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Volume 15 No. 05
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Accepted Papers





Scientific Investigations

Obstructive Sleep Apnea, Sleepiness, and Glycemic Control in Type 2 Diabetes

R. Nisha Aurora, MD, MHS1; Naresh M. Punjabi, MD, PhD2,3
1Department of Medicine, Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey; 2Department of Medicine, Johns Hopkins University, Baltimore, Maryland; 3Department of Epidemiology, Johns Hopkins University, Baltimore, Maryland

ABSTRACT

Study Objectives:

Self-reported sleepiness is common in patients with obstructive sleep apnea (OSA) and is being increasingly recognized as an effect modifier of the association between OSA and cardiovascular outcomes. However, data on whether sleepiness modifies the association between OSA and glycemic outcomes are lacking. The current study sought to characterize the association between glycemic control and sleepiness in people with OSA and type 2 diabetes.

Methods:

Adults with non-insulin requiring type 2 diabetes and undiagnosed moderate to severe OSA were recruited from the community. Demographic data, Epworth Sleepiness Scale (ESS), hemoglobin A1c (HbA1c), as well a type III home sleep test were obtained. The association between self-reported sleepiness and glycemic control was examined using quantile regression.

Results:

The study cohort included 311 participants with 56% of the sample being men. Stratified analyses by sex demonstrated that self-reported sleepiness was associated with a higher HbA1c level, but this association was present only in men with a body mass index (BMI) < 35 kg/m2. Mean HbA1c levels were higher by 0.57% (95% confidence interval: 0.11, 1.02) in men with an ESS ≥ 11 compared to men with an ESS < 11. No such association was observed in men with a BMI ≥ 35 kg/m2 or in women of any BMI category.

Conclusions:

The association between self-reported sleepiness and glycemic control in people with type 2 diabetes and moderate to severe OSA varies a function of BMI and sex. The noted differences in association should be considered when assessing possible treatment effects of therapy for OSA on metabolic outcomes.

Citation:

Aurora RN, Punjabi NM. Obstructive sleep apnea, sleepiness, and glycemic control in type 2 diabetes. J Clin Sleep Med. 2019;15(5):749–755.


BRIEF SUMMARY

Current Knowledge/Study Rationale: Sleepiness is a common symptom in people with type 2 diabetes mellitus as well as obstructive sleep apnea (OSA), but the association between self-reported sleepiness and glycemic control in persons with type 2 diabetes and OSA is not well described. The objective of the current study was to characterize the association between self-reported sleepiness and glycemic control in participants with both type 2 diabetes and moderate to severe OSA.

Study Impact: The findings from the current investigation demonstrate that glycemic control, as determined by hemoglobin A1c, is associated self-reported sleepiness in men who have a body mass index < 35 kg/m2. Sex and body habitus should be considered as potential effect modifiers when examining metabolic outcomes in the setting of OSA treatment.

INTRODUCTION

Over recent years the significance of sleep disorders in glucose metabolism and genesis of type 2 diabetes mellitus has become a topic of considerable research and clinical interest. In particular, obstructive sleep apnea (OSA) has been found to be highly prevalent in people with type 2 diabetes1 and confers an additional risk for adverse cardiovascular outcomes.24 There is an abundance of experimental and clinical evidence demonstrating an association between OSA and altered glucose homeostasis including insulin resistance,5 glucose intolerance,6 and incident type 2 diabetes.7,8 However, several unanswered questions and areas of debate remain. For example, although there is considerable evidence demonstrating that OSA-related intermittent hypoxemia and sleep fragmentation negatively affect glucose metabolism in those with912 and without1315 type 2 diabetes, interventional studies using positive airway pressure (PAP) therapy have yielded mixed results.16,17

Some of the discrepancies noted across published reports may be partially explained by heterogeneity in either or both disorders, including variability in severity and duration of disease. Additionally, biases in sample selection may also explicate some of the noted inconsistencies. For example, it is well known that obesity is a major determinant of metabolic derangements,18 OSA,19,20 and may modify the glycemic response to OSA therapy.21,22 Emerging evidence suggests that clinical presentation may be an important aspect of patient selection and a key determinant of cardiometabolic outcomes in OSA. Recently, it has been demonstrated that excessive daytime sleepiness in OSA may modify the association between OSA and cardiovascular outcomes, prognosticate worse outcomes, and diminish response to OSA therapy.23,24 Given the high prevalence of OSA in type 2 diabetes, it is not surprising that daytime sleepiness is also commonly experienced by patients with type 2 diabetes. An association analogous to the one noted between daytime sleepiness and cardiovascular outcomes may exist and explain the variability in studies of OSA treatment and glycemic outcomes. Currently, there is a dearth of evidence examining the association between daytime sleepiness and glycemic outcomes particularly with concurrent OSA. Thus, the objectives of the current study were to characterize the association between daytime sleepiness and glycemic control in type 2 diabetes and OSA and identify determinants that may modify the association in the presence of both disorders.

METHODS

Study Sample

Adults with type 2 diabetes not requiring insulin were recruited from the general community through letters and advertisement in the Baltimore-Washington area. Eligibility criteria included a hemoglobin A1c (HbA1c) value ≥ 6.5% and presence of moderate to severe OSA. Demographic information such as age, sex, body mass index (BMI), and race was collected. Exclusion criteria included any ongoing therapy for OSA, insulin therapy for type 2 diabetes, inability to complete the required questionnaires or the home sleep study, active participation in an ongoing weight loss program, change in glycemic medications in the previous 6 weeks, active oral steroid use, or any unstable medical condition, such as unstable angina or uncontrolled hypertension. The research protocol was approved by the Johns Hopkins University Institutional Review Board on human research (IRB Approval Number: NA_00036672).

Assessment of Self-Reported Sleepiness, OSA, and Glycemic Control

The Epworth Sleepiness Scale (ESS) was completed along with a battery of other self-reported assessments. The ESS assesses sleep propensity with eight questions on the likelihood of dozing in various situations on a 4-point scale (0–3). The Apnea-Link Plus (ResMed, San Diego, California, United States), a type III portable monitoring device, was given to study participants by trained research assistants for home sleep apnea testing.25,26 Nasal airflow was recorded with a nasal cannula connected to a pressure transducer. Pulse oximetry was used to assess oxyhemoglobin saturation, and respiratory effort was measured with a pneumatic sensor attached to an effort belt. At least 4 hours of interpretable recording time were required for inclusion in the study. All studies were reviewed by a board certified sleep physician (RNA). For the current analyses, the oxygen desaturation index (ODI) was chosen given the higher fidelity of the pulse oximetry signal compared to other respiratory signals such as the nasal cannula and effort belt. The ODI was calculated as the number of desaturations of 3% or more over the total recording time in hours. The ODI was categorized as follows: 5.0–14.9 events/h (mild sleep apnea), 15.0– 29.9 events/h (moderate sleep apnea), and ≥ 30 events/h (severe sleep apnea). To minimize misclassification and maximize diagnostic accuracy on portable monitoring, the current study was limited to people with undiagnosed moderate to severe OSA. For evaluation of glycemic control, point-of-care testing was used to test HbA1c prior to the home sleep study using the DCA Vantage Analyzer (Siemens, Malvern, Pennsylvania, United States). Point-of-care HbA1c testing has been previously demonstrated to be a valid measure of glycemic control when compared to laboratory HbA1c testing with correlation coefficients varying between 0.89 and 0.99.27

Statistical Analysis

For the current analyses, the ESS was modeled as a continuous and categorical variable. The ESS score was categorized using quintiles as follows: 0–5, 6–8, 9–11, 12–14, and ≥ 15. ESS was also considered as a dichotomous variable, with an ESS score ≥ 11 signifying daytime sleepiness. To compare HA1c values across ESS categories, analysis of variance was used. Subsequently, quantile regression was used to examine the association between ESS scores and HbA1c, given that quantile regression is not sensitive to outliers. Age, race, body mass index (BMI), ODI, and medications for type 2 diabetes were included as covariates. Study participants were categorized as either having no ongoing pharmacological therapy for type 2 diabetes, being on one agent, being on multiple hypoglycemic agents, or on a combination drug. To assess whether the association between sleepiness and HbA1c differed by sex or BMI, all analyses were stratified analyses by sex and BMI. A BMI of 35 kg/m2 was used to dichotomize this variable given that it is a threshold differentiating between those with class 1 versus higher obesity categorization. Thus, ESS was modeled as a continuous, categorical variable, and dichotomous variable. Finally, age and BMI were modeled as linear covariate terms. All analyses were conducted using STATA 13.0 (StataCorp LLC, College Station, Texas, United States).

RESULTS

A total of 311 study participants with type 2 diabetes and moderate to severe OSA were recruited. Table 1 shows the demographic, anthropometric, and ODI values for the study sample stratified by sex. Given that the sample consisted primarily of adults with type 2 diabetes and OSA, the sample was older and obese. Men comprised slightly more than half of the sample. The mean HbA1c of the sample was 7.25% (SD: 1.21%) and the median HbA1c was 7.0 with an IQR of 6.5% to 7.8%, suggesting that glycemic control, on average, was fairly acceptable. The mean ESS score was 9.8 (SD: 5.0) and approximately 43% of the sample had an ESS ≥ 11. The proportion of women versus men with an ESS ≥ 11 was similar (44% in each group). Mean age, BMI, and ODI values by ESS quintile for the full sample and stratified by sex are presented in Table 2.

Characteristics of the study sample.

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Table 1

Characteristics of the study sample.

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Characteristics for the sample by ESS quintiles.

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Table 2

Characteristics for the sample by ESS quintiles.

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The association between daytime sleepiness and glycemic control was explored using an ESS cutoff score of 11, stratified by sex. Men with moderate to severe OSA and an ESS score ≥ 11 had significantly worse glycemic control compared to women with an ESS score ≥ 11 (HbA1c: 7.68% versus 7.06%, P = .009). A similar finding was observed in men with an ESS ≥ 11 compared to men with an ESS score < 11 (HbA1c: 7.68% versus 7.06%, P = .001). An association between ESS scores and glycemic control was not seen in the women (HbA1c: 7.06% versus 7.25% for ESS ≥ 11 compared to ESS < 11, P = .36). To examine whether there was a dose-dependent association between self-reported sleepiness and HbA1c, ESS scores were categorized as previously outlined. Differences in mean HbA1c by ESS group were compared for the entire sample of 311 study participants (Figure 1). Stratification by a BMI (< 35 kg/m2 versus ≥ 35 kg/m2) was performed to assess whether severe obesity modified the association between sleepiness and glycemic control. These analyses showed that HbA1c in study participants with more severe sleepiness was higher, but the association was limited to those with a BMI < 35 kg/m2 (P = .03) (Figure 2), and, specifically to men (P = .01 for linear trend) with a class 1 or lower level of obesity (Figure 3).

Unadjusted mean hemoglobin A1c values by Epworth Sleepiness Scale categories.

Mean hemoglobin A1c for each Epworth Sleepiness Scale group for the entire sample. P = .27 for differences between Epworth Sleepiness Scale groups.

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Figure 1

Unadjusted mean hemoglobin A1c values by Epworth Sleepiness Scale categories.

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Unadjusted mean hemoglobin A1c values by Epworth Sleepiness Scale categories, stratified by body mass index.

Includes all races, ages, and both sexes. (A) Participants with a body mass index < 35 kg/m2; * P = .03 for differences between Epworth Sleepiness Scale groups. (B) Participants with a body mass index ≥ 35 kg/m2; P = .33 for differences between Epworth Sleepiness Scale groups.

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Figure 2

Unadjusted mean hemoglobin A1c values by Epworth Sleepiness Scale categories, stratified by body mass index.

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Unadjusted mean hemoglobin A1c by Epworth Sleepiness Scale categories in participants with a body mass index < 35 kg/m2, stratified by sex.

Includes all races and ages. (A) Men; * P = .01 for differences between Epworth Sleepiness Scale groups. (B) Women; * P = .03 for differences between Epworth Sleepiness Scale groups.

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Figure 3

Unadjusted mean hemoglobin A1c by Epworth Sleepiness Scale categories in participants with a body mass index < 35 kg/m2, stratified by sex.

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To account for the potential influence of extreme HbA1c values, quantile regression models were used to examine the association between self-reported sleepiness and glycemic control after accounting for age, race, ODI, and medications for type 2 diabetes, stratified by sex and BMI. In the final adjusted models, findings from the aforementioned analyses were confirmed (Table 3). An association between self-reported sleepiness, defined as an ESS ≥ 11, and glycemic control was only noted in male participants after adjusting for age, race, ODI, and diabetes medications. HbA1c was 0.47% (95% confidence interval [CI]: 0.09, 0.85; P = .02) higher in participants with an ESS ≥ 11 versus those with an ESS < 11. BMI modified the noted association, as stratification by BMI demonstrated a significant association between self-reported sleepiness and glycemic control only in men with a BMI < 35 kg/m2. Within men with a BMI < 35 kg/m2, HbA1c was 0.57% (95% CI: 0.11, 1.02; P = .015) higher in men with an ESS ≥ 11 compared to men with an ESS < 11. No association was seen for women with moderate to severe OSA, self-reported sleepiness, and HbA1c values.

Differences in hemoglobin A1c as a function of sleepiness: those with Epworth Sleepiness Scale score ≥ 11 versus < 11.

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Table 3

Differences in hemoglobin A1c as a function of sleepiness: those with Epworth Sleepiness Scale score ≥ 11 versus < 11.

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DISCUSSION

The results of the current study demonstrated a number of findings regarding the association between self-reported sleepiness, OSA, and glycemic control in persons with non-insulin–requiring type 2 diabetes mellitus. First, sex modified the association between self-reported sleepiness and glycemic control. That is, the association between sleepiness and glycemic control was observed only in men. Second, obesity further modified the noted association between sleepiness and glycemic control in men. Not surprisingly, higher ESS scores were associated with more significant OSA in men with type 2 diabetes. A similar association between higher ESS scores and worsening OSA was not noted in women with type 2 diabetes. Finally, the association between daytime sleepiness and glycemic control in men with a BMI < 35 kg/m2 persisted even after adjusting for age, race, increasing OSA severity, and medication use for type 2 diabetes.

Recently, differences in the prevalence and development of OSA-associated comorbidities as a function of sex have been described. Specifically, a few studies have demonstrated a stronger association between glycemic disturbances, including type 2 diabetes, and OSA with men compared to women.2830 However, the interaction between sex and OSA symptoms in modifying the association between OSA and health outcomes is not well established. There is limited but accumulating evidence that daytime sleepiness may be an important prognostic indicator of worse health outcomes. Previous studies have demonstrated increased mortality in individuals with daytime sleepiness,3133 with one study demonstrating a stronger association between daytime sleepiness and mortality as well as incident cardiovascular disease in women.33 A major limitation in these studies was the lack of polysomnographic data and provider-based diagnosis of sleep disorders. More recently, it is increasingly recognized that patients with sleep apnea present with varying symptoms,3436 and that the presence and/or severity of daytime sleepiness in particular may have implications on cardiometabolic outcomes and perhaps even response to therapy.23,37 Data on glucose metabolism are scant but demonstrate consistent results. In two separate studies38,39 in persons without type 2 diabetes matched for OSA severity and BMI, those with self-reported sleepiness (ESS score ≥ 11) were found to have higher fasting glucose and insulin levels as well as insulin resistance, as determined by the homeostatic model assessment of insulin resistance. However, both studies had modest sample sizes and included either only men38 or a very small percentage of women.39 In a larger study of 579 patients with type 2 diabetes, which included women in the majority, those with an ESS score ≥ 10 were found to have higher HbA1c levels compared to those with an ESS score < 10. However, objective sleep study-diagnosed OSA data were not available.40 Finally, in a large clinic-based sample of 2,149 patients, an association was seen between severe OSA and type 2 diabetes but only in those with an ESS score ≥ 10.41 However, sex-specific differences were not reported. Parallel to the findings with sleepiness and glucose metabolism, similar outcomes have been observed between sleepiness and cardiovascular outcomes.4245 Recently, in a study that included only men, Xie and colleagues24 demonstrated that daytime sleepiness is an independent prognosticator of major adverse cardiac events in patients with moderate to severe OSA after myocardial infarction. Event rates were 72% (with daytime sleepiness) compared to 37% in those with moderate to severe OSA but without daytime sleepiness. The risk of an adverse cardiac event in patients with moderate to severe OSA and daytime sleepiness versus those without daytime sleepiness remained elevated after adjusting for age and minimum oxygen desaturation: heart rate = 3.17 (CI: 1.22, 7.76). Considering sex differences in the setting of OSA and sleepiness is of substantial value. Sleepiness may be a marker of proinflammatory cytokine activity that mediates cardiometabolic disease in the setting of OSA.4648 Amassing evidence suggests a possible differential association between sex and OSA-associated inflammatory burden.4952

Sleepiness may also be important in determining response to OSA therapy and adherence.36 It is plausible that the lack of benefit noted in recent positive airway pressure trials examining cardiometabolic outcomes may be at least partially explained by the inclusion of people without daytime sleepiness.53,54 Lack of daytime sleepiness or asymptomatic patients with OSA may be resistant to the potential cardiometabolic benefits of OSA treatment.55 Pien and colleagues37 reported that the physical component of the Short Form-12 quality of life questionnaire and blood pressure response improves significantly in patients with OSA and significant daytime sleepiness compared to those with OSA and minimal symptoms or insomnia type symptoms. Again, most of the cohort in that study was male. Sex may potentially modify the association between response and adherence to PAP therapy.56 The results of the current investigation add to the growing body of literature that suggests that OSA has heterogeneous manifestations, and that sleepiness in particular may be a marker of increased cardiometabolic risk. However, the results herein also extend the existing literature by demonstrating sex-specific differences and underscoring the need to examine sex-mediated differences in disease as OSA phenotyping emerges as a construct. The findings from the current investigation also confirmed the obvious role of obesity as a key determinant of glycemic outcomes in the setting of OSA. Previous work has demonstrated that response to PAP therapy in persons both with and without type 2 diabetes21,22 varies as a function of obesity. Undoubtedly, adipose tissue is known to be proinflammatory and a modulator of glucose and insulin activity. The overwhelming effects of adiposity may dwarf the relative effectiveness of OSA therapy on glycemic outcomes in persons with obesity.57

There are several strengths in the current study, which include the fairly substantial sample size and a robust representation of women with moderate to severe OSA, as well as the inclusion of a nearly equal number of white and African American participants. Additionally, the full spectrum of ESS scores are represented in the analyses. Limitations include the lack of arousal information with a type 3 portable sleep monitoring device, as well as the use of the ODI rather than the AHI as the primary independent variable. Nasal cannula and effort belts were not as reliable as the pulse oximeter, leading to a higher degree of missing data and loss of power with AHI reporting. Nonetheless, as expected, there was high correlation between AHI and ODI. Other limitations include an absence of data on physical activity and lifestyle, self-assessment of sleep tendency, as well as the fact most of the participants had well-controlled diabetes. Additionally, there was a smaller sample of women with moderate to severe OSA and a BMI < 35 kg/m2, possibly limiting statistical power. Finally, the cross-sectional analyses presented herein precludes making inferences about the directionality of the association between self-reported sleepiness and glycemic control. Nonetheless, the findings reported extend the limited literature on the association between sleepiness and glycemic outcomes in OSA and suggest that sleepiness may be an important determinant of glycemic control in men with a normal, overweight, or mild to moderate obese body type.

Finding new opportunities to mitigate the adverse consequences of type 2 diabetes is paramount. The results herein point to a potential subset of persons with OSA who may benefit from early case-identification of OSA and subsequent prompt intervention as part of secondary prevention measures for type 2 diabetes, which include optimizing glycemic control and treating comorbidities to avert diabetes-associated complications. Future research examining the cardiometabolic benefits of OSA therapy should consider sleepiness, sex, and body habitus as potential modifiers of the association between OSA adverse health outcomes.

DISCLOSURE STATEMENT

All authors have seen and approved the manuscript. Dr. Aurora is supported by NIH grant HL118414. Dr. Punjabi is supported by NIH grants HL078075, HL117167, and DK120051. The authors report no conflicts of interest.

ABBREVIATIONS

BMI

body mass index

ESS

Epworth Sleepiness Scale

HbA1c

hemoglobin A1c

ODI

oxygen desaturation index

OSA

obstructive sleep apnea

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