**4.3 Oxidative stress**

244 Chronic Obstructive Pulmonary Disease – Current Concepts and Practice

Fig. 1. Convergent pahogenesis of chronic obstructive pulmonary disorders, diabetes *mellitus* and metabolic syndrome. COPD: Chronic obstructive pulmonary disease; SAHS: Sleep apnea-hypopnea syndrome; DM: Diabetes *mellitus*; MS: Metabolic syndrome.

in the isolated disease.

linked to COPD.

**4.2 Inflammation** 

neurons. These authors observed a general and markedly higher sympathoexcitation in COPD patients when compared to SAHS, bronchiectasis or healthy subjects. The individual neurone firing probability and mean firing rate were comparable to those recorded in SAHS, but higher than those observed in the healthy group. This finding suggests that muscle vasoconstrictor response is sustained long-after intermittent hypoxia, as it would occur in SAHS patients. Permanent vasoconstriction causes further resistance to the airway flow in any chronic obstructive disorder that is not completely reversed by normoxia. With respect to DM, the noradrenalin liberation of spontaneously active neurons has also been observed

Obesity also increases the MSNA burst incidence, but at lower levels than those seen in COPD or SAHS. Multiple firing of single-unit neurones has not been shown in obese subjects. Advanced age neither seems to be an explanation for the MSNA hyperactivity

Systemic inflammation is a common bound between COPD and DM. Both conditions are a proinflammatory state characterized by transcription and expression of hypoxia-induced factor 1 (HIF-1) and increased levels of serum inflammatory cytokines such as C-reactive protein (CRP), interleukin (IL) 1, IL-6 and tumor necrosis factor α (TNF-α) (Archer & Baker 2009). C-reactive protein (CRP) and the nuclear factor NF-κβ pathway are important

mediators of the inflammatory response in this context (McNicholas, 2009).

Now considering the cellular immune response, the leucocitary drive in COPD is a fountain of reactive oxygen species (ROS). The oxidative response advocates a protein, lipid and DNA damage within the cell. Oxidative stress influences NF-κβ cascade through mitogenesis activating protein kinases (MAP-k) perpetuating inflammation this way.

On the inverse loop, TNF-α stimulates ROS production. Indeed, an additional ROS-related mechanism seems to exacerbate TNF-α and NF- κβ effects on muscle wasting (Oudijk, 2003). TNF-α and ROS act in multiple ways. They have a common source in circulating leukocytes. Activation and dysfunction of leukocytes is shared by COPD, SAHS and DM. The activated leukocyte enhances the expression of adhesion molecules such as CD11b and CD18. This effect predominates in lung tissue if we look at COPD, while systemic endothelium is the main target in SAHS. In addition, exacerbations of COPD may deteriorate the antioxidant response, while ROS are particularly enhanced in SAHS when hypoxia is intermittent in a similar way to injury reperfusion syndrome (McNicholas, 2009). Because ROS production is also a direct effect of hyperglycemia, oxidative stress can be posed as a link between SAHS, DM, overlap syndrome and metabolic syndrome. MS is not so clearly identified in the particular case of COPD. A specific oxidative response in SAHS/ overlap may well account for this difference.

Another effect of neutrophil dysfunction is the inactivation of antiproteases leading to airspace ephitelial damage and mucus hypersecretion. As we see, TNF-α / neutrophil axis is

Chronic Obstructive Pulmonary Disease and Diabetes *Mellitus* 247

Two epidemiological studies have suggested that obesity is the unique or main cause of insulin resistance in SAHS patients (Reichmuth et al, 2005; Stoohs et al., 1996). However, there is growing evidence about alternative links between SAHS and type 2 DM (Tasali et al., 2008). In terms of SAHS severity, only one study had a prospective design and assessed SAHS by polysomnography. An independent relationship between SAHS severity and glucose intolerance was not found after adjustment for body habitus, although the duration

SAHS itself aggravates obesity through several mechanisms that also enhances insulin resistance: neuroendocrine dysregulation and physical inactivity. Neuroendocrine dysregulation includes an enhanced ghrelin and leptin secretion. Hyperleptinemia has been proposed as a previous step to insulin resistance even in the absence of weight gain. Indeed, leptin was the only upregulated gene affecting glucose uptake in both obese and lean mice exposed to intermittent hypoxia (Polotsky et al., 2007). Hyperleptinemia may also be a marker of SAHS severity (Pillar & Shehadeh, 2008). Physical exercise increases after CPAP treatment of SAHS. However, the reversal of the insulin resistance by CPAP is controversial

Ten out of thirteen clinical based studies suggested a body habitus non-related association between SAHS and insulin resistance or glucose intolerance (Tasali et al., 2008). Three studies considered waist-to-hip ratio because central fat distribution seems to be a more relevant mediator of insulin resistance than BMI (Sharma et al., 2007; McArdle et al., 2007; Tassone et al., 2003). There was a positive association in two out of three. In a similar fashion, three studies found higher HOMA-IR and fasting glucose after adjustment for visceral fat measurements (Kono et al., 2007; Makino S et al., 2006; Vgontzas et al., 2000).

Deposit of neck and abdominal fat alter the regular mechanics of ventilation. The most relevant accumulation of neck fat is located inside upper-airway muscles of the pharynx, this way changing the lumen to an oval shape. Also, abdominal fat exerts a mass effect that reduces the distension of the chest walls resulting in a decreased thoracic and tracheal

To conclude with, the impact of obesity on COPD disease is not as clear as the impact on SAHS. An overall role for obesity in SAHS is a common finding despite the diversity of ethnic and geographical origins of the studied subjects. We also have reasons to think that obesity, with its mechanical and metabolic effects, may impair COPD course, particularly in

As an additional mechanism, obesity favors insulin resistance and SAHS development through sleep disturbance. Sleep curtailment, sleep fragmentation and a subsequent disrupted signalling lead to unbalanced energy expenditure and far too much appetite. Recent research proposes impaired sleep as a source of metabolic disturbances in SAHS and

Sleep disturbances are an invariable feature of COPD and SAHS patients. Both chronic and intermittent hypoxemia get worse during sleep. Sleep influences ventilation even in normal subjects due to: a reduced response to the hypoxic drive, a reduced ventilatory efficacy of

of follow up was only four years (Reichmuth et al., 2005).

traction during inspiration (Pillar & Shehadeh, 2008).

initial GOLD stages and/or overlap syndrome.

**4.5 Sleep disorders** 

overlap syndrome patients.

(Pillar & Shehadeh, 2008).

a key in maintenance of the lung COPD phenotype. Finally, advanced COPD stages are characterized by cachectic patients who have an impaired metabolism of proteins, lipids and carbohydrates that is thought to be the maximum expression of systemic inflammation and oxidative stress.

The different pathways involved are complex and the available knowledge on their confluence is still limited. We need to clarify if the mentioned markers of hypoxia, inflammation and oxidative stress have a predictive value of the highest morbidity and mortality in patients with COPD and DM. Moreover, COPD and DM cannot be fully understood if we do not consider classical risk factors, mainly tabaquism in COPD genesis, and obesity in DM. The influence of sleep architecture is an emerging aspect related to DM and hardly studied in COPD with the exception of the overlap syndrome (Zizi et al., 2010). The effect of age and physical activity should also be considered in the COPD and DM interaction. Among these factors, lately research has focused on obesity, sleep disorders and their consequences on respiratory and metabolic environments.

#### **4.4 Insulin resistance and obesity**

The inflamed adipose tissue is a well recognized trigger of insulin resistance. A theoretical link between COPD related inflammation and DM could be the switch to a protein depleted and more adipose skeletal muscle. This change of composition inside the sarcolema induces peripheral insulin resistance in the human organism. Such impairment is not necessarily reflected in a visceral fat excess or in obesity (Festa et al., 2002). If present, both conditions aggravate the risk for diabetes.

Then, obesity could be the cause of insulin resistance in patients with lung obstructive disorders. However, obesity does not seem to be the only mediator of insulin resistance. We need to consider a reduction of glucose uptake mediated by hypoxia. Under hypoxemic conditions, insulin resistance has been proved in both obese and lean mice (Polotsky et al., 2007). An explanation could be the hypoxia mediated sympathetic hyperactivity. But sympathetic hyperactivity does not seem to be the unique source of insulin resistance either. Indeed, pharmacologic blockage of autonomic nervous activity did not reverse the insulin resistance under intermittent hypoxia in animal models (Tasali et al., 2008). As we previously mentioned, oxidative stress is a plausible link between the cyclic hypoxiareoxygenation phenomenon in SAHS and insulin resistance. A first argument is that oxidative stress entails glucose and lipid peroxidation. Secondly, it enhances the inflammatory status through activation of NF-κβ and reduction of nitric oxide bioavailability. Oxidative stress helps to understand why a link between hypoxia and DM seems to be stronger in SAHS than in COPD despite a more sustained and profound hypoxia in the latter. Nevertheless, experimental data about hypoxia and glucose metabolism under mimic SAHS and COPD conditions are still very limited.

The role of obesity in COPD is not established. However, and despite not being the only pathway, obesity is a clear line of causality between SAHS and type 2 DM. Indeed, obesity is the main risk factor for SAHS and type 2 DM. In data from the American National Sleep Foundation, high risk for SAHS is present in one out of four adults and in 57% of obese individuals. The proportion of mild or mild to moderate SAHS attributable to excess weight is 58% (Tasali et al., 2008). Some controversial results have been obtained regarding obesity.

a key in maintenance of the lung COPD phenotype. Finally, advanced COPD stages are characterized by cachectic patients who have an impaired metabolism of proteins, lipids and carbohydrates that is thought to be the maximum expression of systemic inflammation and

The different pathways involved are complex and the available knowledge on their confluence is still limited. We need to clarify if the mentioned markers of hypoxia, inflammation and oxidative stress have a predictive value of the highest morbidity and mortality in patients with COPD and DM. Moreover, COPD and DM cannot be fully understood if we do not consider classical risk factors, mainly tabaquism in COPD genesis, and obesity in DM. The influence of sleep architecture is an emerging aspect related to DM and hardly studied in COPD with the exception of the overlap syndrome (Zizi et al., 2010). The effect of age and physical activity should also be considered in the COPD and DM interaction. Among these factors, lately research has focused on obesity, sleep disorders and

The inflamed adipose tissue is a well recognized trigger of insulin resistance. A theoretical link between COPD related inflammation and DM could be the switch to a protein depleted and more adipose skeletal muscle. This change of composition inside the sarcolema induces peripheral insulin resistance in the human organism. Such impairment is not necessarily reflected in a visceral fat excess or in obesity (Festa et al., 2002). If present, both conditions

Then, obesity could be the cause of insulin resistance in patients with lung obstructive disorders. However, obesity does not seem to be the only mediator of insulin resistance. We need to consider a reduction of glucose uptake mediated by hypoxia. Under hypoxemic conditions, insulin resistance has been proved in both obese and lean mice (Polotsky et al., 2007). An explanation could be the hypoxia mediated sympathetic hyperactivity. But sympathetic hyperactivity does not seem to be the unique source of insulin resistance either. Indeed, pharmacologic blockage of autonomic nervous activity did not reverse the insulin resistance under intermittent hypoxia in animal models (Tasali et al., 2008). As we previously mentioned, oxidative stress is a plausible link between the cyclic hypoxiareoxygenation phenomenon in SAHS and insulin resistance. A first argument is that oxidative stress entails glucose and lipid peroxidation. Secondly, it enhances the inflammatory status through activation of NF-κβ and reduction of nitric oxide bioavailability. Oxidative stress helps to understand why a link between hypoxia and DM seems to be stronger in SAHS than in COPD despite a more sustained and profound hypoxia in the latter. Nevertheless, experimental data about hypoxia and glucose

metabolism under mimic SAHS and COPD conditions are still very limited.

The role of obesity in COPD is not established. However, and despite not being the only pathway, obesity is a clear line of causality between SAHS and type 2 DM. Indeed, obesity is the main risk factor for SAHS and type 2 DM. In data from the American National Sleep Foundation, high risk for SAHS is present in one out of four adults and in 57% of obese individuals. The proportion of mild or mild to moderate SAHS attributable to excess weight is 58% (Tasali et al., 2008). Some controversial results have been obtained regarding obesity.

their consequences on respiratory and metabolic environments.

**4.4 Insulin resistance and obesity** 

aggravate the risk for diabetes.

oxidative stress.

Two epidemiological studies have suggested that obesity is the unique or main cause of insulin resistance in SAHS patients (Reichmuth et al, 2005; Stoohs et al., 1996). However, there is growing evidence about alternative links between SAHS and type 2 DM (Tasali et al., 2008). In terms of SAHS severity, only one study had a prospective design and assessed SAHS by polysomnography. An independent relationship between SAHS severity and glucose intolerance was not found after adjustment for body habitus, although the duration of follow up was only four years (Reichmuth et al., 2005).

SAHS itself aggravates obesity through several mechanisms that also enhances insulin resistance: neuroendocrine dysregulation and physical inactivity. Neuroendocrine dysregulation includes an enhanced ghrelin and leptin secretion. Hyperleptinemia has been proposed as a previous step to insulin resistance even in the absence of weight gain. Indeed, leptin was the only upregulated gene affecting glucose uptake in both obese and lean mice exposed to intermittent hypoxia (Polotsky et al., 2007). Hyperleptinemia may also be a marker of SAHS severity (Pillar & Shehadeh, 2008). Physical exercise increases after CPAP treatment of SAHS. However, the reversal of the insulin resistance by CPAP is controversial (Pillar & Shehadeh, 2008).

Ten out of thirteen clinical based studies suggested a body habitus non-related association between SAHS and insulin resistance or glucose intolerance (Tasali et al., 2008). Three studies considered waist-to-hip ratio because central fat distribution seems to be a more relevant mediator of insulin resistance than BMI (Sharma et al., 2007; McArdle et al., 2007; Tassone et al., 2003). There was a positive association in two out of three. In a similar fashion, three studies found higher HOMA-IR and fasting glucose after adjustment for visceral fat measurements (Kono et al., 2007; Makino S et al., 2006; Vgontzas et al., 2000).

Deposit of neck and abdominal fat alter the regular mechanics of ventilation. The most relevant accumulation of neck fat is located inside upper-airway muscles of the pharynx, this way changing the lumen to an oval shape. Also, abdominal fat exerts a mass effect that reduces the distension of the chest walls resulting in a decreased thoracic and tracheal traction during inspiration (Pillar & Shehadeh, 2008).

To conclude with, the impact of obesity on COPD disease is not as clear as the impact on SAHS. An overall role for obesity in SAHS is a common finding despite the diversity of ethnic and geographical origins of the studied subjects. We also have reasons to think that obesity, with its mechanical and metabolic effects, may impair COPD course, particularly in initial GOLD stages and/or overlap syndrome.

### **4.5 Sleep disorders**

As an additional mechanism, obesity favors insulin resistance and SAHS development through sleep disturbance. Sleep curtailment, sleep fragmentation and a subsequent disrupted signalling lead to unbalanced energy expenditure and far too much appetite. Recent research proposes impaired sleep as a source of metabolic disturbances in SAHS and overlap syndrome patients.

Sleep disturbances are an invariable feature of COPD and SAHS patients. Both chronic and intermittent hypoxemia get worse during sleep. Sleep influences ventilation even in normal subjects due to: a reduced response to the hypoxic drive, a reduced ventilatory efficacy of

Chronic Obstructive Pulmonary Disease and Diabetes *Mellitus* 249

obesity and respiratory conditions were not initially considered. Regarding these items, the authors found an association between A1C levels and sleep duration and quality that remained stable after excluding patients at high SAHS risk. Of note, the highest mean A1C was observed in those with higher versus lower risk for SAHS (9.7% vs. 7.9%, p < 0.01).

In an inverse direction, poor glycemic control and obesity are associated with a less quality of sleep. Intervention studies are needed to precise the sense of causality. To take into account, as a final insight sleep dept is a novel habit that could influence the exponential

We can consider three group of factors in COPD patients: respiratory exacerbations and lung function, nutritional and muscle disorders and finally metabolic syndrome. There are several definitions of MS, but a common element is that all the components are related to the existence of insulin resistance, which will lead to glucose intolerance, abdominal obesity, elevated triglycerides, decreased HDL cholesterol and hypertension. It is estimated that 40- 50% of individuals over 60 years have MS in industrialized countries. In Europe there is a prevalence of 15% (Hu et al., 2004; Botros et al., 2009). In a study of 170 patients with COPD and 30 with chronic bronchitis, Sicras et al. (Sicras et al., 2007) observed that the frequency of MS was 53%, 50%, 53% 37% and 44% in patients with chronic bronchitis, COPD I, II, III and IV respectively. They explained the lower incidence in the latter stages of the disease would

As previously mentioned, insulin resistance and the development of type 2 diabetes is the key point of MS. In this sense, we have discussed that hypoxia, obesity and sleep disturbances reduce the insulin sensitivity. We could say that the association between SAHS and DM resembles the clustering of metabolic diseases found in MS. The components of MS keep bidirectional links, such insulin resistance and obesity, that are plausible between SAHS and DM. Similarly, the sum of SAHS and DM may result in multiplied cardiovascular

We discussed that the association between COPD and MS is far less clear than the parallel course of SAHS and MS. Due to hypoxia, a change towards multiple firing of vasoconstrictor neurons will increase noradrenalin levels, so we could expect at least arterial hypertension in COPD. Surprisingly, patients were not hypertensive in data by Ashley et al. (Ashley et al., 2010), and the authors posed tempering vascular factors that might balance the hypertensive drive. The links between COPD, metabolic syndrome and cardiovascular disease are largely unknown. Most of the data available deals with the association between SAHS, endothelial dysfunction and subsequent cardiovascular morbidity (Zamarron et al., 2008). There is also recent evidence of an increased mortality in overlap patients without CPAP therapy as compared to COPD (42.2 vs. 24.2%, p < 0.001) (Marin et al., 2010). Death was most commonly due to cardiovascular disease. A poorer quality of life was also

The COPD, SAHS and DM shared inflammatory state perpetuates these chronic conditions and have a cardiovascular impact. Hypoxia induced factor (HIF-1) triggers inflammation and angiogenesis inside the atherosclerotic plaque this way facilitating the entry of phagocytes, red blood cells and lipoproteins. CRP is also directly related to atherosclerosis.

demonstrated, even in patients without diurnal sleepiness.

increase of diabetes, obesity and SAHS in our worldwide societies.

**5. Metabolic syndrome** 

be related to weight loss.

effects.

hyperrelaxed accesory muscles and upper-airway dilators and, finally, because lung residual capacity is reduced during sleep and so the pharyngeal traction is. Then, normally decreased nocturnal oxygen saturation becomes a challenge in COPD and SAHS patients. A more blunted chemical response to hypoxic drive is seen in both diseases. A diminished ventilation/perfusion quotient results from an hyperinflated lung, less activity of intercostal muscles and a dissociated diaphragmatic and intercostal activity in COPD patients. A collapsible pharynx is the main cause of hypoxia and sleep disturbance in SAHS. Associated symptoms, comorbid diseases, drugs and sedentary lifestyle also reduce sleep efficacy.

Evidence from extent population-based prospective and experimental studies links short and/or poor sleep and type 2 diabetes (Tasali et al., 2008). The sleep-related diabetes is not necessarily explained by apneas (Ayas et al., 2003; Mallon et al., 2005). Two laboratory studies performed in healthy young lean adults obtained an enhanced insulin resistance and a diminished insulin secretion related to sleep deprivation (Knutsonet al., 2007; Spiegel et al., 1999). Measurements of insulin-glucose homeostasis were based on intravenous glucose overload and minimal model technique respectively. Minimal model (Bergman, 2005) resulted in a glucose disposition index (DI) 40% lower than after sleep recovery. A low DI reflects an insulin secretion that is insufficient to compensate for insulin resistance. A low DI indicates a high risk for type 2 DM. Subjects underwent a relative short sleep restriction (4 h for 6 or 2 nights) however inducing a pre-diabetic state similar to the habitual in older adults. Reduction of slow-wave sleep and sleep fragmentation were assessed in another laboratory set-up (Tasali et al., 2008), resulting a similar marked decrease in insulin sensitivity without a balanced insulin secretion. The decrease in insulin sensitivity was correlated with a rise in heart rate variability as a measure of the daily sympathetic activity. In addition, insulin resistance was more related to sleep slow wave suppression than to sleep fragmentation. These experimental procedures have not been reproduced in specific SAHS and COPD settings.

We conclude that, in addition to hypoxia, sleep curtailment enhances sympathetic activation. Noradrenalin is a counter-regulatory hormone that reduces insulin release and function. There is also a decreased glucose uptake by muscle cells favored by high evening cortisol levels and extended duration of elevated growth hormone (GH) levels at night. Another relevant effect of short sleep is upregulation of appetite. A hormonal deregulation of appetite has been observed in the mentioned laboratory studies (Knutson et al., 2007). Ghrelin and leptin are hormones that exert respective hunger and satiety effects. Leptin inhibits appetite, modulates fat distribution and increases energy expenditure. Sleep debt shortens the adequate time that leptin levels require to balance the previous onset of a ghrelin peak. The ghrelin peak occurs during the first half of the night. An attenuated function of leptin due to leptin-CRP boundage has also been hypothesized (Chen et al., 2006). It is plausible in an inflammatory scene such sleep debt.

In diabetic patients, sleep duration and quality was associated with a poorer glycemic control in data from a cross-sectional study on African-American adults with type 2 DM (Knutson et al., 2006). Sleep characteristics were self-reported. Interestingly, sleep quality was associated with poorer glycemic control only in patients with chronic complications of diabetes. A theoretical explanation would be an impaired autonomic response at the background of those diabetic subjects. They would be more susceptible to a less-quality sleep. The cited results were adjusted for age, gender, insulin treatment and BMI. Central obesity and respiratory conditions were not initially considered. Regarding these items, the authors found an association between A1C levels and sleep duration and quality that remained stable after excluding patients at high SAHS risk. Of note, the highest mean A1C was observed in those with higher versus lower risk for SAHS (9.7% vs. 7.9%, p < 0.01).

In an inverse direction, poor glycemic control and obesity are associated with a less quality of sleep. Intervention studies are needed to precise the sense of causality. To take into account, as a final insight sleep dept is a novel habit that could influence the exponential increase of diabetes, obesity and SAHS in our worldwide societies.
