence): the corresponding change criterion has been termed "least significant change" or LSC. LSC = 2.8 x PE; where PE is the largest precision error of the technique used (or more easily the CV expressed in percentage). This smallest change that is considered statistically significant is

The measurement error can be calculated using Bland and Altman's 95% limits of agreement method(Bland and Altman 1986). Precision expressed by this method gives an absolute and metric estimate of random measurement error, also called SDD. In this case, where there are

**2. Methods of bone mineral density reproducibility measurement** 

change in BMD is expected.

observations.

the value measured.

**2.2 The least significant change (LSC)** 

also expressed in percentage(Gluer 1999).

**2.3 The smallest detectable difference (SDD)** 

**2.1 The coefficient of variation (CV)** 

two observations for each subject, the standard deviation of the differences (SDdiff) estimates the within variability of the measurements. Most disagreements between measurements are expected to be between limits called ''limits of agreement'' defined as dz(1-a/2) SDdiff where d is the mean difference between the pairs of measurements and z(1-a/2) is the 100(1-a/2)th centile of the normal distribution. The value d is an estimate of the mean systematic bias of measurement 1 to measurement 2. d is expected to be 0 because a true change in BMD is not assumed to occur during the interval between the two BMD measurements. Defining a to be 5%, the limits of agreement are +1.96SDdiff and -1.96SDdiff. Thus, about twice the standard deviation (SD) of the difference scores gives the 95% limits of agreement for the two measurements by the machine. A test is considered to be capable of detecting a difference, in absolute units, of at least the magnitude of the limits of agreement.

#### **3. Clinical implications of bone mineral density reproducibility measurement**

In clinical practice, two absolute values (g/cm2) have to be compared, rather than two percentages (T-scores). When serial measurements are obtained in a patient, only changes greater than the LSC (in %) or the SDD (in g/cm2) can be ascribed to treatment effects. Smaller changes may be related to measurement error.

We studied recently the in vivo short term variability of BMD measurement by DXA in three groups of subjects with a wide range of BMD values: healthy young volunteers, postmenopausal women and patients with chronic rheumatic diseases (most of them taking corticosteroids). In all studied subjects, reproducibility expressed by different means was good and independent from clinical and BMD status. Thus, the clinician interpreting a repeated DXA scan of a subject should be aware that a BMD change exceeding the LSC is significant, in our centre arising from a BMD change of at least 3.56% at the total hip and 5.60% at the spine. Expressed as SDD, a BMD change should exceed 0.02 g/cm2 at the total hip and 0.04 g/cm2 at the spine before it can be considered a significant change(El Maghraoui, Do Santos Zounon et al. 2005). Indeed, it has become usual to perform repeated DXA measurement: in postmenopausal women to monitor efficacy of treatment and in patients with chronic rheumatic diseases where high prevalence of bone loss has been demonstrated (Maillefert, Aho et al. 2001; Johnson, Petkov et al. 2005) especially when long term corticosteroid therapy is used. In the reports published, variability is usually expressed as CV and the figures for short term variability are lower than the ones we found [7-9]. However, two studies showed variability data more in line with our results. In Ravaud et al.(Ravaud, Reny et al. 1999) study, two samples of healthy (n=70) and elderly (n=57) postmenopausal women showed a CV (%) of 0.9 and 1.8, respectively, at the spine, and of 0.9 and 2.3, respectively, at the total hip. Eastell showed an LSC (%) of 5.4 at the lumbar spine and 8 at the total hip, respectively, in osteoporotic postmenopausal women (Eastell 1996). It has been suggested that the varying results of reproducibility studies might be explained by the ''population'' investigated; a phantom and healthy young subjects are likely to show more favourable variability than postmenopausal women, possibly in part because of easier positioning for measurement(Gluer, Blake et al. 1995). However, our study failed to show better variability, expressed as CV (%), in young healthy volunteers (El Maghraoui, Do Santos Zounon et al. 2005). Another reason advocated was that osteoarthritis in postmenopausal women may contribute to poorer variability than found in healthy young subjects. The SDD values found in our study were comparable to the figures presented by Ravaud et al. (Ravaud, Reny et al. 1999). In the first group of postmenopausal

Monitoring DXA Measurement in Clinical Practice 23

measured. Thus, these results enhance to encourage the use of the measurement of both hips

(a)

0 0,2 0,4 0,6 0,8 1 1,2 1,4 1,6 **Mean score**

(b)

0 0,511,5 2 **Mean score**

Fig. 1. Graph of the difference score against the mean score of the total hip (a) and the lumbar spine (b) BMD measurements (g/cm2) in our centre [17] using Bland and Altman

method. The outermost (solid) lines represent the SDD.

to improve the reproducibility of DXA at this site.






**Difference score**

0,02

0,04

0,06

0




**Diffrence score**

0,02

0,04

0,06

0

women (mean age 53 years) they describe, the SDD was 0.02 (g/cm2) at the total hip and 0.02 at the lumbar spine. In the second group described, women with a mean age of 80 years, these figures were 0.04 and 0.04, respectively. In Lodder et al.(Lodder, Lems et al. 2004) study (Ninety five women, mean age 59.9 years), the SDD was 0.04 (g/cm2) at the total hip and 0.05 at the lumbar spine. The SDD values of the children studied in this study tended to be lower than the values in the postmenopausal women (table I). Using the SDD, one can state that a (BMD) change larger than the figure found is a true (BMD) change in 95% of the cases. The characteristics of the Bland and Altman method thus allow direct insight into the variability of the measurement under study (figure 1).

It has been shown that reproducibility expressed using the SDD is independent of the BMD value whereas reproducibility expressed using the CV or the derived LSC depend on the BMD value. Ravaud et al.(Ravaud, Reny et al. 1999) reported that using SD, the values of the cut-offs are 0.024, 0.030, 0.020, and 0.021 g/cm2 for postmenopausal women aged 70 years and 0.040, 0.033, 0.033, and 0.038 g/cm2 for postmenopausal osteoporotic women aged >70 years at the spine, femoral neck, greater trochanter, and total hip, respectively. Using CV, cut-offs vary depending on the BMD level. In postmenopausal women aged >70 years, for a BMD level between 0.600 g/cm2 and 1.000 g/cm2, the cut-offs derived from CV vary between 0.015 g/cm2 and 0.024 g/cm2, 0.024 g/cm2 and 0.041 g/cm2, 0.018 g/cm2 and 0.030 g/cm2, 0.015 g/cm2 and 0.025 g/cm2 for the spine femoral neck, greater trochanter, and total hip, respectively. In postmenopausal osteoporotic women aged >70 years, for the same range of BMD level, cut-offs vary between 0.031 g/cm2 and 0.051 g/cm2, 0.038 g/cm2 and 0.063 g/cm2, 0.043 g/cm2 and 0.071 g/cm2, 0.038 g/cm2 and 0.063 g/cm2 for the same bone sites. Consequently, to express variability on a percentage basis using CV leads to underestimate variability in patients with low BMD and to overestimate variability in patients with high BMD. Previous reports in the literature, as well as Ravaud, Lodder's data and our data (table 1) demonstrate that absolute precision errors derived from SD are constant across a wide range of BMD values and independent of the level of BMD. Because of therapeutic consequences, the clinician should be especially careful in judging an apparent BMD change in patients with osteoporosis. Influence of age on BMD reproducibility is controversial. Previous studies have suggested that BMD measurement errors were independent of age even some studies suggested that SDD may vary in extreme ages (children and elderly) probably because of age-related factors other than BMD. However, a few data exist for reproducibility of DXA in women over 70. Ravaud et al. data, as well as those of Fuleihan (Fuleihan, Testa et al. 1995), and Maggio et al. (Maggio, McCloskey et al. 1998) show that the measurement error is greater in older osteoporotic subjects. Several factors such as difficulties in repositioning could explain the increase of measurement error in this kind of patients. Therefore, the use of the SDD in the evaluation of an apparent BMD change gives a more conservative approach than the use of the CV at low BMD. Because of its independence from the BMD level and its expression in absolute units, the SDD is a preferable measure for use in daily clinical practice as compared with the CV and the derived LSC.

In contrast with all previous publications about DXA reproducibility, we found in our centre better results for the hip BMD variability than the lumbar spine. This is due to the fact that our study was the first to use the mean measure of the two femurs (dual femur). In this study, we showed in a group of young healthy volunteers that the SDD was ±0.0218 g/cm2 when both femurs were measured whereas it was ±0.0339 g/cm2 when only one femur was

women (mean age 53 years) they describe, the SDD was 0.02 (g/cm2) at the total hip and 0.02 at the lumbar spine. In the second group described, women with a mean age of 80 years, these figures were 0.04 and 0.04, respectively. In Lodder et al.(Lodder, Lems et al. 2004) study (Ninety five women, mean age 59.9 years), the SDD was 0.04 (g/cm2) at the total hip and 0.05 at the lumbar spine. The SDD values of the children studied in this study tended to be lower than the values in the postmenopausal women (table I). Using the SDD, one can state that a (BMD) change larger than the figure found is a true (BMD) change in 95% of the cases. The characteristics of the Bland and Altman method thus allow direct

It has been shown that reproducibility expressed using the SDD is independent of the BMD value whereas reproducibility expressed using the CV or the derived LSC depend on the BMD value. Ravaud et al.(Ravaud, Reny et al. 1999) reported that using SD, the values of the cut-offs are 0.024, 0.030, 0.020, and 0.021 g/cm2 for postmenopausal women aged 70 years and 0.040, 0.033, 0.033, and 0.038 g/cm2 for postmenopausal osteoporotic women aged >70 years at the spine, femoral neck, greater trochanter, and total hip, respectively. Using CV, cut-offs vary depending on the BMD level. In postmenopausal women aged >70 years, for a BMD level between 0.600 g/cm2 and 1.000 g/cm2, the cut-offs derived from CV vary between 0.015 g/cm2 and 0.024 g/cm2, 0.024 g/cm2 and 0.041 g/cm2, 0.018 g/cm2 and 0.030 g/cm2, 0.015 g/cm2 and 0.025 g/cm2 for the spine femoral neck, greater trochanter, and total hip, respectively. In postmenopausal osteoporotic women aged >70 years, for the same range of BMD level, cut-offs vary between 0.031 g/cm2 and 0.051 g/cm2, 0.038 g/cm2 and 0.063 g/cm2, 0.043 g/cm2 and 0.071 g/cm2, 0.038 g/cm2 and 0.063 g/cm2 for the same bone sites. Consequently, to express variability on a percentage basis using CV leads to underestimate variability in patients with low BMD and to overestimate variability in patients with high BMD. Previous reports in the literature, as well as Ravaud, Lodder's data and our data (table 1) demonstrate that absolute precision errors derived from SD are constant across a wide range of BMD values and independent of the level of BMD. Because of therapeutic consequences, the clinician should be especially careful in judging an apparent BMD change in patients with osteoporosis. Influence of age on BMD reproducibility is controversial. Previous studies have suggested that BMD measurement errors were independent of age even some studies suggested that SDD may vary in extreme ages (children and elderly) probably because of age-related factors other than BMD. However, a few data exist for reproducibility of DXA in women over 70. Ravaud et al. data, as well as those of Fuleihan (Fuleihan, Testa et al. 1995), and Maggio et al. (Maggio, McCloskey et al. 1998) show that the measurement error is greater in older osteoporotic subjects. Several factors such as difficulties in repositioning could explain the increase of measurement error in this kind of patients. Therefore, the use of the SDD in the evaluation of an apparent BMD change gives a more conservative approach than the use of the CV at low BMD. Because of its independence from the BMD level and its expression in absolute units, the SDD is a preferable measure for use in daily clinical practice as compared with the

In contrast with all previous publications about DXA reproducibility, we found in our centre better results for the hip BMD variability than the lumbar spine. This is due to the fact that our study was the first to use the mean measure of the two femurs (dual femur). In this study, we showed in a group of young healthy volunteers that the SDD was ±0.0218 g/cm2 when both femurs were measured whereas it was ±0.0339 g/cm2 when only one femur was

insight into the variability of the measurement under study (figure 1).

CV and the derived LSC.

measured. Thus, these results enhance to encourage the use of the measurement of both hips to improve the reproducibility of DXA at this site.

(b) 0 0,511,5 2 **Mean score**

Fig. 1. Graph of the difference score against the mean score of the total hip (a) and the lumbar spine (b) BMD measurements (g/cm2) in our centre [17] using Bland and Altman method. The outermost (solid) lines represent the SDD.


Mean difference, mean of the difference between the first and the second BMD measurement; SD difference, SD of the difference between the first and the second BMD measurement; SDD, smallest detectable difference (g/cm2); CV, coefficient of variation (%); LSC, least significant change (%); ICC, intraclass correlation coefficient. Table 1. Comparison of BMD measurement reproducibility evaluation in two studies including various groups of patients.

Monitoring DXA Measurement in Clinical Practice 25

Although the variability as expressed by the CV, and especially the SDD, is reassuring, showing good short term variability at group level, the wide range of the differences in BMD and the derived T-scores indicates considerable individual differences between two

indicates that in some patients the diagnosis, based on the diagnostic thresholds of the

In summary, reproducibility of BMD measurement by DXA in different kinds of patients (postmenopausal women, patients with chronic rheumatic diseases, elderly…) expressed by different means is good at a group level. However, the clinician must remain aware that an apparent BMD change in an individual patient may represent a precision error. At each measurement centre, the SDD should be calculated from in vivo reproducibility data. In clinical practice, the SDD should be used to estimate the significance of observed changes, in

The first factor is the time interval between two measurements in the same patient which must be long enough to allow occurrence of a change greater than the SDD or the LSC. Therefore, it depends on the expected rate of change in BMD measurement (which varies according to whether the measurement site is composed predominantly of trabecular or of cortical bone) and the reproducibility of BMD measurement at that site. Thus, in clinical practice, a treatment-induced BMD increase can only be detected in general after 2 years. However, in patients receiving long term steroid therapy, the changes in BMD may be so important that they can be detected at 1 year. Thus, although the spine may not be the best site for the diagnosis of osteoporosis given the high prevalence of spinal degenerative disease, it is the most sensitive site for detecting changes over time. However, our study showed that measurement of both femurs (called "dual femur" in Lunar machines)

In another side, the changes in BMD measurements are influenced by the ability of osteoporosis treatments to increase the BMD at the different skeletal sites. For some treatments such as teriparatide and the more potent bisphosphonates, statistically significant changes in spine BMD occur on time scales of 1 to 2 years in the majority of patients, although for other treatments, such as raloxifene, the changes are often not large enough to be statistically significant. Recently, the strontium ranelate trials show BMD increases at the spine and hip 2-5 times larger than those for BPs and SERMs, and comparable or greater than with teriparatide. However, it is important to appreciate that much of this effect is due to the higher atomic number of strontium compared with calcium. Thus, strontium in bone attenuates X-rays much more strongly than calcium, and BMD is overestimated compared with the true mass of bone mineral present. This effect may persist for many years after the patient stops treatment and may affect the relationship between

Thus, treatment dosages cannot be adjusted on the basis of BMD changes. Moreover, there is no proof that repeating BMD measurements improves compliance, as most patients discontinue antiresorptive medications after a few months because of administration

constraints, side effects, cost of medications or lack of interest.

ΔT scores, for example,

consecutive BMD measurements in some patients. The range in

WHO, would change owing to the measurement variability.

**5. Other factors influencing DXA monitoring** 

increases the reproducibility at this site.

BMD and fracture risk.

absolute values.

Mean difference, mean of the difference between the first and the second BMD measurement; SD difference, SD of the difference between the first and the second BMD measurement; SDD, smallest detectable difference (g/cm2); CV, coefficient of variation (%); LSC, least significant change (%); ICC, intraclass correlation coefficient. Table 1. Comparison of BMD measurement reproducibility evaluation in two studies including various groups of patients.

Although the variability as expressed by the CV, and especially the SDD, is reassuring, showing good short term variability at group level, the wide range of the differences in BMD and the derived T-scores indicates considerable individual differences between two consecutive BMD measurements in some patients. The range in ΔT scores, for example, indicates that in some patients the diagnosis, based on the diagnostic thresholds of the WHO, would change owing to the measurement variability.

In summary, reproducibility of BMD measurement by DXA in different kinds of patients (postmenopausal women, patients with chronic rheumatic diseases, elderly…) expressed by different means is good at a group level. However, the clinician must remain aware that an apparent BMD change in an individual patient may represent a precision error. At each measurement centre, the SDD should be calculated from in vivo reproducibility data. In clinical practice, the SDD should be used to estimate the significance of observed changes, in absolute values.

#### **5. Other factors influencing DXA monitoring**

The first factor is the time interval between two measurements in the same patient which must be long enough to allow occurrence of a change greater than the SDD or the LSC. Therefore, it depends on the expected rate of change in BMD measurement (which varies according to whether the measurement site is composed predominantly of trabecular or of cortical bone) and the reproducibility of BMD measurement at that site. Thus, in clinical practice, a treatment-induced BMD increase can only be detected in general after 2 years. However, in patients receiving long term steroid therapy, the changes in BMD may be so important that they can be detected at 1 year. Thus, although the spine may not be the best site for the diagnosis of osteoporosis given the high prevalence of spinal degenerative disease, it is the most sensitive site for detecting changes over time. However, our study showed that measurement of both femurs (called "dual femur" in Lunar machines) increases the reproducibility at this site.

In another side, the changes in BMD measurements are influenced by the ability of osteoporosis treatments to increase the BMD at the different skeletal sites. For some treatments such as teriparatide and the more potent bisphosphonates, statistically significant changes in spine BMD occur on time scales of 1 to 2 years in the majority of patients, although for other treatments, such as raloxifene, the changes are often not large enough to be statistically significant. Recently, the strontium ranelate trials show BMD increases at the spine and hip 2-5 times larger than those for BPs and SERMs, and comparable or greater than with teriparatide. However, it is important to appreciate that much of this effect is due to the higher atomic number of strontium compared with calcium. Thus, strontium in bone attenuates X-rays much more strongly than calcium, and BMD is overestimated compared with the true mass of bone mineral present. This effect may persist for many years after the patient stops treatment and may affect the relationship between BMD and fracture risk.

Thus, treatment dosages cannot be adjusted on the basis of BMD changes. Moreover, there is no proof that repeating BMD measurements improves compliance, as most patients discontinue antiresorptive medications after a few months because of administration constraints, side effects, cost of medications or lack of interest.

Monitoring DXA Measurement in Clinical Practice 27

Bland, J. M. and D. G. Altman (1986). "Statistical methods for assessing agreement between

Cummings, S. R., D. B. Karpf, et al. (2002). "Improvement in spine bone density and

Eastell, R. (1996). "Assessment of bone density and bone loss." *Osteoporos Int* 6 Suppl 2: 3-5. El Maghraoui, A. and C. Roux (2008). "DXA scanning in clinical practice." *QJM* 101(8): 605-

El Maghraoui, A., A. A. Do Santos Zounon, et al. (2005). "Reproducibility of bone mineral

El Maghraoui, A., L. Achemlal, et al. (2006). "Monitoring of dual-energy X-ray absorptiometry measurement in clinical practice." *J Clin Densitom* 9(3): 281-6. Fuleihan, G. E., M. A. Testa, et al. (1995). "Reproducibility of DXA absorptiometry: a model

Gluer, C. C. (1999). "Monitoring skeletal changes by radiological techniques." *J Bone Miner* 

Gluer, C. C., G. Blake, et al. (1995). "Accurate assessment of precision errors: how to measure the reproducibility of bone densitometry techniques." *Osteoporos Int* 5(4): 262-70. Hochberg, M. C., P. D. Ross, et al. (1999). "Larger increases in bone mineral density during

Hochberg, M. C., S. Greenspan, et al. (2002). "Changes in bone density and turnover explain

Johnson, S. L., V. I. Petkov, et al. (2005). "Improving osteoporosis management in patients

Li, Z., M. P. Meredith, et al. (2001). "A method to assess the proportion of treatment effect

Lodder, M. C., W. F. Lems, et al. (2004). "Reproducibility of bone mineral density

Maggio, D., E. V. McCloskey, et al. (1998). "Short-term reproducibility of proximal femur

Maillefert, J. F., L. S. Aho, et al. (2001). "Changes in bone density in patients with ankylosing spondylitis: a two-year follow-up study." *Osteoporos Int* 12(7): 605-9. Nguyen, T. V., P. N. Sambrook, et al. (1997). "Sources of variability in bone mineral density

Phillipov, G., C. J. Seaborn, et al. (2001). "Reproducibility of DXA: potential impact on serial measurements and misclassification of osteoporosis." *Osteoporos Int* 12(1): 49-54. Ravaud, P., J. L. Reny, et al. (1999). "Individual smallest detectable difference in bone

Roux, C., P. Garnero, et al. (2005). "Recommendations for monitoring antiresorptive therapies in postmenopausal osteoporosis." *Joint Bone Spine* 72(1): 26-31.

measurements: implications for study design and analysis of bone loss." *J Bone* 

with antiresorptive agents." *J Clin Endocrinol Metab* 87(4): 1586-92.

explained by a surrogate endpoint." *Stat Med* 20(21): 3175-88.

measurement in daily practice." *Ann Rheum Dis* 63(3): 285-9.

bone mineral density in the elderly." *Calcif Tissue Int* 63(4): 296-9.

mineral density measurements." *J Bone Miner Res* 14(8): 1449-56.

alendronate therapy are associated with a lower risk of new vertebral fractures in women with postmenopausal osteoporosis. Fracture Intervention Trial Research

the reductions in incidence of nonvertebral fractures that occur during treatment

reduction in risk of vertebral fractures during treatment with antiresorptive drugs."

density measurements using dual X-ray absorptiometry in daily clinical practice."

two methods of clinical measurement." *Lancet* 1(8476): 307-10.

for bone loss estimates." *J Bone Miner Res* 10(7): 1004-14.

**7. References** 

17.

*Am J Med* 112(4): 281-9.

*Osteoporos Int* 16(12): 1742-8.

Group." *Arthritis Rheum* 42(6): 1246-54.

with fractures." *Osteoporos Int* 16(9): 1079-85.

*Res* 14(11): 1952-62.

*Miner Res* 12(1): 124-35.

Above all, BMD is used as a surrogate marker for the fracture risk, yet BMD increases do not reliably reflect a reduction in the fracture risk. Although bisphosphonates, raloxifene, and strontium have not been compared in the same study, they seem to produce comparable reductions in the risk of vertebral fractures, of about 30–50%, whereas BMD changes differ markedly across medications. Studies have shown that BMD gains explain only a small proportion of the vertebral fracture risk reduction: 28% with risedronate(Li, Meredith et al. 2001), 16% with alendronate(Cummings, Karpf et al. 2002), and 4% with raloxifene(Sarkar, Mitlak et al. 2002). It has been suggested that the percentage of BMD change may be related to the change in the relative risk of fracture(Wasnich and Miller 2000). In one study, a linear relationship was found between these two parameters, but a 1% increase in spinal BMD was associated with an only 3% decrease in the relative risk of vertebral fracture(Cummings, Karpf et al. 2002). For peripheral fractures, in contrast, the risk reduction is clearly related to the BMD gain(Hochberg, Greenspan et al. 2002). Common sense indicates that a BMD increase during treatment should be preferable over a BMD decrease. However, data showing that the fracture risk may decrease despite a reduction in BMD have been reported (Watts, Geusens et al. 2005). It has also been shown that the fracture risk was more heavily dependent on BMD at baseline than on BMD changes during treatment (Hochberg, Ross et al. 1999).

#### **6. Conclusion**

Serial BMD measurements can be used to monitor current antiresorptive treatments (raloxifene, bisphosphonates or strontium ranelate). However, adequate quality-control procedures must be used(Roux, Garnero et al. 2005). Measurement error must be considered when evaluating serial assessments. A clear understanding of the interpretation of serial measurements and the statistical principles impacting upon their interpretation is necessary to determine whether a change is real and not simply random fluctuation. It is inadequate to simply use the manufacturer's default precision error, which may underestimate the precision error in the clinical setting. Thus, every centre should calculate its own precision error from in vivo reproducibility data. International societies interested in osteoporosis diagnosis and management such as the International Society for Clinical Densitometry or the International Osteoporosis Foundation should add to their guidelines at least two recommendations about DXA monitoring highlighted in this paper: the measurement of both hips improves the reproducibility at this site and DXA measurement centres should determine and use the individual SDD. Indeed, the use of the SDD is preferable to the use of the CV and LSC because of its independence from BMD level and its expression in absolute units. The exact definition and advices for the measure and use of these parameters in clinical practice should be clearly explained. It is clear that the choice of the optimum site for performing follow-up scans depends on the ratio of the BMD treatment effect to the precision of the measurements. The larger this ratio, the more statistically significant the observed changes are likely to be. Actually, all data agree in showing that the spine is the optimum site. In clinical practice, BMD measurements have to be spaced at least 2 years apart. The main goal of serial BMD measurement is to check that no further bone loss has occurred; estimation of BMD gains is the secondary objective. This should be explained to the patients, many of whom expect to recover normal BMD values.

#### **7. References**

26 Dual Energy X-Ray Absorptiometry

Above all, BMD is used as a surrogate marker for the fracture risk, yet BMD increases do not reliably reflect a reduction in the fracture risk. Although bisphosphonates, raloxifene, and strontium have not been compared in the same study, they seem to produce comparable reductions in the risk of vertebral fractures, of about 30–50%, whereas BMD changes differ markedly across medications. Studies have shown that BMD gains explain only a small proportion of the vertebral fracture risk reduction: 28% with risedronate(Li, Meredith et al. 2001), 16% with alendronate(Cummings, Karpf et al. 2002), and 4% with raloxifene(Sarkar, Mitlak et al. 2002). It has been suggested that the percentage of BMD change may be related to the change in the relative risk of fracture(Wasnich and Miller 2000). In one study, a linear relationship was found between these two parameters, but a 1% increase in spinal BMD was associated with an only 3% decrease in the relative risk of vertebral fracture(Cummings, Karpf et al. 2002). For peripheral fractures, in contrast, the risk reduction is clearly related to the BMD gain(Hochberg, Greenspan et al. 2002). Common sense indicates that a BMD increase during treatment should be preferable over a BMD decrease. However, data showing that the fracture risk may decrease despite a reduction in BMD have been reported (Watts, Geusens et al. 2005). It has also been shown that the fracture risk was more heavily dependent on BMD at baseline than on BMD changes during treatment (Hochberg, Ross et

Serial BMD measurements can be used to monitor current antiresorptive treatments (raloxifene, bisphosphonates or strontium ranelate). However, adequate quality-control procedures must be used(Roux, Garnero et al. 2005). Measurement error must be considered when evaluating serial assessments. A clear understanding of the interpretation of serial measurements and the statistical principles impacting upon their interpretation is necessary to determine whether a change is real and not simply random fluctuation. It is inadequate to simply use the manufacturer's default precision error, which may underestimate the precision error in the clinical setting. Thus, every centre should calculate its own precision error from in vivo reproducibility data. International societies interested in osteoporosis diagnosis and management such as the International Society for Clinical Densitometry or the International Osteoporosis Foundation should add to their guidelines at least two recommendations about DXA monitoring highlighted in this paper: the measurement of both hips improves the reproducibility at this site and DXA measurement centres should determine and use the individual SDD. Indeed, the use of the SDD is preferable to the use of the CV and LSC because of its independence from BMD level and its expression in absolute units. The exact definition and advices for the measure and use of these parameters in clinical practice should be clearly explained. It is clear that the choice of the optimum site for performing follow-up scans depends on the ratio of the BMD treatment effect to the precision of the measurements. The larger this ratio, the more statistically significant the observed changes are likely to be. Actually, all data agree in showing that the spine is the optimum site. In clinical practice, BMD measurements have to be spaced at least 2 years apart. The main goal of serial BMD measurement is to check that no further bone loss has occurred; estimation of BMD gains is the secondary objective. This should be explained to

the patients, many of whom expect to recover normal BMD values.

al. 1999).

**6. Conclusion** 


**3** 

*Finland* 

**Bone Loss and Seasonal Variation in Serial DXA** 

Osteoporosis is a disease of increased skeleton fragility accompanied by low BMD and microarchitectural deterioration. Osteoporosis and bone fragility result in significant morbidity and medical and social costs (Dennison et al., 2005; Cummings et al., 2002). The risk of fractures is greater among women with low BMD although it explains only part of the increased fracture tendency among the elderly (National Osteoporosis Foundation, 1998).The diagnosis of osteoporosis is currently based on axial dual X-ray absorptiometry (DXA) measurements (National Osteoporosis Foundation 1998). In addition to being applicable for fracture prediction, axial DXA has a role in treatment monitoring protocols (Miller et al., 1996; Sowers et al., 1997). Furthermore, serial central DXA measurements have been used for research purposes to evaluate the risk- and preventive factors for postmenopausal bone loss (Burger et al., 1998; Hannan et al., 2000; Sirola et al., 2003) Perimenopausal bone loss rates of over -2 percent /year in spinal and over -1 percent /year in the femoral region have generally been reported (Harris and Dawson-Hughes, 1992; Pouilles et al., 1993; Pouilles et al., 1995; Prior et al., 1998; Ito et al., 1999). In postmenopausal women, age related bone loss continues at age specific rate after the initial fastening during

There are two forms of vitamin D, ergocalciferol (vitamin D2) and cholecalciferol (vitamin D3). Cholecalciferol is the metabolically active form of vitamin D. Vitamin D is produced with either the effect of ultraviolet B radiation or ingested with nutrition and the metabolically active form is produced in the kidneys. It has been suggested that there might be a seasonal variation in bone turnover as assessed with both BMD and biochemical markers (Rosen et al., 1994.; Storm et al., 1998; Rapuri et al., 2002). The sun-light related vitamin D production which varies according to season seems to contribute to this phenomenon (Rapuri et al., 2002; Dawson-Hughes et al., 1997). However, other studies have not demonstrated any such effect as measured either by BMD or by levels of bone marker

**1. Introduction**

the menopausal transition (Hansen et al., 1995).

**Densitometry – A Population-Based Study** 

Jukka S. Jurvelin3, Marjo Tuppurainen1,4 and Heikki Kröger1,2,

Joonas Sirola1,2, Toni Rikkonen1, Risto Honkanen1,

*2Department of Orthopedics, Traumatology and Hand Surgery 3Department of Clinical Physiology & Nuclear Medicine* 

*1University of Eastern Finland, Campus of Kuopio,* 

*4Department of Obstetrics and Gynaecology* 

*Bone and Cartilage Research Unit* 

*2,3,4Kuopio University Hospital,* 


### **Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study**

Joonas Sirola1,2, Toni Rikkonen1, Risto Honkanen1, Jukka S. Jurvelin3, Marjo Tuppurainen1,4 and Heikki Kröger1,2, *1University of Eastern Finland, Campus of Kuopio, Bone and Cartilage Research Unit 2Department of Orthopedics, Traumatology and Hand Surgery 3Department of Clinical Physiology & Nuclear Medicine 4Department of Obstetrics and Gynaecology 2,3,4Kuopio University Hospital, Finland* 

#### **1. Introduction**

28 Dual Energy X-Ray Absorptiometry

Sarkar, S., B. H. Mitlak, et al. (2002). "Relationships between bone mineral density and

Wasnich, R. D. and P. D. Miller (2000). "Antifracture efficacy of antiresorptive agents are related to changes in bone density." *J Clin Endocrinol Metab* 85(1): 231-6. Watts, N. B., P. Geusens, et al. (2005). "Relationship between changes in BMD and

10.

2097-104.

incident vertebral fracture risk with raloxifene therapy." *J Bone Miner Res* 17(1): 1-

nonvertebral fracture incidence associated with risedronate: reduction in risk of nonvertebral fracture is not related to change in BMD." *J Bone Miner Res* 20(12):

> Osteoporosis is a disease of increased skeleton fragility accompanied by low BMD and microarchitectural deterioration. Osteoporosis and bone fragility result in significant morbidity and medical and social costs (Dennison et al., 2005; Cummings et al., 2002). The risk of fractures is greater among women with low BMD although it explains only part of the increased fracture tendency among the elderly (National Osteoporosis Foundation, 1998).The diagnosis of osteoporosis is currently based on axial dual X-ray absorptiometry (DXA) measurements (National Osteoporosis Foundation 1998). In addition to being applicable for fracture prediction, axial DXA has a role in treatment monitoring protocols (Miller et al., 1996; Sowers et al., 1997). Furthermore, serial central DXA measurements have been used for research purposes to evaluate the risk- and preventive factors for postmenopausal bone loss (Burger et al., 1998; Hannan et al., 2000; Sirola et al., 2003) Perimenopausal bone loss rates of over -2 percent /year in spinal and over -1 percent /year in the femoral region have generally been reported (Harris and Dawson-Hughes, 1992; Pouilles et al., 1993; Pouilles et al., 1995; Prior et al., 1998; Ito et al., 1999). In postmenopausal women, age related bone loss continues at age specific rate after the initial fastening during the menopausal transition (Hansen et al., 1995).

> There are two forms of vitamin D, ergocalciferol (vitamin D2) and cholecalciferol (vitamin D3). Cholecalciferol is the metabolically active form of vitamin D. Vitamin D is produced with either the effect of ultraviolet B radiation or ingested with nutrition and the metabolically active form is produced in the kidneys. It has been suggested that there might be a seasonal variation in bone turnover as assessed with both BMD and biochemical markers (Rosen et al., 1994.; Storm et al., 1998; Rapuri et al., 2002). The sun-light related vitamin D production which varies according to season seems to contribute to this phenomenon (Rapuri et al., 2002; Dawson-Hughes et al., 1997). However, other studies have not demonstrated any such effect as measured either by BMD or by levels of bone marker

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 31

offs was based on the assumption that highest BMDs (reflecting serum vitamin D concentration) would be present at late summer and early fall season (within group 2) and the lowest at late winter (within group 3) whereas group 1 would present an intermediate. Also, in order to reveal the greatest differences in seasonal BMD variation between two successive measurements a numeric value of a Seasonal Difference Index (SDI) was calculated as follows:

SDI = (Group number at baseline - Group number at follow-up) Accordingly, the numeric value of SDI varied from -2 to +2 (Table 1). In all, 521 (54.6 %) women were measured within the same season at both measurements (SDI group 0). In 212 (22.2 %) women the follow-up measurement was carried out in a later season (SDI groups -1 and -2) and in 221 (23.2 %) women in earlier season (SDI groups +1 and +2) than the baseline measurement. There was a significant variation in distribution of women into measurement seasons between and within baseline and follow-up measurements (Figure 1 ). Accordingly, any conclusions over differences between specific seasons were considered to

**Follow-up season (Group number) Baseline season (Group number)** January-April (1) May-August (2) September-December (3)

Table 1. Numeric values for the "Seasonal Difference Index" (SDI)a according to baseline

Women were divided into two groups according to their use of hormone therapy (HRT) (tablets and plasters) which was defined as the use of hormonal products for menopausal symptoms. *HRT users* (n=393) had used HRT continuously or occasionally during the follow-up regardless of whether or not they had used hormonal therapy before the baseline (14 women used HRT only before baseline, and were excluded in analysis on HRT effect). *HRT non-users* (n=547) had never used estrogen containing products aimed at postmenopausal therapy. In the OSTPRE cohort, the majority of HRT users were taking estrogen-progesterone combination products (56.2% of all HRT). No data was available on whether HRT was continuous or sequential. Forty-five percent of HRT users (occasional or continuous) had been treated with HRT also prior to baseline. The duration of HRT varied from one month to 7.5 years. The information about the use of hormonal products was obtained from the questionnaires. Comparison between self-reported use of HRT and the national prescription records of The Social Insurance Institution, Finland (KELA), for the whole OSTPRE cohort in 1996–2001 revealed that 97.8% of those who had received an oestrogen drug prescription reported HRT use in inquiries. On the other hand, in 25.5% of the self-reported non-users of HRT some oestrogen use (short-term, median 6.0 months) was

January-April (1) 0 -1 -2 May-August (2) 1 0 -1 September-December (3) 2 1 0

be precluded due to this uneven distribution.

*a) SDI=[Season Group at baseline]-[Season Group at follow-up]*

and follow-up DXA measurement season

**2.3 Other variables 2.3.1 Hormone therapy** 

recorded (Sirola et al., 2003a).

compounds (Patel et al, 2001; Blumsohn et al., 2003)**.** Consequently, the effect of the season when densitometry was performed on bone density is still unresolved. Furthermore, the role of a seasonal difference between two distant follow-up bone density measurements in postmenopausal bone loss has not been studied and thus, nothing is known about the effect of this phenomenon on treatment monitoring or other longitudinal data collection. If present, such seasonal variation could have significant effect on the evaluation of prospective data.

The purpose of the present study was to investigate the effect of densitometry season on early postmenopausal BMD and bone loss in a subset of 954 Finnish women selected from the population-based Osteoporosis Risk Factor and Prevention (OSTPRE)-study.

### **2. Subjects and methods**

#### **2.1 Study population**

The study population was a randomly selected part of the prospective Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study cohort. The OSTPRE cohort was established in 1989 and included all women born in 1932-1941 and who were resident in Kuopio Province, Finland (n=14 220). A postal inquiry including questions about health disorders, medications including HRT, gynaecologic history, nutritional habits, physical activity, lifestyle habits, and anthropometric information was sent to these women at baseline in 1989 (Honkanen et al, 1991). The 5-year (in 1994 follow-up questionnaires were sent to the 13 100 women who responded at baseline and responses were received from 11 954 at 5-year follow-up. The study protocol has been approved by the ethics committee of University of Kuopio and Kuopio University Hospital. Informed written consent from the participants was collected with the postal inquiries.

Of the 13 100 respondents in 1989, 11 055 (84.4 %) were willing to undergo DXA densitometry. A random sample of 2 362 women was selected for densitometry out of which 2025 women actually underwent the procedure during 1989-91. The questionnaire information was updated at the time of bone densitometry. In all, 1 873 women underwent both baseline (1989-91) and follow-up (1994-97) measurements and 1 551 of these had valid serial measurements for both lumbar spine and femoral neck (excluding severe bone deformities, see section *Bone mass measurements*).

For the present study, the following women were additionally excluded: 1) hysterectomized women (for whom it was impossible to define the menopausal status) and bilaterally ovariectomized women (n=445), 2) premenopausal women (n=152). Accordingly, the final study population consisted of 954 women (beginning of menopause either before or during follow-up) aged 48 to 59 years at baseline densitometry. The beginning of menopause was defined as 12 months' amennorhea (WHO Scientific Group, 1996) and the duration of menopause varied from 1 to 26 years at follow-up densitometry. The duration of follow-up varied from 3.8 to 7.9 years (mean 5.8 years).

#### **2.2 Seasonal Difference Index (SDI)**

The study population was divided into three equal groups within the year according to month of measurement at baseline and follow-up: Group 1 (from January to April), Group 2 (from May to August) and Group 3 (from September to December). The basis for selecting these cutoffs was based on the assumption that highest BMDs (reflecting serum vitamin D concentration) would be present at late summer and early fall season (within group 2) and the lowest at late winter (within group 3) whereas group 1 would present an intermediate. Also, in order to reveal the greatest differences in seasonal BMD variation between two successive measurements a numeric value of a Seasonal Difference Index (SDI) was calculated as follows:

#### SDI = (Group number at baseline - Group number at follow-up)

Accordingly, the numeric value of SDI varied from -2 to +2 (Table 1). In all, 521 (54.6 %) women were measured within the same season at both measurements (SDI group 0). In 212 (22.2 %) women the follow-up measurement was carried out in a later season (SDI groups -1 and -2) and in 221 (23.2 %) women in earlier season (SDI groups +1 and +2) than the baseline measurement. There was a significant variation in distribution of women into measurement seasons between and within baseline and follow-up measurements (Figure 1 ). Accordingly, any conclusions over differences between specific seasons were considered to be precluded due to this uneven distribution.


**Follow-up season (Group number)**

*a) SDI=[Season Group at baseline]-[Season Group at follow-up]*

Table 1. Numeric values for the "Seasonal Difference Index" (SDI)a according to baseline and follow-up DXA measurement season

#### **2.3 Other variables**

30 Dual Energy X-Ray Absorptiometry

compounds (Patel et al, 2001; Blumsohn et al., 2003)**.** Consequently, the effect of the season when densitometry was performed on bone density is still unresolved. Furthermore, the role of a seasonal difference between two distant follow-up bone density measurements in postmenopausal bone loss has not been studied and thus, nothing is known about the effect of this phenomenon on treatment monitoring or other longitudinal data collection. If present, such seasonal variation could have significant effect on the evaluation of prospective data.

The purpose of the present study was to investigate the effect of densitometry season on early postmenopausal BMD and bone loss in a subset of 954 Finnish women selected from

The study population was a randomly selected part of the prospective Kuopio Osteoporosis Risk Factor and Prevention (OSTPRE) study cohort. The OSTPRE cohort was established in 1989 and included all women born in 1932-1941 and who were resident in Kuopio Province, Finland (n=14 220). A postal inquiry including questions about health disorders, medications including HRT, gynaecologic history, nutritional habits, physical activity, lifestyle habits, and anthropometric information was sent to these women at baseline in 1989 (Honkanen et al, 1991). The 5-year (in 1994 follow-up questionnaires were sent to the 13 100 women who responded at baseline and responses were received from 11 954 at 5-year follow-up. The study protocol has been approved by the ethics committee of University of Kuopio and Kuopio University Hospital. Informed written consent from the participants

Of the 13 100 respondents in 1989, 11 055 (84.4 %) were willing to undergo DXA densitometry. A random sample of 2 362 women was selected for densitometry out of which 2025 women actually underwent the procedure during 1989-91. The questionnaire information was updated at the time of bone densitometry. In all, 1 873 women underwent both baseline (1989-91) and follow-up (1994-97) measurements and 1 551 of these had valid serial measurements for both lumbar spine and femoral neck (excluding severe bone

For the present study, the following women were additionally excluded: 1) hysterectomized women (for whom it was impossible to define the menopausal status) and bilaterally ovariectomized women (n=445), 2) premenopausal women (n=152). Accordingly, the final study population consisted of 954 women (beginning of menopause either before or during follow-up) aged 48 to 59 years at baseline densitometry. The beginning of menopause was defined as 12 months' amennorhea (WHO Scientific Group, 1996) and the duration of menopause varied from 1 to 26 years at follow-up densitometry. The duration of follow-up

The study population was divided into three equal groups within the year according to month of measurement at baseline and follow-up: Group 1 (from January to April), Group 2 (from May to August) and Group 3 (from September to December). The basis for selecting these cut-

the population-based Osteoporosis Risk Factor and Prevention (OSTPRE)-study.

**2. Subjects and methods**

was collected with the postal inquiries.

deformities, see section *Bone mass measurements*).

varied from 3.8 to 7.9 years (mean 5.8 years).

**2.2 Seasonal Difference Index (SDI)** 

**2.1 Study population**

#### **2.3.1 Hormone therapy**

Women were divided into two groups according to their use of hormone therapy (HRT) (tablets and plasters) which was defined as the use of hormonal products for menopausal symptoms. *HRT users* (n=393) had used HRT continuously or occasionally during the follow-up regardless of whether or not they had used hormonal therapy before the baseline (14 women used HRT only before baseline, and were excluded in analysis on HRT effect). *HRT non-users* (n=547) had never used estrogen containing products aimed at postmenopausal therapy. In the OSTPRE cohort, the majority of HRT users were taking estrogen-progesterone combination products (56.2% of all HRT). No data was available on whether HRT was continuous or sequential. Forty-five percent of HRT users (occasional or continuous) had been treated with HRT also prior to baseline. The duration of HRT varied from one month to 7.5 years. The information about the use of hormonal products was obtained from the questionnaires. Comparison between self-reported use of HRT and the national prescription records of The Social Insurance Institution, Finland (KELA), for the whole OSTPRE cohort in 1996–2001 revealed that 97.8% of those who had received an oestrogen drug prescription reported HRT use in inquiries. On the other hand, in 25.5% of the self-reported non-users of HRT some oestrogen use (short-term, median 6.0 months) was recorded (Sirola et al., 2003a).

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 33

The *height* and *weight* were measured with a stadiometer and calibrated scale by study

*Nutritional calcium* intake of each participant was estimated according to self-reported ingestion of milk products in postal inquiries. The following questions were asked:" How many deciliters of fluid milk products (milk, sour milk, yoghurt, etc.) do you consume daily? " and "How many slices of cheese do you eat daily?". The amount of calcium was approximated

Women were divided into two categories (yes, no) according to the presence/absence of *bone affecting diseases or medications* at baseline. Bone affecting diseases/medications have been described previously by Kröger et al. (Kröger et al. 1994). Diseases were: renal disease, liver disease, insulin-dependent diabetes, malignancies, rheumatoid arthritis, endocrine abnormalities (parathyroid/thyroid glands, adrenals), malabsorption (including lactose malabsorption), total/partial gastrectomy, postovariectomy status, premenopausal amenorrhea, alcoholism and long-term immobilisation. Medications were: corticosteroids, diuretics, cytotoxic drugs, anticonvulsive drugs, anabolic steroids, calcitonin,

*Physical activity level* was calculated based on combined physical activity in work and leisure based on self-reports in the postal inquiries. The physical activity was categorised into low,

The bone mineral density of lumbar spine (L2-L4) and left femoral neck was determined using the same dual X-ray absorptiometry (DXA) (Lunar DPX, Madison, Wisconsin, USA) equipment at both the baseline and five year measurements. The measurements were carried out in Kuopio University Hospital by specially trained personnel. The short term reproducibility of this method has been shown to be 0.9 % for lumbar spine and 1.5 % for femoral neck BMD measurements (Kröger et al., 1992).The long-term reproducibility (coefficient of variation) of the DXA instrument for BMD during the study period, as determined by regular phantom measurements, was 0.4 % (Komulainen et al., 1998). Each DXA measurement print was reviewed and women with bone deformities (osteoarthritis, osteophytes, scoliosis and compression fractures) in either area were excluded from the analyses. At the time of densitometry, also the weight and height of each participant was

Statistical analyses were carried out with the Statistical Package for Social Sciences (SPSS) for Windows, version 17. The annual BMD changes for both measurement sites were calculated as follows: [(BMD at the 5-year follow-up - BMD at baseline) / duration of follow-up in years] and reported as percentage of baseline BMD. In categorical analyses, uni- and multivariate analysis of variance was used and Tukey (crude models) and Least Significant Difference (adjusted models) -post hoc tests were utilized to study differences between multiple groups when applicable. Adjustment for age, height, weight, months since menopause, mean calcium intake, use of HRT (no, occasional, continuous), physical activity level (low, moderate, high),

to be 120 mg/dl for fluid milk products and 87 mg/slice for cheese (Sirola et al., 2003b).

**2.3.2 Adjusting variables** 

bisphosphonates, vitamin D.

moderate and high (Kröger et al., 1994).

**2.4 Bone mass measurements** 

measured in a controlled situation.

**2.5 Statistical methods** 

group nurses at each bone densitometry.

Follow-up measurement month Fig. 1. Distribution of the study population accroding to DXA measurement month.

#### **2.3.2 Adjusting variables**

32 Dual Energy X-Ray Absorptiometry

Baseline measurement month

Follow-up measurement month

Fig. 1. Distribution of the study population accroding to DXA measurement month.

Number of women

Number of women

200

180

160

140

120

100

80

60

40

20 0

120

100

80

60

40

20

0

987654321 121110

987654321 121110

The *height* and *weight* were measured with a stadiometer and calibrated scale by study group nurses at each bone densitometry.

*Nutritional calcium* intake of each participant was estimated according to self-reported ingestion of milk products in postal inquiries. The following questions were asked:" How many deciliters of fluid milk products (milk, sour milk, yoghurt, etc.) do you consume daily? " and "How many slices of cheese do you eat daily?". The amount of calcium was approximated to be 120 mg/dl for fluid milk products and 87 mg/slice for cheese (Sirola et al., 2003b).

Women were divided into two categories (yes, no) according to the presence/absence of *bone affecting diseases or medications* at baseline. Bone affecting diseases/medications have been described previously by Kröger et al. (Kröger et al. 1994). Diseases were: renal disease, liver disease, insulin-dependent diabetes, malignancies, rheumatoid arthritis, endocrine abnormalities (parathyroid/thyroid glands, adrenals), malabsorption (including lactose malabsorption), total/partial gastrectomy, postovariectomy status, premenopausal amenorrhea, alcoholism and long-term immobilisation. Medications were: corticosteroids, diuretics, cytotoxic drugs, anticonvulsive drugs, anabolic steroids, calcitonin, bisphosphonates, vitamin D.

*Physical activity level* was calculated based on combined physical activity in work and leisure based on self-reports in the postal inquiries. The physical activity was categorised into low, moderate and high (Kröger et al., 1994).

#### **2.4 Bone mass measurements**

The bone mineral density of lumbar spine (L2-L4) and left femoral neck was determined using the same dual X-ray absorptiometry (DXA) (Lunar DPX, Madison, Wisconsin, USA) equipment at both the baseline and five year measurements. The measurements were carried out in Kuopio University Hospital by specially trained personnel. The short term reproducibility of this method has been shown to be 0.9 % for lumbar spine and 1.5 % for femoral neck BMD measurements (Kröger et al., 1992).The long-term reproducibility (coefficient of variation) of the DXA instrument for BMD during the study period, as determined by regular phantom measurements, was 0.4 % (Komulainen et al., 1998). Each DXA measurement print was reviewed and women with bone deformities (osteoarthritis, osteophytes, scoliosis and compression fractures) in either area were excluded from the analyses. At the time of densitometry, also the weight and height of each participant was measured in a controlled situation.

#### **2.5 Statistical methods**

Statistical analyses were carried out with the Statistical Package for Social Sciences (SPSS) for Windows, version 17. The annual BMD changes for both measurement sites were calculated as follows: [(BMD at the 5-year follow-up - BMD at baseline) / duration of follow-up in years] and reported as percentage of baseline BMD. In categorical analyses, uni- and multivariate analysis of variance was used and Tukey (crude models) and Least Significant Difference (adjusted models) -post hoc tests were utilized to study differences between multiple groups when applicable. Adjustment for age, height, weight, months since menopause, mean calcium intake, use of HRT (no, occasional, continuous), physical activity level (low, moderate, high),

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 35

**Variable Group 1 Group 2 Group 3 Total (n=424) (n=192) (n=338) (n=954)** 

Duration of follow-up yrs 5,9(0,5) 5,8(0,4) 5,8(0,5) 5,8 (0,5)

Duration of menopause, months 89,4(54,1) 86,2(48,1) 106,6(50,4) 95,0(52,3)d Baseline age, yrs 53,5(3,0) 53,5(2,9) 54,2(2,7) 53,7(2,9)d Baseline height, cm 160,9(5,1) 161,6(5,5) 160,8(5,3) 161,0(5,3)

Baseline weight, kg 69,1(12,4) 68,6(11,4) 69,3(11,9) 69,1(12,0) Weight change (%) 2,9(5,2) 2,9(5,6) 3,0(5,3) 2,9(5,3) Grip strength, kPa 62,0(16,5) 62,2(15,6) 62,4(16,0) 62,2(16,1) Mean calcium intake, mg/day 789(343) 813(311) 799(319) 797(328) Baseline lumbar BMD, g/cm2 1,13(0,17) 1,13(0,16) 1,11(0,16) 1,12(0,16) Baseline femoral neck BMD, g/cm2 0,93(0,13) 0,93(0,13) 0,92(0,12) 0,92(0,13) FU lumbar BMD, g/cm2 1,07(0,17) 1,09(0,15) 1,07(0,16) 1,08(0,16) FU femoral neck BMD, g/cm2 0,88(0,12) 0,90(0,13) 0,89(0,12) 0,89(0,12)

No use 55,8 56,0 64,7 59,0 c Occasional (<90 % of FU) 32,2 34,6 28,2 31,2 Continuous (>90 % of FU) 12,1 9,4 7,2 9,8

No bone affecting disease/medication 60,4 64,1 64,7 62,7 Any previous fracture at baseline 20,9 23,3 20,6 21,3

Previous wrist fracture at baseline 7,2 7,7 5,5 6,7 Alcohol >1 drink/week 35,1 34,4 32,3 33,9 Smoking 9,4 11,1 8,8 9,5 High overall physical activity levelb 29,9 37,8 32,8 32,5

*to December). b) Three categorical variable: low, moderate, high. c) p<0.05 / d) p<0.001* 

*a) Season Groups: Group 1 (from January to April), Group 2 (from May to August) and Group 3 (from September* 

Table 2. Baseline characteristics of the study population according to season groupa (n=954 ).

**Means (SD) of continuous variables** 

**B. Distribution of category variables (%)**

Use of HRT during follow-up

**Season groupa**

duration of follow-up (years) and the use of bone affecting medications or diseases (yes/no) (including vitamin D supplements) as covariates was performed.

#### **3. Results**

The baseline data revealed that there were no significant differences between the three season groups with respect to age, duration of menopause or HRT use (Table 2). There were no differences in the cross-sectional BMD between the season groups at baseline or at the five year follow-up (Table 2).

In order to evaluate a possible contribution of seasonal differences to the follow-up BMD values, the association of SDI with mean annual bone loss was investigated (Figure 2). The bone loss rate in SDI categories -2 and 0 was greater than in SDI categories +1 and +2 (p<0.01) in both lumbar and femoral regions (Figure 2). In lumbar spine, the difference in bone loss rate between SDI categories -1 and +1 was also significant (p=0.015). In femoral neck there was no significant difference between SDI category -1 and the other categories. These effects were independent of any adjustments.

*p-values refer to the differences of the respective SDI group in comparison to SDI 1 and 2 for lumbar spine (LS=full line) and femoral neck (FN=dotted line). a) adjusted for age, height, weight, months since menopause, calcium intake, use of HT (no, occasional, continuous), overall physical activity level (low, moderate, high), duration of follow-up (years) and use of bone affecting medications or diseases (yes/no)* 

Fig. 2. Effect of Seasonal Difference Index (SDI) on mean annual bone loss rate (%) in early postmenopausal women (n=954). Analysis of covariancea

duration of follow-up (years) and the use of bone affecting medications or diseases (yes/no)

The baseline data revealed that there were no significant differences between the three season groups with respect to age, duration of menopause or HRT use (Table 2). There were no differences in the cross-sectional BMD between the season groups at baseline or at the

In order to evaluate a possible contribution of seasonal differences to the follow-up BMD values, the association of SDI with mean annual bone loss was investigated (Figure 2). The bone loss rate in SDI categories -2 and 0 was greater than in SDI categories +1 and +2 (p<0.01) in both lumbar and femoral regions (Figure 2). In lumbar spine, the difference in bone loss rate between SDI categories -1 and +1 was also significant (p=0.015). In femoral neck there was no significant difference between SDI category -1 and the other categories.

*p-values refer to the differences of the respective SDI group in comparison to SDI 1 and 2 for lumbar spine (LS=full line) and femoral neck (FN=dotted line). a) adjusted for age, height, weight, months since menopause, calcium intake, use of HT (no, occasional, continuous), overall physical activity level (low, moderate, high),* 

p < 0.01 (LS)

Fig. 2. Effect of Seasonal Difference Index (SDI) on mean annual bone loss rate (%) in early

*duration of follow-up (years) and use of bone affecting medications or diseases (yes/no)* 

p < 0.001 (FN) p < 0.001 (FN)

p < 0.01 (FN)

postmenopausal women (n=954). Analysis of covariancea

p < 0.01 (LS)

(including vitamin D supplements) as covariates was performed.

These effects were independent of any adjustments.

**3. Results** 

five year follow-up (Table 2).


*a) Season Groups: Group 1 (from January to April), Group 2 (from May to August) and Group 3 (from September to December). b) Three categorical variable: low, moderate, high. c) p<0.05 / d) p<0.001* 

Table 2. Baseline characteristics of the study population according to season groupa (n=954 ).

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 37

p < 0.01

B: SDI category 1 and 2

Fig. 3. Effect of hormone therapy on lumbar spine (full line) and femoral neck (dotted line) bone loss according to seaosal difference index (SDI) category. Analysis of variance

(ANOVA, n=954).

A: SDI category -2 to 0

p > 0.05

A: SDI category -2 to 0

B: SDI category 1 and 2

Fig. 3. Effect of hormone therapy on lumbar spine (full line) and femoral neck (dotted line) bone loss according to seaosal difference index (SDI) category. Analysis of variance (ANOVA, n=954).

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 39

Secondly, in prospective studies, possible distortion in results due to seasonal differences in risk-factor analyses and treatment effects on bone loss rate should be closely considered. Accordingly, it could be worthwhile to create a "seasonal difference index" for each population based on the respective DXA measurement data. The effect of seasonal differences on densitometry-based risk-factors for postmenopausal bone loss remains to be resolved. It might be that seasonal dependency could interact with certain factors lessening their true impacts. Also, the impact of these differences on fracture prediction remains to be

The interpretation of seasonal indices needs to be undertaken with caution. In the present study, the differences between the SDI categories do not necessarily provide information of the exact season that each participant was measured (e.g. SDI +1 could represent a difference between baseline season category 3 and a 5-year season category 2 or a baseline category 2 and a 5-year category 1). Also, the baseline and follow-up season groups (groups 1, 2, 3) itself included quite heterogeneous population. For example, the women measured during the first months of group 1 (January-February) were likely to have significantly lower BMDs in comparison to women presenting the other end (March-April) during which sun light exposure would be higher. We used this season categorisation order to categorize the measurement months into three equal groups within a year (i.e. four months per group: Spring (January-April), Summer (May-August) and Fall/Winter (September-December). This was based on the rapid changes in sun light exposure in the northern latitudes: sun light hours rapidly increase during may and decrease rapidly during september. Furthermore, the distribution of women into measurement months within each group was found to differ between baseline and follow-up densitometry. However, the goal of the present study was not to identify specific "risk seasons", but only to assess variability in the estimated bone loss rate attributable to the seasons when the two successive measurements had been done. Thus, some arbitrary cut-offs, in terms of season groups, were forced to be decided. Accordingly, the hypothesis of high or low bone density according to sun exposure (and vitamin D levels) in baseline and follow-up was precluded by skew distribution of women into different measurement months. Before adaptation for wider use, these indices would need further testing and refinement in order to optimize the categorisation for local

This is the first long-term population-based study investigating the contribution of seasonal difference between two successive DXA measurements on postmenopausal bone loss. Previous studies have shown significant alterations in vitamin D and PTH levels attributable to season (Rapuri et al., 2002; Dawson-Hughes et al., 1997).which could also provide a pathophysiological mechanism for the seasonal bone effects. Some studies have also found seasonal variation in cross-sectional BMD data, bone markers and bone loss rate (Rosen et al., 1994.; Storm et al., 1998; Rapuri et al., 2002; Dawson-Hughes et al., 1997). However, other studies have failed to found any evidence on altered bone metabolism related to seasons as measured with either BMD or bone markers (Patel et al, 2001; Blumsohn et al., 2003). The ability to observe seasonal effects is likely to depend on geographical location. Another study conducted in northern latitudes (Gerdhem et al, 2004) failed to demonstrate any seasonal variation in cross-sectional study design in Sweden. The present large study population, living at northern latitudes (latitude 63 degrees), might facilitate the detection of seasonal differences. However, the present study also showed no significant cross-sectional

resolved in future studies.

purposes.

variation in BMD.

In ANOVA, SDI explained 2.3 percent (R2=0.023) and 1.3 percent (R2=0.013) of the bone mass changes in lumbar spine and femoral neck, respectively. Furthermore, in linear regression models, SDI was positively associated with both lumbar spine and femoral neck bone loss (p<0.001) independent of all adjustments.

In order to mimic the possible effect of SDI on a treatment monitoring protocol, we investigated the effects of HRT on bone loss according to SDI (Figure 3). HRT users had significantly lower bone loss rate in SDI categories -2 to 0 in comparison to HRT non-users (lumbar spine and femoral neck) (p<0.01). In SDI categories 1 to 2, there was no statistically significant difference between HRT users and non-users. These results were not affected by adjustments.

#### **4. Discussion**

The present study evaluated the effect of season on BMD and bone loss with a randomly selected population-based sample of 954 Finnish women. The seasonal difference between two successive axial DXA measurements, estimated with the "Seasonal Difference Index" (SDI), influenced the evaluation of postmenopausal bone loss rate. In addition, this factor interfered with the evaluation of protective bone effects of HRT. The exact direction of these relationships, in terms of specific seasons, was found to complex partly due to the study setting.

The present study sample was large and randomly selected. There were few differences in the baseline variables between the three season groups. The DXA measurements were carried out with same equipment and measurement staff and all bone deformities were excluded. In addition, phantom calibration was performed regularly which should exclude any significant seasonal changes attributable to equipment performance. Furthermore, comprehensive adjustment for any potential confounders was used in the analyses. Hence, it is most unlikely that any major confounding could have occurred in the present study.

Some weaknesses of the present study should also be considered. The follow-up time was relatively long with considerable inter-individual variation. Although the results were adjusted for duration of follow-up and reported in annual percent changes the follow-up period in treatment monitoring is usually only one or two years. However, the long followup and large sample probably facilitated the detection of bone mass changes in the present study. Furthermore, the present study assumed that the pattern of bone loss between the approximately five-year follow-up was linear, masking any possible short-term non-linear patterns. However, adjusting for these changes would have required DXA measurement at the very least at 1 year intervals. Lastly, the lack of information of serum vitamin D levels precluded causal conclusions. However, adjustment for bone affecting medication (including vitamin D containing products, medications and supplements) and calcium intake was performed eliminating bias due to these factors.

The results of the present study have two major applications. Firstly, in treatment monitoring, an attempt should be made to measure bone density within the same season. Naturally, the seasonal limits depend on amount of seasonal variation in sunlight exposure of the study population and should be closely studied. The sub-division used in the present study provides one example in DXA measurements suitable for Scandinavian latitudes.

In ANOVA, SDI explained 2.3 percent (R2=0.023) and 1.3 percent (R2=0.013) of the bone mass changes in lumbar spine and femoral neck, respectively. Furthermore, in linear regression models, SDI was positively associated with both lumbar spine and femoral neck

In order to mimic the possible effect of SDI on a treatment monitoring protocol, we investigated the effects of HRT on bone loss according to SDI (Figure 3). HRT users had significantly lower bone loss rate in SDI categories -2 to 0 in comparison to HRT non-users (lumbar spine and femoral neck) (p<0.01). In SDI categories 1 to 2, there was no statistically significant difference between HRT users and non-users. These results were not affected by

The present study evaluated the effect of season on BMD and bone loss with a randomly selected population-based sample of 954 Finnish women. The seasonal difference between two successive axial DXA measurements, estimated with the "Seasonal Difference Index" (SDI), influenced the evaluation of postmenopausal bone loss rate. In addition, this factor interfered with the evaluation of protective bone effects of HRT. The exact direction of these relationships, in terms of specific seasons, was found to complex partly due to the study

The present study sample was large and randomly selected. There were few differences in the baseline variables between the three season groups. The DXA measurements were carried out with same equipment and measurement staff and all bone deformities were excluded. In addition, phantom calibration was performed regularly which should exclude any significant seasonal changes attributable to equipment performance. Furthermore, comprehensive adjustment for any potential confounders was used in the analyses. Hence, it is most unlikely that any major confounding could have occurred in the present study.

Some weaknesses of the present study should also be considered. The follow-up time was relatively long with considerable inter-individual variation. Although the results were adjusted for duration of follow-up and reported in annual percent changes the follow-up period in treatment monitoring is usually only one or two years. However, the long followup and large sample probably facilitated the detection of bone mass changes in the present study. Furthermore, the present study assumed that the pattern of bone loss between the approximately five-year follow-up was linear, masking any possible short-term non-linear patterns. However, adjusting for these changes would have required DXA measurement at the very least at 1 year intervals. Lastly, the lack of information of serum vitamin D levels precluded causal conclusions. However, adjustment for bone affecting medication (including vitamin D containing products, medications and supplements) and calcium

The results of the present study have two major applications. Firstly, in treatment monitoring, an attempt should be made to measure bone density within the same season. Naturally, the seasonal limits depend on amount of seasonal variation in sunlight exposure of the study population and should be closely studied. The sub-division used in the present study provides one example in DXA measurements suitable for Scandinavian latitudes.

intake was performed eliminating bias due to these factors.

bone loss (p<0.001) independent of all adjustments.

adjustments.

setting.

**4. Discussion** 

Secondly, in prospective studies, possible distortion in results due to seasonal differences in risk-factor analyses and treatment effects on bone loss rate should be closely considered. Accordingly, it could be worthwhile to create a "seasonal difference index" for each population based on the respective DXA measurement data. The effect of seasonal differences on densitometry-based risk-factors for postmenopausal bone loss remains to be resolved. It might be that seasonal dependency could interact with certain factors lessening their true impacts. Also, the impact of these differences on fracture prediction remains to be resolved in future studies.

The interpretation of seasonal indices needs to be undertaken with caution. In the present study, the differences between the SDI categories do not necessarily provide information of the exact season that each participant was measured (e.g. SDI +1 could represent a difference between baseline season category 3 and a 5-year season category 2 or a baseline category 2 and a 5-year category 1). Also, the baseline and follow-up season groups (groups 1, 2, 3) itself included quite heterogeneous population. For example, the women measured during the first months of group 1 (January-February) were likely to have significantly lower BMDs in comparison to women presenting the other end (March-April) during which sun light exposure would be higher. We used this season categorisation order to categorize the measurement months into three equal groups within a year (i.e. four months per group: Spring (January-April), Summer (May-August) and Fall/Winter (September-December). This was based on the rapid changes in sun light exposure in the northern latitudes: sun light hours rapidly increase during may and decrease rapidly during september. Furthermore, the distribution of women into measurement months within each group was found to differ between baseline and follow-up densitometry. However, the goal of the present study was not to identify specific "risk seasons", but only to assess variability in the estimated bone loss rate attributable to the seasons when the two successive measurements had been done. Thus, some arbitrary cut-offs, in terms of season groups, were forced to be decided. Accordingly, the hypothesis of high or low bone density according to sun exposure (and vitamin D levels) in baseline and follow-up was precluded by skew distribution of women into different measurement months. Before adaptation for wider use, these indices would need further testing and refinement in order to optimize the categorisation for local purposes.

This is the first long-term population-based study investigating the contribution of seasonal difference between two successive DXA measurements on postmenopausal bone loss. Previous studies have shown significant alterations in vitamin D and PTH levels attributable to season (Rapuri et al., 2002; Dawson-Hughes et al., 1997).which could also provide a pathophysiological mechanism for the seasonal bone effects. Some studies have also found seasonal variation in cross-sectional BMD data, bone markers and bone loss rate (Rosen et al., 1994.; Storm et al., 1998; Rapuri et al., 2002; Dawson-Hughes et al., 1997). However, other studies have failed to found any evidence on altered bone metabolism related to seasons as measured with either BMD or bone markers (Patel et al, 2001; Blumsohn et al., 2003). The ability to observe seasonal effects is likely to depend on geographical location. Another study conducted in northern latitudes (Gerdhem et al, 2004) failed to demonstrate any seasonal variation in cross-sectional study design in Sweden. The present large study population, living at northern latitudes (latitude 63 degrees), might facilitate the detection of seasonal differences. However, the present study also showed no significant cross-sectional variation in BMD.

Bone Loss and Seasonal Variation in Serial DXA Densitometry – A Population-Based Study 41

Gerdhem P, Mallmin H, Akesson K, Obrant KJ. Seasonal variation in bone density in

Hannan MT, Felson DT, Dawson-Hughes B, Tucker KL, Cupples LA, Wilson PW, Kiel DP

Hansen MA, Overgaard K, Christiansen C (1995) Spontaneous postmenopausal bone loss in different skeletal areas followed up for 15 years. *J Bone Miner Res* 10:205-210 Harris S, Dawson-Hughes B (1992) Rates of change in bone mineral density of spine, heel, femoral neck and radius in healthy postmenopausal women. *Bone Miner* 17:87-95 Honkanen R, Tuppurainen M, Alhava E, Saarikoski S (1991) Kuopio Osteoporosis Risk

*University of Kuopio, Community Health, Statistics and Reviews 3/1991*; Kuopio. Ito M, Nakamura T, Tsurusaki K, Uetani M, Hayashi K (1999) Effects of menopause on age-

Komulainen MH, Kröger H, Tuppurainen MT, Heikkinen A-M, Alhava E, Honkanen R,

postmenopausal women; a 5 year randomised trial. *Maturitas* 31:45-54 Komulainen MH, Kröger H, Tuppurainen MT, Heikkinen A-M, Alhava E, Honkanen R,

population-based 5-year randomised trial. *J Clin Endoc Metab* 84:546-552 Kröger H, Heikkinen J, Laitinen K, Kotaniemi A (1992) Dual-energy X-ray absorptiometry in

Kröger H, Tuppurainen M, Honkanen R, Alhava E, Saarikoski S (1994) Bone mineral density

Miller PD, Bonnick SL, Rosen CJ (1996) Consensus of an international panel on the clinical

National Osteoporosis Foundation (1998) Osteoporosis: review of the evidence for

Patel R, Collins D, Bullock S, Swaminathan R, Blake GM, Fogelman I (2001) The effect of

Pouilles JM, Tremollieres F, Ribot C (1993) The effects of menopause on longitudinal bone

Pouilles JM, Tremollieres F, Ribot C (1995) Effect of menopause on femoral and vertebral

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Rapuri PB, Kinyamu HK, Gallagher JC, Haynatzka V (2002) Seasonal changes in calcitropic

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*Endoc Res* 19(4):397-428

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*Osteoporosis Int* 18 (suppl 4):S1-S88

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Framingham osteoporosis study. *J Bone Miner Res* 15:710-720.

The present study also attempted to evaluate the possible bias in treatment monitoring resulting from seasonal difference via an investigation of how the SDI could affect the bone effect of HRT. In fact, variability in the protective effect of HRT between the SDI groups was detected. This serves as preliminary example of the extensiveness of the distortion in estimation of bone loss rate caused by seasonal difference. The inclusion of occasional use of HRT and less-than-perfect validity of HRT use in "non-users" probably mdified the results (Sirola et al., 2003a) but the trend was clear. Also, the effects may have been affected by lack of power due to small group sizes. However, seasonal densitometry difference may help in the identification of "non-responders" to HRT and be included in the list of other contributing factors (Sirola et al., 2003c; Komulainen et al., 1999). Previously, it has been suggested that calcium may flatten the seasonal differences in bone loss rate among elderly women8 and that there might be seasonal variation in the bone response to vitamin D (Dawson-Hughes et al, 1991). The present study also showed abolition in the difference in the bone loss rate between SDI categories in HRT users.

In summary, seasonal differences should receive closer attention in treatment monitoring protocols and longitudinal risk factor studies. In future studies, the seasonal densitometry difference should be considered as a potential confounder and its effect on risk factor and treatment monitoring data should be assessed. In addition, factors that might lessen the seasonal changes in the bone loss rate, such as calcium, vitamin D and other bone drugs, should be identified. Our study raises, for the first time, the question of whether the results of longitudinal DXA measurements might be significantly distorted by seasonal differences especially in northern latitudes.

#### **5. Acknowledgements**

This work has been financially supported by Kuopio University Hospital, EVO-grant

#### **6. References**


The present study also attempted to evaluate the possible bias in treatment monitoring resulting from seasonal difference via an investigation of how the SDI could affect the bone effect of HRT. In fact, variability in the protective effect of HRT between the SDI groups was detected. This serves as preliminary example of the extensiveness of the distortion in estimation of bone loss rate caused by seasonal difference. The inclusion of occasional use of HRT and less-than-perfect validity of HRT use in "non-users" probably mdified the results (Sirola et al., 2003a) but the trend was clear. Also, the effects may have been affected by lack of power due to small group sizes. However, seasonal densitometry difference may help in the identification of "non-responders" to HRT and be included in the list of other contributing factors (Sirola et al., 2003c; Komulainen et al., 1999). Previously, it has been suggested that calcium may flatten the seasonal differences in bone loss rate among elderly women8 and that there might be seasonal variation in the bone response to vitamin D (Dawson-Hughes et al, 1991). The present study also showed abolition in the difference in

In summary, seasonal differences should receive closer attention in treatment monitoring protocols and longitudinal risk factor studies. In future studies, the seasonal densitometry difference should be considered as a potential confounder and its effect on risk factor and treatment monitoring data should be assessed. In addition, factors that might lessen the seasonal changes in the bone loss rate, such as calcium, vitamin D and other bone drugs, should be identified. Our study raises, for the first time, the question of whether the results of longitudinal DXA measurements might be significantly distorted by seasonal differences

This work has been financially supported by Kuopio University Hospital, EVO-grant

sectional study. *J Bone Miner Res* 18:1274-1281

postmenopausal women. *Ann Intern Med* 115:505-12

Blumsohn A, Naylor KE, Timm W, Eagleton AC, Hannon RA, Eastell R (2003) Absence of

Burger H, de Laet CE, van Daele PL, Weel AE, Witteman JC, Hofman A, Pols HA (1998) Risk

Cummings SR, Melton LJ (2002) Epidemiology and outcomes of osteoporotic fractures.

Dawson-Hughes B, Dallal GE, Krall EA, Harris S, Sokoll LJ, Falconer G (1991) Effects of

Dawson-Hughes B, Harris SS, Dallal GE (1997) Plasma calcidiol, season and serum

Dennison E, Cole Z, Cooper C (2005) Diagnosis and epidemiology of osteoporosis. *Curr* 

marked seasonal change in bone turnover: a longitudinal and multicenter cross-

factors for increased bone loss in an elderly population: the Rotterdam Study. *Am J* 

vitamin D supplementation on wintertime and overall bone loss in healthy

parathyroid hormone concentration in healthy elderly men and women. *Am J Clin* 

the bone loss rate between SDI categories in HRT users.

especially in northern latitudes.

*Epidemiol* 147:871-9.

*Lancet* 359:1761–1767

*Opin Rheumatol* 17(4):456–461

*Nutr* 65:67-71

**5. Acknowledgements**

**6. References** 


**Part 2** 

**Body Composition** 


## **Part 2**

**Body Composition** 

42 Dual Energy X-Ray Absorptiometry

Rosen CJ, Morrison A, Zhou H, Storm D, Hunter SJ, Musgrave K, Chen T, Wei W, Holick

Sirola J, Kröger H, Honkanen R, Jurvelin JS, Sandini L, Tuppurainen M, Saarikoski S (2003b)

Sirola J, Kröger H, Jurvelin JS, Sandini L, Tuppurainen M, Saarikoski S, Honkanen R (2003c)

Sowers MF (1997) Clinical epidemiology and osteoporosis. Measures and their

Storm D, Eslin R, Porter ES, Musgrave K, Vereault D, Patton C, Kessenich C, Mohan S, Chen

bone mineral density and calciotropic hormones. *Bone Miner* 25:83-92 Sirola J, Kröger H, Honkanen R, Sandini L, Tuppurainen M, Jurvelin JS, Saarikoski S (2003a)

prevent weight loss- related bone loss*? Osteoporosis Int* 14: 27-33

population based approach. *J Bone Miner Res* 18: 1036-1043

loss: a prospective study. *Calcif Tissue Int* 72:659-665

interpretation. *Endocrin Metab North Amer* 26:219-231

MF (1994) Elderly women in northern New England exhibit seasonal changes in

Risk factors associated with peri- and postmenopausal bone loss - does HRT

Smoking may impair the bone protective effects of nutritional calcium- a

Interaction of nutritional calcium and HRT in prevention of postmenopausal bone

T, Holick MF, Rosen CJ (1998) Calcium supplementation prevents seasonal bone loss and changes in biochemical markers of bone turnover in elderly New England women: a randomized placebo-controlled trial*. J Clin Endocrinol Metab* 83:3817-25 WHO Scientific Group (1996) Research on the menopause in the 1990's. *A report of the WHO Scientific Group*. World Health Organisation, Geneva, Switzerland, vol 866:1-79.

**4** 

*Japan* 

**The Validity of Body Composition Measurement** 

Tissue energy production varies over time, and for practical purposes, can be organized into three main components, resting energy expenditure (REE), the thermic effect of food, and the thermic effect of physical activity (Ravussin & Bogardus, 1989). To maintain body weight, energy from food intake must equal energy expenditure. The energy requirement is defined as the average dietary energy intake that is predicted to maintain energy balance in healthy adults of a given age, gender, weight, height, and level of physical activity consistent with good health, and can be estimated from REE. REE reflects underlying tissue composition, mass, and metabolic activity (Elia, 1992) and accounts for 60–80% of total daily energy expenditure compared with the thermic effects of feeding and physical activity (~10% and ~15-30%, respectively). The REE has a large impact on the regulation of body mass and energy balance. In the field of obesity research, the presence and genesis of between-individual REE differences is a topic of great interest. To date, some earlier studies demonstrated that body mass, especially fat-free mass (FFM), has been a useful candidate in estimating REE (Ravussin & Bogardus, 1989; Fukagawa et al., 1990; Tataranni & Ravussin, 1995). Therefore, it is important to accurately evaluate REE and to measure body

A strategy for exploring between-individual differences in REE is to apply a tissue organ prediction model (Elia, 1992; Wang et al., 2000; Gallagher et al., 1998 & 2000). Each tissue and organ mass is quantified using either computed tomography (CT) or magnetic resonance imaging (MRI), and assigned assumed specific resting metabolic rates (Elia, 1992). The product of tissue organ mass and specific resting metabolic rate is then taken as the

composition by methods that are practical, precise, and accurate.

tissue organ REE, with the sum representing total body REE.

**1. Introduction** 

**Using Dual Energy X-Ray Absorptiometry for** 

**Estimating Resting Energy Expenditure** 

Chiyoko Usui1,2, Motoko Taguchi3,

*1Department of Health Promotion and Exercise, National Institute of Health and Nutrition,* 

*3Japan Women's College of Physical Education,* 

*4Department of Nutritional Education, National Institute of Health and Nutrition, 5Faculty of Sport Sciences, Waseda University,* 

Kazuko Ishikawa-Takata4 and Mitsuru Higuchi5

*2Research Fellow of the Japan Society for the Promotion of Science,* 

### **The Validity of Body Composition Measurement Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure**

Chiyoko Usui1,2, Motoko Taguchi3, Kazuko Ishikawa-Takata4 and Mitsuru Higuchi5 *1Department of Health Promotion and Exercise, National Institute of Health and Nutrition, 2Research Fellow of the Japan Society for the Promotion of Science, 3Japan Women's College of Physical Education, 4Department of Nutritional Education, National Institute of Health and Nutrition, 5Faculty of Sport Sciences, Waseda University, Japan* 

#### **1. Introduction**

Tissue energy production varies over time, and for practical purposes, can be organized into three main components, resting energy expenditure (REE), the thermic effect of food, and the thermic effect of physical activity (Ravussin & Bogardus, 1989). To maintain body weight, energy from food intake must equal energy expenditure. The energy requirement is defined as the average dietary energy intake that is predicted to maintain energy balance in healthy adults of a given age, gender, weight, height, and level of physical activity consistent with good health, and can be estimated from REE. REE reflects underlying tissue composition, mass, and metabolic activity (Elia, 1992) and accounts for 60–80% of total daily energy expenditure compared with the thermic effects of feeding and physical activity (~10% and ~15-30%, respectively). The REE has a large impact on the regulation of body mass and energy balance. In the field of obesity research, the presence and genesis of between-individual REE differences is a topic of great interest. To date, some earlier studies demonstrated that body mass, especially fat-free mass (FFM), has been a useful candidate in estimating REE (Ravussin & Bogardus, 1989; Fukagawa et al., 1990; Tataranni & Ravussin, 1995). Therefore, it is important to accurately evaluate REE and to measure body composition by methods that are practical, precise, and accurate.

A strategy for exploring between-individual differences in REE is to apply a tissue organ prediction model (Elia, 1992; Wang et al., 2000; Gallagher et al., 1998 & 2000). Each tissue and organ mass is quantified using either computed tomography (CT) or magnetic resonance imaging (MRI), and assigned assumed specific resting metabolic rates (Elia, 1992). The product of tissue organ mass and specific resting metabolic rate is then taken as the tissue organ REE, with the sum representing total body REE.

The Validity of Body Composition Measurement

**2.2 Dual energy X-ray absorptiometry (DXA)** 

(FFM) and fat mass (FM) were calculated by BW and % body fat.

**2.3 Method for the calculation of tissue organ mass** 

**2. Body composition analysis 2.1 Anthropometric measurements** 

meters (kg/m2).

gastrointestinal tract.

**3. Resting energy expenditure (REE)** 

**3.1 Method for measuring REE** 

Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 47

Body weight (BW) was measured to the nearest 0.1 kg by using an electronic scale (Inner Scan; Tanita Co., Japan and UC-321; A&D Co., Ltd., Tokyo, Japan), and height (Ht) was measured to thenearest 0.1cm by using a stadiometer (YL-65 and ST-2M; Yagami Inc., Japan). BW and Ht were measured with subjects wearing light clothing without shoes. Body mass index (BMI) was calculated by dividing BW in kilograms by the square of height in

The percentage of fat (% body fat) and bone mineral content (BMC) of the whole body and appendicular lean soft tissue (LST) were measured by DXA (Hologic QDR-4500 DXA Scanner and Hologic QDT DXA Scanner; Hologic Inc., Whaltham, MA, USA). Fat-free mass

Tissue organ mass was calculated using the previously reported prediction model as follows. Bone mass (BM) was calculated by multiplying BMC times 1.85 (Snyder et al., 1975; Heymsfield et al., 1990). Adipose tissue mass (AT) was assumed to be 85% of total body fat (Heymsfield et al., 2002), leading to the model based on FM. Thus, AT was calculated by multiplying FM times 1.18. Skeletal muscle mass (SM) was calculated using the prediction model of Kim et al. (2002). Finally, residual mass (RM) was calculated as the difference between BW and the sum of the calculated BM, AT and SM. Residual mass includes all of the high-metabolic-rate tissues and organs such as heart, brain, liver, kidneys, spleen, and

SM (kg) = 1.13×LST (kg) - 0.02×age (years) + 0.97

Participants came to the laboratory either on the previous night and stayed overnight, or came in the morning. In the latter case, subjects were asked to minimize any walking while en route from their home to the laboratory before REE determination. The measured REE (REEm) was directly measured by open-circuit indirect calorimetry. Measurements were performed between 0700 and 0900 h after 10–12 h of fasting, except for water, in a room at constant room temperature (23–25℃). After entering the laboratory, subjects rested in the supine position for at least 30 min, and a face mask was put on. In the case of overnight stay, the subjects were quietly awakened at 0630 and were attached a face mask while remaining in bed for 30 minutes. Two samples of expired air were collected in Douglas bags for a duration of 10 min each, and the mean value was used for the analysis. For young subjects,

BM (kg) = BMC (g)×1.85/1000 AT (kg) = FM (kg)×1.18

RM (kg) = BW - (BM + AT + SM)

REE = k1×tissue organ mass1 + k2×tissue organ mass2 + k3×tissue organ mass3 + ···

Dual-energy X-ray absorptiometry (DXA) can easily and accurately assess the body composition, including bone mineral content (BMC), fat mass (FM), and lean soft tissue mass (LST) of the whole body and the segments (Mazes et al., 1990; Svendsen et al., 1991 & 1993). In addition, recent studies support the use of DXA as a means of providing a "metabolic map" for exploring between individual or group differences in observed REE values (Hunter et al., 2001; Hayes et al., 2002; Usui et al., 2009). Specifically, the LST of the arms and legs, as provided by regional DXA estimates, represents primarily lowmetabolic rate skeletal muscle tissue. In contrast, LST of the head and trunk includes all of the tissue organs with high metabolic rates, such as brain, heart, liver, kidneys, spleen, and gastrointestinal tract. Total body-fat estimates reflect low metabolic rate adipose tissue. The various regional and whole-body estimates thus provide a qualitative metabolic body composition map of high and low metabolic rate components. Hayes et al. (2002) demonstrated that REE can be estimated from five measured DXA values: body weight, total body fat mass, bone mineral content, appendicular lean mass, and head area. Their study showed that no bias was detected between measured and predicted REEs (Hayes et al., 2002). These results were quite good and support the overall concept that predictable relations exist between major tissue organ level components and heat production at rest. In view of the finding of Hayes et al. (2002), we improved the estimation model from the five component model to four component model (adipose tissue, skeletal muscle, bone, and residual tissue organs). It was also evaluated the possibility that measurement of the magnitude and distribution of fundamental somatic heat-producing units using DXA can be used to estimate REE in both young and elderly women with different aerobic fitness levels (Usui et al., 2009). We suggested that REE in adult women can be estimated from four tissue organ components by using DXA regardless of age and aerobic fitness levels.

On the other hand, the traditional REE estimation approach, now widely applied, is to link REE with body composition determinants using a two-compartment model consisting of FM and FFM (Wang et al., 2000). Empirical regression models are developed with REE set as the dependent variable, and age, race, sex, height, and weight (FFM and FM) set as potential predictor variables. The Harris–Benedict equation (Harris & Benedict, 1919), Schofield equation (Schofield, 1985), and the Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University (FAO/WHO/UNU) equation (FAO/WHO/UNU, 1985) are internationally used. In Japan, Dietary Reference Intakes for Japanese (DRI (Japan)) provides basal metabolic rate (BMR ≒ REE) standards according to sex and age categories (Ministry of Heaith, Labour and Welfare of Japan, 2009). REE can be calculated as BMR standards multiplied by body weight. In addition, Ganpule et al. (2007) recently developed new predictive equations (NIHN (Japan)) for REE in Japanese.

In the present chapter, we evaluated the validity of body composition measurement using DXA for estimating REE. The REEs which are predicted by the equations of Harris– Benedict, Schofield, FAO/WHO/UNU, DRI (Japan), and NIHN (Japan) will then be crossvalidated against our measured and predicted values by using DXA.

#### **2. Body composition analysis**

46 Dual Energy X-Ray Absorptiometry

REE = k1×tissue organ mass1 + k2×tissue organ mass2 + k3×tissue organ mass3 + ···

Dual-energy X-ray absorptiometry (DXA) can easily and accurately assess the body composition, including bone mineral content (BMC), fat mass (FM), and lean soft tissue mass (LST) of the whole body and the segments (Mazes et al., 1990; Svendsen et al., 1991 & 1993). In addition, recent studies support the use of DXA as a means of providing a "metabolic map" for exploring between individual or group differences in observed REE values (Hunter et al., 2001; Hayes et al., 2002; Usui et al., 2009). Specifically, the LST of the arms and legs, as provided by regional DXA estimates, represents primarily lowmetabolic rate skeletal muscle tissue. In contrast, LST of the head and trunk includes all of the tissue organs with high metabolic rates, such as brain, heart, liver, kidneys, spleen, and gastrointestinal tract. Total body-fat estimates reflect low metabolic rate adipose tissue. The various regional and whole-body estimates thus provide a qualitative metabolic body composition map of high and low metabolic rate components. Hayes et al. (2002) demonstrated that REE can be estimated from five measured DXA values: body weight, total body fat mass, bone mineral content, appendicular lean mass, and head area. Their study showed that no bias was detected between measured and predicted REEs (Hayes et al., 2002). These results were quite good and support the overall concept that predictable relations exist between major tissue organ level components and heat production at rest. In view of the finding of Hayes et al. (2002), we improved the estimation model from the five component model to four component model (adipose tissue, skeletal muscle, bone, and residual tissue organs). It was also evaluated the possibility that measurement of the magnitude and distribution of fundamental somatic heat-producing units using DXA can be used to estimate REE in both young and elderly women with different aerobic fitness levels (Usui et al., 2009). We suggested that REE in adult women can be estimated from four tissue organ components by using DXA

On the other hand, the traditional REE estimation approach, now widely applied, is to link REE with body composition determinants using a two-compartment model consisting of FM and FFM (Wang et al., 2000). Empirical regression models are developed with REE set as the dependent variable, and age, race, sex, height, and weight (FFM and FM) set as potential predictor variables. The Harris–Benedict equation (Harris & Benedict, 1919), Schofield equation (Schofield, 1985), and the Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University (FAO/WHO/UNU) equation (FAO/WHO/UNU, 1985) are internationally used. In Japan, Dietary Reference Intakes for Japanese (DRI (Japan)) provides basal metabolic rate (BMR ≒ REE) standards according to sex and age categories (Ministry of Heaith, Labour and Welfare of Japan, 2009). REE can be calculated as BMR standards multiplied by body weight. In addition, Ganpule et al. (2007) recently developed new predictive equations

In the present chapter, we evaluated the validity of body composition measurement using DXA for estimating REE. The REEs which are predicted by the equations of Harris– Benedict, Schofield, FAO/WHO/UNU, DRI (Japan), and NIHN (Japan) will then be cross-

validated against our measured and predicted values by using DXA.

regardless of age and aerobic fitness levels.

(NIHN (Japan)) for REE in Japanese.

#### **2.1 Anthropometric measurements**

Body weight (BW) was measured to the nearest 0.1 kg by using an electronic scale (Inner Scan; Tanita Co., Japan and UC-321; A&D Co., Ltd., Tokyo, Japan), and height (Ht) was measured to thenearest 0.1cm by using a stadiometer (YL-65 and ST-2M; Yagami Inc., Japan). BW and Ht were measured with subjects wearing light clothing without shoes. Body mass index (BMI) was calculated by dividing BW in kilograms by the square of height in meters (kg/m2).

#### **2.2 Dual energy X-ray absorptiometry (DXA)**

The percentage of fat (% body fat) and bone mineral content (BMC) of the whole body and appendicular lean soft tissue (LST) were measured by DXA (Hologic QDR-4500 DXA Scanner and Hologic QDT DXA Scanner; Hologic Inc., Whaltham, MA, USA). Fat-free mass (FFM) and fat mass (FM) were calculated by BW and % body fat.

#### **2.3 Method for the calculation of tissue organ mass**

Tissue organ mass was calculated using the previously reported prediction model as follows. Bone mass (BM) was calculated by multiplying BMC times 1.85 (Snyder et al., 1975; Heymsfield et al., 1990). Adipose tissue mass (AT) was assumed to be 85% of total body fat (Heymsfield et al., 2002), leading to the model based on FM. Thus, AT was calculated by multiplying FM times 1.18. Skeletal muscle mass (SM) was calculated using the prediction model of Kim et al. (2002). Finally, residual mass (RM) was calculated as the difference between BW and the sum of the calculated BM, AT and SM. Residual mass includes all of the high-metabolic-rate tissues and organs such as heart, brain, liver, kidneys, spleen, and gastrointestinal tract.

> BM (kg) = BMC (g)×1.85/1000 AT (kg) = FM (kg)×1.18 SM (kg) = 1.13×LST (kg) - 0.02×age (years) + 0.97 RM (kg) = BW - (BM + AT + SM)

#### **3. Resting energy expenditure (REE)**

#### **3.1 Method for measuring REE**

Participants came to the laboratory either on the previous night and stayed overnight, or came in the morning. In the latter case, subjects were asked to minimize any walking while en route from their home to the laboratory before REE determination. The measured REE (REEm) was directly measured by open-circuit indirect calorimetry. Measurements were performed between 0700 and 0900 h after 10–12 h of fasting, except for water, in a room at constant room temperature (23–25℃). After entering the laboratory, subjects rested in the supine position for at least 30 min, and a face mask was put on. In the case of overnight stay, the subjects were quietly awakened at 0630 and were attached a face mask while remaining in bed for 30 minutes. Two samples of expired air were collected in Douglas bags for a duration of 10 min each, and the mean value was used for the analysis. For young subjects,

The Validity of Body Composition Measurement

2007) equations (Table 1).

**4.2 Statistical analysis** 

**REE** 

**4.1 Subjects** 

**3.3 Predictive equations of REE for women** 

Faculty of Sport Sciences of Waseda University, in Saitama.

Statistical significance was set at p < 0.05 for all predictors.

Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 49

Other predictive REEs were calculated using the Harris–Benedict (Harris & Benedict, 1919), Schofield (Schofield, 1985), FAO/WHO/UNU (FAO/WHO/UNU, 1985), DRI (Japan) (Ministry of Heaith, Labour and Welfare of Japan, 2009), and NIHN (Japan) (Ganpule et al.,

**4. The validity of body composition measurement using DXA for estimating** 

The data used for the current analysis were collected from different 3 experimental studies that followed a similar methodology (Usui et al., 2009; Taguchi et al., 2011; Hasegawa et al., 2011). Total of 288 healthy women (216 young women; age: 21.8 ± 2.0 (18-29) years and 72 elderly women; age: 63.3 ± 6.4 (50-77) years) were recruited for the study. The elderly subjects had passed three years or more (13.5 ± 7.4 years) after menopause. None had used medications that affect bone and estrogen replacement was eliminated from the analysis. All subjects were informed about the purpose and possible risks of the study and were then provided written informed consent. These studies were conducted according to the guidelines laid down in the Declaration of Helsinki. All procedures involving human subjects were approved by the Ethical Committee of the National Institute of Health and Nutrition in Tokyo, the Institutional Ethical Committee Review Board of Japan Women's College of Physical Education in Tokyo, and the Human Research Ethical Committee of the

Results are presented as the mean ± standard deviation (SD). Statistical analyses were carried out with the Sigma Stat 3.5 (Systat Software Inc., CA, USA). Statistical analysis was performed using the Student's t-test for parametric variables and the Mann-Whitney rank sum test for nonparametric variables to determine differences between young and elderly women. In addition to the mean ± SD of the difference, total error was used to determine how accurately predicted REE matched measured REE. This statistic includes two sources of variation, one attributable to the lack of association between the two sets of measurement (standard error of estimate) and one attributable to the difference between the means (van der Ploeg et al., 2001). Statistical significance of differences between measured and predicted values in all subjects was analyzed by one-way repeatedmeasures analysis of variance (ANOVA) and Dunnett's post hoc test. Evaluation of bias between measured and predicted REE was conducted with a Bland–Altman analysis (Bland and Altman, 1986). Fixed bias was indicated if the 95% coefficient interval (CI) for the difference between measured and predicted REE did not include zero. Predictive equations that are accurate display a tight prediction interval around zero. The 95%CI below zero signify an underestimation, while the 95%CI above zero signify an overestimation. The correlation of the difference between measured and predicted REE values and the mean from the both REE values was utilized to assess proportional bias.

all measurements were made during the follicular phase of the menstrual cycle. An oxygen and carbon dioxide analyzer (Arco-1000A; Arco system, Japan and AE-300; Minato Medical Science, Tokyo) was used to analyze the rate of oxygen consumption and carbon dioxide production. The volume of expired air was determined using a dry gas volume meter (DC-5; Shinagawa, Japan) and converted to standard temperature, standard pressure and dry gas. Gas exchange results were converted to REE (kcal/day) using Weir's equation (Weir, 1949). To examine whether overnight stay before the REE measurement caused a significant difference in the observed REE, analysis of covariance with REE as the dependent variable and age, height, FFM, and FM as covariates was employed. No significant effect of the measurement conditions was observed (overnight stay: 1169 ± 12 kcal/day (mean ± standard error (SE)), came in the early morning on the day: 1170 ± 7 kcal/day (mean ± SE), F = 0.001, p = 0.980).

#### **3.2 Method for estimating REE**

Estimation of REE using DXA was obtained based on the sum of four body compartments (BM, AT, SM and RM) times the corresponding tissue respiration rates as follows. The specific resting metabolic rate of the four compartments was assumed from previously reported data, bone (2.3 kcal/kg), AT (4.5 kcal/kg), skeletal muscle (13 kcal/kg) and residual (54 kcal/kg) (Holliday et al., 1967; Grande, 1989; Elia, 1992; Hayes et al., 2002; Heymsfield et al., 2002).


REE (kcal/day) = 2.3×BM + 4.5×AT + 13×SM + 54×RM

BM: bone mass (kg), AT: adipose tissue mass (kg), SM: skeletal muscle mass (kg), RM: residual mass (kg), BW: body weight (kg), Ht: height (cm), A: age (years).

Table 1. Predictive equations of resting energy expenditure for women used in the present study.

#### **3.3 Predictive equations of REE for women**

Other predictive REEs were calculated using the Harris–Benedict (Harris & Benedict, 1919), Schofield (Schofield, 1985), FAO/WHO/UNU (FAO/WHO/UNU, 1985), DRI (Japan) (Ministry of Heaith, Labour and Welfare of Japan, 2009), and NIHN (Japan) (Ganpule et al., 2007) equations (Table 1).

#### **4. The validity of body composition measurement using DXA for estimating REE**

#### **4.1 Subjects**

48 Dual Energy X-Ray Absorptiometry

all measurements were made during the follicular phase of the menstrual cycle. An oxygen and carbon dioxide analyzer (Arco-1000A; Arco system, Japan and AE-300; Minato Medical Science, Tokyo) was used to analyze the rate of oxygen consumption and carbon dioxide production. The volume of expired air was determined using a dry gas volume meter (DC-5; Shinagawa, Japan) and converted to standard temperature, standard pressure and dry gas. Gas exchange results were converted to REE (kcal/day) using Weir's equation (Weir, 1949). To examine whether overnight stay before the REE measurement caused a significant difference in the observed REE, analysis of covariance with REE as the dependent variable and age, height, FFM, and FM as covariates was employed. No significant effect of the measurement conditions was observed (overnight stay: 1169 ± 12 kcal/day (mean ± standard error (SE)), came in the early morning on the day: 1170 ± 7 kcal/day (mean ± SE), F

Estimation of REE using DXA was obtained based on the sum of four body compartments (BM, AT, SM and RM) times the corresponding tissue respiration rates as follows. The specific resting metabolic rate of the four compartments was assumed from previously reported data, bone (2.3 kcal/kg), AT (4.5 kcal/kg), skeletal muscle (13 kcal/kg) and residual (54 kcal/kg) (Holliday et al., 1967; Grande, 1989; Elia, 1992; Hayes et al., 2002;

REE (kcal/day) = 2.3×BM + 4.5×AT + 13×SM + 54×RM

**30-59 (0.034×BW + 3.538)×1000/4.186 60 over (0.038×BW + 2.755)×1000/4.186**

**NIHN (Japan) - (0.0481×BW + 0.0234×Ht - 0.0138×A - 0.9708)×1000/4.186**

BM: bone mass (kg), AT: adipose tissue mass (kg), SM: skeletal muscle mass (kg), RM: residual mass

Table 1. Predictive equations of resting energy expenditure for women used in the present

**30-59 (36.4×BW - 104.6×Ht/100 + 3619)/4.186 60 over (38.5×BW + 2665.2×Ht/100 - 1264)/4.186**

**DXA - 2.3×BM + 4.5×AT + 13×SM + 54×RM**

**FAO/WHO/UNU 18-29 (55.6×BW + 1397.4×Ht/100 + 146)/4.186**

**30-49 21.7 ×BW 50 over 20.7 ×BW**

**Schofield 18-29 (0.062×BW + 2.036)×1000/4.186**

**Harris-Benedict - 655.0955 + 9.5634×BW + 1.8496×Ht - 4.6756×A**

= 0.001, p = 0.980).

Heymsfield et al., 2002).

**Predictive equations**

study.

**(kcal/day) Age range**

**DRI (Japan) 18-29 22.1 ×BW**

(kg), BW: body weight (kg), Ht: height (cm), A: age (years).

**3.2 Method for estimating REE** 

The data used for the current analysis were collected from different 3 experimental studies that followed a similar methodology (Usui et al., 2009; Taguchi et al., 2011; Hasegawa et al., 2011). Total of 288 healthy women (216 young women; age: 21.8 ± 2.0 (18-29) years and 72 elderly women; age: 63.3 ± 6.4 (50-77) years) were recruited for the study. The elderly subjects had passed three years or more (13.5 ± 7.4 years) after menopause. None had used medications that affect bone and estrogen replacement was eliminated from the analysis. All subjects were informed about the purpose and possible risks of the study and were then provided written informed consent. These studies were conducted according to the guidelines laid down in the Declaration of Helsinki. All procedures involving human subjects were approved by the Ethical Committee of the National Institute of Health and Nutrition in Tokyo, the Institutional Ethical Committee Review Board of Japan Women's College of Physical Education in Tokyo, and the Human Research Ethical Committee of the Faculty of Sport Sciences of Waseda University, in Saitama.

#### **4.2 Statistical analysis**

Results are presented as the mean ± standard deviation (SD). Statistical analyses were carried out with the Sigma Stat 3.5 (Systat Software Inc., CA, USA). Statistical analysis was performed using the Student's t-test for parametric variables and the Mann-Whitney rank sum test for nonparametric variables to determine differences between young and elderly women. In addition to the mean ± SD of the difference, total error was used to determine how accurately predicted REE matched measured REE. This statistic includes two sources of variation, one attributable to the lack of association between the two sets of measurement (standard error of estimate) and one attributable to the difference between the means (van der Ploeg et al., 2001). Statistical significance of differences between measured and predicted values in all subjects was analyzed by one-way repeatedmeasures analysis of variance (ANOVA) and Dunnett's post hoc test. Evaluation of bias between measured and predicted REE was conducted with a Bland–Altman analysis (Bland and Altman, 1986). Fixed bias was indicated if the 95% coefficient interval (CI) for the difference between measured and predicted REE did not include zero. Predictive equations that are accurate display a tight prediction interval around zero. The 95%CI below zero signify an underestimation, while the 95%CI above zero signify an overestimation. The correlation of the difference between measured and predicted REE values and the mean from the both REE values was utilized to assess proportional bias. Statistical significance was set at p < 0.05 for all predictors.

The Validity of Body Composition Measurement

**predicted REE**

Dunnett's post hoc test).

Table 4. Bland–Altman analysis.

**4.4 Discussion** 

against the measured REE of healthy women.

**REEm (kcal/day) 1170 ± 167**

Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 51

**DXA 1181 ± 152 11 ± 104 105 Harris-Benedict 1324 ± 123 154 ± 121 <sup>c</sup> 196 Schofield 1273 ± 119 104 ± 111 <sup>c</sup> 151 FAO/WHO/UNU 1281 ± 118 111 ± 109 <sup>c</sup> 156 DRI (Japan) 1191 ± 166 22 ± 114 115 NIHN (Japan) 1184 ± 136 14 ± 110 111**

Means ± SD.; a Mean error = predicted REE – measured REE. ; b Total error (kcal/day) = √(∑(predicted REE – measured REE)2/n). ; c p<0.05 vs. measured REE (one-way repeated-measures ANOVA and

Table 3. Measured and predicted REE and cross-validation of REE prediction equations

**predicted REE** r *p* values

95%CI

**DXA** -1.1 ~ 23.0 N.S. -0.152 p = 0.010 **Harris-Benedict** 140.2 ~ 168.3 -0.395 p < 0.001 **Schofield** 90.8 ~ 116.5 -0.471 p < 0.001 **FAO/WHO/UNU** 98.5 ~ 123.8 -0.481 p < 0.001 **DRI (Japan)** 8.4 ~ 34.8 -0.010 p = 0.868 N.S. **NIHN (Japan)** 1.6 ~ 27.2 -0.307 p < 0.001

With the increasing availability of imaging methods, CT, MRI, and DXA, estimating methods of REE are developing and providing a "new" viewpoint of REE from the modeling perspective of organs and tissues. The importance of the developed approach is that it not only provides a qualitative estimate of REE, but of the actual distribution of heatproducing tissue components. As whole-body CT and MRI cannot assess body composition easily and quickly, previous investigators have explored the use of DXA as an available and practical alternative for developing qualitative tissue organ REE predictions. Hayes et al. (2002) demonstrated that REE can be estimated from five measured DXA values: body weight, total body fat mass, bone mineral content, appendicular lean mass, and head area. Their findings suggested that when REE is reviewed in the context of major heat-producing

**All subjects Total error<sup>b</sup> (n = 288) (kcal/day) (kcal/day) (kcal/day)**

**REE Mean error<sup>a</sup>**

Bland-Altman analysis

fixed bias proportional bias

#### **4.3 Results**

Table 2 presents the comparisons of subjects characteristics and body composition. Ht was significantly lower in the elderly women (50 over age group) than in young women (18-29 age group). However, no significant difference in BW was noted between two groups. The elderly women had significantly higher levels of % body fat and FM, and lower levels of FFM than the young group. When the FFM was separated into BM, SM, and RM, young women had significantly higher BM, SM, and RM than elderly women.


Means ± SD, Ht: Height, BW: body weight, BMI: body mass index, FFM: fat-free mass, FM: fat mass, BM: bone mass, AT: adipose tissue mass, SM: skeletal muscle mass, RM: residual mass, \* p<0.05 vs. 18- 29 age group (Student's t-test), † p<0.05 vs. 18-29 age group (Mann-Whitney rank sum test).

Table 2. Physical characteristics in the healthy women.

Table 3 show measured and predicted REEs, and the mean and total errors of predicted REEs. No significant differences were observed from the REEm in predicted REE values by using DXA, DRI (Japan) equation, and NIHN (Japan) equation in all subjects. On the other hand, the mean errors of REE predicted by the Harris–Benedict, Schofield, and FAO/WHO/UNU equations were significantly higher than the REEm in all subjects. Total error of estimated REE by using DXA was lowest in the group. Total error using NIHN (Japan) equation was the second to the lowest. In particular, total error of the Harris– Benedict equation was largest in healthy women.

The systemic bias between the measured and predicted REEs by the Bland-Altman analysis are shown in Table 4 and Fig. 1. Fixed bias was not present in only estimated REE by using DXA. However, the REE estimated by using DXA had a proportional bias (Fig. 1 (a)). Systematic bias (fixed and proportional bias) was presented in predicted REE by the Harris– Benedict, Schofield, FAO/WHO/UNU, and NIHN (Japan) equations.


Means ± SD.; a Mean error = predicted REE – measured REE. ; b Total error (kcal/day) = √(∑(predicted REE – measured REE)2/n). ; c p<0.05 vs. measured REE (one-way repeated-measures ANOVA and Dunnett's post hoc test).

Table 3. Measured and predicted REE and cross-validation of REE prediction equations against the measured REE of healthy women.


Table 4. Bland–Altman analysis.

#### **4.4 Discussion**

50 Dual Energy X-Ray Absorptiometry

Table 2 presents the comparisons of subjects characteristics and body composition. Ht was significantly lower in the elderly women (50 over age group) than in young women (18-29 age group). However, no significant difference in BW was noted between two groups. The elderly women had significantly higher levels of % body fat and FM, and lower levels of FFM than the young group. When the FFM was separated into BM, SM, and RM, young

**Age (years) 32.1 ± 18.4 21.8 ± 2.0 63.3 ± 6.4** † **Ht (cm) 159.7 ± 6.8 161.6 ± 6.2 153.9 ± 5.2 \* BW (kg) 54.8 ± 7.4 54.9 ± 7.8 54.3 ± 6.0**

**FFM (kg) 41.4 ± 5.8 42.6 ± 5.9 37.7 ± 3.5** † **FM (kg) 13.4 ± 4.0 12.3 ± 3.4 16.6 ± 3.9** † **% body fat 24.3 ± 5.8 22.3 ± 4.6 30.2 ± 4.8 \* BM (kg) 3.8 ± 0.7 4.0 ± 0.6 3.1 ± 0.5** † **AT (kg) 15.8 ± 4.7 14.5 ± 4.0 19.6 ± 4.6** † **SM (kg) 19.5 ± 3.5 20.5 ± 3.3 16.5 ± 1.9** † **RM (kg) 15.7 ± 2.1 15.9 ± 2.2 15.2 ± 1.5** †

Means ± SD, Ht: Height, BW: body weight, BMI: body mass index, FFM: fat-free mass, FM: fat mass, BM: bone mass, AT: adipose tissue mass, SM: skeletal muscle mass, RM: residual mass, \* p<0.05 vs. 18-

Table 3 show measured and predicted REEs, and the mean and total errors of predicted REEs. No significant differences were observed from the REEm in predicted REE values by using DXA, DRI (Japan) equation, and NIHN (Japan) equation in all subjects. On the other hand, the mean errors of REE predicted by the Harris–Benedict, Schofield, and FAO/WHO/UNU equations were significantly higher than the REEm in all subjects. Total error of estimated REE by using DXA was lowest in the group. Total error using NIHN (Japan) equation was the second to the lowest. In particular, total error of the Harris–

The systemic bias between the measured and predicted REEs by the Bland-Altman analysis are shown in Table 4 and Fig. 1. Fixed bias was not present in only estimated REE by using DXA. However, the REE estimated by using DXA had a proportional bias (Fig. 1 (a)). Systematic bias (fixed and proportional bias) was presented in predicted REE by the Harris–

29 age group (Student's t-test), † p<0.05 vs. 18-29 age group (Mann-Whitney rank sum test).

Table 2. Physical characteristics in the healthy women.

Benedict equation was largest in healthy women.

Benedict, Schofield, FAO/WHO/UNU, and NIHN (Japan) equations.

**All 18-29 50 over n = 288 n = 216 n = 72**

**Age range**

**) 21.5 ± 2.6 21.0 ± 2.5 22.9 ± 2.3 \***

women had significantly higher BM, SM, and RM than elderly women.

**4.3 Results** 

**BMI (kg/m2**

With the increasing availability of imaging methods, CT, MRI, and DXA, estimating methods of REE are developing and providing a "new" viewpoint of REE from the modeling perspective of organs and tissues. The importance of the developed approach is that it not only provides a qualitative estimate of REE, but of the actual distribution of heatproducing tissue components. As whole-body CT and MRI cannot assess body composition easily and quickly, previous investigators have explored the use of DXA as an available and practical alternative for developing qualitative tissue organ REE predictions. Hayes et al. (2002) demonstrated that REE can be estimated from five measured DXA values: body weight, total body fat mass, bone mineral content, appendicular lean mass, and head area. Their findings suggested that when REE is reviewed in the context of major heat-producing

The Validity of Body Composition Measurement

ratio of organ–tissue mass to body size.

women.

Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 53

units differing in metabolic activity, normal-weight men and women are actually quite similar. In addition, this five component model allowed REE prediction over a wide range of FFM, because the mean REE prediction errors in intermediate weight, underweight and obese subjects were not significantly different between groups (Bosy-Westphal et al., 2004). These findings provided indirect evidence for a view that, for practical purposes within humans, the specific metabolic rate is constant with increasing organ mass, and that interindividual REE differences in under- or overweight are more a reflection of an altered

On the other hand, there is evidence that REE is lower in the elderly, even after adjustment for tissue and organ mass (Gallagher et al., 2000) or age-related differences in body composition (Van Pelt et al., 1997, 2001). This raises the important question of the validity of assumed tissue- and organ-specific metabolic rates. The specific metabolic rates applied in the present study represent literature averages. The extent to which these heat production rates are valid remains unknown. Moreover, the specific metabolic rates for some components reported in the literature vary widely. However, we demonstrated that estimation based on the four tissue organs by using DXA allows successful calculation of REE in female adults regardless of age and aerobic fitness levels. (Usui et al., 2009). Our findings suggested the possibility that REE is regulated mainly by the mass of the tissue organs with lower and higher metabolic rates, including skeletal muscle and intestinal organs, rather than a decline in the specific metabolic rate of different tissue organs associated with advancing age and decreasing aerobic fitness levels in young and elderly

In addition, as for the four component model of REE prediction, Taguchi et al. (2011) evaluated that the relationship between REE and body composition in Japanese female athletes with a wide range of body sizes. Their results indicated, in good agreement with previous study (Bosy-Westphal et al., 2004), that REE for female athletes with a wide range of body sizes can be attributed to changes in organ tissue mass, and not changes in organ tissue metabolic rate. Moreover, Hasegawa et al. (2011) investigated the differences in body composition and REE between young women with low (BMI < 18.5 kg/m2) and normal BMI (18.5≦BMI<25 kg/m2), and suggested the possibility that low BMI subjects with normal menstrual cycle do not have any differences in specific metabolic rates of different tissue organs compared to those with normal BMI. According to Bland-Altman analysis in the present chapter, although there is a proportional bias in estimation of REE by using DXA, this proportional bias is relatively small compared with other estimation methods for REE (Table 4 and Fig. 1). Furthermore, fixed bias was not present in only estimated REE by using DXA. These findings suggested that estimation of the four tissue organs by using DXA can

easily, practically, precisely, and accurately predict REE in healthy men and women.

The present chapter also focused on the validity of body composition measurement using DXA for estimating REE. Our results showed that predicted REE values by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were not significantly different from the REEm, while Harris–Benedict, Schofield, and FAO/WHO/UNU equations significantly higher than the REEm in healthy women (Table 3). DRI (Japan) and NIHN (Japan) equations were developed based on the data for Japanese subjects with standard body size (Ministry of Heaith, Labour and Welfare of Japan, 2009; Ganpule et al., 2007). Schofield and FAO/WHO/UNU equations were developed based on data from a population of many

Plots of the differences between measured REE and predicted REE. Open circle (○): young subjects (n = 216), black circle (●): elderly subjects (n = 72), the solid line ( ): regression line in all subjects (n = 288), REEm: measured by expiratory gas exchange, REEDXA: estimated by using DXA, REEH-B: predicted by Harris– Benedict equation, REEscho: predicted by Schofield equations, REEF/W/U: predicted by FAO/WHO/UNU equations, REEDRI: predicted by DRI (Japan) equations, REENIHN: predicted by NIHN (Japan) equation.

Fig. 1. Bland–Altman plot.




**REENIHN**

Plots of the differences between measured REE and predicted REE. Open circle (○): young subjects (n = 216), black circle (●): elderly subjects (n = 72), the solid line ( ): regression line in all subjects (n = 288), REEm: measured by expiratory gas exchange, REEDXA: estimated by using DXA, REEH-B: predicted by Harris– Benedict equation, REEscho: predicted by Schofield equations, REEF/W/U: predicted by FAO/WHO/UNU equations, REEDRI: predicted by DRI (Japan) equations, REENIHN: predicted by NIHN (Japan) equation.

**minus REEm (kcal/day)**

**REEF/W/U**

**minus REEm (kcal/day)**

**REEH-B**

**minus REEm (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**all; r = -0.395, p < 0.001**

**all; r = -0.481, p < 0.001**

**all; r = -0.307, p < 0.001**

**mean REEm and REEH-B (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**mean REEm and REEF/W/U (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**mean REEm and REENIHN (kcal/day)**




Fig. 1. Bland–Altman plot.

**REEDRI**

**minus REEm (kcal/day)**

**REEScho**

**minus REEm (kcal/day)**

**REEDXA**

**minus REEm (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**all; r = -0.152, p < 0.01**

(a) (b)

**all; r = -0.471, p < 0.001**

(c) (d)

(e) (f)

**mean REEm and REEDXA (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**mean REEm and REEScho (kcal/day)**

600 800 1000 1200 1400 1600 1800 2000

**mean REEm and REEDRI (kcal/day)**

units differing in metabolic activity, normal-weight men and women are actually quite similar. In addition, this five component model allowed REE prediction over a wide range of FFM, because the mean REE prediction errors in intermediate weight, underweight and obese subjects were not significantly different between groups (Bosy-Westphal et al., 2004). These findings provided indirect evidence for a view that, for practical purposes within humans, the specific metabolic rate is constant with increasing organ mass, and that interindividual REE differences in under- or overweight are more a reflection of an altered ratio of organ–tissue mass to body size.

On the other hand, there is evidence that REE is lower in the elderly, even after adjustment for tissue and organ mass (Gallagher et al., 2000) or age-related differences in body composition (Van Pelt et al., 1997, 2001). This raises the important question of the validity of assumed tissue- and organ-specific metabolic rates. The specific metabolic rates applied in the present study represent literature averages. The extent to which these heat production rates are valid remains unknown. Moreover, the specific metabolic rates for some components reported in the literature vary widely. However, we demonstrated that estimation based on the four tissue organs by using DXA allows successful calculation of REE in female adults regardless of age and aerobic fitness levels. (Usui et al., 2009). Our findings suggested the possibility that REE is regulated mainly by the mass of the tissue organs with lower and higher metabolic rates, including skeletal muscle and intestinal organs, rather than a decline in the specific metabolic rate of different tissue organs associated with advancing age and decreasing aerobic fitness levels in young and elderly women.

In addition, as for the four component model of REE prediction, Taguchi et al. (2011) evaluated that the relationship between REE and body composition in Japanese female athletes with a wide range of body sizes. Their results indicated, in good agreement with previous study (Bosy-Westphal et al., 2004), that REE for female athletes with a wide range of body sizes can be attributed to changes in organ tissue mass, and not changes in organ tissue metabolic rate. Moreover, Hasegawa et al. (2011) investigated the differences in body composition and REE between young women with low (BMI < 18.5 kg/m2) and normal BMI (18.5≦BMI<25 kg/m2), and suggested the possibility that low BMI subjects with normal menstrual cycle do not have any differences in specific metabolic rates of different tissue organs compared to those with normal BMI. According to Bland-Altman analysis in the present chapter, although there is a proportional bias in estimation of REE by using DXA, this proportional bias is relatively small compared with other estimation methods for REE (Table 4 and Fig. 1). Furthermore, fixed bias was not present in only estimated REE by using DXA. These findings suggested that estimation of the four tissue organs by using DXA can easily, practically, precisely, and accurately predict REE in healthy men and women.

The present chapter also focused on the validity of body composition measurement using DXA for estimating REE. Our results showed that predicted REE values by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were not significantly different from the REEm, while Harris–Benedict, Schofield, and FAO/WHO/UNU equations significantly higher than the REEm in healthy women (Table 3). DRI (Japan) and NIHN (Japan) equations were developed based on the data for Japanese subjects with standard body size (Ministry of Heaith, Labour and Welfare of Japan, 2009; Ganpule et al., 2007). Schofield and FAO/WHO/UNU equations were developed based on data from a population of many

The Validity of Body Composition Measurement

**6. Acknowledgments** 

Japanese Olympic Committee.

1522-1555

Online ISSN 1522-1555

8923, Online ISSN 1749-6632

0954-3007, Online ISSN 1476-5640

pp. 307–310, Print ISSN 0140-6736

0565, Online ISSN 1476-5497

Raven Press, ISBN 978-088-1678-71-0, New York

**7. References** 

Using Dual Energy X-Ray Absorptiometry for Estimating Resting Energy Expenditure 55

We express our appreciation to the subjects for their cooperation in this study. We thank the members of the National Institute of Health and Nutrition and Waseda University for their help in this experiment. This study was supported by a Research Grant for Academic Frontier Projects from the Ministry of Education, Culture, Sports, Science and Technology (05F-02), a Waseda University Grant for Special Research Projects (2005A-932, 2006B-242), Health Sciences Research Grants from the Ministry of Health, Labor and Welfare, Medical Health Care Research Grants from the Consolidated Research Institute for Advanced Science and Medical Care at Waseda University, Research grant for Waseda University Global COE program: Sports Sciences for Promotion of Active Life (projectsIII-2), Research Grant from Japan Women's College of Physical Education, and Research Grants from the

Bland, J.M. & Altman, D.G. (1986). Statistical methods for assessing agreement between two

Bosy-Westphal, A.; Reinecke, U.; Schlörke, T.; Illner, K.; Kutzner, D.; Heller M. & Müller,

Elia, M. (1992). Organ and tissue contribution to metabolic rate. In: *Energy Metabolism: Tissue* 

FAO/WHO/UNU. (1985). *Energy and protein requirements, report of a joint FAO/WHO/UNU expert consultation. Technical Report Series 724*, WHO, ISBN 92-4-120724-8, Geneva. Fukagawa, N.K.; Bndini, L.G. & Young, J.B. (1990). Effect of age on body composition and

Gallagher, D.; Belmonte, D.; Deurenberg, P.; Wang, Z.; Krasnow, N.; Pi-Sunyer, F.X. &

Gallagher, D.; Allen, A.; Wang, Z.; Heymsfield, S.B. & Krasnow, N. (2000). Smaller organ

Ganpule, A.A.; Tanaka, S.; Ishikawa–Takata, K. & Tabata, I. (2007). Interindividual

methods of clinical measurement. *The Lancet*, Vol.327, No.8476, (08 February 1986),

M.J. (2004). Effect of organ and tissue masses on resting energy expenditure in underweight, normal weight and obese adults. *International Journal of Obesity and Related Metabolic Disorders,* Vol.28, No.1, (January 2004), pp. 72–79, Print ISSN 0307-

*Determinants and Cellular Corollaries*, Kinney, J. M. & Tucker, H. N. (eds), pp. 61–80,

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tissue mass in the elderly fails to explain lower resting metabolic rate. *Annals of the New York Academy of Sciences,* Vol.904, (May 2000), pp. 449–455, Print ISSN 0077-

variability in sleeping metabolic rate in Japanese subjects. *European Journal of Clinical Nutrition*, Vol.61, No.11, (Epub February 2007), pp. 1256–1261, Print ISSN

races (Schofield, 1985; FAO/WHO/UNU, 1985). However, the data used to develop the Schofield equations were mostly from young European military and police recruits with 45% being of Italian descent. Harris–Benedict equation was developed using data obtained in healthy normal weight Caucasian men and women aged 15–74 years. Asians are reported to have lower REE than Europeans by 10–12% (Hayter et al., 1994), even after adjustment for body composition. According to a recent study by Miyake et al. (2011), mean difference and total error values were smaller using the DRI (Japan) equations and NIHN (Japan) equation than the internationally used equations (Harris–Benedict, Schofield, FAO/WHO/UNU) in both sexes. In particular, the total error was lower for the NIHN (Japan) equation than the other equations in most age groups (Miyake et al., 2011). In the present chapter, the mean errors of the predicted REEs by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were smaller than those of internationally used equations in all subjects (Table 3). In addition, total error of estimated REE by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were also lower than other total errors (105 kcal/day, 115 kcal/day, 111 kcal/day, respectively). Moreover, while these three prediction methods had some kind of bias, the bias was relatively small compared with other internationally used equations (Table 4 & Fig. 1 (a), (e), (f)). The results of this chapter supported the notions that the DRI (Japan) and NIHN (Japan) equations of REE are more accurate in healthy Japanese subjects, and that estimating REE from four tissue organ components by using DXA will be one of the useful methods for the dietary management (energetic assessment) in healthy Japanese subjects.

Our investigation has a few limitations. First, we did not test middle-aged (30–49 years) and very old (> 80 years) women and the adult men of all ages. It is unclear whether age-related functional decline in cells or tissue organs make a major impact on the heat production. Second, it was performed only in a Japanese population. Hayter et al. (1994) reported that Asians have lower REE than Europeans by 10–12%, even after adjustment for body composition. Third, because all the participants were healthy adult women, little is known as to whether this four component model for estimating REE by using DXA can accurately estimate REE in patients with a chronic disease, such as diabetes mellitus or cardiac disorder. Recently, DXA and regression modeling of REE showed that skeletal muscle is hypermetabolic in patients with HIV lipoatrophy (Kosmiski et al., 2009). Accordingly, the specific metabolic rate of each tissue organ may differ from those in healthy adults over 80 years of age, other ethnic subjects, or patients with a chronic disease. Future studies are needed to extend these observations and to analyze gender-related, genetics, hormonal, ethnic, and other determinant factors of REE.

#### **5. Conclusion**

The results of the present chapter suggest that REE in healthy adult women can be accurately estimated from four tissue organ components by using DXA, because the mean error, total error, and systematic bias between the predicted and measured REE was relatively small compared with other estimation methods for REE. Therefore, we expect that the DXA will be one of the useful methods not only for examining body composition and bone mineral density but also for estimating REE in a broad range of fields. In the future, DXA method may offer a bridge to effective prevention, treatment and care with the dietary management and exercise regimen for life style-related disease, such as obesity, osteoporosis, and type 2 diabetes.

#### **6. Acknowledgments**

54 Dual Energy X-Ray Absorptiometry

races (Schofield, 1985; FAO/WHO/UNU, 1985). However, the data used to develop the Schofield equations were mostly from young European military and police recruits with 45% being of Italian descent. Harris–Benedict equation was developed using data obtained in healthy normal weight Caucasian men and women aged 15–74 years. Asians are reported to have lower REE than Europeans by 10–12% (Hayter et al., 1994), even after adjustment for body composition. According to a recent study by Miyake et al. (2011), mean difference and total error values were smaller using the DRI (Japan) equations and NIHN (Japan) equation than the internationally used equations (Harris–Benedict, Schofield, FAO/WHO/UNU) in both sexes. In particular, the total error was lower for the NIHN (Japan) equation than the other equations in most age groups (Miyake et al., 2011). In the present chapter, the mean errors of the predicted REEs by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were smaller than those of internationally used equations in all subjects (Table 3). In addition, total error of estimated REE by using DXA, DRI (Japan) equations, and NIHN (Japan) equation were also lower than other total errors (105 kcal/day, 115 kcal/day, 111 kcal/day, respectively). Moreover, while these three prediction methods had some kind of bias, the bias was relatively small compared with other internationally used equations (Table 4 & Fig. 1 (a), (e), (f)). The results of this chapter supported the notions that the DRI (Japan) and NIHN (Japan) equations of REE are more accurate in healthy Japanese subjects, and that estimating REE from four tissue organ components by using DXA will be one of the useful methods for the dietary management (energetic assessment) in healthy Japanese subjects.

Our investigation has a few limitations. First, we did not test middle-aged (30–49 years) and very old (> 80 years) women and the adult men of all ages. It is unclear whether age-related functional decline in cells or tissue organs make a major impact on the heat production. Second, it was performed only in a Japanese population. Hayter et al. (1994) reported that Asians have lower REE than Europeans by 10–12%, even after adjustment for body composition. Third, because all the participants were healthy adult women, little is known as to whether this four component model for estimating REE by using DXA can accurately estimate REE in patients with a chronic disease, such as diabetes mellitus or cardiac disorder. Recently, DXA and regression modeling of REE showed that skeletal muscle is hypermetabolic in patients with HIV lipoatrophy (Kosmiski et al., 2009). Accordingly, the specific metabolic rate of each tissue organ may differ from those in healthy adults over 80 years of age, other ethnic subjects, or patients with a chronic disease. Future studies are needed to extend these observations and to analyze gender-related, genetics, hormonal,

The results of the present chapter suggest that REE in healthy adult women can be accurately estimated from four tissue organ components by using DXA, because the mean error, total error, and systematic bias between the predicted and measured REE was relatively small compared with other estimation methods for REE. Therefore, we expect that the DXA will be one of the useful methods not only for examining body composition and bone mineral density but also for estimating REE in a broad range of fields. In the future, DXA method may offer a bridge to effective prevention, treatment and care with the dietary management and exercise regimen for life style-related disease, such as obesity,

ethnic, and other determinant factors of REE.

osteoporosis, and type 2 diabetes.

**5. Conclusion** 

We express our appreciation to the subjects for their cooperation in this study. We thank the members of the National Institute of Health and Nutrition and Waseda University for their help in this experiment. This study was supported by a Research Grant for Academic Frontier Projects from the Ministry of Education, Culture, Sports, Science and Technology (05F-02), a Waseda University Grant for Special Research Projects (2005A-932, 2006B-242), Health Sciences Research Grants from the Ministry of Health, Labor and Welfare, Medical Health Care Research Grants from the Consolidated Research Institute for Advanced Science and Medical Care at Waseda University, Research grant for Waseda University Global COE program: Sports Sciences for Promotion of Active Life (projectsIII-2), Research Grant from Japan Women's College of Physical Education, and Research Grants from the Japanese Olympic Committee.

#### **7. References**


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**5** 

*Poland* 

Magdalena Krzykała

**Dxa as a Tool for the Assessment of** 

*University School of Physical Education in Poznań* 

*Anthropology and Biometry Department,* 

**Morphological Asymmetry in Athletes** 

Symmetry and asymmetry – two opposite phenomenon, does coexist in nature and both are very essential for science. There are many definition of symmetry, depending on research area. In biology, a dominant view is the left-right bilateral symmetry describes health and high genetic quality (Gould & Gould, 1989). In physics, symmetry includes all features of a physical system that exhibit the property of symmetry—that is, under certain transformations, aspects of these systems are "unchanged according to a particular observation" (en.wikipedia.org). In mathematics, the intellectual pursuit of the Universal formulation of symmetry (Group Theory) has led to major discoveries in physics, and to Einstein's general relativity theory (Engler, 2005). In chemistry, left-right balance is a critical component in the notion of symmetry and refers to regular arrangements of molecules and the more symmetrical, the more aesthetic (Muller, 2003). Additionally symmetry and chemistry have been in interplay in spectroscopy, crystallography, reactivity and conformational analysis. Besides, symmetry considerations continua to assist chemistry in systematizing and interpreting observation and in discovering new reactions, molecules, and other material (Hargittai & Hargittai, 2005). In art, symmetry refers to left-right, topbottom balance (of forms, colors, lines etc) in the composition as a whole being essential

On the one hand symmetry means proper proportions, harmony, and balance between two elements of some totality and is connected with beauty. On the other hand there is another definition of this term- bilateral symmetry (right-left symmetry). Both of them concern body build (morphological symmetry), but can also refer to some human movements (Starosta,

According to Frey (1949) "symmetry signifies rest and binding, asymmetry motion and loosening, the one order and law, the other arbitrariness and accident, the one formal

According to Webster dictionary (Webster, 1991) symmetry is quality of possessing exactly corresponding parts on either side of an axis and in biology – regularity in form or similarity of structure, whereas asymmetry it is lack of symmetry, uneven disposition on each side of

component of art's aesthetic quality (Jacobsen et al, 2006).

rigidity and constrains, the other life, play and freedom".

**1. Introduction** 

1990).

an central line or point.


### **Dxa as a Tool for the Assessment of Morphological Asymmetry in Athletes**

Magdalena Krzykała

*University School of Physical Education in Poznań Anthropology and Biometry Department, Poland* 

#### **1. Introduction**

58 Dual Energy X-Ray Absorptiometry

Van Pelt, R.E.; Dinneno, F.A.; Seals, D.R. & Jones, P.P. (2001). Age-related decline in RMR in

Van Pelt, R.E.; Jones, P.P.; Davy, K.P.; Desouza, C.A.; Tanaka, H.; Davy, B.M. & Seals D.R.

Wang, Z.; Heshka, S.; Gallagher, D.; Boozer, C.N.; Kotler, D.P. & Heymsfield, S.B. (2000).

Weir, J.B. (1949). New methods for calculating metabolic rate with special reference to

pp. E633–E639, Print ISSN 0193-1849, Online ISSN 1522-1555

Online ISSN 1522-1555

Print ISSN 0022-3751, Online ISSN 1469-7793

1997), pp. 3208–3212, Print ISSN 0021-972x, Online ISSN 1945-7197

physically active men: relation to exercise volume and energy intake. *American Journal of Physiology Endocrinology and Metabolism*, Vol.281, No.3, (September 2001),

(1997). Regular exercise and the age-related decline in resting metabolic rate in women. *The Journal of Clinical Endocrinology and Metabolism*, Vol.82, No.10, (October

Resting energy expenditure fat-free mass relationship: new insights provided by body composition modeling. *American Journal of Physiology Endocrinology and Metabolism*, Vol.279, No.3, (September 2000), pp. E539–E545, Print ISSN 0193-1849,

protein metabolism. *Journal of Physiology*, Vol.109, No.1-2, (August 1949), pp. 1-9,

Symmetry and asymmetry – two opposite phenomenon, does coexist in nature and both are very essential for science. There are many definition of symmetry, depending on research area. In biology, a dominant view is the left-right bilateral symmetry describes health and high genetic quality (Gould & Gould, 1989). In physics, symmetry includes all features of a physical system that exhibit the property of symmetry—that is, under certain transformations, aspects of these systems are "unchanged according to a particular observation" (en.wikipedia.org). In mathematics, the intellectual pursuit of the Universal formulation of symmetry (Group Theory) has led to major discoveries in physics, and to Einstein's general relativity theory (Engler, 2005). In chemistry, left-right balance is a critical component in the notion of symmetry and refers to regular arrangements of molecules and the more symmetrical, the more aesthetic (Muller, 2003). Additionally symmetry and chemistry have been in interplay in spectroscopy, crystallography, reactivity and conformational analysis. Besides, symmetry considerations continua to assist chemistry in systematizing and interpreting observation and in discovering new reactions, molecules, and other material (Hargittai & Hargittai, 2005). In art, symmetry refers to left-right, topbottom balance (of forms, colors, lines etc) in the composition as a whole being essential component of art's aesthetic quality (Jacobsen et al, 2006).

On the one hand symmetry means proper proportions, harmony, and balance between two elements of some totality and is connected with beauty. On the other hand there is another definition of this term- bilateral symmetry (right-left symmetry). Both of them concern body build (morphological symmetry), but can also refer to some human movements (Starosta, 1990).

According to Frey (1949) "symmetry signifies rest and binding, asymmetry motion and loosening, the one order and law, the other arbitrariness and accident, the one formal rigidity and constrains, the other life, play and freedom".

According to Webster dictionary (Webster, 1991) symmetry is quality of possessing exactly corresponding parts on either side of an axis and in biology – regularity in form or similarity of structure, whereas asymmetry it is lack of symmetry, uneven disposition on each side of an central line or point.

Dxa as a Tool for the Assessment of Morphological Asymmetry in Athletes 61

whole body composition, but research show that regional BMD, FFM and FM distribution is equally important, in relation to training and performance (Bell et al., 2005). Tend to this

Many researches proved that morphological asymmetry - the difference between the right and the left sides of the body exist in sport (Dorado et al., 2002; Auerbach & Ruff 2006; Starosta, 1990) and it is very important to observe the scale of this phenomenon in order to its elimination if it will be necessary. Morphological asymmetry can concerns both side-toside differences between extremities, pelvis, trunk and total body with upper and lower body diversification. Analysis concerns mostly body dimension (length of limbs), level of body fat, lean and body density. In humans, some level of asymmetry in body dimensions is rather norm than the exception (Al-Eisa et al., 2004). It is stated that the lifelong preference for one extremity -e.g. the left arm or the left leg -as well as a predilection for a certain direction when turning around or rotating about one's longitudinal axis could lead to asymmetry which occurred in morphological characteristics and which can even be osseous in the case of competitive athletes. The body response on training stimulus will be vary

It is obvious that morphological side–to–side diversification depends on sport specificity. Participation in asymmetric sport disciplines is connected with asymmetric changes in soft tissues (Ducher et al., 2005; Haapasalo et al., 1998). As was noticed, soft tissues indicate greater lateral differences than skeletal measurements (Van Dusen, 1939; Chhibber & Singh, 1970; Kimura & Asaeda, 1974;). Observation from literature suggests that in most cases the upper limb is laterally dominant in size on the right side while the lower limb is larger on the left side (Singh, 1970; McGrew & Marchant, 1997). Some studies stated that asymmetries are more pronounced in the upper extremities in comparison to lower extremities when the right side tends to be larger than the left (Munter, 1936; Tomkinson et al., 2003; Malina & Buschang, 2004; Ulijaszek & Mascie-Taylor, 2005). Malinowski (2004) stated that average, the right arm and forearm are longer, and larger are their circumferences. Left hand is longer and narrowest. The right upper limb is longer about 1 cm in comparison to left one, whereas left lower limb is longer about 10-13 mm. Also left foot, left thigh and calf

As was stated by some authors (Manning et al., 2002) small percentage changes in left or right trait size may result in large percentage changes in asymmetries, so precise and the most suitable method is need to evaluated regional morphological diversification among

There are different methods which could be use to do such analysis, which differ in the time, expense and accuracy of the results. These procedures are subject to some error which can result from measurement procedures, from the equations selected to calculate body fat

The most traditional method to assess the size of particularly body segments is anthropometry. It uses circumferences, SKF thicknesses, skeletal breadth and segment lengths for total and regional body composition evaluation (Heyward & Wagner, 2004). Some standardized procedures should be taken into consideration to increase the accuracy

percent, or from both. A standard error for most procedure is about 3 to 4 %.

direction more and more scientists conduct research in this area.

according to its timing, duration and intensity (Malina, 1979).

circumferences are longer.

very specific part of population – athletes.

**2.1 Methods for regional body composition assessment** 

The opposite word to symmetry is asymmetry. Three kinds of asymmetry were distinguished by Wolański (1955): 1/ morphological – differences in size and shape of organs or body parts situated on left or right side of the body; 2/ functional – connected with one of hemispheres domination (usually left); 3/ dynamic – differences between left and right extremities in strength, muscles hardness and elasticity.

Bilateral asymmetry in humans, as was stated by same authors (Zeidel & Hessamian, 2010), was fashioned by millions of years of adaptive evolution and it implies perfection.

There are three types of bilateral asymmetry by Van Valen (1962): 1/directional asymmetry (DA): when some traits develop more on one side than the other, e.g., the human brain; 2/ antisymmetry: asymmetric development is typical, but unpredictable, e.g., larger signaling claw of the male fiddler crabs or handedness in human; 3/ fluctuating asymmetry (FA): "randomly produced deviations from perfect symmetry of two sides of quantitative traits in an individual for which the population mean of R-L differences is zero and their variability is near-normally distributed".

Normal human body asymmetry appear very soon and the manifestation of morphological asymmetry intensify with aging what is connected with functional asymmetry (Malinowski, 2004). Level of asymmetry among population reflects its developmental stability, hence differences between right and left bilateral trait are very good predictor of developmental stability both on the individually and population level. Fluctuating asymmetry has been the most widely used measure of developmental stability (Palmer & Strobeck, 1992).

Potentially human body is divided into two symmetrical parts but in fact there are some deviations (aberrations): 1) internal aberrations: asymmetry of even (kidney) and odd (pancreas, heart, spleen, liver, stomach etc) of internal organs. It refers to size, shape, location, constitution or function, 2) external aberration: refer to extremities asymmetry and handedness. There is strict connection of those aberrations with hemisphere domination (Czachowska-Sieszycka, 1983). The more increasing of the left hemisphere in domination, the more differences in left and right size of the brain. The fact that human brain is asymmetrically organized is known for about 140 years. Platon stated that symmetry is ideal (beauty is symmetrical and proportional) and that there are perfect harmony between one side to the other. That is why anthropologists in XIX w thought that human brain must be symmetrical. Paul Broca debunked this theory (Broca, 1865) and stated that right hemisphere damaged cripple speech rarely but left hemisphere - in most cases. On the basis of this information he found out that speech centers are localized only on the one side of the brain. Nowadays, the research including asymmetry of human brain are more and more advanced due to a new technology. It is valid, because the information about right and left hemisphere asymmetry could also help in better understanding of human body morphological diversification.

#### **2. Morphological asymmetry in sport**

In sport there is a need to seek some acts to achievements the highest results, especially in highest level athletes. Among other factors like training methods modification or biological regeneration also certain level of morphological parameters is very important. Body compartments, among other factors, play an important role in physical performance (Petersen et al., 2006). Generally, the body composition of athletes are consider in terms of

The opposite word to symmetry is asymmetry. Three kinds of asymmetry were distinguished by Wolański (1955): 1/ morphological – differences in size and shape of organs or body parts situated on left or right side of the body; 2/ functional – connected with one of hemispheres domination (usually left); 3/ dynamic – differences between left

Bilateral asymmetry in humans, as was stated by same authors (Zeidel & Hessamian, 2010),

There are three types of bilateral asymmetry by Van Valen (1962): 1/directional asymmetry (DA): when some traits develop more on one side than the other, e.g., the human brain; 2/ antisymmetry: asymmetric development is typical, but unpredictable, e.g., larger signaling claw of the male fiddler crabs or handedness in human; 3/ fluctuating asymmetry (FA): "randomly produced deviations from perfect symmetry of two sides of quantitative traits in an individual for which the population mean of R-L differences is zero and their variability

Normal human body asymmetry appear very soon and the manifestation of morphological asymmetry intensify with aging what is connected with functional asymmetry (Malinowski, 2004). Level of asymmetry among population reflects its developmental stability, hence differences between right and left bilateral trait are very good predictor of developmental stability both on the individually and population level. Fluctuating asymmetry has been the

Potentially human body is divided into two symmetrical parts but in fact there are some deviations (aberrations): 1) internal aberrations: asymmetry of even (kidney) and odd (pancreas, heart, spleen, liver, stomach etc) of internal organs. It refers to size, shape, location, constitution or function, 2) external aberration: refer to extremities asymmetry and handedness. There is strict connection of those aberrations with hemisphere domination (Czachowska-Sieszycka, 1983). The more increasing of the left hemisphere in domination, the more differences in left and right size of the brain. The fact that human brain is asymmetrically organized is known for about 140 years. Platon stated that symmetry is ideal (beauty is symmetrical and proportional) and that there are perfect harmony between one side to the other. That is why anthropologists in XIX w thought that human brain must be symmetrical. Paul Broca debunked this theory (Broca, 1865) and stated that right hemisphere damaged cripple speech rarely but left hemisphere - in most cases. On the basis of this information he found out that speech centers are localized only on the one side of the brain. Nowadays, the research including asymmetry of human brain are more and more advanced due to a new technology. It is valid, because the information about right and left hemisphere asymmetry could also help in better understanding of human body

In sport there is a need to seek some acts to achievements the highest results, especially in highest level athletes. Among other factors like training methods modification or biological regeneration also certain level of morphological parameters is very important. Body compartments, among other factors, play an important role in physical performance (Petersen et al., 2006). Generally, the body composition of athletes are consider in terms of

was fashioned by millions of years of adaptive evolution and it implies perfection.

most widely used measure of developmental stability (Palmer & Strobeck, 1992).

and right extremities in strength, muscles hardness and elasticity.

is near-normally distributed".

morphological diversification.

**2. Morphological asymmetry in sport** 

whole body composition, but research show that regional BMD, FFM and FM distribution is equally important, in relation to training and performance (Bell et al., 2005). Tend to this direction more and more scientists conduct research in this area.

Many researches proved that morphological asymmetry - the difference between the right and the left sides of the body exist in sport (Dorado et al., 2002; Auerbach & Ruff 2006; Starosta, 1990) and it is very important to observe the scale of this phenomenon in order to its elimination if it will be necessary. Morphological asymmetry can concerns both side-toside differences between extremities, pelvis, trunk and total body with upper and lower body diversification. Analysis concerns mostly body dimension (length of limbs), level of body fat, lean and body density. In humans, some level of asymmetry in body dimensions is rather norm than the exception (Al-Eisa et al., 2004). It is stated that the lifelong preference for one extremity -e.g. the left arm or the left leg -as well as a predilection for a certain direction when turning around or rotating about one's longitudinal axis could lead to asymmetry which occurred in morphological characteristics and which can even be osseous in the case of competitive athletes. The body response on training stimulus will be vary according to its timing, duration and intensity (Malina, 1979).

It is obvious that morphological side–to–side diversification depends on sport specificity. Participation in asymmetric sport disciplines is connected with asymmetric changes in soft tissues (Ducher et al., 2005; Haapasalo et al., 1998). As was noticed, soft tissues indicate greater lateral differences than skeletal measurements (Van Dusen, 1939; Chhibber & Singh, 1970; Kimura & Asaeda, 1974;). Observation from literature suggests that in most cases the upper limb is laterally dominant in size on the right side while the lower limb is larger on the left side (Singh, 1970; McGrew & Marchant, 1997). Some studies stated that asymmetries are more pronounced in the upper extremities in comparison to lower extremities when the right side tends to be larger than the left (Munter, 1936; Tomkinson et al., 2003; Malina & Buschang, 2004; Ulijaszek & Mascie-Taylor, 2005). Malinowski (2004) stated that average, the right arm and forearm are longer, and larger are their circumferences. Left hand is longer and narrowest. The right upper limb is longer about 1 cm in comparison to left one, whereas left lower limb is longer about 10-13 mm. Also left foot, left thigh and calf circumferences are longer.

As was stated by some authors (Manning et al., 2002) small percentage changes in left or right trait size may result in large percentage changes in asymmetries, so precise and the most suitable method is need to evaluated regional morphological diversification among very specific part of population – athletes.

#### **2.1 Methods for regional body composition assessment**

There are different methods which could be use to do such analysis, which differ in the time, expense and accuracy of the results. These procedures are subject to some error which can result from measurement procedures, from the equations selected to calculate body fat percent, or from both. A standard error for most procedure is about 3 to 4 %.

The most traditional method to assess the size of particularly body segments is anthropometry. It uses circumferences, SKF thicknesses, skeletal breadth and segment lengths for total and regional body composition evaluation (Heyward & Wagner, 2004). Some standardized procedures should be taken into consideration to increase the accuracy

Dxa as a Tool for the Assessment of Morphological Asymmetry in Athletes 63

chemical composition of the underlying tissue (Pietrobelli et al., 1996). Two kinds of absorptiometry were distinguished: single-photon absorptiometry and dual-photon absorptiometry which were precisely described in Lukaski comprehensive study (1987). It was stated that both technologies provide highly reproducible estimates of lean tissue mainly in adults, because the coefficient of variation for them for repeated measurements is less than 2 %. The advantage of DXA technology is reduced of radiation exposure and more readily available data about changes in bone mineral content in whole and regional skeleton. As was stated by Webber (1995) the x-ray exposure is low and corresponding to natural radiation and radioactivity received during 5 days of normal living. Additionally, this method is more relevant in longitudinal study to track changes in body composition (Malina et al., 2004) because of the good precision of particular DXA device (about 1% for BMC and 2-3% for total body fat) for assessing whole body composition (De Lorenzo et al.,

Kistrop with co-authors (Kistrop et al., 2000) found that measuring total and regional body composition by DXA can improve the prediction of energy expenditure what could be a

Each method has its own advantages and disadvantages, also DXA. Some authors observed (Calbet et al., 1998) that this method giving a general idea about the specific sport loading influence on BMC. On the other hand it is not possible to assess the effect of sport participation to particular bones during DXA analysis because it includes all bone structures. There are also some concerns about measurements bias related to the impact of a significant change in the lean tissue hydration. Some study however show not significantly alters the estimates for the bone, lean and fat mass (Pietrobelli et al., 1998). Another problem is that thicknesses are different depends on body regions and individual body shapes, so there could be large variation in percent fat in DXA study (Steward & Hannan, 2000b; Lohman & Chen, 2005). When comparing results of regional body composition from DXA scanners of different manufacturers, caution must be done because of some factors which are different among both of them like: pixel size, X-ray voltages or algorithms for shape and edge detection (Tothill et al., 1994; Steward & Hannan, 2000a). As was stated by some authors (Lohman & Chen, 2005) differences between DXA instruments from leading manufactures like Hologic, Lunar and Norland were 7% for body fat and about 15% for bone mineral content, some times ago. Nowadays, DXA software are more and more accurate with new calibration modes increasing precision and accuracy (O'Connor, 2006). iDXA (Lunar) for example have a little bit precision with total-body assessment and almost identical values in regional BMD measurements in comparison to Lunar Prodigy (Faulkner, 2006). Despite all limitations DXA is a widely used method, owing to its ease of use, availability, low-radiation exposure, good accuracy and reproducibility for the assessment of regional body composition. It is also seems to be less dependent on biological consistency than other methods (Haarbo et al., 1991; Kohrt, 1995) and is most useful for research purposes (Speiser et al., 2005). It was documented that regional adiposity by DXA is potentially more accurate than anthropometry measures, and more practical than computed tomography or magnetic resonance imaging scans (Henche et al., 2008). Taking into account all of those data, DXA seems to be one of the most suitable method for evaluation side-to-

valid information for athletes who try to lose fat mass or gain lean muscle.

side morphological diversification among athletes.

1998).

and reliability of measurements like taking three measurements within ± 0.2 for body segments with relatively small girths (calf, arm, forearm) and three measurements within ± 1.0 cm for longer body segments like waist, abdomen and buttock; or using a small sliding caliper with greater precision to measure breadth of smaller segments (elbow, wrist) and so on (Wilmore et al., 1988). Schell and co-authors (Schell et al., 1985) emphasized that asymmetry of paired dimensions is a big methodological problem in anthropometry. Additionally, analysis of asymmetry should be done with advanced statistical tests. Moreover, interpretation of given results must be done with cautions especially, if observed differences are so small that could be lower than technical errors of measurement (Moreno et al., 2002).

There are also some newer methods to assess regional diversification of some tissues. Traditional BIA method, allows to assess only whole body composition (wrist-to-ankle), however segmental BIA (SBIA) system based on eight symmetrically re-positioned electrodes can give information about resistance and reactance values from right and left arms and legs and torso (www.rjlsystem.com/pdf-files/segmental\_bia.pdf). Because of human body segments are not uniform in length or cross-sectional area, there will be different resistance to the flow of current through them (Heyward & Wagner., 2004). Fuller and Elia (1989) stated that those body segments which have small cross-sectional areas have simultaneously the greatest effect on impedance. The resistance ratio of upper limb-trunklower limb theoretically equal 13.8 : 1 : 11.8. SBIA measuring particularly body segments like upper and lower limb or trunk may be a good method for person with altered fluid distribution, so also for athletes (Organ et al., 1994). Segmental muscle changes for example during physical activity are visible by studying the individual arms and legs as a comparative percentage or a percent change over time. Segmental measurements can be successful made by traditional electrodes Akern Sre (Florence, Italy) or by segmental body composition analyzers (Omron, Tanita, BioSpace) which incorporate foot and hand contact points (where standing person holding two rods with fingers and thumb of each hand) what has disadvantage: high resistance of the body ankle and wrist are included in the measurements. Additionally the ankle, wrist, lower leg and forearm contribute more than 50 percent of the measurement (Scheltinge et al., 1991).

MRI (Magnetic Resonans Imaging) – another method to assess regional diversification, can be used to assess whole mass and muscle content of any body area, giving high-quality images of different tissues (Malina et al., 2004). Also computerized tomography CT is often used in regional determination of body segments with body fat, muscle and bone area assessment (Buckley et al., 1987; Forbes et al., 1988; Jordao et al., 2004). Magnetic resonance imaging and computed tomography are regard as the reference methods for regional body composition assessment but routine use of them is impeded by access, high cost, and in case of CT, significant exposure to ionizing radiation. Some authors propose then DXA (Dual Energy X-ray absorptiometry) as a more valid and precise method for measuring total and regional body composition (Chen et al., 2007; Andreoli et al., 2009). Generally, DXA, CT and MRI methods are consider to be a standard for precision and accuracy for body composition measurements (Chettle & Fremlin, 1984; Ellis, 2000). DXA is primary method to estimate body composition of the total body and specific regions (bone mineral, fat-free soft tissue and fat). The main assumption of absorptiometry method relies on measuring attenuation of X-rays with high-and-low-photon energies what is dependent on the thickness, density and

and reliability of measurements like taking three measurements within ± 0.2 for body segments with relatively small girths (calf, arm, forearm) and three measurements within ± 1.0 cm for longer body segments like waist, abdomen and buttock; or using a small sliding caliper with greater precision to measure breadth of smaller segments (elbow, wrist) and so on (Wilmore et al., 1988). Schell and co-authors (Schell et al., 1985) emphasized that asymmetry of paired dimensions is a big methodological problem in anthropometry. Additionally, analysis of asymmetry should be done with advanced statistical tests. Moreover, interpretation of given results must be done with cautions especially, if observed differences are so small that could be lower than technical errors of measurement (Moreno

There are also some newer methods to assess regional diversification of some tissues. Traditional BIA method, allows to assess only whole body composition (wrist-to-ankle), however segmental BIA (SBIA) system based on eight symmetrically re-positioned electrodes can give information about resistance and reactance values from right and left arms and legs and torso (www.rjlsystem.com/pdf-files/segmental\_bia.pdf). Because of human body segments are not uniform in length or cross-sectional area, there will be different resistance to the flow of current through them (Heyward & Wagner., 2004). Fuller and Elia (1989) stated that those body segments which have small cross-sectional areas have simultaneously the greatest effect on impedance. The resistance ratio of upper limb-trunklower limb theoretically equal 13.8 : 1 : 11.8. SBIA measuring particularly body segments like upper and lower limb or trunk may be a good method for person with altered fluid distribution, so also for athletes (Organ et al., 1994). Segmental muscle changes for example during physical activity are visible by studying the individual arms and legs as a comparative percentage or a percent change over time. Segmental measurements can be successful made by traditional electrodes Akern Sre (Florence, Italy) or by segmental body composition analyzers (Omron, Tanita, BioSpace) which incorporate foot and hand contact points (where standing person holding two rods with fingers and thumb of each hand) what has disadvantage: high resistance of the body ankle and wrist are included in the measurements. Additionally the ankle, wrist, lower leg and forearm contribute more than 50

MRI (Magnetic Resonans Imaging) – another method to assess regional diversification, can be used to assess whole mass and muscle content of any body area, giving high-quality images of different tissues (Malina et al., 2004). Also computerized tomography CT is often used in regional determination of body segments with body fat, muscle and bone area assessment (Buckley et al., 1987; Forbes et al., 1988; Jordao et al., 2004). Magnetic resonance imaging and computed tomography are regard as the reference methods for regional body composition assessment but routine use of them is impeded by access, high cost, and in case of CT, significant exposure to ionizing radiation. Some authors propose then DXA (Dual Energy X-ray absorptiometry) as a more valid and precise method for measuring total and regional body composition (Chen et al., 2007; Andreoli et al., 2009). Generally, DXA, CT and MRI methods are consider to be a standard for precision and accuracy for body composition measurements (Chettle & Fremlin, 1984; Ellis, 2000). DXA is primary method to estimate body composition of the total body and specific regions (bone mineral, fat-free soft tissue and fat). The main assumption of absorptiometry method relies on measuring attenuation of X-rays with high-and-low-photon energies what is dependent on the thickness, density and

percent of the measurement (Scheltinge et al., 1991).

et al., 2002).

chemical composition of the underlying tissue (Pietrobelli et al., 1996). Two kinds of absorptiometry were distinguished: single-photon absorptiometry and dual-photon absorptiometry which were precisely described in Lukaski comprehensive study (1987). It was stated that both technologies provide highly reproducible estimates of lean tissue mainly in adults, because the coefficient of variation for them for repeated measurements is less than 2 %. The advantage of DXA technology is reduced of radiation exposure and more readily available data about changes in bone mineral content in whole and regional skeleton. As was stated by Webber (1995) the x-ray exposure is low and corresponding to natural radiation and radioactivity received during 5 days of normal living. Additionally, this method is more relevant in longitudinal study to track changes in body composition (Malina et al., 2004) because of the good precision of particular DXA device (about 1% for BMC and 2-3% for total body fat) for assessing whole body composition (De Lorenzo et al., 1998).

Kistrop with co-authors (Kistrop et al., 2000) found that measuring total and regional body composition by DXA can improve the prediction of energy expenditure what could be a valid information for athletes who try to lose fat mass or gain lean muscle.

Each method has its own advantages and disadvantages, also DXA. Some authors observed (Calbet et al., 1998) that this method giving a general idea about the specific sport loading influence on BMC. On the other hand it is not possible to assess the effect of sport participation to particular bones during DXA analysis because it includes all bone structures. There are also some concerns about measurements bias related to the impact of a significant change in the lean tissue hydration. Some study however show not significantly alters the estimates for the bone, lean and fat mass (Pietrobelli et al., 1998). Another problem is that thicknesses are different depends on body regions and individual body shapes, so there could be large variation in percent fat in DXA study (Steward & Hannan, 2000b; Lohman & Chen, 2005). When comparing results of regional body composition from DXA scanners of different manufacturers, caution must be done because of some factors which are different among both of them like: pixel size, X-ray voltages or algorithms for shape and edge detection (Tothill et al., 1994; Steward & Hannan, 2000a). As was stated by some authors (Lohman & Chen, 2005) differences between DXA instruments from leading manufactures like Hologic, Lunar and Norland were 7% for body fat and about 15% for bone mineral content, some times ago. Nowadays, DXA software are more and more accurate with new calibration modes increasing precision and accuracy (O'Connor, 2006). iDXA (Lunar) for example have a little bit precision with total-body assessment and almost identical values in regional BMD measurements in comparison to Lunar Prodigy (Faulkner, 2006). Despite all limitations DXA is a widely used method, owing to its ease of use, availability, low-radiation exposure, good accuracy and reproducibility for the assessment of regional body composition. It is also seems to be less dependent on biological consistency than other methods (Haarbo et al., 1991; Kohrt, 1995) and is most useful for research purposes (Speiser et al., 2005). It was documented that regional adiposity by DXA is potentially more accurate than anthropometry measures, and more practical than computed tomography or magnetic resonance imaging scans (Henche et al., 2008). Taking into account all of those data, DXA seems to be one of the most suitable method for evaluation side-toside morphological diversification among athletes.

Dxa as a Tool for the Assessment of Morphological Asymmetry in Athletes 65

asymmetry in the upper body of fast bowlers in cricket was observed in Grobbelaar study (Grobbelaar et al., 2000). It concerns the relaxed arm (3.8 %), tensed arm (4.7%), forearm

The morphological characteristics of fencers show a typical asymmetry of the limbs as a result of the asymmetrical sport activity practicing what is advantage in gaining success. Fencing produces typical functional asymmetries that emphasize the very high level of specific function, strength and control required in this sport discipline (Roi et al., 2008). Muscle mass of dominant lower limb of high-class fencer is bigger than contrlateral limb (Nystrom et al., 1990). Tsolakis et al (2006) stated, on the example of fencers, that there was difference in morphological asymmetry of arm and leg depended on age, where arm asymmetries were specific for the age of 10-13 years, whereas leg asymmetries were observed among 14-17 year-old athletes. Because of asymmetrical nature of this sport, some specific injuries are characteristics in the shoulders, the back and the pelvic girdle, so there is

necessity of including prevention in daily fencers training (Kucera & Henn, 2003).

(professional national league and semi-professional state league).

years. Similar findings were done by Prives (1969).

Manning and Pickup (Manning, & Pickup, 1998) stated that the national league athletes would be more symmetric, because symmetry is positively related with physical performance in adult males. Besides, symmetric males run faster than asymmetric males (Manning & Pickup, 1998). According to Chinn study "Morphological asymmetry is more pointed out in high level athlete in case of asymmetrical sport disciplines" (Chinn et al., 1974). Tomkinson et al stated (2003) that there are no differences in variance in fluctuating asymmetry between adult basketball and soccer, competing at two different standards

There is also some assumption that symmetry positive correlated with body size what mean that the larger men is (higher and heavier), the more symmetric he is. But in women body size correlated negatively with symmetry (Manning, 1995). Additionally this author observed positive correlation between body mass index (BMI) and asymmetry among women (the bigger BMI= the bigger asymmetry). On the other hand some research indicated that the bigger person (both men and women)– the greater asymmetry (Graham et al., 1998). Studies of scientists from many disciplines suggest that age, sex and environmental stress like extreme unilateral work are the most influential factors for morphological asymmetry (Wolański, 1962; Singh, 1970; Mascie-Taylor et al., 1981; Malina, 1983). Some authors also found (Hetland et al., 1998) that there are different factors significantly contributed to the regional body composition. On the basic of 108 (86 recreational and 22 elite runners) male long-distance runners they stated that training was the strongest determinant in the legs and the arms, whereas androgenic activity was important in the abdominal region. Some data suggest that persistent unilateral training may also influence specific bone lengths and width. As was stated by Buskirk et al (1956) on the example of seven tennis players (nationally level), the athletes had greater length and width of bones of the dominant hands and forearms than the non-dominant extremity. Authors suggested that those laterality differences were results of vigorous exercise on the bone growth during the adolescent

In Jones et al study (1977) there was a significant hypertrophy of cortical bone of the humerus in the dominant arm compared to the non-playing arm of 84 male professional tennis players. There were sex differences in cortical thickness of the dominant arm

(2.8%) and ½ chest circumferences (6.4%).

#### **2.2 Morphological asymmetry of athletes - research overview**

Many studies with athletes body asymmetry exploited anthropometric method. Malina and Buschang (2004) observed greater hypertrophy in the musculature of the dominant side in athletes. Also another authors (Calbet et al., 2001; Dorado et al., 2002) found, on the example of golfers, that they had muscle hypertrophy in the dominant compared with non-dominant arm under training influence. The right-left differences in morphological parameters (mainly in forearm girth, arm girth, elbow width) were observed also in 134 athletes aged 21-32 years, engaged in many different asymmetric movement sports like tennis, canoeing, kayaking and boxing (Krawczyk et al., 1998). According to those research, most significant asymmetry was found among tennis players, then in kayakers, canoeists, rowers and skaters and concerned mainly forearm girth. The extreme directional asymmetry in the use of the limbs among tennis players is connected with physiological and anatomical changes in those body segments (Lucki, 2006). On the basis of 25 male collegiate tennis players age 19- 24 from four NCAA Division I she stated that there was significant asymmetry between the limbs, related to cross-sectional measurements (circumferences, widths). Some authors (Maughan et al., 1986) observed greater proportion of muscle and smaller proportion of fat in dominant arm than the opposite limb in tennis players. Numerous studies indicated increased bone density in the dominant limb among tennis players (Ruff et al., 1994; Ducher et al., 2005; Lucki & Nicolay, 2007). Analysis of the bilateral asymmetry among collegiate (19-24 years old) tennis players showed that forearm circumferences of the dominant limb was greater than in the opposite limb (3-10% in female and 2-13% in male tennis players) (Lucki & Nikolay, 2007). Jone's (Jones et al., 1977) study examining site-specific accretion of bone of professional tennis players, with differences being up to 30%. Tarociński (1977) stated that difference in circumferences of the left and the right upper extremities was 1-2 cm in male athlete (age 12-15) playing tennis for four years, whereas in Marchwicki study (1927) those differences was smaller and equal 0,3 – 1,1 cm. also among young tennis players. In lower extremities, in turn, there was smaller diversification with left leg advantage (Tarociński, 1977). Similar finding was done later on the example of baseball players (Komi 1996; Bubanj & Obradovic, 2002).

Abraho and Mello (2008) made comparison of the young athletes playing tennis for at most two years (age 6-10 years) with the male instructors between 22 y and 37 years of age engaged in this sport discipline at least eight years. They noticed the increase of the incidence of right somatic measures superior to the left, because the excessive time of training of asymmetric sport. This situation has influence on postural deviation according to those research.

When one considers that carrying loads with the preferred hand means a stress on the arm muscles of the same side and a simultaneous activation of the contra-lateral muscles for the stabilization of one's balance, these functional asymmetries become plausible. It is also remarkable that hurdlers, high jumpers and pole vaulters exhibit higher muscle contractility in their swing leg than in their take-off leg (Absauomov, 1976) because of the higher mechanical load. Kruger et al (2005) determined the degree of upper body morphological asymmetry in 19 elite international male javelin throwers, age of 26,4 ± 4.4 years. They found larger variables on the dominant side for thirteen of the fourteen variables (especially for triceps skinfold 5.9%, half-chest girth 4.9%,, forearm girth 3.9%, biceps skinfold 2.5%) what could have health consequences and performance limiting effects. The morphological

Many studies with athletes body asymmetry exploited anthropometric method. Malina and Buschang (2004) observed greater hypertrophy in the musculature of the dominant side in athletes. Also another authors (Calbet et al., 2001; Dorado et al., 2002) found, on the example of golfers, that they had muscle hypertrophy in the dominant compared with non-dominant arm under training influence. The right-left differences in morphological parameters (mainly in forearm girth, arm girth, elbow width) were observed also in 134 athletes aged 21-32 years, engaged in many different asymmetric movement sports like tennis, canoeing, kayaking and boxing (Krawczyk et al., 1998). According to those research, most significant asymmetry was found among tennis players, then in kayakers, canoeists, rowers and skaters and concerned mainly forearm girth. The extreme directional asymmetry in the use of the limbs among tennis players is connected with physiological and anatomical changes in those body segments (Lucki, 2006). On the basis of 25 male collegiate tennis players age 19- 24 from four NCAA Division I she stated that there was significant asymmetry between the limbs, related to cross-sectional measurements (circumferences, widths). Some authors (Maughan et al., 1986) observed greater proportion of muscle and smaller proportion of fat in dominant arm than the opposite limb in tennis players. Numerous studies indicated increased bone density in the dominant limb among tennis players (Ruff et al., 1994; Ducher et al., 2005; Lucki & Nicolay, 2007). Analysis of the bilateral asymmetry among collegiate (19-24 years old) tennis players showed that forearm circumferences of the dominant limb was greater than in the opposite limb (3-10% in female and 2-13% in male tennis players) (Lucki & Nikolay, 2007). Jone's (Jones et al., 1977) study examining site-specific accretion of bone of professional tennis players, with differences being up to 30%. Tarociński (1977) stated that difference in circumferences of the left and the right upper extremities was 1-2 cm in male athlete (age 12-15) playing tennis for four years, whereas in Marchwicki study (1927) those differences was smaller and equal 0,3 – 1,1 cm. also among young tennis players. In lower extremities, in turn, there was smaller diversification with left leg advantage (Tarociński, 1977). Similar finding was done later on the example of baseball

Abraho and Mello (2008) made comparison of the young athletes playing tennis for at most two years (age 6-10 years) with the male instructors between 22 y and 37 years of age engaged in this sport discipline at least eight years. They noticed the increase of the incidence of right somatic measures superior to the left, because the excessive time of training of asymmetric sport. This situation has influence on postural deviation according to

When one considers that carrying loads with the preferred hand means a stress on the arm muscles of the same side and a simultaneous activation of the contra-lateral muscles for the stabilization of one's balance, these functional asymmetries become plausible. It is also remarkable that hurdlers, high jumpers and pole vaulters exhibit higher muscle contractility in their swing leg than in their take-off leg (Absauomov, 1976) because of the higher mechanical load. Kruger et al (2005) determined the degree of upper body morphological asymmetry in 19 elite international male javelin throwers, age of 26,4 ± 4.4 years. They found larger variables on the dominant side for thirteen of the fourteen variables (especially for triceps skinfold 5.9%, half-chest girth 4.9%,, forearm girth 3.9%, biceps skinfold 2.5%) what could have health consequences and performance limiting effects. The morphological

**2.2 Morphological asymmetry of athletes - research overview** 

players (Komi 1996; Bubanj & Obradovic, 2002).

those research.

asymmetry in the upper body of fast bowlers in cricket was observed in Grobbelaar study (Grobbelaar et al., 2000). It concerns the relaxed arm (3.8 %), tensed arm (4.7%), forearm (2.8%) and ½ chest circumferences (6.4%).

The morphological characteristics of fencers show a typical asymmetry of the limbs as a result of the asymmetrical sport activity practicing what is advantage in gaining success. Fencing produces typical functional asymmetries that emphasize the very high level of specific function, strength and control required in this sport discipline (Roi et al., 2008). Muscle mass of dominant lower limb of high-class fencer is bigger than contrlateral limb (Nystrom et al., 1990). Tsolakis et al (2006) stated, on the example of fencers, that there was difference in morphological asymmetry of arm and leg depended on age, where arm asymmetries were specific for the age of 10-13 years, whereas leg asymmetries were observed among 14-17 year-old athletes. Because of asymmetrical nature of this sport, some specific injuries are characteristics in the shoulders, the back and the pelvic girdle, so there is necessity of including prevention in daily fencers training (Kucera & Henn, 2003).

Manning and Pickup (Manning, & Pickup, 1998) stated that the national league athletes would be more symmetric, because symmetry is positively related with physical performance in adult males. Besides, symmetric males run faster than asymmetric males (Manning & Pickup, 1998). According to Chinn study "Morphological asymmetry is more pointed out in high level athlete in case of asymmetrical sport disciplines" (Chinn et al., 1974). Tomkinson et al stated (2003) that there are no differences in variance in fluctuating asymmetry between adult basketball and soccer, competing at two different standards (professional national league and semi-professional state league).

There is also some assumption that symmetry positive correlated with body size what mean that the larger men is (higher and heavier), the more symmetric he is. But in women body size correlated negatively with symmetry (Manning, 1995). Additionally this author observed positive correlation between body mass index (BMI) and asymmetry among women (the bigger BMI= the bigger asymmetry). On the other hand some research indicated that the bigger person (both men and women)– the greater asymmetry (Graham et al., 1998).

Studies of scientists from many disciplines suggest that age, sex and environmental stress like extreme unilateral work are the most influential factors for morphological asymmetry (Wolański, 1962; Singh, 1970; Mascie-Taylor et al., 1981; Malina, 1983). Some authors also found (Hetland et al., 1998) that there are different factors significantly contributed to the regional body composition. On the basic of 108 (86 recreational and 22 elite runners) male long-distance runners they stated that training was the strongest determinant in the legs and the arms, whereas androgenic activity was important in the abdominal region. Some data suggest that persistent unilateral training may also influence specific bone lengths and width. As was stated by Buskirk et al (1956) on the example of seven tennis players (nationally level), the athletes had greater length and width of bones of the dominant hands and forearms than the non-dominant extremity. Authors suggested that those laterality differences were results of vigorous exercise on the bone growth during the adolescent years. Similar findings were done by Prives (1969).

In Jones et al study (1977) there was a significant hypertrophy of cortical bone of the humerus in the dominant arm compared to the non-playing arm of 84 male professional tennis players. There were sex differences in cortical thickness of the dominant arm

Dxa as a Tool for the Assessment of Morphological Asymmetry in Athletes 67

respectively: p= 0.0086) and of the trunk (1.111 ± 0.0609 g/cm2 vs. 1.137 ± 0.0729 g/cm2, respectively: p=0.0008). In turn there were no differences between right arm 1.017 g/cm2 and for left arm 1.014 g/cm2 as well as between total bone mineral density (the right and the left sides of the body) (1.326 ± 0.0741 g/cm2 vs. 1.338 ± 0.0754 g/cm2, respectively: p=0.1310). The larger amount of body density was in the left legs, left trunk and total body of athletes. The individual differences between the left and right sides of a particularly body segment show that the biggest diversity among them was in the case of legs. Significant differences in side-to-side lean morphology were observed in every measured parameter. The lean of the arms, legs, trunk and total was higher in the left side of the body. The lean of right arm was 3738 ± 454.9 g vs. 4046 ± 420.7 g for left arm respectively (p=0.0000), lean of right leg was 10578 ± 1050.9 g vs. 10904 ± 1054.5 g, respectively (p=0.0000). Also lean amount was larger in the left side of trunk in comparison to the right (difference: 14371 ± 1580.6 g to 13996 ± 1436.7 g). Significance difference was observed in total lean (30444 ± 2759.5 g for right vs. 31208 ± 2814.5 g for left total, respectively (p=0.0073). There was clear similarity in arms and legs among field hockey players, as opposed to the trunk and total LEAN. Similar findings were observed in a case of body fat analyzing. Across all body segments, the amount of

morphological asymmetry was significantly greater in the left side of the body.

athletes combined nor within individual sport disciplines.

In a study of thirty competitive young adult Rugby Union players, Bell et al (2005) observed the arm-lower body contrast in lean soft tissue mass suggesting, that upper and lower limbs contribute equally to playing performance. Also bone mineral mass (BMM) was different according to playing position. The legs were dominant in forwards (-0.76) and the trunk in backs (0.67). The measurements were done using DXA (Hologic QDR/1000W. software version V5.73). Using DXA (QDR-1500, Hologic Corporation, Waltham, MA) in ten healthy postmenopausal tennis players Sanchis-Moysi et al (2004) observed linear relationship between the extent of years of training and the magnitude of the inter-arm difference in bone size- participation in tennis after menarche is connecting with greater bone size and mass in the loaded arm. Some data suggested also that muscle mass is independent predictor of regional and total BMD on the example of gymnasts and elite swimmers with DXA method (Taaffe et al., 1995). Also Calbet et al (2001) study showed a high correlation between the BMD and the muscle mass in the left leg among 33 right-leg dominant male soccer players. Nevil et al (2003) considered the effects of specific training on BMD asymmetry between arms and legs among Caucasians athletes from ten sport disciplines (runners, cyclists, triathletes, racket players, rock climbers, swimmers, rugby players, rowers, kayakers and bodybuilders), at a university or higher level, trained for a minimum of four hour per week, for a minimum of three years. Using DXA on a Hologic QDR 1000W (Bedford, MA) scanner they stated that the differences between the dominant and non-dominant arms were significant but different depends on sport. For example it was greater in the racket players and lesser in the rowers. In the case of right and left legs, there was no asymmetric difference in BMD between them for all

The McClanahan et al (2002) study investigated the effects of participation in different sports (NCAA Division I-A baseball, basketball, football, golf, soccer, tennis, cross-country, indoor/outdoor track and volleyball) on side-o-side diversification in bone mineral density (BMD) of the upper and lower limbs. On the basic of 184 collegiate athletes both sexes, and using dual energy X-ray absorptiometry, they revealed greater BMD of the right arms compared with the left arms for all teams. The largest differences were found in the case of

compared to non-dominant arm among athletes (man had 35% greater cortical thickness of the dominant arm, whereas women - 28%). Hypertrophy of the humeral cortex has been reported also in the throwing arms of professional baseball pitchers (King et al., 1969).

Asymmetry could have different value and it is stated that when it access some level, it may hinder some special activity practice, could negatively affect the health (being connected with functional asymmetry and some changes for example in the area of backbone). According to Wilk et al study (2002) therapist should focus on restoring the functional shoulder asymmetry during rehabilitation of shoulder injuries in the case of throwing athlete. Asymmetries between lower limbs during athletic movements are thought to increase the risk of injury and compromise performance (Cronin, 2010). In the case of athletes who are engaged with sport discipline including one-sided hips rotation (tennis, golf, squash) and suffer from LBP, more lower limbs asymmetry and pelvic asymmetry (LLD-leg length discrepancy) were observed (Egan et al., 1995; Van Dillen et al., 2008). Some authors asked the question if the asymmetry lead to the back pain or did the back pain lead to the asymmetry (Bussey, 2010). This is not yet determined evolutionary dilemma. She stated that asymmetries may be functional adaptations, meaning that the body has successfully adapted to the asymmetrical loading demands of the sport in order to decrease the excessive strain in some tissues. Similar finding did previously Koszczyc (1991) who observed that shoulder and whole upper limb asymmetry (which can appear in progressive stadium of ontogeny) could be effect of body adaptation to increased mechanical loads. Bussey (2010) conducted the study based on 60 women divided into three groups consisted of elite athletes at the national and international level (bilateral group: triathlon, crosscountry running, single scull rowing ; unilateral group: field hockey, ice hockey, speedskating) and control group. She speculated that some level of pelvis asymmetry may be a natural effect of lateral dominance which decrease with bilateral activities like running or cycling or increase with unilateral nature of sport discipline like hockey.

Field hockey is one-side dominant sport. It means that athletes have preference of one side of the body over the other. There are many different specific movements in this discipline. Many of them involve a rapid rotation of the hips, shoulders and arms for example push-in movement (Kerr & Ness, 2006). This movement stated with the body counter-rotated right side of the body is behind the left side (with respect to the direction of ball trajectory). Similar findings was described by Mc Laughlin (1997). Kerr found that pelvic and shoulder girdle maximum angular velocities occurred concurrently from left arm to the right arm. Many published studies focused on many components of field hockey like the hit (Burges-Limerick et al., 1991), the push (Alexander, 1983), the slap shot (Cresswell & Elliott, 1987), the push-in and trap phases of the penalty corner (Kerr & Ness, 2006), describing specificity of characteristic movements, which could appeared in athletes morphology.

Krzykała (2010) studied the effects of specific one-side dominant training on morphological asymmetry, on the basic of twenty competitive male field hockey players 18-34 years of age. All athletes had played representative hockey at a senior (national) polish level of the game. Average training years of all competitors was 17 y and hesitated between 13 y and 24 y. The anthropometric characteristics in both sides of the body was done by dual energy x-ray absorptiometry, using LUNAR PRODIGY ADVANCE (GE, Madison, WI, USA) densitometer with enCORE software (GE Helthcare v.10.50.086). There were significant differences in body mass density of the legs (1.576 ± 0.0909 g/cm2 vs. 1.611 ± 0.1062 g/cm2,

compared to non-dominant arm among athletes (man had 35% greater cortical thickness of the dominant arm, whereas women - 28%). Hypertrophy of the humeral cortex has been reported also in the throwing arms of professional baseball pitchers (King et al., 1969).

Asymmetry could have different value and it is stated that when it access some level, it may hinder some special activity practice, could negatively affect the health (being connected with functional asymmetry and some changes for example in the area of backbone). According to Wilk et al study (2002) therapist should focus on restoring the functional shoulder asymmetry during rehabilitation of shoulder injuries in the case of throwing athlete. Asymmetries between lower limbs during athletic movements are thought to increase the risk of injury and compromise performance (Cronin, 2010). In the case of athletes who are engaged with sport discipline including one-sided hips rotation (tennis, golf, squash) and suffer from LBP, more lower limbs asymmetry and pelvic asymmetry (LLD-leg length discrepancy) were observed (Egan et al., 1995; Van Dillen et al., 2008). Some authors asked the question if the asymmetry lead to the back pain or did the back pain lead to the asymmetry (Bussey, 2010). This is not yet determined evolutionary dilemma. She stated that asymmetries may be functional adaptations, meaning that the body has successfully adapted to the asymmetrical loading demands of the sport in order to decrease the excessive strain in some tissues. Similar finding did previously Koszczyc (1991) who observed that shoulder and whole upper limb asymmetry (which can appear in progressive stadium of ontogeny) could be effect of body adaptation to increased mechanical loads. Bussey (2010) conducted the study based on 60 women divided into three groups consisted of elite athletes at the national and international level (bilateral group: triathlon, crosscountry running, single scull rowing ; unilateral group: field hockey, ice hockey, speedskating) and control group. She speculated that some level of pelvis asymmetry may be a natural effect of lateral dominance which decrease with bilateral activities like running or

cycling or increase with unilateral nature of sport discipline like hockey.

of characteristic movements, which could appeared in athletes morphology.

Field hockey is one-side dominant sport. It means that athletes have preference of one side of the body over the other. There are many different specific movements in this discipline. Many of them involve a rapid rotation of the hips, shoulders and arms for example push-in movement (Kerr & Ness, 2006). This movement stated with the body counter-rotated right side of the body is behind the left side (with respect to the direction of ball trajectory). Similar findings was described by Mc Laughlin (1997). Kerr found that pelvic and shoulder girdle maximum angular velocities occurred concurrently from left arm to the right arm. Many published studies focused on many components of field hockey like the hit (Burges-Limerick et al., 1991), the push (Alexander, 1983), the slap shot (Cresswell & Elliott, 1987), the push-in and trap phases of the penalty corner (Kerr & Ness, 2006), describing specificity

Krzykała (2010) studied the effects of specific one-side dominant training on morphological asymmetry, on the basic of twenty competitive male field hockey players 18-34 years of age. All athletes had played representative hockey at a senior (national) polish level of the game. Average training years of all competitors was 17 y and hesitated between 13 y and 24 y. The anthropometric characteristics in both sides of the body was done by dual energy x-ray absorptiometry, using LUNAR PRODIGY ADVANCE (GE, Madison, WI, USA) densitometer with enCORE software (GE Helthcare v.10.50.086). There were significant differences in body mass density of the legs (1.576 ± 0.0909 g/cm2 vs. 1.611 ± 0.1062 g/cm2, respectively: p= 0.0086) and of the trunk (1.111 ± 0.0609 g/cm2 vs. 1.137 ± 0.0729 g/cm2, respectively: p=0.0008). In turn there were no differences between right arm 1.017 g/cm2 and for left arm 1.014 g/cm2 as well as between total bone mineral density (the right and the left sides of the body) (1.326 ± 0.0741 g/cm2 vs. 1.338 ± 0.0754 g/cm2, respectively: p=0.1310). The larger amount of body density was in the left legs, left trunk and total body of athletes. The individual differences between the left and right sides of a particularly body segment show that the biggest diversity among them was in the case of legs. Significant differences in side-to-side lean morphology were observed in every measured parameter. The lean of the arms, legs, trunk and total was higher in the left side of the body. The lean of right arm was 3738 ± 454.9 g vs. 4046 ± 420.7 g for left arm respectively (p=0.0000), lean of right leg was 10578 ± 1050.9 g vs. 10904 ± 1054.5 g, respectively (p=0.0000). Also lean amount was larger in the left side of trunk in comparison to the right (difference: 14371 ± 1580.6 g to 13996 ± 1436.7 g). Significance difference was observed in total lean (30444 ± 2759.5 g for right vs. 31208 ± 2814.5 g for left total, respectively (p=0.0073). There was clear similarity in arms and legs among field hockey players, as opposed to the trunk and total LEAN. Similar findings were observed in a case of body fat analyzing. Across all body segments, the amount of morphological asymmetry was significantly greater in the left side of the body.

In a study of thirty competitive young adult Rugby Union players, Bell et al (2005) observed the arm-lower body contrast in lean soft tissue mass suggesting, that upper and lower limbs contribute equally to playing performance. Also bone mineral mass (BMM) was different according to playing position. The legs were dominant in forwards (-0.76) and the trunk in backs (0.67). The measurements were done using DXA (Hologic QDR/1000W. software version V5.73). Using DXA (QDR-1500, Hologic Corporation, Waltham, MA) in ten healthy postmenopausal tennis players Sanchis-Moysi et al (2004) observed linear relationship between the extent of years of training and the magnitude of the inter-arm difference in bone size- participation in tennis after menarche is connecting with greater bone size and mass in the loaded arm. Some data suggested also that muscle mass is independent predictor of regional and total BMD on the example of gymnasts and elite swimmers with DXA method (Taaffe et al., 1995). Also Calbet et al (2001) study showed a high correlation between the BMD and the muscle mass in the left leg among 33 right-leg dominant male soccer players. Nevil et al (2003) considered the effects of specific training on BMD asymmetry between arms and legs among Caucasians athletes from ten sport disciplines (runners, cyclists, triathletes, racket players, rock climbers, swimmers, rugby players, rowers, kayakers and bodybuilders), at a university or higher level, trained for a minimum of four hour per week, for a minimum of three years. Using DXA on a Hologic QDR 1000W (Bedford, MA) scanner they stated that the differences between the dominant and non-dominant arms were significant but different depends on sport. For example it was greater in the racket players and lesser in the rowers. In the case of right and left legs, there was no asymmetric difference in BMD between them for all athletes combined nor within individual sport disciplines.

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One limb performance during some certain activities causes its higher fitness level than another limb. Thus training limb become dominate limb, in spite of athletes predisposition to right handedness, left handedness or ambidextratity (Starosta, 1990). Chilibeck et al (2000) asked the question if more bone deposition is a result of greater use of dominant limbs during physical activity or it is connected with fact that the dominant limb is genetically larger. They noticed, using dual energy x-ray absorptiometry, that a greater lifetime of preferential loading of the dominant arm is connected with a greater differences between arms in older group. Also earlier research reported greater mineralization for the dominant humerus but not for dominant radius and ulna, compared to the non-dominant ones, among baseball players 8-19 years of age (Watson, 1973). Additionally, they observed differences in the mineral content between the dominant and non-dominant increasing with age. Tomkinson study show that the increased metabolic rate that accompanies training will tend to increase fluctuating asymmetry in males athletes, at least if the training were conducted during the athlete's growth phase, when disturbances will have the greatest effect (Tomkinson et al., 2003).

#### **3. Conclusion**

All above information indicate that DXA is suitable method in athlete study. It allows to gain many valid data about not only overall but also regional body composition. Additionally it is precious method in longitudinal study of body composition assessment, tracking regional changes of some tissues during training program.

That kind of information could be important in a better understanding morphological characteristics of athletes, talent development, sports results improvement and with some injuries prevention. Further longitudinal research in this area could provide deeper insight into characteristic responses on specific training that require asymmetrical limb (side of body) use. Information about side-to-side differences in bone density may be valid for example for strength and conditioning professionals who want to include bilateral training programs minimizing stress-related injuries and maximizing sport results (McClanahan et al., 2002). It could be important to individualized symmetrization process among the athletes, because they do not have the same level of morphological differences. It is obvious that this process should be done methodologically (Starosta, 1990). After the few first stages, movements symmetrization could be used during training and the competitions, what could give quite big advantage over the opponents during the game. It relays on development through the training less fit side of the body and preservation harmony between symmetrical and asymmetrical movement (motor) preparation (Starosta, 1990).

#### **4. References**


men's and women's tennis and men's baseball. The differences in the lower limbs were less

One limb performance during some certain activities causes its higher fitness level than another limb. Thus training limb become dominate limb, in spite of athletes predisposition to right handedness, left handedness or ambidextratity (Starosta, 1990). Chilibeck et al (2000) asked the question if more bone deposition is a result of greater use of dominant limbs during physical activity or it is connected with fact that the dominant limb is genetically larger. They noticed, using dual energy x-ray absorptiometry, that a greater lifetime of preferential loading of the dominant arm is connected with a greater differences between arms in older group. Also earlier research reported greater mineralization for the dominant humerus but not for dominant radius and ulna, compared to the non-dominant ones, among baseball players 8-19 years of age (Watson, 1973). Additionally, they observed differences in the mineral content between the dominant and non-dominant increasing with age. Tomkinson study show that the increased metabolic rate that accompanies training will tend to increase fluctuating asymmetry in males athletes, at least if the training were conducted during the athlete's growth phase,

All above information indicate that DXA is suitable method in athlete study. It allows to gain many valid data about not only overall but also regional body composition. Additionally it is precious method in longitudinal study of body composition assessment,

That kind of information could be important in a better understanding morphological characteristics of athletes, talent development, sports results improvement and with some injuries prevention. Further longitudinal research in this area could provide deeper insight into characteristic responses on specific training that require asymmetrical limb (side of body) use. Information about side-to-side differences in bone density may be valid for example for strength and conditioning professionals who want to include bilateral training programs minimizing stress-related injuries and maximizing sport results (McClanahan et al., 2002). It could be important to individualized symmetrization process among the athletes, because they do not have the same level of morphological differences. It is obvious that this process should be done methodologically (Starosta, 1990). After the few first stages, movements symmetrization could be used during training and the competitions, what could give quite big advantage over the opponents during the game. It relays on development through the training less fit side of the body and preservation harmony between

when disturbances will have the greatest effect (Tomkinson et al., 2003).

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Response to Grip Forces Exterted by Intercollegiate Tennis Players. *American* 


**6** 

 *Greece* 

**Body Composition in Disabilities** 

*2University of Athens, Laboratory for Research of the Musculoskeletal System* 

Disability leads to immobilisation associated with profound changes in body composition. The potential risks involved with these changes i.e. loss of lean tissue mass (LM) and bone mineral density (BMD) vs. gain in fat mass (FM) in body composition have implications for the health of the disabled individuals (Jones et al., 1998). Body fat has been identified as a significant predictor of mortality in humans making body composition measurement to quantify nutritional and health status an important issue for human health. (Seidell et al., 1996; Bender et al., 1998; Van Der Ploeg et al., 2003). Moreover, some disorders such as carbohydrate intolerance, insulin resistance, lipid abnormalities, and heart disease occur prematurely and at a higher prevalence in disabled populations may be related to adverse changes in body composition that result from immobilization and skeletal muscle

In traumatic and pathological lesions of the central nervous system (CNS) there are differences according to the evolution or not of the lesion (i.e. progressive multiple sclerosis vs. complete paraplegia), the type of injury (i.e. lesion with a level of injury vs. upper motor neuron pyramidal lesion), life expectancy, the residual mobility and functionality, the ability to walk and stand (i.e. incomplete paraplegia vs. quadriplegia vs. high-low paraplegia) and drug treatment (i.e. frequent corticosteroid therapy in multiple sclerosis vs. long-term therapy with anticoagulants in paraplegia). In addition there are differences in the degree of spasticity which is likely to play a regulatory role in maintaining bone density (Dionyssiotis et al., 2011a). We need to take into account the element of fatigue and muscle weakness in disabilities, especially in diseases like multiple sclerosis, which significantly reduces the

The relative difference in energy expenditure between individuals with multiple sclerosis (MS) and able-bodied subjects is probably lower than the relative difference in physical activity, because individuals with MS have a higher energy expenditure of physical activity (Olgiati et al., 1988). Reduced physical activity (and probably reduced energy expenditure) in MS need to be accompanied by a reduction in energy intake otherwise body fat will increase (Lambert et al., 2002). Subjects with those motor disorders often face problems of depression and limit mobility (Dionyssiotis, 2011b). Moreover, in children with cerebral palsy (CP) studies suggest that increased stretch reflexes and muscle tone, weakness of

**1. Introduction** 

denervation (Spungen et al., 2003).

mobility of these patients (Krupp et al., 2010).

 **of Central Nervous System** 

*1Physical and Social Rehabilitation Center Amyntæo* 

Yannis Dionyssiotis1,2


### **Body Composition in Disabilities of Central Nervous System**

#### Yannis Dionyssiotis1,2

*1Physical and Social Rehabilitation Center Amyntæo 2University of Athens, Laboratory for Research of the Musculoskeletal System Greece* 

#### **1. Introduction**

74 Dual Energy X-Ray Absorptiometry

The New Lexicon Webster's Encyclopedic Dictionary of the English Language. Deluxe

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whole-body bone mineral: comparison between Hologic, Lunar and Norland dual energy X-ray absorptiometers. *British Journal of Radiology*, Vol.67, pp. 1210- 1217. Tsolakis, CH.; Bogdanis, GC. & Vagenas, G. (2006). Anthropometric profile and limb

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Disability leads to immobilisation associated with profound changes in body composition. The potential risks involved with these changes i.e. loss of lean tissue mass (LM) and bone mineral density (BMD) vs. gain in fat mass (FM) in body composition have implications for the health of the disabled individuals (Jones et al., 1998). Body fat has been identified as a significant predictor of mortality in humans making body composition measurement to quantify nutritional and health status an important issue for human health. (Seidell et al., 1996; Bender et al., 1998; Van Der Ploeg et al., 2003). Moreover, some disorders such as carbohydrate intolerance, insulin resistance, lipid abnormalities, and heart disease occur prematurely and at a higher prevalence in disabled populations may be related to adverse changes in body composition that result from immobilization and skeletal muscle denervation (Spungen et al., 2003).

In traumatic and pathological lesions of the central nervous system (CNS) there are differences according to the evolution or not of the lesion (i.e. progressive multiple sclerosis vs. complete paraplegia), the type of injury (i.e. lesion with a level of injury vs. upper motor neuron pyramidal lesion), life expectancy, the residual mobility and functionality, the ability to walk and stand (i.e. incomplete paraplegia vs. quadriplegia vs. high-low paraplegia) and drug treatment (i.e. frequent corticosteroid therapy in multiple sclerosis vs. long-term therapy with anticoagulants in paraplegia). In addition there are differences in the degree of spasticity which is likely to play a regulatory role in maintaining bone density (Dionyssiotis et al., 2011a). We need to take into account the element of fatigue and muscle weakness in disabilities, especially in diseases like multiple sclerosis, which significantly reduces the mobility of these patients (Krupp et al., 2010).

The relative difference in energy expenditure between individuals with multiple sclerosis (MS) and able-bodied subjects is probably lower than the relative difference in physical activity, because individuals with MS have a higher energy expenditure of physical activity (Olgiati et al., 1988). Reduced physical activity (and probably reduced energy expenditure) in MS need to be accompanied by a reduction in energy intake otherwise body fat will increase (Lambert et al., 2002). Subjects with those motor disorders often face problems of depression and limit mobility (Dionyssiotis, 2011b). Moreover, in children with cerebral palsy (CP) studies suggest that increased stretch reflexes and muscle tone, weakness of

Body Composition in Disabilities of Central Nervous System 77

No differences were found in BMI between paraplegics in the acute phase of injury and controls, which is a finding in accordance with other studies in which, despite the same BMI, the body composition and the distribution of fat and fat free mass were altered in patients with spinal cord injury, with the fat free mass being statistically significantly lower in paraplegic patients in total body composition and in the lower, but not the upper limbs. As far as the fat mass is concerned, it was statistically significantly higher (kilograms and %)

These findings show that using the BMI does not contribute substantially in determining the body composition of paraplegics and lowers the percentage of fat in this population, finding that agrees with other studies and shows that the anthropometric measurement with BMI in paraplegics, underestimates fat in body composition when measurements are compared

Body mass index is a very simple measurement of fat; however it does not distinguish the individual components of weight. The applicability of conventional BMI cut off values is into question (Buchholz, 2005; McDonald et al., 2007). BMI is an insensitive marker of obesity in subjects with SCI and measuring fat with BMI in chronic paraplegic patients is not

To standardize or index physiological variables, such as resting metabolic rate and power fat free mass (FFM) is usually used (Van Der Ploeg et al., 2003). Skeletal muscle represents 50% of the non fat component in the total body (Clarys et al., 1984; Modlesky et al., 2004) and exact quantification of the amount of skeletal muscle is important to assess nutritional status, disease risk, danger of illnesses, physical function, atrophic effects of aging, and

Because muscle wasting is a common sign of cerebral palsy (CP), even in well nourished children, the validity of using muscle wasting as evidence or measurement of malnutrition in CP is in doubt. Studies found that the triceps, midthigh, and calf skinfold thicknesses of the affected side were greater than those of the no affected side among children with hemiplegic CP (Stevenson et al., 1995). Useful information regarding fat provide triceps, subscapular skinfolds and arm-fat area (Patrick & Gisel, 1990). Other studies support the concept that the validity of skinfold thickness as an assessment of limb fat storage is dependent on the preservation of limb muscles (Ingemann-Hansen T et al., 1977) and suggested good sensitivity and specificity of triceps skinfold thickness for predicting midupper arm fat area probably were attributable to good preservation of mid-upper arm

In disabled children techniques for measuring skinfolds are well established and standardised (Lohman et al., 1988) and equations are available for calculation of body fat from skin fold thickness (Slaughter et al., 1988) although unvalidated in this population, as are normative values for skinfold thickness (Frisancho, 1981; Kuperminc & Stevenson, 2008). Consequently, use of skinfold thickness as a measurement, especially for the affected limb, should be used with discretion in the assessment of children with CP, who tend to have

In cerebral palsy neither bioelectrical impedance analysis nor predictive equations for skinfold thickness generated from normal, able-bodied adults accurately determined

in the total body composition in the upper and lower limbs (Maimoun et al., 2006).

enough to determine subject's percentage of fat in the body (Olle et al., 1993).

muscle-wasting diseases (Forbes, 1987; Mojtahedi et al., 2008).

muscles among children with CP (Samson-Fang et al., 2000).

muscle wasting.

with healthy subjects (Jones et al., 1998).

involved musculature, and severe limitation of movement reduce the capacity to perform normal movements creating ambulation barriers limiting physical activity. The dependency on mobility devices, common in all disabilities, and the frequent periods of immobilization after multiple operative procedures contribute to the hypoactivity status of such children. It could be assumed that, under these conditions, body composition may be significantly compromised (Chad et al., 2000).

Studies found that lean mass of the contralateral limb was lower compared to the ipsilateral limb in upper motor neuron injury, as occurs in stroke (Ryan et al., 2000; 2002). Similar findings of reduced muscle mass and increased intramuscular fat have been also published in individuals with incomplete spinal cord injury (SCI) (Gorgey et al., 2007) suggesting that reduced muscle mass is fundamentally related to poor fitness and physical performance capacity after stroke (Hafer-Macko et al., 2008).

On the other side the clinical equivalence of diseases with different physiopathology, location, evolution, etc. could be similar; i.e. a severe form of MS can result in a wheelchair bound patient a clinical figure equivalent to paraplegia or a MS patient may have a more appropriate walking gait pattern vs. a patient with incomplete paraplegia but may also be unable to walk at all, is bedridden and vice versa (Dionyssiotis, 2011b; 2011c; 2011d). In addition to these differences and according to osteoporosis the role of factors which do not change, such as race or gender of patients has not been yet clarified, although there are few studies in women debating that bone mass in women with disabilities is more affected than men (Smeltzer et al., 2005; Coupaud et al., 2009).

Therefore, the purpose of this chapter was to present the bone-mineral density, bonemineral content, and bone-mineral-free lean and fat tissue mass alterations of ambulatory and non-ambulatory subjects with disabilities of the central nervous system.

#### **2. Body composition measurements**

#### **2.1 Anthropometric and various techniques of body composition measurements**

In a study which investigated a chronic spinal cord injury (SCI) population with paraplegia (Dionyssiotis, 2008a, Dionyssiotis et al., 2008b) values of body mass index (BMI, kg/m2) did not present statistical significance in relation to the controls, which is a finding in line with the literature (Maggioni et al., 2003; Mamoun et al., 2004).. Nevertheless, there are studies which demonstrate the usefulness of BMI as an indicator of obesity, in body composition in people with spinal cord injury (Gupta et al., 2006). These studies, however, included both tetraplegics and middle-aged people unlike the Greek one which included relatively young individuals (Dionyssiotis et al., 2008a). Whether the criteria of BMI may assess obesity in people with spinal cord injury the latest studies show the opposite (McDonald et al., 2007).

BMI of a male paraplegic group was slightly greater compared to a tetraplegic one and distribution of BMI by level of injury was similar with 37.5% and 40.5% of the male tetraplegic and male paraplegic groups, respectively, falling into the recommended BMI range. Approximately 50% in each male group were overweight by BMI, and 12.5% and 10.8%, respectively, were classified as obese. Overall, when compared with the general population-observed distribution by BMI, a greater proportion of men with SCI fell into the desirable BMI range and fewer fell into the obese category (Groah et al., 2009).

involved musculature, and severe limitation of movement reduce the capacity to perform normal movements creating ambulation barriers limiting physical activity. The dependency on mobility devices, common in all disabilities, and the frequent periods of immobilization after multiple operative procedures contribute to the hypoactivity status of such children. It could be assumed that, under these conditions, body composition may be significantly

Studies found that lean mass of the contralateral limb was lower compared to the ipsilateral limb in upper motor neuron injury, as occurs in stroke (Ryan et al., 2000; 2002). Similar findings of reduced muscle mass and increased intramuscular fat have been also published in individuals with incomplete spinal cord injury (SCI) (Gorgey et al., 2007) suggesting that reduced muscle mass is fundamentally related to poor fitness and physical performance

On the other side the clinical equivalence of diseases with different physiopathology, location, evolution, etc. could be similar; i.e. a severe form of MS can result in a wheelchair bound patient a clinical figure equivalent to paraplegia or a MS patient may have a more appropriate walking gait pattern vs. a patient with incomplete paraplegia but may also be unable to walk at all, is bedridden and vice versa (Dionyssiotis, 2011b; 2011c; 2011d). In addition to these differences and according to osteoporosis the role of factors which do not change, such as race or gender of patients has not been yet clarified, although there are few studies in women debating that bone mass in women with disabilities is more affected than

Therefore, the purpose of this chapter was to present the bone-mineral density, bonemineral content, and bone-mineral-free lean and fat tissue mass alterations of ambulatory

**2.1 Anthropometric and various techniques of body composition measurements** 

In a study which investigated a chronic spinal cord injury (SCI) population with paraplegia (Dionyssiotis, 2008a, Dionyssiotis et al., 2008b) values of body mass index (BMI, kg/m2) did not present statistical significance in relation to the controls, which is a finding in line with the literature (Maggioni et al., 2003; Mamoun et al., 2004).. Nevertheless, there are studies which demonstrate the usefulness of BMI as an indicator of obesity, in body composition in people with spinal cord injury (Gupta et al., 2006). These studies, however, included both tetraplegics and middle-aged people unlike the Greek one which included relatively young individuals (Dionyssiotis et al., 2008a). Whether the criteria of BMI may assess obesity in people with spinal cord injury the latest studies show the opposite (McDonald et al., 2007). BMI of a male paraplegic group was slightly greater compared to a tetraplegic one and distribution of BMI by level of injury was similar with 37.5% and 40.5% of the male tetraplegic and male paraplegic groups, respectively, falling into the recommended BMI range. Approximately 50% in each male group were overweight by BMI, and 12.5% and 10.8%, respectively, were classified as obese. Overall, when compared with the general population-observed distribution by BMI, a greater proportion of men with SCI fell into the

and non-ambulatory subjects with disabilities of the central nervous system.

desirable BMI range and fewer fell into the obese category (Groah et al., 2009).

compromised (Chad et al., 2000).

capacity after stroke (Hafer-Macko et al., 2008).

men (Smeltzer et al., 2005; Coupaud et al., 2009).

**2. Body composition measurements** 

No differences were found in BMI between paraplegics in the acute phase of injury and controls, which is a finding in accordance with other studies in which, despite the same BMI, the body composition and the distribution of fat and fat free mass were altered in patients with spinal cord injury, with the fat free mass being statistically significantly lower in paraplegic patients in total body composition and in the lower, but not the upper limbs. As far as the fat mass is concerned, it was statistically significantly higher (kilograms and %) in the total body composition in the upper and lower limbs (Maimoun et al., 2006).

These findings show that using the BMI does not contribute substantially in determining the body composition of paraplegics and lowers the percentage of fat in this population, finding that agrees with other studies and shows that the anthropometric measurement with BMI in paraplegics, underestimates fat in body composition when measurements are compared with healthy subjects (Jones et al., 1998).

Body mass index is a very simple measurement of fat; however it does not distinguish the individual components of weight. The applicability of conventional BMI cut off values is into question (Buchholz, 2005; McDonald et al., 2007). BMI is an insensitive marker of obesity in subjects with SCI and measuring fat with BMI in chronic paraplegic patients is not enough to determine subject's percentage of fat in the body (Olle et al., 1993).

To standardize or index physiological variables, such as resting metabolic rate and power fat free mass (FFM) is usually used (Van Der Ploeg et al., 2003). Skeletal muscle represents 50% of the non fat component in the total body (Clarys et al., 1984; Modlesky et al., 2004) and exact quantification of the amount of skeletal muscle is important to assess nutritional status, disease risk, danger of illnesses, physical function, atrophic effects of aging, and muscle-wasting diseases (Forbes, 1987; Mojtahedi et al., 2008).

Because muscle wasting is a common sign of cerebral palsy (CP), even in well nourished children, the validity of using muscle wasting as evidence or measurement of malnutrition in CP is in doubt. Studies found that the triceps, midthigh, and calf skinfold thicknesses of the affected side were greater than those of the no affected side among children with hemiplegic CP (Stevenson et al., 1995). Useful information regarding fat provide triceps, subscapular skinfolds and arm-fat area (Patrick & Gisel, 1990). Other studies support the concept that the validity of skinfold thickness as an assessment of limb fat storage is dependent on the preservation of limb muscles (Ingemann-Hansen T et al., 1977) and suggested good sensitivity and specificity of triceps skinfold thickness for predicting midupper arm fat area probably were attributable to good preservation of mid-upper arm muscles among children with CP (Samson-Fang et al., 2000).

In disabled children techniques for measuring skinfolds are well established and standardised (Lohman et al., 1988) and equations are available for calculation of body fat from skin fold thickness (Slaughter et al., 1988) although unvalidated in this population, as are normative values for skinfold thickness (Frisancho, 1981; Kuperminc & Stevenson, 2008). Consequently, use of skinfold thickness as a measurement, especially for the affected limb, should be used with discretion in the assessment of children with CP, who tend to have muscle wasting.

In cerebral palsy neither bioelectrical impedance analysis nor predictive equations for skinfold thickness generated from normal, able-bodied adults accurately determined

Body Composition in Disabilities of Central Nervous System 79

To determine whether bioelectrical impedance analysis and anthropometry can be used to determine body composition for clinical and research purposes in children with cerebral palsy 8 individuals (two female, mean age=10 years, mean gross motor function classification=4.6 [severe motor impairment]) recruited from an outpatient tertiary care setting underwent measurement of fat mass, fat-free mass, and percentage body fat using BIA, anthropometry (two and four skinfold equations), and dual-energy x-ray absorptiometry. Correlation were excellent for determination of fat-free mass for all methods (i.e., all were above 0.9) and moderate for determination of fat mass and percent body fat (range=0.4 to 0.8). Moreover, skinfolds were better predictors of percent body fat, while bioelectrical impedance was a better predictor for fat mass (Liu et al., 2005). On the contrary another study investigated the pattern of body composition in 136 subjects with spastic quadriplegic cerebral palsy, 2 to 12 years of age, by anthropometric measures, or by anthropometric and total body water (TBW) measures (n = 28), compared with 39 control subjects. Body composition and nutritional status indicators were significantly reduced. Calculation of body fat from two skinfolds correlated best with measures of fat mass from

Magnetic resonance imaging (MRI) provides remarkably accurate estimates of skeletal muscle in vivo (Modlesky et al., 2004). MRI and also quantitative computed tomography (QCT) have been validated in studies of humancadavers in the assessment of regional skeletal muscle (Mitsiopoulos et al., 1998). Although, these devices have disadvantages of

Recently, dual-energy X-ray absorptiometry (DXA) has gained acceptance as a reference method for body composition analysis (Mahon et al., 2007; LaForgia et al., 2009). Originally designed to determine bone density, DXA technology has subsequently been adopted for the assessment of whole body composition and offers estimation rapidly, non-invasively and with minimal radiation exposure (Van Der Ploeg et al., 2003; Dionyssiotis et al., 2008a). Moreover, is well tolerated in subjects who would be unable to tolerate other body composition techniques, such as underwater weighing (hydro-densitometry) (Laskey, 1996). DXA software determines the bone mineral and soft tissue composition in different regions of the body being a three-compartment model that quantifies: (i) bone mineral density and content (BMD, BMC), (ii) fat mass (FM); and (iii) lean mass (LM), half of which is closely correlated with muscle mass and also yields regional as well as total body values (Rittweger

DXA analyzes differently the dense pixels in body composition. Soft tissue pixels are analyzed for two materials: fat and fat-free tissue mass. Variations in the fat mass/fat free tissue mass composition of the soft tissue produce differences in the respective attenuation coefficients at both energy levels. The ratio at the two main energy peaks is automatically calculated of the X-ray attenuation providing separation of the soft tissue compartment into fat mass and fat-free tissue mass (lean mass) (Peppler & Mazess, 1981; Pietrobelli et al., 1996). A bone-containing pixel is analyzed for "bone mass" (bone mineral content, BMC) and soft tissue as the two materials. Thus, the fat mass/fat free tissue mass of the soft tissue component of the bone pixels cannot be measured, but only estimated

TBW (Stallings et al., 1995; Kuperminc & Stevenson, 2008).

et al., 2000) for example in the arms, legs, and trunk (figure 1).

high radiation exposure and are expensive.

(Ferretti et al., 2001).

**2.2 Dual-energy X-ray absorptiometry (DXA)** 

percentage body fat (Hildreth et al., 1997). Body mass index (BMI), triceps skinfold thickness, subscapular skinfold thickness, suprailiac skinfold thickness, and circumferences of the biceps, waist, forearm, and knee were all significantly correlated with percentage body fat (Bandini et al., 1991).

BMI in patients with MS was statistically less compared to age comparable controls (Formica et al., 1997). In a recent study both total body fat and mass percent showed consistent significant dependence from BMI, as among normal subjects. Multiple linear regression analysis of bone mineral percent at all studied sites showed consistent dependence from BMI (increased with higher BMI) for both patient and control groups (Sioka et al., 2011).

Changes in body composition in spinal cord injured subjects can be assessed with various techniques including isotope-labelled water (Jones et al., 1998) total body potassium counting (Lussier et al., 1983; Spungen et al., 1992) anthropometric measures (Bulbulian et al., 1987) hydrodensitometry (Lussier et al., 1983; Sedlock, 1990) dual photon absorptiometry (DPA) (Spungen et al., 1992; Changlai, 1996) and dual energy X-ray absorptiometry (DXA) (Jones et al., 1998). However, some of these methods are not particularly suitable for use in the SCI population.

The hydrodensitometric model was regarded as the "gold standard" for body composition assessment. This model partitions the body into two compartments of constant densities [fat mass: 0.9007 g/cm3 and FFM: 1.100 g/cm3] and assumes that the relative amounts of the FFM components [water, protein, protein, bone mineral (BM), and non-BM] are fixed (Brozek et al., 1963; Van Der Ploeg et al., 2003). Hydrodensitometry is clearly inappropriate for individuals who deviate from these fixed and/or assumed values (e.g., children, elderly, blacks, obese), and its application is, therefore, somewhat limited (Womersley et al., 1976; Schutte, 1984; Lohman, 1986; Fuller et al., 1996).

Bioelectrical impedance analysis (BIA) has been used to measure cerebral palsy subjects. However, the inclusion of weight in the BIA predictive equation may reduce its accuracy in determining change in lean body mass (Forbes et al., 1992). The inability of BIA to accurately predict percentage body fat in the sample may be related to several factors. In the BIA method where the impedance of a geometrical system (i.e., the human body) is dependent on the length of the conductor (height) and its configuration, it is almost impossible to measure accurately height in subjects with CP because of their muscle contractures. An over- or underestimation of height by 2.5 cm can result in a l.0-L error in the estimation of TBW, producing a small error in the estimation of percentage body fat (< 5%). The second major problem is body asymmetry which renders the assumption of a symmetrical configuration of the human body invalid in this case. (National Institutes of Health Technology Assessment Conference Statement, 1994; Hildreth et al., 1997).

Isotope dilution measures the water compartment of the whole body rather than a single area assumed to mimic the composition of the whole body. Thus, the use of a stable isotope to measure body composition is ideal for people with CP because it is non-invasive, does not require the subject to remain still for the measurement, and is independent of height and body symmetry. However, the prohibitive cost of the isotopes and the need for a mass spectrometry facility and highly trained technicians make this method impractical for routine clinical use (Hildreth et al., 1997).

percentage body fat (Hildreth et al., 1997). Body mass index (BMI), triceps skinfold thickness, subscapular skinfold thickness, suprailiac skinfold thickness, and circumferences of the biceps, waist, forearm, and knee were all significantly correlated with percentage

BMI in patients with MS was statistically less compared to age comparable controls (Formica et al., 1997). In a recent study both total body fat and mass percent showed consistent significant dependence from BMI, as among normal subjects. Multiple linear regression analysis of bone mineral percent at all studied sites showed consistent dependence from BMI (increased with higher BMI) for both patient and control groups

Changes in body composition in spinal cord injured subjects can be assessed with various techniques including isotope-labelled water (Jones et al., 1998) total body potassium counting (Lussier et al., 1983; Spungen et al., 1992) anthropometric measures (Bulbulian et al., 1987) hydrodensitometry (Lussier et al., 1983; Sedlock, 1990) dual photon absorptiometry (DPA) (Spungen et al., 1992; Changlai, 1996) and dual energy X-ray absorptiometry (DXA) (Jones et al., 1998). However, some of these methods are not particularly suitable for use in

The hydrodensitometric model was regarded as the "gold standard" for body composition assessment. This model partitions the body into two compartments of constant densities [fat mass: 0.9007 g/cm3 and FFM: 1.100 g/cm3] and assumes that the relative amounts of the FFM components [water, protein, protein, bone mineral (BM), and non-BM] are fixed (Brozek et al., 1963; Van Der Ploeg et al., 2003). Hydrodensitometry is clearly inappropriate for individuals who deviate from these fixed and/or assumed values (e.g., children, elderly, blacks, obese), and its application is, therefore, somewhat limited (Womersley et al., 1976;

Bioelectrical impedance analysis (BIA) has been used to measure cerebral palsy subjects. However, the inclusion of weight in the BIA predictive equation may reduce its accuracy in determining change in lean body mass (Forbes et al., 1992). The inability of BIA to accurately predict percentage body fat in the sample may be related to several factors. In the BIA method where the impedance of a geometrical system (i.e., the human body) is dependent on the length of the conductor (height) and its configuration, it is almost impossible to measure accurately height in subjects with CP because of their muscle contractures. An over- or underestimation of height by 2.5 cm can result in a l.0-L error in the estimation of TBW, producing a small error in the estimation of percentage body fat (< 5%). The second major problem is body asymmetry which renders the assumption of a symmetrical configuration of the human body invalid in this case. (National Institutes of Health

Isotope dilution measures the water compartment of the whole body rather than a single area assumed to mimic the composition of the whole body. Thus, the use of a stable isotope to measure body composition is ideal for people with CP because it is non-invasive, does not require the subject to remain still for the measurement, and is independent of height and body symmetry. However, the prohibitive cost of the isotopes and the need for a mass spectrometry facility and highly trained technicians make this method impractical for

Technology Assessment Conference Statement, 1994; Hildreth et al., 1997).

body fat (Bandini et al., 1991).

(Sioka et al., 2011).

the SCI population.

Schutte, 1984; Lohman, 1986; Fuller et al., 1996).

routine clinical use (Hildreth et al., 1997).

To determine whether bioelectrical impedance analysis and anthropometry can be used to determine body composition for clinical and research purposes in children with cerebral palsy 8 individuals (two female, mean age=10 years, mean gross motor function classification=4.6 [severe motor impairment]) recruited from an outpatient tertiary care setting underwent measurement of fat mass, fat-free mass, and percentage body fat using BIA, anthropometry (two and four skinfold equations), and dual-energy x-ray absorptiometry. Correlation were excellent for determination of fat-free mass for all methods (i.e., all were above 0.9) and moderate for determination of fat mass and percent body fat (range=0.4 to 0.8). Moreover, skinfolds were better predictors of percent body fat, while bioelectrical impedance was a better predictor for fat mass (Liu et al., 2005). On the contrary another study investigated the pattern of body composition in 136 subjects with spastic quadriplegic cerebral palsy, 2 to 12 years of age, by anthropometric measures, or by anthropometric and total body water (TBW) measures (n = 28), compared with 39 control subjects. Body composition and nutritional status indicators were significantly reduced. Calculation of body fat from two skinfolds correlated best with measures of fat mass from TBW (Stallings et al., 1995; Kuperminc & Stevenson, 2008).

Magnetic resonance imaging (MRI) provides remarkably accurate estimates of skeletal muscle in vivo (Modlesky et al., 2004). MRI and also quantitative computed tomography (QCT) have been validated in studies of humancadavers in the assessment of regional skeletal muscle (Mitsiopoulos et al., 1998). Although, these devices have disadvantages of high radiation exposure and are expensive.

#### **2.2 Dual-energy X-ray absorptiometry (DXA)**

Recently, dual-energy X-ray absorptiometry (DXA) has gained acceptance as a reference method for body composition analysis (Mahon et al., 2007; LaForgia et al., 2009). Originally designed to determine bone density, DXA technology has subsequently been adopted for the assessment of whole body composition and offers estimation rapidly, non-invasively and with minimal radiation exposure (Van Der Ploeg et al., 2003; Dionyssiotis et al., 2008a). Moreover, is well tolerated in subjects who would be unable to tolerate other body composition techniques, such as underwater weighing (hydro-densitometry) (Laskey, 1996). DXA software determines the bone mineral and soft tissue composition in different regions of the body being a three-compartment model that quantifies: (i) bone mineral density and content (BMD, BMC), (ii) fat mass (FM); and (iii) lean mass (LM), half of which is closely correlated with muscle mass and also yields regional as well as total body values (Rittweger et al., 2000) for example in the arms, legs, and trunk (figure 1).

DXA analyzes differently the dense pixels in body composition. Soft tissue pixels are analyzed for two materials: fat and fat-free tissue mass. Variations in the fat mass/fat free tissue mass composition of the soft tissue produce differences in the respective attenuation coefficients at both energy levels. The ratio at the two main energy peaks is automatically calculated of the X-ray attenuation providing separation of the soft tissue compartment into fat mass and fat-free tissue mass (lean mass) (Peppler & Mazess, 1981; Pietrobelli et al., 1996). A bone-containing pixel is analyzed for "bone mass" (bone mineral content, BMC) and soft tissue as the two materials. Thus, the fat mass/fat free tissue mass of the soft tissue component of the bone pixels cannot be measured, but only estimated (Ferretti et al., 2001).

Body Composition in Disabilities of Central Nervous System 81

in spinal cord injured subjects (Modlesky et al., 2004). DXA technique has been used in assessment of SCI and appears to be tolerated well by this population (Szollar et al., 1997;

Fig. 2. Whole body and regional distribution of fat mass, lean mass, bone mineral content (BMC) and bone mineral density (BMD) from control male subject using whole body DEXA Norland X-36 and values of measured parameters. Modified and translated with permission

Spinal cord injury (SCI) always results in substantial and rapid bone loss predominately in areas below the neurological level of injury. The predominant finding of SCI on bone is a large loss of bone during the first year of injury (Spungen et al., 2003) and an ongoing demineralisation 3 years after trauma in tibia (Biering-Sörensen et al., 1988) with a progressive bone loss over 12 to 16 months prior to stabilizing (Lazo et al., 2001) was

Cancellous bone is more affected than cortical bone after SCI. In a prospective study, six acute tetraplegics were followed up for 12 months, and the trabecular and cortical BMD's of the tibia were found to be decreased by 15 and 7% (Frey-Rindova et al., 2000), while in

Uebelhart et al., 1995; Chow et al., 1996).

from Dionyssiotis, 2008a.

**3.1 Spinal cord injury** 

demonstrated.

**3. Physiopathological context** 

Fig. 1. Whole body and regional distribution of fat mass, lean mass, bone mineral content (BMC) and bone mineral density (BMD) from paraplegic subject thoracic 6 using whole body DXA (Norland X-36, Fort Atkinson, Wisconsin, USA) and values of measured parameters. Modified and translated with permission from Dionyssiotis, 2008a.

The important issue on this is the investigation of distribution of bone mineral, fat and mass throughout the body. These changes induce the risk for diseases such as diabetes, coronary heart disease, dyslipidaimias and osteoporosis (Bauman et al., 1992; Bauman & Spungen, 1994; Kocina, 1997; Garland et al., 1992). There is a need to quantify the alterations in body composition to prevent these diseases and their complications. Studies also reported that bone density measurements at one site cannot usefully predict the bone density elsewhere (Heymsfield et al., 1989) because different skeletal regions, even with similar quantities of trabecular or cortical bone, may respond variably in different physiopathological conditions (Laskey, 1996).

In disabled conditions the accuracy of skeletal muscle measured by DXA may be compromised when muscle atrophy is present. A lower ratio of muscle to adipose-tissuefree mass indicates a lower proportion of muscle in the fat-free soft tissue mass. Crosssectional area of skeletal muscle in the thighs after SCI is extensively reduced (Castro et al., 1999). If this is the case muscle mass would be overestimated by prediction models that assume that muscle represents all or a certain proportion of the fat-free soft tissue mass, i.e.

Fig. 1. Whole body and regional distribution of fat mass, lean mass, bone mineral content (BMC) and bone mineral density (BMD) from paraplegic subject thoracic 6 using whole body DXA (Norland X-36, Fort Atkinson, Wisconsin, USA) and values of measured parameters. Modified and translated with permission from Dionyssiotis, 2008a.

The important issue on this is the investigation of distribution of bone mineral, fat and mass throughout the body. These changes induce the risk for diseases such as diabetes, coronary heart disease, dyslipidaimias and osteoporosis (Bauman et al., 1992; Bauman & Spungen, 1994; Kocina, 1997; Garland et al., 1992). There is a need to quantify the alterations in body composition to prevent these diseases and their complications. Studies also reported that bone density measurements at one site cannot usefully predict the bone density elsewhere (Heymsfield et al., 1989) because different skeletal regions, even with similar quantities of trabecular or cortical bone, may respond variably in different physiopathological conditions

In disabled conditions the accuracy of skeletal muscle measured by DXA may be compromised when muscle atrophy is present. A lower ratio of muscle to adipose-tissuefree mass indicates a lower proportion of muscle in the fat-free soft tissue mass. Crosssectional area of skeletal muscle in the thighs after SCI is extensively reduced (Castro et al., 1999). If this is the case muscle mass would be overestimated by prediction models that assume that muscle represents all or a certain proportion of the fat-free soft tissue mass, i.e.

(Laskey, 1996).

in spinal cord injured subjects (Modlesky et al., 2004). DXA technique has been used in assessment of SCI and appears to be tolerated well by this population (Szollar et al., 1997; Uebelhart et al., 1995; Chow et al., 1996).

Fig. 2. Whole body and regional distribution of fat mass, lean mass, bone mineral content (BMC) and bone mineral density (BMD) from control male subject using whole body DEXA Norland X-36 and values of measured parameters. Modified and translated with permission from Dionyssiotis, 2008a.

#### **3. Physiopathological context**

#### **3.1 Spinal cord injury**

Spinal cord injury (SCI) always results in substantial and rapid bone loss predominately in areas below the neurological level of injury. The predominant finding of SCI on bone is a large loss of bone during the first year of injury (Spungen et al., 2003) and an ongoing demineralisation 3 years after trauma in tibia (Biering-Sörensen et al., 1988) with a progressive bone loss over 12 to 16 months prior to stabilizing (Lazo et al., 2001) was demonstrated.

Cancellous bone is more affected than cortical bone after SCI. In a prospective study, six acute tetraplegics were followed up for 12 months, and the trabecular and cortical BMD's of the tibia were found to be decreased by 15 and 7% (Frey-Rindova et al., 2000), while in

Body Composition in Disabilities of Central Nervous System 83

The neurological level of the lesion i.e. the extent of impairment of motor and sensory function is important, because tetraplegics are more likely to lose more bone mass throughout the skeleton than paraplegics (Tsuzuku et al., 1999). In paraplegics legs' BMC was reduced vs. controls, independently of the neurological level of injury and negatively correlated with the duration of paralysis in total paraplegic group, but after investigation according to the neurological level of injury this correlation was due to the strong correlation of high paraplegics' legs BMC with the duration of paralysis, meaning that the neurological level of injury determines the extent of bone loss (Dionyssiotis et al., 2009). The similar severity of demineralization in the sublesional area was shown between paraplegics and tetraplegics, and the extent of the bone loss may be variable (Demirel et al., 1998;

The duration of paralysis has an inverse relationship with leg percentage-matched BMD and trunk percentage-matched BMD (Clasey et al., 2004). In addition in complete paraplegics, with high (thoracic 4-7) and low (thoracic 8-12) neurological level of injury, upper limbs FM and lower limbs BMD were correlated with the duration of paralysis in total paraplegic group but after investigation according the neurological level of injury this correlation was due to the strong correlation of high paraplegics' lower limbs BMD with the duration of paralysis. The explanation of this strong correlation could possibly lie on higher incidence of standing in the group of low paraplegics and direct effect of loading lower limbs while standing and walking with orthotic equipment. Moreover, the association of the duration of paralysis with parameters below and above the neurological level of injury (upper limbs FM) raises the question of the existence of a hormonal mechanism as an influential regulator in paraplegics' body composition (Dionyssiotis, 2008a; Dionyssiotis et al., 2008b; 2009).

Actually, little is known regarding the nature and time frame of the influence of complete SCI on human skeletal muscle because published data are coming from cross-sectional studies, where different groups with few subjects have been examined at different times, usually in the chronic phase of paralysis. Disuse was thought to be the mechanism responsible for the skeletal muscle atrophy in paraplegics, but muscle fibres following SCI begin to change their functional properties early post injury. Muscle fiber cross-sectional area (CSA) has been suggested to decline from 1 to 17 months after injury and thereafter to reach its nadir. Conversion to type II fibers has been suggested to occur between 4 months and 2 years after injury, resulting in even slow-twitch muscle becoming predominantly fast twitch thereafter (Castro et al., 1999). Metabolic enzymes levels in skeletal muscle might be expected to be reduced after SCI because of inactivation. In support of this contention, succinic dehydrogenase (SDH) activity, a marker of aerobic-oxidative capacity, has been reported to be 47–68% below control values in fibers of tibialis anterior muscle years after

The muscle atrophy in SCI is of central type and depends on the disuse and loss of upper connections of the lower motor neuron, sometimes associated to the loss of anterior horn cells and transinaptic degeneration. The last alteration may be responsible for the denervation changes seen in early stages post SCI. In the later stages (10-17 months post SCI) diffuse muscle atrophy with reduction of the muscle fascicle dimension is associated to fat infiltration and endomysial fibrosis. In all stages post SCI, almost all patients showed myopathic changes, as internal nuclei, fibre degeneration and cytoplasmic vacuolation due

Tsuzuku et al., 1999; Dauty et al., 2000).

injury in support of this contention (Scelsi, 2001).

to lipid accumulation (Scelsi, 2001)

paraplegics trabecular metaphysical-epiphyseal areas of the distal femur and the proximal tibia are the most affected sites (Jiang et al., 2006). A cross-sectional study (Dauty et al., 2000) in SCI subjects demonstrated a significant demineralization at the distal femur (-52%) and the proximal tibia (-70%), respectively.

There is no demineralization of the upper limbs in paraplegics. Studies reported a minor increase of BMD while at the lumbar spine trabecular bone demineralization remains relatively low compared to long bones cortical bone demineralization of (Dauty et al., 2000). Normal (Chantraine et al., 1986; Biering-Sorensen et al., 1988; Kunkel et al., 1993) or even higher than normal values were found (Ogilvie et al., 1993), a phenomenon known as "dissociated hip and spine demineralization" (Leslie, 1993) One reason for preservation of bone mass in the vertebral column is because of its continued weight-bearing function in paraplegics but also lumbar spine arthrosis, bone callus, vertebral fracture, aortic calcification, osteosynthesis material, etc. Degenerative changes in the spine may be the most possible reason to give falsely higher values of BMD (Dauty et al., 2000).

Fig. 3. The picture depicts the analysis of bone mineral density (BMD) in high and low level paraplegics and controls. A statistically significant reduction in total BMD (p<0.001) and lower limbs BMD in body composition compared to able-bodied males was observed. On the contrary, upper limbs BMD was higher in low paraplegics and controls, an unexpected finding explained in the paper of Dionyssiotis et al., 2008b. Diagram modified and translated from Dionyssiotis, 2008a.

paraplegics trabecular metaphysical-epiphyseal areas of the distal femur and the proximal tibia are the most affected sites (Jiang et al., 2006). A cross-sectional study (Dauty et al., 2000) in SCI subjects demonstrated a significant demineralization at the distal femur (-52%) and

There is no demineralization of the upper limbs in paraplegics. Studies reported a minor increase of BMD while at the lumbar spine trabecular bone demineralization remains relatively low compared to long bones cortical bone demineralization of (Dauty et al., 2000). Normal (Chantraine et al., 1986; Biering-Sorensen et al., 1988; Kunkel et al., 1993) or even higher than normal values were found (Ogilvie et al., 1993), a phenomenon known as "dissociated hip and spine demineralization" (Leslie, 1993) One reason for preservation of bone mass in the vertebral column is because of its continued weight-bearing function in paraplegics but also lumbar spine arthrosis, bone callus, vertebral fracture, aortic calcification, osteosynthesis material, etc. Degenerative changes in the spine may be the

**BMD ANALYSIS**

**High paraplegics Low paraplegics**
