|Year : 2021 | Volume
| Issue : 2 | Page : 48-54
A cross-sectional study to assess the risk factors for the presence and severity of obstructive sleep apnea among patients with type 2 diabetes mellitus at a tertiary care hospital, Gangtok
Divij Sharma1, Bidita Khandelwal2, Sumit Kar3
1 Sikkim Manipal Institute of Medical Sciences, Gangtok, Sikkim, India
2 Department of Medicine, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Gangtok, Sikkim, India
3 Department of Community Medicine, Sikkim Manipal Institute of Medical Sciences, Sikkim Manipal University, Gangtok, Sikkim, India
|Date of Submission||22-Nov-2020|
|Date of Acceptance||16-Dec-2020|
|Date of Web Publication||09-Jun-2021|
Dr. Divij Sharma
41, Rohini Building, Colaba, RC Church, Mumbai - 400 005, Maharashtra
Source of Support: None, Conflict of Interest: None
Background and Objectives: Diabetes mellitus (T2DM) and obstructive sleep apnea (OSA) are common disorders that not only often coexist but also have a bidirectional association where each condition exacerbates the other. The present study was performed to ascertain the occurrence and predictors of risk factors of OSA in patients with type 2DM. Materials and Methods: A cross-sectional hospital-based study recruiting 164 patients for over 2 months was conducted, in which each diabetic patient was assigned to complete a questionnaire on various variables followed by a general physical examination for associated comorbidities diabetic complications (neuropathy, nephropathy, and retinopathy). Their fasting blood glucose (FBG) and hemoglobin A1c (HbA1c) levels were recorded, following which they were administered and assessed using Berlin Questionnaire and Epworth Sleepiness Score for risk category and daytime sleepiness. Patients with already diagnosed OSA including ones receiving treatment for it were excluded from the study. Results: Of the 164 diabetic patients recruited in the study, 64 (39%) were at high risk for OSA in contrast to the 100 (61%) who were at low risk for OSA. Neck circumference, waist circumference, presence of hypertension and more than one comorbidities along with patients who experienced witnessed apnea at least three times a week, excessive daytime sleepiness, and habitual snorers found to be significant risk factors and practices in posing DM patients at a higher risk for OSA. Patients with body mass index ≥25 were more likely to have a high risk of OSA. FBG and HbA1c were not significant risk factors for OSA. Conclusions: OSA has a high prevalence in subjects with T2DM which reinforces the clinicians to remain observant for signs and symptoms of OSA in diabetic patients and monitor their compliance in terms of weight management, diet control, and medication adherence.
Keywords: Berlin questionnaire, diabetes mellitus, Epworth Sleepiness Score, fasting blood glucose, obstructive sleep apnea
|How to cite this article:|
Sharma D, Khandelwal B, Kar S. A cross-sectional study to assess the risk factors for the presence and severity of obstructive sleep apnea among patients with type 2 diabetes mellitus at a tertiary care hospital, Gangtok. J Prim Care Spec 2021;2:48-54
|How to cite this URL:|
Sharma D, Khandelwal B, Kar S. A cross-sectional study to assess the risk factors for the presence and severity of obstructive sleep apnea among patients with type 2 diabetes mellitus at a tertiary care hospital, Gangtok. J Prim Care Spec [serial online] 2021 [cited 2021 Aug 5];2:48-54. Available from: http://www.jpcs.com/text.asp?2021/2/2/48/317998
| Introduction|| |
Obstructive sleep apnea (OSA) is a sleep-related breathing disorder characterized by the collapse of the upper airway leading to the cessation of airflow in the setting of continued respiratory effort. Intermittent hypoxia is an important mechanism that provides a pathophysiological link between OSA and DM. Hypoxia enhances the sympathetic activity and drives oxidative stress and chronic inflammation, which are postulated to contribute to abnormality in glucose metabolism. The sleep fragmentation and sleep deprivation associated with OSA may have an additive, adverse impact on insulin sensitivity.
Diabetes mellitus (T2DM) and OSA are common disorders that often coexist. In conjunction with the above, another explanation for this overlap is the presence of shared risk factors such as obesity, age, gender, body mass index (BMI), triglyceride level, smoking, and more. A study suggests that OSA and diabetes not only coexist but also have a bidirectional association where each condition exacerbates the other. The prevalence and presence of risk factors of OSA have extensively been studied in the previous studies.,,, Besides, the weight gain that occurs in patients with T2DM with treatment intensification might result in the development of or worsening of preexisting OSA. OSA is associated with numerous comorbidities such as increased risk of road traffic accidents, hypertension, hyperlipidemia, increased inflammation, increased risk of cardiovascular disease, and mortality, leading to a poor overall quality of life.,,, Increased severity of OSA is also associated with increased hemoglobin A1c (HbA1c) levels.
There is compelling documentation to suggest that type 2 diabetes independently increases the likelihood of sleep-disordered breathing. The prevalence of OSA in people with type 2 diabetes is mercurial, estimating from 18% in primary care to 58% in an older cohort and as high as 86% in obese populations with type 2 diabetes. However, there is considerable diverseness in the findings of these longitudinal studies when adjusted for confounders such as age, sex, and BMI. This suggests that shared risk factors are important mediators of the association between OSA and type 2 DM and should be considered in the clinical evaluation and management constructions of individual patients.
Thus, the current evidence from the aforementioned studies reveals that OSA is more prevalent among patients with type 2 diabetes compared to those without diabetes, independent of shared risk factors. These findings suggest that DM patients are not being structurally screened for it; as a result, OSA is going largely unrecognized, and thus, millions of diabetics may currently suffer from this comorbidity. In sum, these staggering statistics call attention to the need for a better understanding of the links between OSA and type 2 DM. The aims and objectives of this study were:
- To ascertain the occurrence and predictors of risk factors of OSA in patients with type 2 DM
- To study the relationship between OSA and type 2 DM in individuals with BMI ≥25 and BMI <25
- To assess the risk for OSA in controlled (HbA1c <6.5 or fasting blood glucose [FBG] <126 mg/dL) and uncontrolled (HbA1c >6.5 or FBG >126 mg/dL) type 2 DM.
| Materials and Methods|| |
A cross-sectional hospital-based study was undertaken after approval of the Institutional Ethics Committee at a tertiary care hospital in Sikkim among 164 patients with T2DM attending medicine outpatient department over a period of 2 months. The diagnosis of type 2 DM was confirmed according to the American Diabetes Association. Nonconsenting subjects and those with any previously diagnosed respiratory illness were excluded from the study. Berlin Questionnaires and Epworth Sleepiness Score were administered as a tool to screen for risk factors of OSA.
According to the hospital medical records department (MRD) statistics, an average of 90–100 patients of T2DM attend the medicine department per month. Congruent to the student duration of 2 months, an estimated sample of 190–200 patients was taken. A total of 194 patients were included in the study, with 185 giving informed consent. Twenty-one of them were patients with already diagnosed OSA including ones receiving treatment for it and were excluded from the study. Hence, a sample of 164 patients was taken in the study.
After obtaining given written informed consent, each patient completed a pretested self-administered questionnaire on demographic data, alcohol intake, smoking, medical history, including duration of diabetes, current treatment, prior diagnosis of hypertension and medications, and was assessed and examined for associated comorbidities such as dyslipidemia, chronic liver or kidney disease, stroke, endocrine disease, and psychological abnormality and diabetic complications (neuropathy, nephropathy, and retinopathy). Height, weight, waist, and neck circumferences (NCs) were measured. Glycated HbA1c values within 3 months of duration of study were obtained from the patient's records and if that was not available, FBG was taken as a parameter for good or bad glycemic control. However, as recent HBA1c was not recorded in all patients, FBG was deemed to be taken as a reliable indicator of good or poor glucose control. All of the above information was recorded. Patients who had their most recent fasting glucose below 126 mg/dL (7 mmol/L) were considered controlled, while those with fasting glucose values equal or above 126 mg/dL were considered uncontrolled. To determine whether a significant association of sleep parameters with insulin resistance was similar in obese and nonobese subjects, subjects were categorized into normal, overweight, and obese groups based on the WHO recommendation for Asians, i.e., BMI <25 and BMI ≥25.
The Berlin Questionnaire is a validated instrument widely used to screen for OSA in view of its good sensitivity and high negative predictive value in ruling out severe OSA. It includes questions on snoring, witnessed apnea, wake time sleepiness, and self-reported hypertension. It is classified into three categories. Category 1 includes five questions on snoring and witnessed apnea.
Category 2 includes three questions on wake time sleepiness and drowsiness behind the steering wheel.
Category 3 comprises a diagnosis of high blood pressure and BMI based on physical assessment. Category 1 and 2 will be considered positive if two or more questions are reported positive at least three times/week, while category 3 is positive if a participant has been diagnosed as hypertensive or had BMI >30 kg/m2.
Snoring is classified as simple for those who snore two times/week or less and habitual for those who snore greater than three times/week and as loudly as talking. Nonsnorers are those who answered negative to the question: “Do you snore?”
Subjects who score “≥2” are classified as high risk for OSA (HROSA), whereas subjects who score “<2” are classified as low risk.
Epworth Sleepiness Scale
Sleepiness is measured using the Epworth Sleepiness Scale (ESS). The ESS is a validated questionnaire that is used to assess the risk of daytime somnolence and is valid and sensitive.
It estimates a participant's likelihood to doze off or fall asleep in eight different scenarios associated with daily activities. Each item is measured by a 4-point scale, with a possible score ranging from 0 to 24. A score of greater than “10” is regarded as indicative of excessive daytime sleepiness.
All patient data were re-identified prior to analysis. The analysis was done using Microsoft Word Excel 2016. Categorical data were presented using proportions and frequencies, while continuous data were presented in mean and standard deviation. Where appropriate, the Chi-square test or Fisher's exact test was used to test for differences between proportions, while the Student's t-test was used to compare continuous variables. P < 0.05 was statistically significant.
| Results|| |
Baseline parameters of 164 T2DM patients are shown in [Table 1]. Of the 164 patients, 85 (52%) were male and 79 (48%) were female. Men had larger necks (P < 0.001), while the women were more likely to have larger waist circumferences (WCs) (P = 0.006).
The categorization of the study population according to the OSA risk group is depicted in [Figure 1].
|Figure 1: Characteristics of the study population by obstructive sleep apnea risk group|
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About 55.5% of the respondents had a BMI ≥25 kg·m−2 compared with 44.5% of respondents with BMI <25 kg·m−2. BMI ≥30 kg·m−2 was found in 40 (24.3) respondents. The data in [Table 2] show that those with a BMI ≥25 were more likely to have a high risk of OSA (P < 0.05).
|Table 2: Distribution of body mass index of the study population by obstructive sleep apnea risk group|
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Sixty-one percent of the participants had uncontrolled diabetes, i.e., FBG ≥126 based on their most recent glucose test. According to the data in [Table 3], it was observed that FBG (P = 0.14) was not a significant risk factor for OSA.
|Table 3: Co-relation of obstructive sleep apnea indices with fasting blood glucose (short-term control)|
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There was no statistically significant difference in HbA1c levels between the “high” or “low” risk groups (P = 0.47). This shows that there is no association between long-term control of blood sugar measured by HbA1c and the risk of OSA [Table 4]. However, HbA1c was reported in only 93 patients.
|Table 4: Co-relation of obstructive sleep apnea indices with hemoglobin A1c (long-term control|
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The effect of certain sociodemographic and clinical variables associated with HROSA and low risk for OSA (LROSA) are elucidated in [Table 5] and [Table 6].
|Table 5: Sociodemographic and clinical characteristics according to the obstructive sleep apnea status|
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|Table 6: Clinical characteristics according to the obstructive sleep apnea status|
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Our results showed that patients who experienced witnessed apnea at least three times a week (P < 0.0036), excessive daytime sleepiness (P = 0.0002), and habitual snorers (P < 0.0001) were more likely to have a high risk of OSA.
Current smokers and alcoholics comprised 40% and 48.8% of the total patients assessed, respectively. In our study, there was no statistical significance between patients with HROSA and LROSA in terms of age (P = 0.84), sex (P = 0.92), smoking (P = 0.57), and alcohol (0.93).
In terms of treatment taken for T2DM, no treatment taken implied inadequate compliance with oral medications or insulin injectable analogs. After analysis, in patients with HROSA and LROSA, no statistical significance was found in terms of treatment taken by patients (P = 0.54).
Of the 164 patients, 98 (59.7%) patients suffered from at least 1 or more comorbidities in conjunction with T2DM. The prevalence in relation to associated comorbidities was hypertension in 62 patients (37.8%), of whom 37 had HROSA and 25 had LROSA and were treated by antihypertensive medications; dyslipidemia in 28 patients (17%), of whom 15 had HROSA and 13 had LROSA; chronic kidney disease in 27 patients (16%), of whom 20 had HROSA and 7 had LROSA; a history of stroke in 22 patients (13.4%), of whom 10 had HROSA and 12 had LROSA; depression in 20 patients (12.2%), of whom 11 had HROSA and 9 had LROSA; and endocrine abnormalities in 16 patients (9.7%). Many other disorders were also identified, including peptic ulcer disease, chronic liver disease, anxiety, and insomnia. A statistically significant relationship was identified between the presence of hypertension (P < 0.0001) and comorbidities (P < 0.0001) in the prevalence of OSA in T2DM patients.
Patients with higher ESS score and neck and WCs were statistically proven to have HROSA (P < 0.0001).
The majority of diabetes patients, i.e., 131 of them (80%), reported that they had never heard of OSA and that it had never been discussed with them. Sixty-nine patients (42%) of HROSA expressed interest in learning more about this disorder. It was reported from the patients that majority of clinicians rarely or never asked questions about OSA symptoms.
| Discussion|| |
It is observed that 39% of patients with type 2 diabetes mellitus had a HROSA. The gold standard for the diagnosis of OSA is overnight polysomnography. However, we cannot rely on in-laboratory polysomnography alone, and simpler and less expensive diagnostic tests are needed. Questionnaire-based surveys are useful screening tools as they are simple, rapid, yet effective tools for efficient selection of those with a HROSA.
A significant finding observed in this study was that 9 (5.4%) patients, 3 low risk and 6 high risk, who occasionally snored attributed the reason to improper pillow covers and padding which inadvertently caused mild discomfort during their sleep. Physicians diagnosing OSA and screening its risk factors should bear in mind the patient's sleep position and type of pillow and mattress used and can further help them to identify the most suitable material for their pillows. Based on the findings, particularly the HROSA patients should be advised to change pillows in an attempt to improve sleep and treatment adherence. Although the gel pillow was not associated with changes in the efficacy of CPAP therapy, in a study by Salvaggio A et al., it showed to improve sleep comfort, particularly in the sleepiest patients.
NC is a newly identified clinical feature that may be associated with OSAS. Our finding supports previous studies that showed that NC is an important predictor of OSA syndrome. A study of 150 patients referred to a sleep clinic reported that NC corrected for height is a more useful predictor of OSA. In a study conducted in Turkey, higher NC value was determined to be an independent risk factor for severe OSA (P = 0.01). Similarly, WC which is used as a parameter for central obesity was reported to be an independent risk factor of OSA in patients with T2DM (P < 0.0001). This finding is consistent with a study where WC was the only significant predictor of the presence of OSA and independent of other variables in T2DM patients, a 1-cm increase in WC was associated with a 10% increase in the predicted odds of the presence of OSA. A reason being that higher NC, BMI, and WC are correlated with aerobic capacity, physical inactivity, and excess body fluid causing respiratory compromise.
Hypertension was also found to be a significant risk factor in posing DM patients at a higher risk for OSA. Notably, recent epidemiological studies provide strong evidence that OSA itself confers independent risks for the development of hypertension.
Among obese patients (BMI >40) with type 2 diabetes, who represent the vast majority of individuals with type 2 diabetes in the United States, the prevalence of OSA has recently been estimated at a staggering 86%. Although BMI was the best predictor of OSA, type 2 diabetes conferred a significant extra increase in the likelihood of having OSA after allowing for BMI, age, and neck size. This is consistent with the observations of this study that obesity is one of the strongest risk factors for OSA and that OSA symptoms can improve or resolve with weight loss interventions. Excessive daytime sleepiness, increased frequency of snoring, and witnessed apnea of at least three or more times/week in this study were also identified as significant predictors of presence of OSA among T2DM patients.
There was no statistical significance between patients with HROSA and LROSA in terms of treatment taken for diabetes (P = 0.54), although a minuscule sample of patients (5%) reported that their habitual habit of snoring had gotten better gradually over the years upon following regular and stringent diabetic control measures. To back this, a study was conducted which depicted that weight loss through lifestyle intervention and pharmacotherapy has proved effective in reducing OSA severity and glycemic status in obese patients with type 2 diabetes.
Comorbidities and complications due to diabetes also play a vital role in increasing the likelihood or exacerbating preexisting OSA. In this study, the presence of one or more than one comorbidity statistically posed them at a HROSA (P < 0.0001).
According to the literature, increased severity of OSA was associated with heightened A1c levels, but in our study, uncontrolled HbA1c level was not proven as a significant risk factor contributing to the development of OSA (P = 0.47). However, our findings are consistent with the study conducted in health centers where there was no statistically significant difference in HbA1c levels between the high- or low-risk groups.
In our study, OSA was quite prevalent in smokers and alcoholic subjects. Studies state that smokers are 2.5 times more likely to have OSA than nonsmokers and wider literature suggests that it is plausible that alcohol increases the risk of OSA because alcohol consumption reduces genioglossal muscle tone, predisposing patients to upper airway collapse, and generally increasing upper airway resistance. High alcohol intakes also contribute to dietary energy intake, and hence in some cases a high BMI, which is itself a risk factor for OSA and T2DM. However, the results were conflicting in this study, as alcohol (P = 0.93) and smoking (P = 0.57) were not proven as statistically significant determinants in the risk of OSA.
We also observed that variables such as treatment taken for T2DM and duration of diabetes were not statistically significant. Reason for this could be that the treatment duration ranged from 1 month to 22 years. This large range in treatment duration may have resulted in different treatment effects, and the difference in patient adherence to diabetes therapy which could have influenced our results.
Our study had the following limitations: first, the gold standard for diagnosing OSA that is “polysomnography” was not used, thus to actually assess the prevalence of OSA, it remains unclear whether questionnaires, as convenient and practical as they are, perhaps are not exceedingly effective for screening OSA in patients with type 2 diabetes, and second, glycated HbA1c values of all patients are not reported which would have been a more reliable measure of the level of glucose control.
| Conclusions|| |
Our study has shown that OSA has a high prevalence in subjects with T2DM. Diabetes and OSA are two common conditions that are associated with significant morbidity. Not only were a good proportion of the diabetic patients found to be at HROSA but they also reported low awareness about OSA and unwary of its consequences on health. This supports the need for sleep evaluation referral and higher vigilance. Furthermore, treatment of sleep apnea improves insulin sensitivity and could benefit the metabolic profiles of these patients, although this is not well defined. Clinicians must remain observant for signs and symptoms of OSA in diabetic patients and monitor their compliance in terms of weight management, diet control, and medication adherence. Therefore, it is essentially prudent to adopt the practice of rigorous screening of OSA and related sleep-obesity syndromes among diabetic patients to identify those who warrant treatment because hypoxemia in moderate-to-severe OSA triggers hyperglycemia and can ultimately worsen hyperglycemia in T2DM patients.
Declaration of patient consent
The authors certify that they have obtained all appropriate patient consent forms. In the form, the patient(s) have given their consent for their images and other clinical information to be reported in the journal. The patients understand that their names and initials will not be published and due efforts will be made to conceal their identity.
Financial support and sponsorship
Authors acknowledge the Indian Council of Medical Research (ICMR), New Delhi, for sanctioning Short Term Studentship (STS) (ICMR-STS no. 2019-00304) to the first author.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Epstein LJ, Kristo D, Strollo PJ Jr, Friedman N, Malhotra A, Patil SP, et al
. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea in adults. J Clin Sleep Med 2009;5:263-76.
Kent BD, McNicholas WT, Ryan S. Insulin resistance, glucose intolerance and diabetes mellitus in obstructive sleep apnoea. J Thorac Dis 2015;7:1343-57.
Chasens ER. Obstructive sleep apnea, daytime sleepiness, and type 2 diabetes. Diabetes Educ 2007;33:475-82.
Young T, Peppard PE, Gottlieb DJ. Epidemiology of obstructive sleep apnea: A population health perspective. Am J Respir Crit Care Med 2002;165:1217-39.
Punjabi NM. The epidemiology of adult obstructive sleep apnea. Proc Am Thorac Soc 2008;5:136-43.
Young T, Palta M, Dempsey J, Skatrud J, Weber S, Badr S. The occurrence of sleep-disordered breathing among middle-aged adults. N Engl J Med 1993;328:1230-5.
Peppard PE, Young T, Palta M, Dempsey J, Skatrud J. Longitudinal study of moderate weight change and sleep-disordered breathing. JAMA 2000;284:3015-21.
Young T, Blustein J, Finn L, Palta M. Sleep-disordered breathing and motor vehicle accidents in a population-based sample of employed adults. Sleep 1997;20:608-13.
Hla KM, Young T, Finn L, Peppard PE, Szklo-Coxe M, Stubbs M. Longitudinal association of sleep-disordered breathing and nondipping of nocturnal blood pressure in the Wisconsin sleep cohort study. Sleep 2008;31:795-800.
Peker Y, Hedner J, Norum J, Kraiczi H, Carlson J. Increased incidence of cardiovascular disease in middle-aged men with obstructive sleep apnea: A 7-year follow-up. Am J Respir Crit Care Med 2002;166:159-65.
Young T, Finn L, Peppard PE, Szklo-Coxe M, Austin D, Nieto FJ, et al
. Sleep disordered breathing and mortality: Eighteen-year follow-up of the Wisconsin sleep cohort. Sleep 2008;31:1071-8.
Resnick HE, Redline S, Shahar E, Gilpin A, Newman A, Walter R, et al
. Diabetes and sleep disturbances: Findings from the sleep heart health study. Diabetes Care 2003;26:702-9.
Heffner JE, Rozenfeld Y, Kai M, Stephens EA, Brown LK. Prevalence of diagnosed sleep apnea among patients with type 2 diabetes in primary care. Chest 2012;141:1414-21.
Foster GD, Sanders MH, Millman R, Zammit G, Borradaile KE, Newman AB, et al
. Obstructive sleep apnea among obese patients with type 2 diabetes. Diabetes Care 2009;32:1017-9.
Grimaldi D, Beccuti G, Touma C, Van Cauter E, Mokhlesi B. Association of obstructive sleep apnea in rapid eye movement sleep with reduced glycemic control in type 2 diabetes: Therapeutic implications. Diabetes Care 2014;37:355-63.
American Diabetes Association. Standards of medical care in diabetes. Diabetes Care 2019;42:S1-2.
International Diabetes Institute, World Health Organization. The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Melbourne, Australia: Health Communications Australia Pty. Ltd.; 2000. p. 15-21.
Tan A, Yin JD, Tan LW, van Dam RM, Cheung YY, Lee CH. Using the berlin questionnaire to predict obstructive sleep apnea in the general population. J Clin Sleep Med 2017;13:427-32.
Johns MW. A new method for measuring daytime sleepiness: The Epworth Sleepiness scale. Sleep 1991;14:540-5.
Salvaggio A, Lo Bue A, Isidoro SI, Romano S, Marrone O, Insalaco G. Gel pillow designed specifically for obstructive sleep apnea treatment with continuous positive airway pressure. J Bras Pneumol 2016;42:362-6.
Davies RJ, Ali NJ, Stradling JR. Neck circumference and other clinical features in the diagnosis of the obstructive sleep apnoea syndrome. Thorax 1992;47:101-5.
Ahbab S, Ataoğlu HE, Tuna M, Karasulu L, Cetin F, Temiz LU, et al
. Neck circumference, metabolic syndrome and obstructive sleep apnea syndrome; Evaluation of possible linkage. Med Sci Monit 2013;19:111-7.
Lavie P, Herer P, Hoffstein V. Obstructive sleep apnoea syndrome as a risk factor for hypertension: Population study. BMJ 2000;320:479-82.
West SD, Nicoll DJ, Stradling JR. Prevalence of obstructive sleep apnoea in men with type 2 diabetes. Thorax 2006;61:945-50.
Reutrakul S, Mokhlesi B. Obstructive sleep apnea and diabetes: A state of the art review. Chest 2017;152:1070-86.
Pillai A, Warren G, Gunathilake W, Idris I. Effects of sleep apnea severity on glycemic control in patients with type 2 diabetes prior to continuous positive airway pressure treatment. Diabetes Technol Ther 2011;13:945-9.
Wetter DW, Young TB, Bidwell TR, Badr MS, Palta M. Smoking as a risk factor for sleep-disordered breathing. Arch Intern Med 1994;154:2219-24.
Krol RC, Knuth SL, Bartlett D Jr. Selective reduction of genioglossal muscle activity by alcohol in normal human subjects. Am Rev Respir Dis 1984;129:247-50.
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]