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Obstructive Sleep Apnoea and Type 2 Diabetes

Published Online: June 8th 2014 US Endocrinology, 2014;10(1):35–43 DOI: http://doi.org/10.17925/USE.2014.10.01.35
Authors: Abd A Tahrani, Asad Ali
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Abstract:
Overview

With the growing prevalence of obesity, the burden of type 2 diabetes is increasing. Obstructive sleep apnoea (OSA) is a very common medical condition that is associated with increased risk of cardiovascular disease and mortality. Obesity is a common risk factor for OSA and type 2 diabetes and hence it is not surprising that OSA and type 2 diabetes are interlinked. OSA has been shown to be an independent risk factor for the development of incident pre-diabetes/type 2 diabetes. OSA is also associated with worse glycaemic control and vascular disease in patients with type 2 diabetes. However, evidence for the benefits of OSA treatment in patients with type 2 diabetes is still lacking. The aim of this article is to provide an overview of OSA, the relationships between OSA and dysglycaemia and the impact of OSA in patients with type 2 diabetes, highlighting recent advances in the field.

Keywords

Diabetes, sleep apnoea, obesity, pre-diabetes, impaired glucose tolerance, hypertension, cardiovascular disease, retinopathy, nephropathy, neuropathy, glycated haemoglobin

Article:

Diabetes is a global epidemic with an estimated worldwide prevalence of 8.3 % (382 million) in 2013 that is forecast to rise to 10.1 % (592 million) in 2035.1 Type 2 diabetes accounts for >90 % of all cases, and costs an estimated 10–12 % of the world’s health expenditure in 2010 to 2013.1–3 In addition to the high prevalence of diabetes, 316 million people have impaired glucose tolerance (IGT) that is projected to increase to 471 million by 2035.1

Diabetes is a global epidemic with an estimated worldwide prevalence of 8.3 % (382 million) in 2013 that is forecast to rise to 10.1 % (592 million) in 2035.1 Type 2 diabetes accounts for >90 % of all cases, and costs an estimated 10–12 % of the world’s health expenditure in 2010 to 2013.1–3 In addition to the high prevalence of diabetes, 316 million people have impaired glucose tolerance (IGT) that is projected to increase to 471 million by 2035.1

Type 2 diabetes is a complex metabolic disorder in which the interaction between multiple genetic and environmental factors results in a heterogeneous and progressive condition with variable degrees of insulin resistance (IR) and pancreatic b-cell dysfunction.4 Overweight and obesity are major contributors to the IR.4–6 When b-cells are unable to secrete sufficient insulin to overcome IR, type 2 diabetes ensues.4,6 Obstructive sleep apnoea (OSA) is a common disorder characterised by upper airway instability during sleep, resulting in markedly reduced (hypopnoea) or absent (apnoea) airflow.7 These apnoea/hypopnoea episodes are usually accompanied by recurrent oxygen desaturations and cyclical changes in blood pressure (BP) and heart rate and disturbances in sleep architecture such as loss of slow-wave sleep (SWS) and rapid eye movement (REM) sleep.7

Although obesity is the main driver for IR and b-cell dysfunction, several aspects of sleep-related disorders have recently emergedas potentially important contributors to IR and to the development of IGT/type 2 diabetes.8 Short sleep duration and disturbances in the circadian rhythm are associated with IR and increase the risk of type 2 diabetes.9–12 Hence there is a lot of interest among endocrinologists and sleep specialists to further our understanding of the role of sleep in patients with dysglycaemia.

The aim of this paper is to give an overview of OSA and review the evidence for the relationship between OSA and type 2 diabetes with particular focus on more recent studies.

Obstructive Sleep Apnoea
Definitions
The American Academy of Sleep Medicine (AASM) guideline has defined an apnoea as cessation or ≥90 % reduction in airflow for a period of ≥10 seconds; hypopnoea has different definitions detailed in the AASM but a commonly used definition is ≥30 % reduction in airflow for ≥10 seconds associated with ≥4 % drop in oxygen saturations.13 Apnoeas are classified into obstructive or central, based on the presence or absence of respiratory/abdominal efforts. Examples of apnoeas and hypopnoeas can be found in Figure 1. The apnoea–hypopnoea index (AHI) is the average number apnoea and hypopnoea episodes per hour during sleep and is a marker of OSA severity.7 An AHI ≥5 events/hour is consistent with the diagnosis of OSA;14 however, in the context of research, different studies in the literature used different AHI cut-offs to define OSA including 5, 10, 15 and 30 events/hour. OSA can be classified as mild, moderate and severe based on AHI 5 to <15, 15 to <30 and ≥30 events/hours. The respiratory disturbance index (RDI) is another OSA measure that includes the AHI in addition to respiratory effort-related arousal, which is defined as a sequence of breaths characterised by increasing respiratory effort leading to an arousal from sleep, but that does not meet criteria for an apnoea or hypopnoea.7 Another measure of OSA is the oxygen desaturation index (ODI), which is the average number of oxygen desaturations per hour during sleep.

Obstructive Sleep Apnoea Epidemiology and Risk Factors
The prevalence of OSA varies considerably between studies, mainly due to differences in the population studied, study designs and the method and criteria used to diagnose OSA. In addition, as obesity and age are major risk factors for OSA, it is likely that OSA prevalence is increasing with the increasing prevalence of obesity and an ageing population. It is estimated that 17–26 % of men and 9–28 % of women have OSA and that 9–14 % of men and 2–7 % of women have moderate to severe OSA.15 This OSA prevalence is reported from studies that used a twostage sampling design that allows some degree of estimate of the ‘selfselection’ bias, which is usually a significant problem in OSA studies. Ethnicity and gender have significant impacts on OSA prevalence. Some studies showed a higher adjusted OSA prevalence in Afro-Caribbeans (increased twofold) compared with White Europeans,15 while others did not confirm this.16 The Chinese population has a high OSA prevalence (8.8 % in men and 3.7 % in women) despite being less obese than White Europeans,17,18 which highlights the importance of factors other than obesity (such as upper airway anatomy) in the development of OSA.19 In South Asians, OSA prevalence varies between 3.7 % in a semi-urban population to 19.5 % in middle-age urban men.20–22 Men have two to three times increased risk of OSA compared with women:15 differences in sex hormones, upper airway size and ventilatory control have been implicated in the gender differences.23,24 OSA prevalence in elderly men is three to six times that in younger men;15,23,25 however, this age effect plateaus around the age of 65 years.16 Changes in pharyngeal anatomy and upper airway collapsibility are likely responsible for the age impact on OSA prevalence.23

Excess body weight is by far the most important risk factor for OSA, although not all OSA patients are obese. In the Wisconsin sleep study, eachincrease in body mass index (BMI) by one standard deviation, resulted in a fourfold increase in OSA prevalence.26 Weight gain is a strong predictor of developing OSA or worsening of pre-existing OSA27,28 and weight loss(via lifestyle modifications or surgical intervention) improve/cure OSA.29,30 Several mechanisms might be responsible for the association between obesity and OSA. Obesity can alter normal upper airway mechanics during sleep by increased parapharyngeal fat deposition resulting in a smaller upper airway, altering the neural compensatory mechanisms that maintain airway patency, reducing the functional residual capacity with a resultant decrease in the stabilising caudal traction on the upper airway and affecting the chemosensitivity to O2 and CO2, which reduces ventilator drive.31 Other OSA risk factors include current smoking, excess alcohol intake and genetic factors.15,23

Obstructive Sleep Apnoea Pathophysiology
OSA is a very complex disorder, and pathogenesis involves multiple mechanisms (see Figure 2). Upper airway size and collapsibility plays an important role; smaller airways are more likely to collapse.32 The presence of upper airway deficits in patients with OSA is supported by data showing that upper airway muscles (genioglossus) activity is increased in patients with OSA,33 suggesting that these muscles are compensating for an underlying upper airway deficit32 and that continuous positive airway pressure (CPAP) treatment improves muscle hyperactivity.34 Sleep onset is associated with greater reductions in upper airway muscle tone in OSA patients, which explains the occurrence of apnoea/hypopnoea episodes at sleep onset and during REM sleep.32 Other factors contributing to OSA pathogenesis include changes in lung volume, abnormalities in ventilatory control and stability, changes in chemosensitivity to CO2 and higher arousal thresholds.32

Obstructive Sleep Apnoea Clinical Features and Diagnosis
A thorough history and examination are still essential parts of the assessment of patients with OSA despite the fact that several reports have shown the limited value of symptoms in predicting OSA.35 Snoring is the most common symptom of OSA and only 6 % of OSA patients (or their partners) have not reported snoring.7 Snoring, however, has a poor predictive value as many snorers do not have OSA.7 Nonetheless, lack of snoring almost rules out OSA.7 Witnessed apnoeas are another important symptom that is usually reported by the partner. However, witnessed apnoeas do not correlate with disease severity and around 6 % of the ‘normal’ population appear to have experienced apnoeas without OSA.7 Other nocturnal symptoms such as choking (which is possibly a ‘proper’ rather than a ‘micro’ arousal to terminate apnoea), insomnia, nocturia and diaphoresis have been reported.7 Daytime symptoms include EDS, fatigue, morning headache and autonomic symptoms.7

The gold standard to diagnose OSA is polysomnography that typically includes the recording of 12 channels such as electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), oronasal airflow, chest wall effort, abdominal effort, body position, snore microphone, electrocardiogram (ECG) and oxyhaemoglobin saturation.7 Portable home-based respiratory devices are another alternative.7 Pulse oximetry is another good way to diagnose sleep apnoea but it cannot differentiate between obstructive and central apnoeas and it has a wide range of sensitivity (31–98 %) and specificity (41–100 %). The AASM recommends use of a type II device as a minimum.7

Obstructive Sleep Apnoea as a Risk Factor for Type 2 Diabetes
Several prospective studies have shown an increased risk of type 2 diabetes in patients with OSA and that OSA is an independent risk factor for incident type 2 diabetes after adjustment for age, obesity and other possible confounders.36–42 Most of these studies used polysomnography to diagnose OSA, but these included a relatively small number of participants as polysomnography is time-consuming and requires significant resources. The diagnosis of type 2 diabetes in these studies was mostly based on ‘physician diagnosis’ or fasting plasma glucose. However, more recent studies that used oral glucose tolerance tests to assess glycaemic status found similar results to other studies (see Table 1). A meta-analysis of published studies that used objective measures to diagnose OSA found that moderate to severe (but not mild) OSA was associated with increased risk of developing type 2 diabetes (moderate to severe OSA: relative risk [RR] 1.63, 95 % confidence interval [CI] 1.09– 2.45; mild OSA RR 1.22, 95 % CI 0.91–1.63).43

The impact of OSA on incident type 2 diabetes is likely to be related to its impact on IR and b-cell dysfunction. While some cross-sectional studies showed an association between OSA and IR,44–64 others did not.65–71 Several factors might be responsible for these conflicting results. Studies that did not show such an association typically included fewer participants and were possibly underpowered. Variation in excessive daytime sleepiness (EDS), which has been shown to be associated with IR,72,73 between study populations might also contribute to the variation in results. Further evidence for the relationship between OSA and IR comes from a study which assessed the impact of OSA on IR (homeostatic model assessment [HOMA]-IR) longitudinally over 11- year follow-up. This showed that OSA, AHI, ODI and minimal oxygen saturations were independently associated with IR after adjustment for age, baseline BMI, hypertension, BMI change over follow-up and CPAP treatment.42 CPAP treatment was shown to lower IR in some studies74,75 but not in others.48,65,76–78 However, several meta-analyses showed that CPAP treatment lowers IR,79,80 particularly in those compliant with treatment and using CPAP >4 hours per night.81

Obesity remains a major possible confounder for the relationship between OSA and IR. Recent studies tried to address this matter by reassessing this association in lean individuals.82,83 Healthy lean men (BMI 22.6 kg/m2) with OSA had 27 % lower insulin sensitivity and 37 % higher insulin secretion than age, BMI, family history and exercise levelmatched control after ingestion of a glucose load despite comparable glucose levels between groups.82 This suggests that OSA in lean men is associated with IR and a compensatory rise in insulin secretion to maintain normoglycaemia. Another study showed that OSA was associated with dysglycaemia/pre-diabetes in lean but not obese Koreans.84 Furthermore, in conditions in which IR is not driven by obesity, such as acromegaly, OSA has been associated with IR and dysglycaemia.85 Hence it is likely that OSA is associated with IR independent of obesity and that this is amenable to treatment with CPAP.

Contrary to the relationship between OSA and IR, the impact of OSA on b-cells is rather limited. In vitro and animal studies showed that intermittent hypoxia increases b-cell death, and results in b-cell dysfunction,86,87 but the intermittent hypoxia used in these studies was greater than that which occurs in humans with OSA. Two studies in humans showed that OSA was associated with b-cell dysfunction in patients with88 and without type 2 diabetes.44 Much more data are needed in terms of the longitudinal impact of OSA on b-cells and the impact of CPAP.

Type 2 Diabetes as a Risk Factor for Obstructive Sleep Apnoea
Unlike the well-examined impact of OSA on the risk of developing type 2 diabetes, the impact of type 2 diabetes on OSA incidence and the natural history of OSA have not been examined. It is plausible that type 2 diabetes could result in the development or worsening of pre-existing OSA. This might be related in part to the weight gain that occur in patients with type 2 diabetes with treatment intensification (particularly in the pre-incretin therapy era) and weight gain is a strong predictor of developing OSA or worsening of pre-existing OSA.27,28 Other possible mechanisms include loss of upper airway innervations or the autonomic dysfunction that can occur in patients with type 2 diabetes, which is implicated in the central respiratory centre response to hypercapnic stimulus89 and may result in changes in respiratory control resulting in sleep apnoea.90

One study has examined the presence of witnessed sleep apnoeas (sleep reported) in 3,565 participants at baseline and after 6 years and found that HOMA-IR was an independent predictor of incident witnessed apnoeas (odds ratio [OR] 1.31 [1.13–1.51]) after adjustment for age, sex and waist circumference.91 Other independent predictors of witnessed sleep apnoeas included waist circumference (1.34 [95 % CI 1.19–1.52]), triglycerides (1.24 [1.09–1.41]) and smoking (1.52 [1.12– 2.05])91 – all of these factors might confer an increased risk of OSA in patients with type 2 diabetes.

Future studies need to examine the natural history of sleep apnoea (both obstructive and central) in patients with type 2 diabetes as this information will be important to develop the appropriate screening strategies for sleep apnoea in patients with type 2 diabetes and to understand the metabolic and non-metabolic consequences of sleep apnoea in patients with type 2 diabetes. The ongoing Sleep AHEAD study might provide some useful information about the natural history of OSA in patients with type 2 diabetes.

Obstructive Sleep Apnoea Prevalence in Patients with Type 2 Diabetes
Obesity is a major risk factor for OSA and type 2 diabetes and, as described above, OSA is an independent predictor of incident type 2 diabetes and is associated with IR and b-cell dysfunction. Hence, it is not surprising that several epidemiological studies showed that OSA is very common in patients with type 2 diabetes (8.5–85 %, 23.8–70 % for moderate to severe OSA).64,92–103 Methodological and population differences account for the significant differences observed between studies in OSA prevalence.

Studies with lower OSA prevalence tended to include a White European population and were conducted in primary care settings,94,102,103 while studies of higher prevalence were conducted in secondary care settings or included Afro-Caribbeans.97,101 Data regarding the prevalence of OSA in South Asians with type 2 diabetes is limited to one study that showed a higher prevalence in White Europeans compared with South Asians with type 2 diabetes, but the South Asians in this study had lower BMI and waist circumference that White Europeans.101 As OSA is common in patients with type 2 diabetes, the international Diabetes Federation (IDF) recommended screening for OSA in this high-risk population,104 although the extent to which this recommendation is followed is to be determined and appropriate validated screening methods in patients with type 2 diabetes are still lacking. Whether the prevalence of OSA is higher in patients with type 2 diabetes compared with similar populations without type 2 diabetes remains unclear.

Obstructive Sleep Apnoea and Glycaemic Control in Patients with Type 2 Diabetes
Due to its association with obesity, IR and b-cell dysfunction, it is plausible to speculate that OSA might worsen glycaemic measures in patients with type 2 diabetes. However, as with most aspects of OSA-related research, obesity is a major confounder. Several studies of relatively small sample size (n=31–92) showed that OSA and OSA severity are associated with poorer glycaemic control (both glycated haemoglobin [HbA1c] and fasting plasma glucose) and glycaemic variability after adjustments for age, sex, race, BMI, number of diabetes medications, level of exercise, years of diabetes and total sleep time in some studies.100,105–108 The adjusted mean increase in HbA1c between patients with and without OSA varied between 0.7 to 3.69 % depending on the OSA severity. However, not all studies showed such an association;95 however, in this study only 22 % of participants had full polysomnography and the duration of the sleep study was just 4 hours.109 Another study showed no association between AHI and HbA1c in patients with type 2 diabetes after adjustment for confounders but there was an association between lowest oxygen saturation correlated with HbA1c.110 Hence, most studies suggest a relationship between OSA and glycaemic measures in patients with type 2 diabetes, but some studies did not show such an association and causation is difficult to prove due to the cross-sectional nature of these studies, the confounding effects of obesity and the lack of prospective studies assessing the impact of OSA on HbA1c longitudinally.

To further add to the complexity, recent data suggest that the association between HbA1c and OSA in patients with type 2 diabetes is dependent on the sleep stage by showing that AHI is independently associated with HbA1c during REM but not during non-REM sleep.111 The difference in HbA1c between the lowest and highest REM AHI quartile was about 1 %.111 This could explain some of the variability observed in previous studies as patients with similar total AHI might have different distributions of REM and non-REM AHI.

Several studies examined the impact of CPAP treatment on glycaemic measures in patients with type 2 diabetes,76,92,112–117 only one of which was randomised,76 with the rest being uncontrolled pre-post assessments. The uncontrolled studies showed improvements in insulin sensitivity,92,112 postprandial hyperglycaemia,113 glycaemic variability116 and/or HbA1c.113,114 The one randomised controlled trial showed no change in HbA1c after 3 months of CPAP therapy. The lack of a positive effect could be attributed to the small study sample, the limited duration of follow-up and the suboptimal adherence to CPAP (3.6 hours/night), or to a true lack of effect. A meta-analysis found that CPAP did not significantly reduce HbA1c (0.08 % [95 % CI –0.26 to 0.42) in patients with type 2 diabetes.80

The association between REM AHI with HbA1c, rather than non REM AHI (described above), is another factor to be taken into account. Most of the REM sleep occurs towards the end of the night, hence longer usage of CPAP might be needed to produce an effect on glycaemic measures. This was tested in a recent study presented in the SLEEP 2013 conference where patients with type 2 diabetes (mean BMI 39.2 kg/m2, diabetes duration 3.2 years), were randomised to 1 week in-laboratory (8 hours sleep) CPAP or sham CPAP to maximise CPAP adherence under direct supervision. CPAP resulted in a decrease of 11.2 and 19.8 mg/dl in the average 24-hour and post-breakfast glucose levels, respectively. The dawn phenomena was also reduced by 45 %.118 In addition to the better CPAP usage reported in this study, the diabetes duration was also relatively short, which might contribute to the CPAP effect.

Further randomised controlled trials are needed to assess the impact of CPAP on glycaemic control in patients with type 2 diabetes; these trials might need to focus on better CPAP compliance, longer CPAP usage per night and possibly targeting patients with shorter diabetes duration. Another completed randomised trial (Effect of PAP Treatment on Glycemic Control in Patients With Type 2 Diabetes [GLYCOSA]) should report soon (http://clinicaltrials.gov/ct2/show/study /NCT00509223?sect=X6015).

Obstructive Sleep Apnoea and Hypertension in Patients with Type 2 Diabetes
The links between OSA and arterial hypertension and the impact of CPAP treatment on BP in patients without diabetes are well established.119–124 Emerging evidence suggests that the same might be true in patients with type 2 diabetes. In a retrospective cohort study in patients with OSA and type 2 diabetes, CPAP was associated with a mean change of –6.81 mmHg (95 % CI –9.94 to –3.67 mmHg) and –3.69 mmHg (–5.53 to –1.85 mmHg) in systolic and diastolic BP, respectively, after nine to 12 months of treatment.125 A randomised parallel group intervention trial showed similar results after 3 months of CPAP treatment.126 Randomised placebo and active controlled studies are needed.

Obstructive Sleep Apnoea and Vascular Complications in Patients with Type 2 Diabetes
Macro and micro-vascular complications remain the major cause of morbidity and mortality in patients with type 2 diabetes. Several lines of evidence suggest increased risk of cardiovascular disease (CVD) in patients with OSA and observational studies suggest that CPAP treatment is associated with reduction in CVD.127–129 In addition, in patients with stable coronary artery disease, AHI correlated positively with the plaque volume assessed by intravascular ultrasound (r=0.6; p=0.01)130 or computed tomography (CT) angiogram (r=0.4; p=0.02).131 Furthermore, patients with OSA are more likely to develop acute myocardial infarction between 12 am and 6 am compared with non- OSA patients matched for comorbidities (32 % versus 7 %; p=0.01) supporting the role of the nocturnal events that occur in OSA patients in the development of CVD.132

Data regarding the impact of OSA on CVD in patients with type 2 diabetes are limited. A cross-sectional analysis from the Look AHEAD study showed that AHI is associated with stroke (adjusted OR 2.57, 95 % CI 1.03–6.42), but there was no association with coronary artery disease,133 but the CVD was self-reported. In a more recent study of 132 consecutive asymptomatic patients with type 2 diabetes and normal exercise echocardiographic findings for ≤8 years, sleep disordered breathing (SDB) was associated with incident coronary artery disease (adjusted hazard ratio [HR] 2.2, 95 % CI 1.2–3.9; p=0.01) and heart failure (adjusted HR 3.5, 95 % CI 1.4–9.0; p <0.01) after a median follow-up of 4.9 years.134 Several studies examined the relationship between OSA and diabetesrelated microvascular complications – most of these studies were crosssectional in nature and interventional data are lacking. In Japanese patients undergoing vitreous surgery for advanced diabetic retinopathy (DR), lower oxygen saturations were associated with proliferative DR after adjustment for age, HbA1c and hypertension.135 In a study from the UK, OSA was independently associated with DR and maculopathy after adjusting for age, BMI, diabetes duration and hypertension in men with type 2 diabetes.136

Similarly, in another study from the UK, patients with OSA were three to four times more likely to have sight-threatening DR, preproliferative/proliferative DR or maculopathy after adjustment for a wide range of confounders including gender and ethnicity.137 Longitudinally, patients with OSA were more likely to develop advanced DR (adjusted OR 6.6, 95 % CI 1.2–35.1; p=0.03); and patients who were compliant with CPAP treatment had lower progression to advanced DR compared with non-complaint patients.137 In a proof of concept uncontrolled, hypothesis-generating study, CPAP treatment for 6 months was associated with improvement in visual acuity without an impact on macular oedema/thickness.138

Similar to the associations with DR, OSA was found to be associated with diabetic nephropathy (defined as albuminuria and/or reduced eGFR) in patients with type 2 diabetes after adjustment for possible confounder.139 After a 2.5-year follow-up of the same cohort, OSA was an independent predictor of study-end eGFR and eGFR decline.139 In a study of Japanese patients with type 2 diabetes, ODI ≥5 was independently associated with microalbuminuria in women but not in men after adjustment for confounders.140

A cross-sectional study found that patients with OSA were more likely to have diabetic neuropathy (OR 2.82, 95 % CI 1.44–5.52) and foot insensitivity (OR 3.97; 95 % CI 1.80–8.74 ) compared with those without OSA.101

Several mechanisms might explain the observed associations between OSA and vascular disease in patients with type 2 diabetes. Hyperglycaemia and OSA result in the activation of similar pathways (e.g. Aldose reductase, protein kinase C, advanced glycation end products) and increased oxidative and nitrosative stress that can lead to increased inflammation, cellular and endothelial dysfunction and vascular disease (see Figure 3).141–162 In recent studies, OSA was associated with increased oxidative stress, nitrosative stress and impaired microvascular complications in patients with type 2 diabetes.101,139

Obstructive Sleep Apnoea and Type 2 Diabetes – The Mechanisms
The mechanisms underlying the relationship between OSA and type 2 diabetes are likely to be complex and multifactorial involving multiple neural and endocrine pathways (see Figure 4).

Intermittent hypoxia is an important component of OSA and contributes significantly to its pathological consequences. Intermittent hypoxia for as little as 5 hours in healthy volunteers can reduce insulin sensitivity without a compensatory increase in insulin secretion, suggesting an impact on b-cell function as well.163 The intermittent hypoxia and the repetitive episodes of re-oxygenation following desaturations that occur in OSA can simulate ischaemia–reperfusion injury and result in the formation of reactive oxygen species (ROS) and reactive nitrogen species (RNS) causing oxidative and nitrosative stress resulting in cellular and DNA damage and increased oxidised lipids, DNA and carbohydrates as shown in patients with OSA and animals exposed to intermittent hypoxia.101,144,146,161 The impact of intermittent hypoxia on IR could be in part due to the hypoxia-inducible factor-1 (HIF-1) that is increased in OSA either secondary to hypoxia itself164 or oxidative stress.165 HIF-1 upregulates sterol regulatory element-binding protein (SREBP)-1,166 which is associated with increased lipid biosynthesis and IR.167 HIF is also involved in systemic inflammation.168 Interestingly, in mice with partial deficiency of HIF-1, intermittent hypoxia does not result in IR.8,166

OSA is also associated with many hormonal changes that can affect glucose metabolism including activation of the hypothalamic– pituitary–adrenal (HPA) axis,169,170 increased ghrelin171 and increased catecholamines secretion172 all of which can be corrected with CPAP. OSA is also associated with changes in adipokine secretion including lower adiponectin levels,161,173,174 and higher leptin levels.48,49 OSA is associated with increased sympathetic activity161 that plays an important role in the regulation of glucose and fat metabolism and the development of type 2 diabetes.175 Both recurrent hypoxia176 and recurrent arousals177 probably contribute to the activation of the sympathetic system.

In addition, OSA is associated with elevated inflammatory cytokines such as interleukin (IL)-6, tumour necrosis factor alpha (TNF-a) and nuclear factor kappa-light-chain-enhancer of activated B cells (NF-kB),161 which contributes to IR and b-cell dysfunction. In addition to intermittent hypoxia, OSA is associated with changes in sleep architecture such as reduction in SWS and sleep quality, which have been associated with reduction in insulin sensitivity and dysglycaemia.178

OSA might also be a risk factor for the developing of histologically proven non-alcoholic fatty liver disease (NAFLD) and for progressing to NASH.61,179 Nocturnal desaturations were found to be associated with hepatic inflammation, hepatocyte ballooning and liver fibrosis.61 Another study also found that subjects with histological NASH had significantly lower mean nocturnal oxygen saturation, and higher AHI compared with non-NASH controls.179

All the above-listed mechanisms can impact on IR and/or b-cell function resulting in impaired glucose metabolism and eventually type 2 diabetes.

Summary and Conclusion
OSA is a common medical condition that is strongly linked to obesity. It is associated with increased IR and b-cell dysfunction and is a risk factor for the development of incident glucose intolerance/type 2 diabetes. Obesity remains a major confounder for the relationship between OSA and glycaemic abnormalities but recent studies suggest that these associations hold true even in lean individuals with OSA. The mechanisms underlying this relationship between OSA and dysglycaemia are complex and likely to involve several neural and endocrine mechanisms. OSA is common in patients with type 2 diabetes with most studies showing that more than half of patients with type 2 diabetes have some degree of OSA. OSA is associated with worsening glycaemic control in patients with type 2 diabetes and recent studies suggest that OSA is associated with increased macro- and microvascular complications and possibly hypertension in patients with type 2 diabetes. The mechanisms linking OSA to vascular disease in type 2 diabetes are likely to involve similar pathways to those stimulated by hyperglycaemia in type 2 diabetes. The impact of CPAP treatment in patients with dysglycaemia or type 2 diabetes remains controversial. While CPAP seems to improve IR, convincing evidence for the impact of CPAP on glycaemic control in patients with type 2 diabetes is still lacking. Recent observational data suggest that CPAP might have a favourable impact on diabetes-related vascular complication but evidence from randomised controlled trials is awaited. While most of the research in the field of OSA and glucose metabolism focussed on euglycaemic individuals or patients with pre-diabetes, research into the impact of OSA in patients with type 2 diabetes is rapidly expanding and gaining momentum. Further research into the impact of mild OSA in patients with type 2 diabetes and randomised controlled trials into the efficacy of OSA treatments on diabetes-related outcomes are needed. In addition, more work needs to be carried out to understand the impact of type 2 diabetes on OSA.

Article Information:
Disclosure

Abd A Tahrani is a clinician scientist supported by the National Institute for Health Research. The views expressed in this article are those of the authors and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health. The authors have no conflicts of interest to declare.

Correspondence

Abd A Tahrani, 2nd floor Institute of Biomedical Research, The Medical School, University of Birmingham, Birmingham B15 2TT, UK. E: a.a.tahrani@bham.ac.uk
An erratum to this article can be found below.

Received

2014-02-03T00:00:00

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