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Prevalence of Type 2 Diabetes in Patients with Obstructive Sleep Apnea in a Multi-Ethnic Sample

Published Online:https://doi.org/10.5664/jcsm.27489Cited by:43

ABSTRACT

Study Objectives:

Relationship of obstructive sleep apnea (OSA) with insulin resistance and type 2 diabetes in Caucasians has been studied, but this association has not been investigated in Hispanic and African-Americans. The objective of this study is to determine the prevalence of type 2 diabetes in patients evaluated for OSA in a predominantly African American and Hispanic sample. The secondary objective is to evaluate the relationship of REM related OSA and type 2 diabetes.

Methods:

1008 consecutive patients who had a comprehensive polysomnography were evaluated. OSA was defined as an obstructive apnea-hypopnea index (AHI) of ≥ 5 per hour. REM AHI of ≥ 10 was considered to indicate REM related OSA.

Results:

The prevalence of type 2 diabetes was 30.1% in the group with OSA compared to 18.6% in those without OSA. The subjects with OSA had significantly increased odds of type 2 diabetes compared with those without OSA (odds ratio = 1.8, 95% confidence interval: 1.3–2.6) but this association became non-significant when controlled for confounding variables and covariates (odds ratio = 1.3, 95% confidence interval: 0.9–2.0). Middle-aged participants with OSA had 2.8 times higher odds for type 2 diabetes, when compared to younger or middle aged without OSA, controlling for covariates. Finally, the odds of type 2 diabetes were 2.0 times higher in patients with REM AHI of ≥ 10/h independent of confounding variables.

Conclusions:

OSA is not independently associated with type 2 diabetes in a predominantly African American and Hispanic sample. However, the relationship of REM related OSA with type 2 diabetes may be statistically significant.

Citation:

Mahmood K; Akhter N; Eldeirawi K; Önal E; Christman JW; Carley DW; Herdegen JJ. Prevalence of type 2 diabetes in patients with obstructive sleep apnea in a multi-ethnic sample. J Clin Sleep Med 2009;5(3):215-221.

INTRODUCTION

More than 24 million Americans have diabetes mellitus (DM), and nearly one million new cases of diabetes are diagnosed every year.1 Type 2 diabetes accounts for 90% to 95% of all cases of diabetes. Diabetes mellitus was the sixth leading cause of death in 2002, with the risk of death almost twice that of non-diabetic patients of similar age.2 According to the Centers for Disease Control and Prevention, African Americans have a 1.8-fold increase and Hispanic Americans have a 1.7-fold increase in the prevalence of diabetes mellitus compared to Caucasian Americans.2

In addition to ethnicity, sleep disordered breathing (SDB) is independently associated with glucose intolerance and insulin resistance,3,4 and may also contribute to the pathogenesis of type 2 DM. These findings were reported by the Sleep Heart Health Study, which comprised 93.4% Caucasian Americans.4 The Wisconsin Sleep Cohort study reported a high prevalence of type 2 DM with SDB in a population that comprised 96% Caucasian Americans,5 but the incidence was not increased after 4 and 8 years of follow-up. The relationship between SDB and type 2 DM has not been defined in Hispanic or African Americans. The objective of our study is to determine whether obstructive sleep apnea (OSA) is associated with type 2 DM in a sample consisting of predominantly Hispanic and African Americans.

Studies have suggested that REM related SDB is more common in mild to moderate cases of OSA,6 and thus, may be a marker of early obstructive sleep apnea. We hypothesized that the relationship of REM related OSA and type 2 DM could be independent in this racially diverse sample.

RESEARCH DESIGN AND METHODS

Study Design

This was a retrospective study in which we reviewed the records of 1008 consecutive patients who had polysomnography in the Sleep Science Center at the University of Illinois at Chicago Medical Center (UICMC) from June 2004 to February 2006. This research was approved by the Institutional Review Board (IRB) at the University of Illinois at Chicago (UIC). The patients were referred to the Sleep Science Center for evaluation of snoring, witnessed apnea episodes while asleep, unrefreshing sleep, or excessive daytime somnolence. Exclusion criteria were age < 18 years, pregnancy, or diagnosed neuromuscular disease.

Methods

Comprehensive nocturnal polysomnography was performed in the Sleep Science Center with recording of electroencephalography, electro-oculography, oro-nasal airflow, snoring, chest and abdominal respiratory movements, chin and bilateral leg electromyography, body position, electrocardiography, and finger pulse-oximeter. Obstructive apnea was identified as cessation of airflow ≥ 10 sec, with continued chest and abdominal movement. Hypopnea was identified as ≥ 30% reduction in airflow accompanied by 4% decrease in oxygen saturation and/or followed by an arousal, with continued chest and abdominal movement. Obstructive apnea-hypopnea index (AHI) was defined as the number of obstructive apneas or hypopneas per hour of sleep.

Subjects who had an AHI ≥ 5/h were defined as having obstructive sleep apnea (OSA). Subjects who had an AHI < 5/h were included in the non-OSA group. REM AHI was defined as the number of obstructive apneas or hypopneas per hour of REM sleep. REM AHI ≥ 10/h was considered REM related OSA. The presence or absence of type 2 DM was determined by reviewing medical records, patient self-report, or the use of anti-diabetic medications. Patients with type 1 DM, as determined by the patients' physician notes and medications, were not included in the analysis.

Statistical Analysis

SAS version 8.2 (by SAS Institute, Inc., Cary, North Carolina) was used for statistical analysis. Student's t-test was used to compare continuous variables and chi-square test to compare categorical variables between the OSA and non-OSA groups. Multivariate logistic regression analysis was used to evaluate the association of type 2 DM (primary outcome) with different covariates and risk factors, including obstructive sleep apnea, age, gender, race, body mass index (BMI), neck size, smoking, alcohol and drug abuse, oxygen nadir, and duration of oxygen desaturation during polysomnography. Some of the major risk factors for type 2 DM, such as levels of physical activity and family history, were not accurately available in the charts, and hence, were not utilized. A 2-tailed p value ≤ 0.05 was considered to indicate statistical significance.

RESULTS

The characteristics and comorbidities of the study population are summarized in Table 1. A total of 1008 consecutive subjects were included in the study. There were 66.9% African Americans, 16.9% Caucasians, 14.9% Hispanics, and 1.3% Asians. OSA was diagnosed in 745 individuals (74%) while 263 individuals in the non-OSA group (26%) served as the control. The subjects in the OSA group were, on average, 6.5 years older than the non-OSA group. There were 52.8% males in OSA group while only 28.5% in the non-OSA group. As expected, the patients with OSA had a higher BMI (42 kg/m2 vs. 37 kg/m2) and a bigger neck size (17 vs. 15.5 inches) than the non-OSA group. Overall, 37% of the patients in OSA group were current smokers compared to 21.9% in non-OSA group.

Table 1 Characteristics of the Patient Population

CHARACTERISTICSAHI < 5 (n = 263)AHI ≥ 5 (n = 745)p value for difference b/w means or proportions
Age (years)45 ± 14.651.5 ± 12.8 < 0.0001
Gender
    Male75 (28.5%)393 (52.8%)< 0.0001
    Female188 (71.5%)352 (47.2%)
Race*
    Caucasian47 (18.2%)121 (16.5%)0.02
    Hispanic39 (15.1%)109 (14.8%)
    African American165 (63.7%)500 (68%)
    Asian8 (3.1%)5 (0.7%)
BMI (kg/m2)37.0 ± 1042.0 ± 11< 0.0001
Neck (inches)**15.5 ± 1.617 ± 1.9< 0.0001
Smoking current49 (21.9%)235 (37%)< 0.0001
Lowest oxygen saturation84.2 ± 6.774.9 ± 10.7< 0.0001
Time with oxygen saturation more that 90%96.1%85.6%< 0.0001

Student's t-test was used to compare continuous variables and chi-square test to compare categorical variables between the OSA and control groups. p-value ≤ 0.05 or less indicates statistical significance.

*Information was missing on 14 subjects for race.

**Information on the neck size was available for 593 subjects only. AHI, apnea-hypopnea index; BMI, body mass index (weight in kg/ height in m2).

As detailed in Table 2, the prevalence of type 2 DM was 30.1% in the group with OSA compared to 18.6% in the non-OSA group (unadjusted OR 1.8, 95% CI 1.3–2.6; p = 0.0012). Among diabetics, the mean hemoglobin A1c was higher in patients with OSA than those without OSA (mean HbA1c 6.8 versus 6.5, respectively; p = 0.08). In addition, patients with OSA had a higher prevalence of hypertension (58.7% vs. 37.8%) and CHF (7.9% vs. 3%) than those without OSA.

Table 2 Comorbidities of the Patient Population

COMORBIDITYAHI < 5 (n = 263)AHI ≥ 5 (n = 745)p value for difference b/w means or proportions
Type 2 DM49 (18.6%)224 (30.1%)0.0003*
HbA1c Mean: 6.5Mean: 6.80.08
Alcohol use32 (14.5%)103 (16.9%)0.42
Drug abuse7 (3.2%)28 (4.6%)0.39
CAD16 (6.1%)63 (8.5%)0.22
Hypertension99 (37.8%)437 (58.7%)< 0.0001*
CHF8 (3.0%)59 (7.9%)0.006*
Hyperlipidemia46 (17.5%)150 (20.1%)0.35
Asthma64 (24.3%)129 (17.3%)0.01*
COPD15 (5.4%)45 (6.1%)0.49
Liver disease10 (3.6%)19 (2.6%)0.37
AIDS3 (1.1%)5 (0.7%)0.51
ESRD4 (1.4%)17 (2.3%)0.4
Hypothyroidism17 (6.2%)39 (5.3%)0.58
Depression48 (18.3%)99 (13.3%)0.05*

Student's t-test was used to compare continuous variables and chi-square test to compare categorical variables between the OSA and control groups. P-value ≤ 0.05 indicates statistical significance (*). DM, diabetes mellitus; HbA1c, glycosylated hemoglobin; CAD, coronary artery disease; CHF, congestive heart failure; COPD, chronic obstructive pulmonary disease; AIDS, acquired immunodeficiency syndrome; ESRD, end-stage renal disease.

Univariate logistic regression analysis (Table 3, model 1) showed a significant association between the primary outcome of type 2 DM and presence of OSA, with an odds ratio of 1.8 (95% CI 1.3–2.6; p = 0.0012). We assessed for potential confounding effects of BMI, age, gender, race, neck size, smoking, oxygen nadir, and duration of oxygen desaturation. Among these, only BMI and age were found statistically to be potential confounders. Multivariate logistic regression analysis was used to further examine the relationship between type 2 diabetes and AHI, while controlling for potentially important clinical covariates and confounders including BMI, age, gender, race, oxygen nadir and duration with oxygen saturation > 90% (Table 3, model 1). In multiple logistic regression analysis, the odds ratio for type 2 DM in patients with OSA compared to those without OSA was 1.3 (95 % confidence interval, 0.9–2.0; p = 0.15) after controlling for covariates. We also found a direct and dose-dependent association of type 2 DM with age and BMI. In addition current smoking, alcohol abuse or neck size did not have a statistically significant relationship to type 2 diabetes (information not shown).

Table 3 (Model 1): Results of Logistic Regression Analysis with Type 2 DM as the Dependent Variable and OSA (AHI ≥ 5) with Other Covariates as Independent Variables

CovariatesOdds Ratio95% Confidence Intervalp-value
Unadjusted
AHI ≥ 51.81.3-2.60.0012
Adjusted
AHI ≥ 51.30.9-2.00.15
Age, years
    18-401.0 (Reference)
    40-602.31.5-3.6< 0.0001
    > 605.03.1-8.2< 0.0001
BMI, kg/m2
    20-301.0 (Reference)
    30-402.61.4-4.60.0014
    000000040-502.91.6-5.40.0005
    > 505.22.7-10.1< 0.0001
Male gender0.90.6-1.20.63
Race/Ethnicity
    White1.0 (Reference)
    Hispanic2.01.2-3.40.01
    Black1.20.8-1.90.41
Oxygen nadir
    > 90%1.0 (Reference)
    75-90%0.50.2-1.40.20
    < 75%0.50.2-1.50.21
Time (oxygen saturation above 90%)
    > 90%1.0 (Reference)
    75-90%1.30.8-2.00.25
    < 75%1.20.7-1.80.53

AHI, apnea-hypopnea index; BMI, body mass index

The severity of OSA was categorized as mild, moderate, and severe, based on AHI of 5 to 14.9, 15 to 29.9, and ≥ 30, respectively. The odds for type 2 DM were high in all the categories of OSA in crude analysis compared to patients without OSA (Table 4, model 2). However, when controlling for covariates, the results were not statistically significant (Table 4, model 2).

Table 4 (Model 2): Results of Logistic Regression Analysis with Type 2 DM as the Dependent Variable and OSA Severity Categories with Other Covariates as Independent Variables

CovariatesOdds Ratio95% Confidence Intervalp-value
Unadjusted
OSA severity
    None1.0 (Reference)
    Mild1.81.2-2.80.005
    Moderate1.50.95-2.50.07
    Severe1.91.3-2.80.001
Adjusted
OSA severity
    None1.0 (Reference)
    Mild1.50.9-2.40.06
    Moderate1.10.7-1.90.61
    Severe1.20.7-1.90.44
Age, years
    18-401.0 (Reference)
    40-602.41.5-3.6< 0.0001
    > 605.13.1-8.3< 0.0001
BMI, kg/m2
    20-301.0 (Reference)
    30-402.61.5-4.70.0013
    40-503.01.6-5.60.0004
    > 505.42.8-10.4< 0.0001
Male gender0.90.7-1.30.78
Race/Ethnicity
    White1.0 (Reference)
    Hispanic2.01.2-3.50.01
    Black1.20.8-1.90.39
Oxygen nadir
    > 90%1.0 (Reference)
    75-90%0.50.2-1.60.20
    < 75%0.50.2-1.40.24
Time (oxygen saturation above 90%)
    > 90%1.0 (Reference)
    75-90%1.40.9-2.20.18
    < 75%1.20.7-1.90.46

OSA severity, none: AHI < 5, mild: AHI 5-14.9, moderate: AHI 15-29.9, severe: AHI ≥ 30. AHI, apnea-hypopnea index; BMI, body mass index

We examined the association of type 2 DM and OSA in the different ethnic and age subgroups. We did not find any significant association in Caucasian or African-American subgroups. However, Hispanics had high prevalence of type 2 DM and OSA did not affect this relationship (Figure 1). The middle-aged group with OSA had higher odds of having type 2 diabetes mellitus than younger or middle-aged participants without OSA (odds ratio 2.8, 95% confidence interval 1.4–5.6; p = 0.003), after adjusting for BMI, gender, and race (Figure 2). There was a high prevalence of type 2 DM in older patients, with or without OSA.

Figure 1
Figure 1

Adjusted odds ratio for type 2 diabetes in racial subgroups stratified according to presence or absence of obstructive sleep apnea, controlling for BMI, gender, and age.

Figure 2
Figure 2

Adjusted odds ratio for type 2 diabetes mellitus in age subgroups stratified according to presence or absence of obstructive sleep apnea, controlling for BMI, gender and race. *Statistically significant compared to younger subjects without OSA and/or middle aged subjects without OSA. Younger 18-40 years, Middle-aged > 40-60 years, Older > 60 years

Relationship of REM AHI and Type 2 Diabetes

Subjects with REM AHI ≥ 10/h had significantly higher odds of type 2 diabetes than subjects with REM AHI < 10/h, with odds ratio of 2.7 (95% CI, 1.8–3.9; p < 0.0001, Table 5, model 3). BMI and age were found to be possible confounders. However, the relationship remained statistically significant (odds ratio 2.1, 95% CI 1.3–3.3; p = 0.001), even after controlling for confounders and covariates of clinical interest like BMI, age, race, gender, and oxygenation parameters as described in model 3 (Table 5).

Table 5 (Model 3): Results of Logistic Regression Analysis with Type 2 DM as the Dependent Variable and REM Related OSA with Other Covariates as Independent Variables

CovariatesOdds Ratio95% Confidence Intervalp-value
Unadjusted
REM AHI ≥ 10/hr2.71.8-3.9< 0.0001
Adjusted
REM AHI ≥ 10/hr2.11.3-3.30.001
Age, years
    18-401.0 (Reference)
    40-602.01.3-3.30.004
    > 603.92.2-6.9< 0.0001
BMI, kg/m2
    20-301.0 (Reference)
    30-402.41.2-4.60.009
    40-502.51.3-5.10.009
    > 505.52.6-11.7< 0.0001
Male gender1.00.7-1.50.83
Race/Ethnicity
    White1.0 (Reference)
    Hispanic1.80.9-3.50.07
    Black1.00.6-1.70.97
Oxygen nadir
    > 90%1.0 (Reference)
    75-90%0.40.1-1.40.15
    < 75%0.30.1-1.40.13
Time (oxygen saturation above 90%)
    > 90%1.0 (Reference)
    75-90%1.20.7-2.10.42
    < 75%1.00.6-1.80.99

AHI, apnea-hypopnea index; BMI, body mass index

DISCUSSION

We found that the prevalence of type 2 diabetes was 30.1% in patients with OSA, compared to 18.6% in patients without OSA. Our result is similar to that reported by Meslier et al,3 who found a prevalence of type 2 diabetes of 30% in OSA patients and 13.9% in non-OSA patients in a cross-sectional analysis.

In our study, the unadjusted odds ratio for the risk of type 2 diabetes in subjects with OSA was statistically significant. However, the association became non-significant when we controlled for BMI, age and other covariates of potential interest like race and gender.

Our results are consistent with several studies on the association of sleep disordered breathing with glucose tolerance, insulin resistance and type 2 diabetes. Glucose intolerance and insulin resistance are considered known risk factors and possible precursors to type 2 DM. In cross-sectional studies, AHI and hypoxemia have been shown to be associated with glucose intolerance and insulin resistance, independent of BMI, age, gender and other confounders. Most of these studies were done on predominantly Caucasian or Asian samples, and the effect of race was not evaluated.3,4,710 Punjabi et al. reported in 2,656 predominantly Caucasian subjects included in the Sleep Heart Health Study Cohort that subjects with mild SDB (AHI 5-14.9) and moderate to severe SDB (AHI ≥ 15) had increased odds of glucose intolerance; adjusted odds ratios of 1.27 (95% CI 0.98–1.64) and 1.46 (95% CI 1.09–1.97), respectively. Hypoxemia during sleep was also associated with glucose intolerance, independent of age, BMI, waist circumference and gender.4 A recent study suggested that mild oxyhemoglobin desaturation of less than 4% during sleep may predispose to fasting hyperglycemia.10 Few prospective studies have concluded that habitual snoring may independently increase the risk of type 2 diabetes.11,12 The Wisconsin Sleep Cohort study examined the prevalence and incidence of type 2 diabetes in 1,387 patients, mostly Caucasians, and observed an odds ratio of 2.3 for type 2 diabetes with an AHI of 15 or greater compared to an AHI of less than 5 (95% CI 1.28–4.11), after adjusting for age, gender and body habitus but AHI was not predictive of developing diabetes within 4 years.5 Several studies have addressed the efficacy of CPAP in treatment of glucose intolerance and diabetes but the results have not been conclusive.1315

There are several possible mechanisms by which OSA could be associated with glucose intolerance and type 2 DM. Severe OSA results in an increased neurogenic sympathetic activity and circulating levels of norepinephrine,4,16,17 which could result in increased glycogenolysis, lipolysis, and insulin resistance. It is also possible that SDB leads to release of cortisol resulting in higher glucose concentration and excessive insulin secretion.18,19 Yet another possibility is that adipocyte-derived inflammatory mediators, such as IL-6, TNF-α, and leptin, which are released as a result of cyclic hypoxia, contribute to insulin resistance and hyperglycemia.2025 Sleep loss and poor sleep quality have been associated with the risk of type 2 diabetes in several studies as well.2629

When examining the relationship of type 2 DM and OSA in different ethnic and age subgroups, Hispanics had a high prevalence of type 2 DM, irrespective of OSA. We also observed that the odds of type 2 DM in middle aged patients with OSA were 2.8 times as high as odds for younger or middle aged participants without OSA. The older participants had higher odds of type 2 DM regardless of OSA status. These ethnic and age related differences would be interesting to explore in prospective fashion.

REM related SDB is more common in mild to moderate cases of OSA, especially in women and in patients younger than 55 years of age.3032 We found a strong and independent association of REM AHI with type 2 diabetes. This relationship may be explained on the premise that there may be more pronounced neurohormonal perturbations and cytokine release during REM sleep caused by apnea and hypopnea, as compared to NREM sleep.3336 This may increase the risk of insulin resistance and type 2 diabetes. Another reason for high REM AHI may be sleep fragmentation leading to reduced REM sleep time, as has been described in OSA. Even if a high REM AHI is secondary to sleep fragmentation, it could still serve as an important marker for the prevalence of type 2 diabetes.

The current investigation has several strengths. To the best of our knowledge, this study is the first to examine the relationship between OSA and type 2 DM in a sample composed predominantly of African Americans and Hispanics. Though there is a paucity of studies in sleep disordered breathing directed towards this population,37 there are numerous investigations of outcomes including DM, hypertension, coronary artery disease, etc., indicating the increased vulnerability of these ethnic groups. Several studies have reported that after controlling for obesity, socioeconomic status, and health care access, Hispanics and African Americans had worse glycemic control than Caucasians, based on data from the National Health and Nutrition Examination Surveys (NHANES).38 In addition, it has been shown that socioeconomic risk factors might not fully explain the high cardiovascular disease mortality in African Americans compared to Caucasians.39 The CDC has also recently reported that the prevalence of obesity is higher in African American and Hispanic women than Caucasian women.40 It is possible that the prevalence of OSA may be higher in African Americans and Hispanics than the traditional data generally quoted from Caucasians, and it may contribute to the poor metabolic and cardiovascular outcomes.

Other strengths of our study include the large sample size of 1008 subjects, with a wide range of age, BMI, and AHI, and inclusion of both genders. Furthermore, all of our patients were evaluated with comprehensive overnight polysomnography. This study has a number of weaknesses that should be considered. It is cross-sectional and retrospective in design and, therefore, cannot establish the temporal association of diabetes with OSA or with REM AHI. In addition, the study relied on existing medical records for data collection including the diagnosis of type 2 DM. It is possible that we may have missed some cases of undiagnosed type 2 DM, and some of the important risk factors for diabetes like family history and physical activity could not be accurately determined. However, several other epidemiologic studies have relied on an established diagnosis of diabetes.4 Another limitation of this study is that it used a patient sample referred to a sleep disorders center, instead of a general population sample. There were several differences between the OSA and control groups, including BMI, age, gender, neck size, and smoking, which might have influenced the results of our study. However many of our associations persisted even after adjusting for confounding variables.

In summary, our study shows that obstructive sleep apnea is not independently associated with type 2 diabetes in a predominantly African American and Hispanic sample. However, the relationship of REM related OSA with type 2 diabetes may be statistically significant in this population. These findings are preliminary and prospective studies are needed to further examine this hypothesis.

DISCLOSURE STATEMENT

This was not an industry supported study. The authors have indicated no financial conflicts of interest.

ABBREVIATIONS

AHI

apnea-hypopnea index

BMI

body mass index

CDC

Centers for Disease Control and Prevention

DM

diabetes mellitus

NHANES

National Health and Nutrition Examination Surveys

OSA

obstructive sleep apnea

REM

rapid eye movement

REFERENCES

  • 1 American Diabetes AssociationEconomic costs of diabetes in the US in 2007Diabetes Care200831596615, 18308683

    CrossrefGoogle Scholar
  • 2 Centers for Disease Control and PreventionNational diabetes fact sheet: general information and national estimates on diabetes in the United States, 20052005Atlanta, GAU.S. Department of Health and Human Services, Centers for Disease Control and PreventionAvailable at: http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2005.pdf

    Google Scholar
  • 3 Meslier NGagnadoux FGiraud PPerson COuksel HUrban TRacineux JLImpaired glucose-insulin metabolism in males with obstructive sleep apnoea syndromeEur Respir J20032215660, 12882466

    CrossrefGoogle Scholar
  • 4 Punjabi NMShahar ERedline SGottlieb DJGivelber RResnick HESleep Heart Health Study InvestigatorsSleep-disordered breathing, glucose intolerance, and insulin resistance: the Sleep Heart Health StudyAm J Epidemiol200416052130, 15353412

    CrossrefGoogle Scholar
  • 5 Reichmuth KJAustin DSkatrud JBYoung TAssociation of sleep apnea and type II diabetes: a population-based studyAm J Respir Crit Care Med200517215905, 16192452

    CrossrefGoogle Scholar
  • 6 Haba-Rubio JJanssens JPRochat TSforza ERapid eye movement-related disordered breathing: clinical and polysomnographic featuresChest200512833507, 16304283

    CrossrefGoogle Scholar
  • 7 Ip MSLam BNg MMLam WKTsang KWLam KSObstructive sleep apnea is independently associated with insulin resistanceAm J Respir Crit Care Med20021656706, 11874812

    CrossrefGoogle Scholar
  • 8 Coughlin SRMawdsley LMugarza JACalverley PMWilding JPObstructive sleep apnoea is independently associated with an increased prevalence of metabolic syndromeEur Heart J20042573541, 15120883

    CrossrefGoogle Scholar
  • 9 Punjabi NMSorkin JDKatzel LIGoldberg APSchwartz ARSmith PLSleep-disordered breathing and insulin resistance in middle-aged and overweight menAm J Respir Crit Care Med200216567782, 11874813

    CrossrefGoogle Scholar
  • 10 Stamatakis KSanders MHCaffo B, et al.Fasting glycemia in sleep disordered breathing: lowering the threshold on oxyhemoglobin desaturationSleep200831101824, 18652097

    Google Scholar
  • 11 Elmasry AJanson CLindberg EGislason TTageldin MABoman GThe role of habitual snoring and obesity in the development of diabetes: a 10-year follow-up study in a male populationJ Intern Med20002481320, 10947876

    CrossrefGoogle Scholar
  • 12 Al-Delaimy WKManson JEWillett WCStampfer MJHu FBSnoring as a risk factor for type II diabetes mellitus: a prospective studyAm J Epidemiol200215538793, 11867347

    CrossrefGoogle Scholar
  • 13 Babu ARHerdegen JFogelfeld LShott SMazzone TType 2 diabetes, glycemic control, and continuous positive airway pressure in obstructive sleep apneaArch Intern Med200516544752, 15738376

    CrossrefGoogle Scholar
  • 14 Harsch IASchahin SPRadespiel-Tröger M, et al.Continuous positive airway pressure treatment rapidly improves insulin sensitivity in patients with obstructive sleep apnea syndromeAm J Respir Crit Care Med200416915662, 14512265

    CrossrefGoogle Scholar
  • 15 West SDNicoll DJWallace TMMatthews DRStradling JREffect of CPAP on insulin resistance and HbA1c in men with obstructive sleep apnoea and type 2 diabetesThorax20076296974, 17557769

    CrossrefGoogle Scholar
  • 16 Peled NGreenberg APillar GContributions of hypoxia and respiratory disturbance index to sympathetic activation and blood pressure in obstructive sleep apnea syndromeAm J Hypertens19981112849, 9832170

    CrossrefGoogle Scholar
  • 17 Carlson JTHedner JElam MEjnell HSellgren JWallin BGAugmented resting sympathetic activity in awake patients with obstructive sleep apneaChest199310317638, 8404098

    CrossrefGoogle Scholar
  • 18 Leproult RCopinschi GBuxton OVan Cauter ESleep loss results in an elevation of cortisol levels the next eveningSleep19972086570, 9415946

    Google Scholar
  • 19 Bratel TWennlund ACarlstrom KPituitary reactivity, androgens and catecholamines in obstructive sleep apnoea: effects of continuous positive airway pressure treatment (CPAP)Respir Med19999317, 10464840

    CrossrefGoogle Scholar
  • 20 Liu HLiu JXiong SShen GZhang ZXu YThe change of interleukin-6 and tumor necrosis factor in patients with obstructive sleep apnea syndromeJ Tongji Med Univ2000202002, 11215046

    CrossrefGoogle Scholar
  • 21 Pradhan ADManson JERifai NBuring JERidker PMC-reactive protein, interleukin 6, and risk of developing type 2 diabetes mellitusJAMA200128632734, 11466099

    CrossrefGoogle Scholar
  • 22 Lang CHDobrescu CBagby GJTumor necrosis factor impairs insulin action on peripheral glucose disposal and hepatic glucose outputEndocrinology19921304352, 1727716

    CrossrefGoogle Scholar
  • 23 Uysal KTWiesbrock SMMarino MWHotamisligil GSProtection from obesity-induced insulin resistance in mice lacking TNF-alpha functionNature199738961014, 9335502

    CrossrefGoogle Scholar
  • 24 Phillips BGKato MNarkiewicz KChoe ISomers VKIncreases in leptin levels, sympathetic drive, and weight gain in obstructive sleep apneaAm J Physiol Heart Circ Physiol2000279H2347, 10899061

    CrossrefGoogle Scholar
  • 25 Harsch IAKonturek PCKoebnick C, et al.Leptin and ghrelin levels in patients with obstructive sleep apnoea: effect of CPAP treatmentEur Respir J2003222517, 12952256

    CrossrefGoogle Scholar
  • 26 Nilsson PMRoost MEngstrom GHedblad BBerglund GIncidence of diabetes in middle-aged men is related to sleep disturbancesDiabetes Care20042724649, 15451917

    CrossrefGoogle Scholar
  • 27 Mallon LBroman JEHetta JHigh incidence of diabetes in men with sleep complaints or short sleep duration: a 12-year follow-up study of a middle-aged populationDiabetes Care20052827627, 16249553

    CrossrefGoogle Scholar
  • 28 Yaggi HKAraujo ABMcKinlay JBSleep duration as a risk factor for the development of type 2 diabetesDiabetes Care20062965761, 16505522

    CrossrefGoogle Scholar
  • 29 Knutson KLRyden AMMander BAVan Cauter ERole of sleep duration and quality in the risk and severity of type 2 diabetes mellitusArch Intern Med2006166176874, 16983057

    CrossrefGoogle Scholar
  • 30 Loadsman JAWilcox IIs obstructive sleep apnea a rapid eye movement—predominant phenomenon?Br J Anaesth2000853548, 11103173

    CrossrefGoogle Scholar
  • 31 Haba-Rubio JJanssens JPRochat TSforza ERapid eye movement-related disordered breathing: clinical and polysomnographic featuresChest200512833507, 16304283

    CrossrefGoogle Scholar
  • 32 Koo BBDostal JIoachimescu OBudur KThe effects of gender and age on REM-related sleep-disordered breathingSleep Breath20081225964, 18074161

    CrossrefGoogle Scholar
  • 33 Somers VKDyken MEMark ALAbboud FMSympathetic-nerve activity during sleep in normal subjectsN Engl J Med19933283037, 8419815

    CrossrefGoogle Scholar
  • 34 Trinder JKleiman JCarrington M, et al.Autonomic activity during human sleep as a function of time and sleep stageJ Sleep Res20011025364, 11903855

    CrossrefGoogle Scholar
  • 35 Redwine LHauger RLGillin JCIrwin MEffects of sleep and sleep deprivation on interleukin-6, growth hormone, cortisol, and melatonin levels in humansJ Clin Endocrinol Metab2000853597603, 11061508

    Google Scholar
  • 36 Baharav AKotagal SGibbons V, et al.Fluctuations in autonomic nervous activity during sleep displayed by power spectrum analysis of heart rate variabilityNeurology19954511837, 7783886

    CrossrefGoogle Scholar
  • 37 Villaneuva ATBuchanan PRYee BJGrunstein RREthnicity and obstructive sleep apnoeaSleep Med Rev2005941936, 16183307

    CrossrefGoogle Scholar
  • 38 Saydah SCowie CEberhardt MSDe Rekeneire NNarayan KMRace and ethnic differences in glycemic control among adults with diagnosed diabetes in the United StatesEthn Dis20071752935, 17985509

    Google Scholar
  • 39 Jones-Webb RYu XO'Brien JHannan PWall MOswald JDoes socioeconomic position moderate the effects of race on cardiovascular disease mortality?Ethn Dis20041448996, 15724767

    Google Scholar
  • 40 Obesity among adults in the United States--no statistically significant change since 2003-2004Data Brief Number 1, November 2007 http://www.cdc.gov/nchs/pressroom/07newsreleases/obesity.htm

    Google Scholar