Abstract
Study Objectives
To examine the associations of sleep measures with hemoglobin A1c (HbA1c) among individuals with and without type 2 diabetes.
Methods
Data were from 2049 Multi-Ethnic Study of Atherosclerosis participants taking part in a sleep ancillary study. Cross-sectional linear regression models examined associations of actigraphy estimates of sleep (sleep duration, variability, and maintenance efficiency) and polysomnography measures (obstructive sleep apnea [OSA] and hypoxemia) with HbA1c, stratified by diabetes status. Primary models were adjusted for demographics, lifestyle behaviors, and obesity.
Results
Among individuals with diabetes (20 per cent population), those who slept <5 hr/night had greater HbA1c than those who slept 7–8 hr/night (7.44 vs. 6.98 per cent, ptrend = 0.04), with no attenuation of associations after adjusting for OSA/hypoxemia. In women with diabetes, but not men, those in the lowest quartile of sleep maintenance efficiency had greater HbA1c than those in the highest quartile of sleep maintenance efficiency (7.60 vs. 6.97 per cent, ptrend < 0.01). Among those without diabetes, individuals with severe OSA or in the highest quartile of hypoxemia had significantly greater HbA1c than those without OSA or who were in the lowest quartile of hypoxemia (5.76 vs. 5.66 per cent, ptrend = 0.01; 5.75 vs. 5.66 per cent, ptrend < 0.01, respectively). Associations did not meaningfully differ by race/ethnicity.
Conclusions
Among individuals with diabetes, HbA1c was significantly higher in men and women with short sleep duration and in women with poor sleep maintenance efficiency, suggesting a role for behavioral sleep interventions in the management of diabetes. Among individuals without diabetes, untreated severe OSA/hypoxemia may adversely influence HbA1c.
Keywords: sleep duration, sleep duration variability, sleep maintenance efficiency, sleep-disordered breathing, diabetes, hemoglobin A1c, actigraphy, polysomnography
Statement of Significance
Evidence suggests that sleep disturbances are associated with poor glycemic control. Existing studies are limited by self-reported sleep measures, focus on one type of exposure (e.g. sleep duration), relatively small samples, and racially/ethnically homogenous populations. Using wrist actigraphy and home polysomnography, among participants with diabetes we observed elevated hemoglobin A1c (HbA1c) among men and women with short sleep duration (<5 hr/night), and among women who had poor sleep maintenance efficiency. We also observed small elevations in HbA1c in individuals without diabetes who had untreated sleep apnea or hypoxemia. Findings suggest a role for behavioral interventions to increase sleep duration and efficiency to improve management of diabetes. The data also suggest that untreated sleep apnea/hypoxemia may contribute to dysregulation of glycemic control.
Introduction
The high and increasing prevalence of type 2 diabetes is a major public health concern. Growing evidence suggests that sleep disorders, such as obstructive sleep apnea (OSA), short or long sleep duration, and sleep fragmentation are associated with poor glycemic control and type 2 diabetes [1, 2]. There are multiple potential physiological and behavioral pathways linking sleep with glucose regulation. For example, short sleep duration and OSA lead to increased sympathetic nervous system activity, higher cortisol secretion, altered growth hormone metabolism, inflammation, and changes in appetite-regulating hormones, all of which may contribute to poor glycemic control and type 2 diabetes [2, 3]. Furthermore, OSA has been associated with risk of developing diabetes, independent of body mass index (BMI) [4, 5].
Hemoglobin A1c (HbA1c), an indicator of glucose concentrations over the past several months, is used as the gold standard for glycemic control [6] and is more strongly associated with risks of cardiovascular disease and all-cause mortality when compared with serum glucose [7]. Several studies evaluating sleep duration and quality in relation to HbA1c have been conducted; however, the majority rely on self-reported measures of sleep [8, 9], which are only moderately correlated with objectively assessed sleep [10], and did not adjust for sleep apnea. Furthermore, most studies using objective estimates of sleep, such as wrist actigraphy, are limited by their relatively small sample sizes [11–13]. Additionally, the majority of studies have focused exclusively on diabetic populations; yet, sleep may also play an important role in glycemic control in those without diabetes. Lastly, there is limited data on sleep abnormalities and glycemic control in racially and ethnically diverse populations. The American Heart Association recently called for inclusion of more diverse populations in sleep research studies [14].
Previously in the Multi-Ethnic Study of Atherosclerosis (MESA), Bakker and colleagues examined the associations of sleep disturbances with abnormal fasting glucose. They found that moderate-to-severe OSA, using the apnea–hypopnea index (AHI), was associated with abnormal fasting glucose, with associations appearing stronger in African Americans and Caucasians compared with Chinese or Hispanics [15]. Sleep duration was not associated with abnormal fasting glucose concentrations after adjusting for the AHI. While that study enhanced the existing literature, the authors did not examine sleep disturbances in relation to HbA1c, which may be a superior biomarker since it reflects hyperglycemia over a longer time frame and better predicts incident diabetes [6]. Furthermore, the authors assessed sleep-disordered breathing (SDB) using the AHI only, whereas nocturnal hypoxemia is also an important physiological marker of sleep that has been associated with a variety of metabolic parameters, including fasting glucose and diabetes [16–18]. Additionally, sleep duration variability and sleep maintenance efficiency were not examined and may play an important role in glycemic control due to the negative effects of sleep disruption on metabolism.
To address these existing gaps in knowledge, we examined the associations of sleep duration, variability, and maintenance efficiency using 7 day wrist actigraphy, as well as SDB, assessed by the AHI and nocturnal hypoxemia using home polysomnography with HbA1c among individuals with and without type 2 diabetes. We hypothesized that short sleep duration, high sleep duration variability, poor sleep maintenance efficiency, and greater levels of SDB will be associated with higher HbA1c concentrations, among individuals with and without type 2 diabetes. We also examined whether associations for sleep duration, variability, and maintenance efficiency persisted after adjusting for measures of SDB, and whether associations for SDB persisted after adjusting for sleep duration. Finally, we examined whether the associations of sleep and HbA1c vary by race/ethnicity, age, or sex.
Methods
Study population
The MESA is an ongoing multisite cohort of adults. Participants were initially recruited in 2000–2002 (examination 1) from six field centers in the United States (Baltimore, Maryland; Chicago, Illinois; St. Paul, Minnesota; Los Angeles, California; New York, New York; and Forsyth County, North Carolina). At recruitment, 6814 participants, who were African American (28 per cent), Chinese (12 per cent), Hispanic (22 per cent), and non-Hispanic white (38 per cent), were 45–84 years old and were free from known cardiovascular disease. Follow-up examinations occurred approximately every 2 years, with the MESA Exam 5 occurring between 2010 and 2012. The MESA Sleep ancillary study was conducted in conjunction with Exam 5 (2010–2013), when all study MESA Exam 5 participants who did not report regular use of oral devices, nocturnal oxygen, or nightly positive airway pressure devices (n = 113) were invited to undergo 7 day actigraphy, home polysomnography, and a MESA sleep questionnaire. A total of 2261 individuals participated in the sleep examination (59.7 per cent). Individuals who participated in the sleep study were younger, more likely to be Chinese-American or Hispanic, less educated, nonsmokers, less likely to consume alcohol, and with a higher average BMI and HbA1c, when compared with those who declined to participate (Supplementary Table 1). The present cross-sectional study uses data from MESA Exam 5 and the concurrent MESA Sleep ancillary study. Participants were excluded if they were missing data on actigraphy (N = 110), HbA1c (N = 37), diabetes status (N = 2), or covariates of interest (N = 63), resulting in a final sample of 2049 for the actigraphy analyses. An additional 168 individuals were missing data on polysomnography measures, resulting in a final sample of 1881 for the SDB analyses. Institutional review board’s approval was obtained at each study site, and written informed consent was obtained from all participants for the core MESA exam, as well as the MESA Sleep Ancillary Study.
Exposures
Sleep duration, night to night sleep duration variability, and maintenance efficiency were estimated by actigraphy. Participants were asked to wear the ActiWatch Spectrum wrist actigraph (Philips Respironics, Murrysville, PA) on the nondominant hand for 7 days and nights. Data were sent to a central sleep reading center at Brigham and Women’s Hospital in Boston, Massachusetts, where output was scored. Actigraphic data were scored in 30 s epochs as sleep or wake using Actiware-Sleep version 5.59 software as described previously [19]. A minimum of 3 days of actigraphy data with >50 per cent reliable data were required for study inclusion. Approximately 6 per cent of the study population had 3–6 days of valid sleep data, 89 per cent had 7 days, 4 per cent had 8 days, and <1 per cent had 9–13 days. Sleep duration was estimated as the sum of epochs scored as sleep in each main sleep interval (manually identified based on a self-actuated event marker, sleep diary, and light sensor) averaged over all days of valid recording. Interscorer and intrascorer reliability was assessed; intraclass correlation coefficients for sleep duration exceeded 0.90. Sleep duration was defined as the average hours of estimated sleep per night across all days and modeled as a categorical variable: <5, 5–5.9, 6–6.9, 7–8, and >8 hr/night. Night to night sleep duration variability was estimated in minutes using the within-person standard deviation of sleep duration from an average of 7 nights of actigraphy (range 3–13 nights) and was modeled in quartiles according to the distribution: ≤48.0, 48.1–70.0, 71.0–99.0, and >99.0 within-person standard deviations. Sleep maintenance efficiency was defined as the percent of time estimated to be asleep between sleep onset and lights on (not including sleep latency, or the time it takes to transition from full wakefulness to sleep), and categorized according to quartiles of the distribution. The four categories were ≤89.5 per cent, 89.6%–91.9%, 92.0%–93.7%, and ≥93.8 per cent.
In-home polysomnography was conducted using the Compumedics Somte system (Compumedics, Abbottsville, Australia), as described previously [15, 20]. Sleep recordings were transmitted to the sleep reading center (Brigham and Women’s Hospital) where data were scored blinded to all other data. The AHI was calculated as the sum of all apneas and hypopneas with ≥4 per cent oxygen desaturation. Intrascorer reliability for the AHI exceeded 0.94. We categorized OSA severity using the following AHI categories: <5, 5–14.9, 15–29.9, and ≥30, representing none/minimal, mild, moderate, and severe OSA, respectively [21]. Nocturnal hypoxemia was defined as the percentage of sleep time with less than 90 per cent oxyhemoglobin saturation. As described previously, the level of agreement between total sleep time measured by actigraphy and polysomnography was fair (κ = 0.29) [22].
Outcome
Fasting anticoagulated whole blood samples were collected at Exam 5. The whole blood was diluted in a sample preparation vial containing an aqueous solution of ethylenediaminetetraacetic acid and potassium cyanide (HbA1c Sample Preparation Kit, Bio-Rad, Hercules, CA 94547). HbA1c was measured by high-performance liquid chromatography using a Tosoh G7 HPLC Glycohemoglobin Analyzer (Tosoh Medics, Inc., San Francisco, CA). The laboratory interassay CV range was 1.4%–1.9%. The method was calibrated utilizing standard values derived by the National Glycohemoglobin Standardization Program (NGSP).
Covariates
Race/ethnicity, sex, age, and marital status (married/living with partner or other) were self-reported at baseline (Exam 1). Education (<high school diploma, high school diploma/some college, and college degree or higher), smoking status (never, former, and current), current alcohol use (yes/no), daily sleeping pill use (yes/no), and depressive symptoms were self-reported at Exam 5. Depressive symptoms were measured using the 20-item Center for Epidemiologic Studies Depression Scale and modeled as a dichotomous variable using a cutpoint of 16, with scores equal to this or higher indicating clinically significant depression [23]. Shift work (day vs. afternoon, night, split, irregular shift/on-call, and rotating) was assessed using the MESA sleep questionnaire. Measures of obesity included BMI and waist circumference. Weight and height were measured using a balance beam scale and stadiometer, respectively. BMI was calculated as weight in kilograms divided by height in meters squared. Waist circumference was measured at the level of the umbilicus. Diabetes was defined using the standard MESA definition, based on reported diagnosis of diabetes, taking medications identified as treatment for diabetes, elevated glucose (≥126 mg/dL) and/or elevated HbA1c (≥ 6.5 per cent) [24].
Statistical analysis
Descriptive statistics were calculated and stratified by diabetes status and sleep duration categories. Multivariable linear regression was used to test the associations of actigraphy estimates of sleep (sleep duration, variability, and maintenance efficiency) and polysomnography measures of SDB (OSA severity defined by the AHI and nocturnal hypoxemia) with continuously measured HbA1c from Exam 5. Each exposure variable was modeled categorically, due to prior evidence indicating nonlinear associations with health outcomes [8, 25], and entered into separate regression models; all models were tested for linear trends. Because the mechanisms of glycemic control are different between people with and without type 2 diabetes, all analyses were stratified by diabetes status. Model 1 adjusted for field center, age, race/ethnicity, and sex. Model 2 adjusted for education, marital status, smoking status, alcohol use, hypnotic medication use, and depressive symptoms. Model 3 additionally adjusted for continuously measured waist circumference. Model 4 adjusted for continuously measured BMI in place of waist circumference. In sensitivity analyses, we additionally adjusted for diabetes medication use among those with diabetes. We also explored additional adjustment for indices of SDB when the primary exposure was an actigraphy measure (i.e. sleep duration, sleep variability, and sleep maintenance efficiency), and additional adjustment for sleep duration when the exposures were sleep variability, maintenance efficiency, or indices of SDB. Interactions were tested between sleep measures and race/ethnicity, age, and sex using cross-product terms in the models.
Results
Participant characteristics
As seen in Table 1, participants averaged 68.5 ± 9.2 years, 53.5 per cent were female, and average HbA1c was 6.0 ± 0.9. Approximately 20 per cent of the study population met the criteria for diabetes using the standard MESA definition described earlier (N = 402). Individuals with diabetes were more likely to be older, African American or Hispanic, less educated, less likely to report alcohol consumption, and had higher depression scores, with a higher average BMI and waist circumference when compared with those without diabetes. Those with diabetes also had shorter sleep duration, higher levels of sleep duration variability, poorer sleep maintenance efficiency, and were more likely to have moderate-to-severe OSA than those without diabetes. When examining participant characteristics stratified by sleep duration categories (Table 2), individuals with short sleep duration were more likely to be male, African American, current smokers, with higher depression scores, BMI, and waist circumference, and were less likely to be married or living with a partner when compared with those with longer sleep duration. Individuals with short sleep duration also had greater sleep duration variability, poorer sleep maintenance efficiency, and were more likely to have moderate-to-severe OSA compared with those with longer sleep duration.
Table 1.
Participant characteristics by type 2 diabetes status: the Multi-Ethnic Study of Atherosclerosis (2010–2013)
| Participant characteristics | Total sample N = 2049 |
No diabetes N = 1647 |
Diabetes N = 402 |
P* |
|---|---|---|---|---|
| Age, mean years ± SD | 68.5 ± 9.2 | 68.3 ± 9.2 | 69.3 ± 8.9 | 0.042 |
| Female, n (%) | 1097 (53.5) | 892 (54.2) | 205 (49.0) | 0.254 |
| Race/ethnicity, n (%) | <0.001 | |||
| Non-Hispanic white | 778 (38.0) | 688 (41.8) | 90 (22.4) | |
| Chinese-American | 227 (11.1) | 194 (11.8) | 33 (8.2) | |
| African American | 560 (27.3) | 408 (24.8) | 152 (37.8) | |
| Hispanic | 484 (23.6) | 357 (21.7) | 127 (31.6) | |
| Education, n (%) | <0.001 | |||
| Less than a high school diploma | 287 (14.0) | 204 (12.4) | 83 (20.7) | |
| High school diploma or some college | 946 (46.2) | 739 (44.9) | 207 (51.5) | |
| College degree or higher | 816 (39.8) | 704 (42.7) | 112 (27.9) | |
| Married/living with partner, n (%) | 1239 (60.5) | 1006 (61.1) | 233 (58.0) | 0.251 |
| Smoking, n (%) | 0.234 | |||
| Current | 152 (7.4) | 115 (7.0) | 37 (9.2) | |
| Former | 948 (46.3) | 759 (46.1) | 189 (47.0) | |
| Never | 949 (46.3) | 773 (46.9) | 176 (43.8) | |
| Presently drinking alcohol, n (%) | 894 (43.6) | 761 (46.2) | 133 (33.1) | <0.001 |
| Hypnotic medication use ≥5 times/week, n (%) | 128 (6.3) | 106 (6.4) | 22 (5.5) | 0.474 |
| CES-D ≥ 16, n (%) | 303 (14.8) | 227 (13.8) | 76 (18.9) | 0.010 |
| BMI, mean kg/m2 ± SD (N = 2048) | 28.8 ± 5.6 | 28.2 ± 5.4 | 31.5 ± 5.8 | <0.001 |
| Waist circumference, mean cm ± SD (N = 2046) | 99.7 ± 14.6 | 97.9 ± 13.9 | 107.1 ± 14.9 | <0.001 |
| Sleep duration, mean hours ± SD | 6.5 ± 1.4 | 6.5 ± 1.3 | 6.4 ± 1.5 | 0.038 |
| Sleep duration variability, mean ± SD | 75.7 ± 38.0 | 73.5 ± 36.9 | 84.7 ± 41.1 | <0.001 |
| Sleep maintenance efficiency, median % ± IQR | 92.0 ± 4.2 | 92.1 ± 4.1 | 91.5 ± 4.7 | 0.008 |
| Apnea–hypopnea index, median ± IQR (N = 1881) | 9.1 ± 16.9 | 8.1 ± 15.8 | 13.1 ± 21.9 | <0.001 |
| Obstructive sleep apnea, n (%) (N = 1881) | <0.001 | |||
| None | 640 (34.0) | 550 (36.4) | 90 (24.4) | |
| Mild | 592 (31.5) | 482 (31.9) | 110 (29.8) | |
| Moderate | 359 (19.1) | 270 (17.9) | 89 (24.1) | |
| Severe | 290 (15.4) | 210 (13.9) | 80 (21.7) | |
| Nocturnal hypoxemia†, median % ± IQR (N = 1881) | 0.7 ± 3.4 | 0.5 ± 3.0 | 1.5 ± 6.4 | <0.001 |
| HbA1c, mean ± SD | 6.0 ± 0.9 | 5.7 ± 0.4 | 6.8 ± 1.3 | <0.001 |
CES-D = Center for Epidemiologic Studies Depression Scale; IQR = interquartile range.
*p-Value tests for a difference by diabetes status using t-tests, Wilcoxon–Mann Whitney tests, or chi-square tests, as appropriate.
†Percent of sleep time with oxyhemoglobin saturation <90%.
Table 2.
Participant characteristics by sleep duration categories: the Multi-Ethnic Study of Atherosclerosis (2010–2013), N = 2049
| Participant characteristics | Sleep duration categories, hr | P* | ||||
|---|---|---|---|---|---|---|
| <5.0 N = 267 |
5.0–5.9 N = 366 |
6.0–6.9 N = 649 |
7–8 N = 539 |
>8 N = 228 |
||
| Age, mean years ± SD | 69.2 ± 10.0 | 68.0 ± 9.0 | 67.6 ± 8.8 | 68.2 ± 9.0 | 71.7 ± 9.0 | 0.021 |
| Female, n (%) | 97 (36.3) | 185 (50.6) | 333 (51.3) | 335 (62.2) | 147 (64.5) | <0.001 |
| Race/ethnicity, n (%) | <0.001 | |||||
| Non-Hispanic white | 62 (23.2) | 90 (24.6) | 256 (39.5) | 258 (47.9) | 112 (49.1) | |
| Chinese-American | 36 (13.5) | 45 (12.3) | 65 (10.0) | 63 (11.7) | 18 (7.9) | |
| African American | 116 (43.5) | 130 (35.5) | 181 (27.9) | 96 (17.8) | 37 (16.2) | |
| Hispanic | 53 (19.9) | 101 (27.6) | 147 (22.7) | 122 (22.6) | 61 (26.8) | |
| Education, n (%) | 0.136 | |||||
| Less than a high school diploma | 35 (13.1) | 45 (12.3) | 82 (12.6) | 77 (14.3) | 48 (21.1) | |
| High school diploma or some college | 122 (45.7) | 175 (47.8) | 311 (47.9) | 242 (44.9) | 96 (42.1) | |
| College degree or higher | 110 (41.2) | 146 (39.9) | 256 (39.5) | 220 (40.8) | 84 (36.8) | |
| Married/living with partner, n (%) | 145 (54.3) | 201 (54.9) | 421 (64.9) | 346 (64.2) | 126 (55.3) | <0.001 |
| Smoking, n (%) | <0.001 | |||||
| Current | 35 (13.1) | 29 (7.9) | 46 (7.1) | 22 (4.1) | 20 (8.8) | |
| Former | 130 (48.7) | 164 (44.8) | 291 (44.8) | 265 (49.2) | 98 (43.0) | |
| Never | 102 (38.2) | 173 (47.3) | 312 (48.1) | 252 (46.8) | 110 (48.3) | |
| Presently drinking alcohol, n (%) | 107 (40.1) | 146 (39.9) | 303 (46.7) | 251 (46.6) | 87 (38.2) | 0.032 |
| Hypnotic medication use ≥5 times/week, n (%) | 15 (5.6) | 19 (5.2) | 36 (5.6) | 41 (7.6) | 17 (7.5) | 0.450 |
| CES-D ≥ 16, n (%) | 51 (19.1) | 71 (19.4) | 79 (12.2) | 68 (12.6) | 34 (14.9) | 0.004 |
| BMI, mean kg/m2 ± SD (N = 2048) | 29.9 ± 5.8 | 29.5 ± 5.7 | 28.8 ± 5.6 | 28.1 ± 5.4 | 28.2 ± 5.4 | <0.001 |
| Waist circumference, mean cm ± SD (N = 2046) | 102.8 ± 14.6 | 101.2 ± 14.7 | 99.7 ± 14.7 | 97.3 ± 13.9 | 99.0 ± 14.6 | <0.001 |
| Sleep duration, mean hours ± SD | 4.0 ± 0.9 | 5.6 ± 0.3 | 6.5 ± 0.3 | 7.4 ± 0.3 | 8.5 ± 0.5 | <0.001 |
| Sleep duration variability, mean ± SD | 94.0 ± 36.9 | 87.1 ± 38.5 | 74.0 ± 37.4 | 65.2 ± 35.8 | 65.4 ± 32.4 | <0.001 |
| Sleep maintenance efficiency, median % ± IQR | 91.2 ± 5.2 | 91.3 ± 4.6 | 91.7 ± 4.2 | 92.6 ± 3.9 | 92.9 ± 3.1 | <0.001 |
| Apnea–hypopnea index, median ± IQR (N = 1881) | 14.8 ± 26.2 | 9.5 ± 15.8 | 7.8 ± 15.8 | 7.5 ± 15.9 | 8.3 ± 15.6 | <0.001 |
| Obstructive sleep apnea, n(%) (N = 1881) | <0.001 | |||||
| None | 46 (18.6) | 110 (32.9) | 230 (38.1) | 186 (37.4) | 68 (34.3) | |
| Mild | 81 (32.8) | 113 (33.8) | 176 (29.1) | 155 (31.1) | 67 (33.8) | |
| Moderate | 52 (21.1) | 69 (20.7) | 117 (19.4) | 87 (17.5) | 34 (17.2) | |
| Severe | 68 (27.5) | 42 (12.6) | 81 (13.4) | 70 (14.1) | 29 (14.7) | |
| Nocturnal hypoxemia†, median % ± IQR (N=1881) | 1.8 ± 6.9 | 0.7 ± 3.7 | 0.6 ± 2.8 | 0.5 ± 2.7 | 0.6 ± 3.4 | <0.001 |
| HbA1c, mean ± SD | 6.2 ± 1.1 | 6.0 ± 0.8 | 6.0 ± 0.9 | 5.9 ± 0.8 | 5.9 ± 0.8 | <0.001 |
CES-D = Center for Epidemiologic Studies Depression Scale; IQR = interquartile range.
*p-Value tests for trend across sleep variability categories using one-way ANOVA or tests for a difference using Kruskal–Wallis or chi-square tests, as appropriate.
†Percent of sleep time with oxyhemoglobin saturation <90%.
Actigraphy measures and HbA1c
Table 3 shows the adjusted means of HbA1c by categories of the actigraphy-derived measures, stratified by diabetes status. Among those with diabetes, individuals with short average sleep duration had significantly higher HbA1c concentrations than those who slept 7–8 hr/night. More specifically, the adjusted mean HbA1c concentrations were 7.54 per cent among those who slept <5 hr/night when compared with 6.94 per cent among those who slept 7–8 hr/night after adjustment for demographics (Model 1, mean difference = 0.60 per cent, p = 0.004). The association was slightly attenuated but remained statistically significant after additional adjustment for education, marital status, and lifestyle factors (Model 2, mean difference = 0.51 per cent, p = 0.019), waist circumference (Model 3, mean difference = 0.46 per cent, p = 0.029), BMI (Model 4, mean difference = 0.50 per cent, p = 0.020), and in sensitivity analyses (Supplementary Table 2), after adjusting separately for the AHI (mean difference = 0.50 per cent, p = 0.031) or hypoxemia (mean difference = 0.47 per cent, p = 0.040). There was also a significant linear trend, with lower HbA1c concentrations across higher sleep duration categories among those with diabetes (all p < 0.05). For context, a review of oral antidiabetic agents found an average reduction of 0.5%–1.25% in HbA1c concentrations after 6 months of therapy [26].
Table 3.
Adjusted means of HbA1c by categories of actigraphy estimated sleep duration, variability, and efficiency: the Multi-Ethnic Study of Atherosclerosis (2010–2013), N = 2049
| Sleep duration, hr | ||||||
|---|---|---|---|---|---|---|
| HbA1c | <5.0 | 5.0–5.9 | 6.0–6.9 | 7–8 (Referent) | >8 | p for trend |
| No diabetes | N = 196 | N = 298 | N = 526 | N = 448 | N = 179 | |
| Model 1 | 5.75 | 5.68 | 5.71 | 5.68 | 5.66 | 0.074 |
| Model 2 | 5.75 | 5.69 | 5.71 | 5.68 | 5.65 | 0.050 |
| Model 3 | 5.74 | 5.68 | 5.71 | 5.69 | 5.65 | 0.165 |
| Model 4 | 5.73 | 5.68 | 5.71 | 5.69 | 5.66 | 0.232 |
| Diabetes | N = 71 | N = 68 | N = 123 | N = 91 | N = 49 | |
| Model 1 | 7.54** | 7.03 | 7.18 | 6.94 | 6.91 | 0.008 |
| Model 2 | 7.47* | 7.00 | 7.21 | 6.96 | 6.95 | 0.041 |
| Model 3 | 7.44* | 6.98 | 7.22 | 6.98 | 6.90 | 0.041 |
| Model 4 | 7.46* | 6.70 | 7.21 | 6.96 | 6.95 | 0.044 |
| Sleep duration variability, within-person standard deviation | ||||||
| ≤48.0 (Referent) |
48.1–70.0 | 70.1–99.0 | >99.0 | |||
| No diabetes | N = 436 | N = 454 | N = 382 | N = 375 | ||
| Model 1 | 5.70 | 5.66 | 5.72 | 5.71 | 0.438 | |
| Model 2 | 5.70 | 5.66 | 5.71 | 5.71 | 0.474 | |
| Model 3 | 5.71 | 5.66 | 5.71 | 5.70 | 0.733 | |
| Model 4 | 5.71 | 5.66 | 5.71 | 5.70 | 0.677 | |
| Diabetes | N = 84 | N = 80 | N = 109 | N = 129 | ||
| Model 1 | 6.84 | 7.17 | 7.23* | 7.22* | 0.049 | |
| Model 2 | 6.86 | 7.19 | 7.24* | 7.19 | 0.087 | |
| Model 3 | 6.88 | 7.18 | 7.21 | 7.18 | 0.143 | |
| Model 4 | 6.86 | 7.19 | 7.24* | 7.19 | 0.111 | |
| Sleep maintenance efficiency, % | ||||||
| ≤89.5 | 89.6–91.9 | 92.0–93.7 | ≥93.8 (Referent) | |||
| No diabetes | N = 392 | N = 409 | N = 420 | N = 426 | ||
| Model 1 | 5.70 | 5.73* | 5.69 | 5.67 | 0.154 | |
| Model 2 | 5.70 | 5.73 | 5.69 | 5.67 | 0.224 | |
| Model 3 | 5.69 | 5.73 | 5.69 | 5.68 | 0.445 | |
| Model 4 | 5.69 | 5.72 | 5.69 | 5.68 | 0.416 | |
| Diabetes | N = 122 | N = 103 | N = 82 | N = 95 | ||
| Model 1 | 7.34 | 7.09 | 6.91 | 7.12 | 0.121 | |
| Model 2 | 7.33 | 7.11 | 6.93 | 7.08 | 0.100 | |
| Model 3 | 7.32 | 7.07 | 6.94 | 7.09 | 0.134 | |
| Model 4 | 7.33 | 7.11 | 6.93 | 7.08 | 0.104 | |
Model 1 adjusted for age, sex, race/ethnicity, and field center. Model 2 adjusted for Model 1, plus education, marital status, smoking status, alcohol use, hypnotic medication, and depressive symptoms. Model 3 adjusted for Model 2, plus waist circumference. Model 4 adjusted for Model 2, plus BMI.
*p < 0.05; **p < 0.01; ***p < 0.001.
Also among individuals with diabetes, HbA1c concentrations were greater in those in the highest two quartiles of sleep duration variability when compared with those in the lowest quartile of sleep duration variability (Model 1, mean difference = 0.38%–0.39%, p < 0.05 for all). This difference was attenuated after further adjustment for waist circumference, but not BMI (Model 4, mean difference = 0.38 per cent, p = 0.047). In sensitivity analyses (including adjustment for waist circumference), HbA1c concentrations were significantly greater among those in the highest vs. lowest quartile of sleep duration variability after additional adjustment for AHI (mean difference = 0.39 per cent, p = 0.05) and separately for hypoxemia (mean difference = 0.40 per cent, p = 0.04), with no differences observed after adjustment for sleep duration (Supplementary Table 2). Among those without diabetes, neither sleep duration nor sleep duration variability was significantly associated with HbA1c. No associations were observed between sleep maintenance efficiency and HbA1c in those with or without diabetes.
Sleep-disordered breathing measures and HbA1c
Table 4 shows the adjusted means of HbA1c by SDB measures. Among those without diabetes, HbA1c concentrations were higher in those with mild, moderate, and severe OSA when compared with those without OSA after adjustment for demographics and lifestyle factors (Models 1 and 2, mean difference = 0.06%–0.16%, p < 0.05 for all). After additional adjustment for waist circumference or BMI, HbA1c concentrations were higher in those with severe OSA only, compared with those without OSA (Model 3 and 4, mean difference = 0.09%–10%, p < 0.05 for all). There was also a significant linear trend, with HbA1c increasing across levels of OSA severity among those without diabetes. There were no associations between HbA1c and OSA severity among those with diabetes. However, the mean difference in HbA1c concentrations between those without OSA and those with severe OSA was 0.11%–0.15% (Models 3–4), which was similar in magnitude to those without diabetes. A similar pattern was observed for nocturnal hypoxemia, with small but significantly higher HbA1c concentrations observed among those in the highest quartile of hypoxemia compared with those in the lowest quartile after adjustment (Models 3–4, mean difference = 0.08%–0.09%, p < 0.05) among those without diabetes. There was also a significant linear trend, with higher HbA1c concentrations observed across higher quartiles of nocturnal hypoxemia. Among those with diabetes, there was a significant positive linear trend between hypoxemia categories and HbA1c concentrations after adjustment for demographics and lifestyle factors (Models 1 and 2, ptrend < 0.05). This association was attenuated with additional adjustment for waist circumference, but not BMI (Model 3, ptrend <0.05). It is important to note that the mean difference between those in the lowest and highest quartiles of nocturnal hypoxemia among those with diabetes (Models 3–4, 0.20%–0.28%) was larger than those without diabetes. In sensitivity analyses, the associations for OSA and hypoxemia with HbA1c among those without diabetes remained significant after additional adjustment for sleep duration (Supplementary Table 2).
Table 4.
Adjusted means of HbA1c by categories of polysomnography sleep-disordered breathing: the Multi-Ethnic Study of Atherosclerosis (2010–2013), N = 1881
| Obstructive sleep apnea, apnea–hypopnea index | |||||
|---|---|---|---|---|---|
| <5.0 None (Referent) |
5.0–14.9 Mild |
15–29.9 Moderate |
≥30 Severe |
p for trend | |
| No diabetes | N = 550 | N = 482 | N = 270 | N = 210 | |
| Model 1 | 5.64 | 5.70* | 5.71* | 5.80*** | <0.001 |
| Model 2 | 5.64 | 5.70* | 5.71* | 5.80*** | <0.001 |
| Model 3 | 5.66 | 5.70 | 5.70 | 5.76** | 0.010 |
| Model 4 | 5.67 | 5.70 | 5.69 | 5.75* | 0.035 |
| Diabetes | N = 90 | N = 110 | N = 89 | N = 80 | |
| Model 1 | 7.10 | 7.05 | 7.18 | 7.26 | 0.343 |
| Model 2 | 7.10 | 7.07 | 7.17 | 7.25 | 0.390 |
| Model 3 | 7.12 | 7.04 | 7.17 | 7.23 | 0.501 |
| Model 4 | 7.10 | 7.07 | 7.17 | 7.25 | 0.416 |
| Nocturnal hypoxemia, %† | |||||
| <0.03 (Referent) |
0.04–0.65 | 0.66–3.44 | ≥3.45 | ||
| No diabetes | N = 393 | N = 396 | N = 374 | N = 349 | |
| Model 1 | 5.63 | 5.67 | 5.71** | 5.78*** | <0.001 |
| Model 2 | 5.63 | 5.67 | 5.71** | 5.78*** | <0.001 |
| Model 3 | 5.66 | 5.67 | 5.70 | 5.75** | 0.003 |
| Model 4 | 5.67 | 5.67 | 5.69 | 5.75* | 0.010 |
| Diabetes | N = 50 | N = 100 | N = 96 | N = 123 | |
| Model 1 | 7.04 | 6.86 | 7.28 | 7.29 | 0.036 |
| Model 2 | 7.08 | 6.85 | 7.28 | 7.29 | 0.047 |
| Model 3 | 7.09 | 6.86 | 7.24 | 7.29 | 0.080 |
| Model 4 | 7.04 | 6.84 | 7.28 | 7.32 | 0.041 |
†Percent of sleep time with oxyhemoglobin saturation <90%.
Model 1 adjusted for age, sex, race/ethnicity, and field center. Model 2 adjusted for Model 1, plus education, marital status, smoking status, alcohol use, hypnotic medication, and depressive symptoms. Model 3 adjusted for Model 2, plus waist circumference. Model 4 adjusted for Model 2, plus BMI.
*p < 0.05; **p < 0.01; ***p < 0.001.
Interactions between sleep measures and race/ethnicity, age, and sex
In exploratory analyses, there was a significant interaction between sleep duration variability and race/ethnicity among those without diabetes (p = 0.02). Associations between sleep duration variability and HbA1c were observed in Chinese and Hispanics, but not non-Hispanic whites or African Americans (Supplementary Table 3); however, the average differences by sleep variability in HbA1c concentrations for Chinese and Hispanics were small (Model 3, mean difference in both groups = 0.15 per cent, p < 0.05). A significant interaction was also observed between sleep maintenance efficiency and sex among those with diabetes (p = 0.02). Among women, HbA1c concentrations were significantly higher among those in the lowest quartile of sleep maintenance efficiency compared with those in the highest quartile after adjustment for demographics, lifestyle factors, and waist circumference (Model 3, mean difference = 0.63 per cent, p = 0.03; Supplementary Table 4). There was also a significant inverse linear trend across quartiles of sleep maintenance efficiency, with higher sleep maintenance efficiency associated with a graded decrease in HbA1c concentrations. These associations remained significant after additional adjustment for the AHI and hypoxemia in sensitivity analyses (data not shown). Among men, associations were less consistent between sleep maintenance efficiency categories and HbA1c. There were also differences in the association between OSA and HbA1c by sex, with associations found in men but not women (Supplementary Table 5). The differences in HbA1c among men were observed when comparing those with no OSA to those with mild OSA, with higher HbA1c observed in those with no OSA (Model 3, mean difference = −0.81 per cent, p = 0.002); no differences were observed when comparing those with no OSA to those with moderate or severe OSA.
In sensitivity analyses (data not shown), we examined the associations of HbA1c with continuous measures of sleep duration, variability, maintenance efficiency, and OSA severity. Findings were similar to those using categorical sleep measures. Additionally, with the aim of looking at long sleep duration, we also categorized sleep duration as <5, 5.0–5.9, 6.0–6.9, 7–7.9, 8–8.9, and ≥9 hr/night. Only 37 individuals (25 nondiabetics and 12 diabetics) slept ≥9 hr/night, and there were no significant differences in HbA1c between the long sleepers (≥9 hr) and those who slept 7–7.9 hr/night. Lifestyle factors associated with short sleep duration (<5 hr/night) included shift work and depressive symptoms; sample sizes were insufficient to examine stratified analyses. However, in sensitivity analyses, we restricted our analyses to nonshift workers, and separately, among those without depression. Results did not materially differ. Finally, adjustment for diabetes medication use among those with diabetes did not alter study findings.
Discussion
In this multiethnic, community-based cohort study, where sleep duration, variability, maintenance efficiency, and SDB were objectively evaluated using both actigraphy and polysomnography, we found a graded association between shorter average sleep duration and higher HbA1c concentrations among participants with diabetes. Among women with diabetes, but not men, poor sleep maintenance efficiency was also associated with higher HbA1c concentrations. These associations persisted after adjusting for multiple potential confounders including SDB. Individuals without diabetes but with severe untreated OSA and nocturnal hypoxemia also had small increases in HbA1c concentrations. Our findings add to the growing body of literature demonstrating clinically meaningful associations between short sleep duration in men and women and poor sleep maintenance efficiency in women with glycemic control (assessed using HbA1c) among individuals with diabetes. Our findings are novel for considering not only objectively estimated average sleep duration, but also night-to-night variation in sleep duration, sleep maintenance efficiency, and multiple measures of SDB.
Our findings that short sleep duration was significantly associated with higher concentrations of HbA1c among those with diabetes are largely consistent with the existing literature using self-reported measures of sleep. A systematic review and meta-analysis conducted by Lee and colleagues found that short self-reported sleep duration, assessed by sleep questionnaires, was associated with higher HbA1c levels [8]. The absolute difference observed using self-reported measures of sleep was smaller than that observed in the present study using actigraphy-estimated sleep (mean HbA1c difference = 0.23 vs. 0.46 per cent, respectively). Fewer studies have examined the associations of objective measures of sleep duration with HbA1c. Siwasaranond and colleagues reported a significant inverse correlation between sleep duration and HbA1c in 90 patients with diabetes using 7 days of wrist actigraphy [27]. Studies have also reported an association between long sleep duration and elevated HbA1c [8, 25], which was not observed in the current study. However, long sleep duration (≥9 hr/night) was observed in less than 2 per cent of the study population; therefore, we had poor precision to detect differences in HbA1c in this group. Although the results from prior studies may be true, it is also possible that previously observed associations between long sleep duration and HbA1c may be due to residual confounding, reverse causality, or to measurement error, with overestimation of sleep duration using self-reported data [28, 29].
Sleep maintenance efficiency was inversely associated with HbA1c among women, but not men. An earlier study by Trento and colleagues reported an inverse association between sleep efficiency and HbA1c using wrist actigraphy in a sample of men and women with and without type 2 diabetes [11]. It is unclear why the association of sleep maintenance efficiency and HbA1c differed by sex in our study. Other studies, including MESA, have previously reported associations between sleep efficiency and markers of obesity in women, but not men [30–32]. The Cardiovascular Health Study Research Group also reported stronger associations between sleep disturbances and cardiovascular disease in women than men [33]. While not measuring glycemic control, these studies are relevant as obesity and cardiovascular disease are strongly associated with type 2 diabetes, and thus elevated HbA1c [34]. Sleep disturbances have been shown to increase proinflammatory cytokines (IL-6, CRP, and TNF-α) in women, but not men [35, 36]. Inflammatory markers in turn play an important role in the development of type 2 diabetes [37]. Therefore, the observed sex differences for sleep maintenance efficiency and HbA1c may be due to the increased proinflammatory cytokine response observed in women in response to poor sleep efficiency.
It is important to note that the average observed differences in HbA1c among those with diabetes between individuals with short sleep duration when compared with those sleeping 7–8 hr/night (mean difference = 0.48 per cent), and in women, between those in the lowest quartile of sleep maintenance efficiency compared with those in the highest quartile (mean difference = 0.63 per cent) are clinically relevant. For example, these differences in HbA1c concentrations are comparable to those observed after 6 months of oral antidiabetic medication use (mean difference = 0.50%–1.25%) [26]. Furthermore, the Look AHEAD study, a multicenter randomized clinical trial examining the effects of an intensive lifestyle intervention, reported a 0.36 per cent reduction in HbA1c after 4 years among participants in the treatment group [38]. Therefore, the observed differences in HbA1c for sleep duration and efficiency are comparable to both pharmacological and behavioral interventions targeting glycemic control. These results have important clinical implications, given that sleep duration and sleep maintenance efficiency can be improved with behavioral interventions [39–41]. Our data therefore suggest a potential value in sleep interventions for improving glycemic control in patients with diabetes.
Small but significant differences in HbA1c were also observed among individuals without diabetes when comparing those with severe OSA with those without OSA (mean difference = 0.11 per cent), and for those in the highest versus lowest quartile of nocturnal hypoxemia (mean difference = 0.09 per cent). The clinical importance of these findings is unclear. However, individuals with normal HbA1 concentrations may experience wide fluctuations in glucose levels during the day [42], and individuals with OSA specifically may experience both increased variability in fasting glucose [43] and increased nocturnal levels of glucose and free fatty acids [44]. Especially in people without diabetes, changes in HbA1c may not be sufficiently sensitive to detect day to day and diurnal fluctuations in glucose that may reflect the impact of OSA on glycemic status. Although we did not observe significant associations between OSA and nocturnal hypoxemia with HbA1c among those with diabetes, it is important to note that the differences in HbA1c were the same or greater than those observed among those without diabetes. Lack of statistically significant differences may be due to insufficient power given the smaller sample with diabetes.
Similarly, we report small but significant differences (mean difference = 0.15 per cent) among individuals without diabetes, in the associations of sleep duration variability and HbA1c by race, with associations observed in Chinese and Hispanics, but not non-Hispanic whites or African Americans. We also observed an unexpected association between OSA and HbA1c in men with diabetes, with those having mild OSA experiencing significantly lower HbA1c than those with no OSA (mean difference = −0.81 per cent). However, given the small sample size and lack of a dose–response relationship, these findings should be interpreted with caution.
A novel aspect of the study was the examination of sleep duration variability in relation to HbA1c. HbA1c concentrations were significantly higher among those in the upper two quartiles of sleep duration variability when compared with those in the lowest quartile of sleep duration variability; however, this association was attenuated after adjustment for waist circumference and sleep duration. Few other studies have examined the associations of sleep duration variability with markers of glycemic control. In a prior small study by Baron and colleagues, greater variability in sleep duration was associated with significantly higher HbA1c after adjustment for age and average sleep duration in a small sample of older adults with insomnia (N = 17) who wore a wrist actigraph for 7 days [45]. A recent study by Chontong et al. also found that higher sleep duration variability was associated with greater HbA1c (median 7.8 versus 7.2 per cent) among 41 patients with type 1 diabetes. Sleep variability may impact metabolism through effects on variability in eating, physical activity, and other behaviors. Further research is needed to more broadly address whether associations with sleep duration variability and glycemia control are partly reflective of variability in the alignment of sleep and other health behaviors.
Strengths of this study include the large, multiethnic community-based study sample, objective evaluation of a comprehensive set of sleep indices from both wrist actigraphy and home polysomnography, use of HbA1c as a marker of glycemic control, and stratification by type 2 diabetes status. Furthermore, we were able to explore effect modification by race/ethnicity, age, and sex, which are notably absent in the existing literature due to the use of homogenous populations and smaller sample sizes. A limitation of the present study is the cross-sectional study design, which limits causal inference. It is possible that those with elevated HbA1c have poorer quality sleep; for example, pain associated with diabetic neuropathy is associated with sleep interference [46, 47]. Additionally, individuals under active treatment for OSA were not included in this study, potentially biasing the results to individuals with less symptomatic disease. Given that the mechanisms between sleep abnormalities and glycemic control are not fully understood, it is possible that we did not adjust for all relevant confounders. For example, we did not adjust for diet and physical activity in our models as their association with our outcomes is not well understood and could be mediators or even occur secondary to the outcomes of interest (e.g. individuals may change diet given a diabetes diagnosis). We also did not adjust for insulin or diabetes medication dosage; however, adjustment for diabetes medication (yes/no) did not alter study results.
In conclusion, among individuals with diabetes, we observed associations between average sleep duration in men and women, and sleep maintenance efficiency in women only with HbA1c. Both sleep duration and sleep maintenance efficiency are amenable to behavioral interventions; therefore, these results have clinical implications as they suggest a potential value in sleep interventions for improving glycemic control in those with diabetes. Our study adds to the growing literature suggesting the potential value for addressing sleep health as strategy for improving diabetes management. Our study also suggests that untreated SDB may contribute to abnormalities in glycemic control. Well-designed prospective studies and intervention trials are needed to further elucidate the causal roles of sleep disturbances with glycemic control, including assessment of potential genes related to sleep and glucose homeostasis, and the impact of sleep-focused interventions in the prevention or management of diabetes. Future work should also consider other sleep behaviors, such as napping, which may also play a role in glycemic control.
Supplementary Material
Supplementary material is available at SLEEP online.
Supplementary Material
Acknowledgments
The authors thank the other investigators, the staff, and the participants of the MESA study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.
Funding
This work was supported by grants and contracts T32-HL-007779, T32-HL-082610, R01HL098433, N01-HC-95159, N01-HC-95160, N01-HC-95161,N01-HC-95162, N01-HC-95163, N01-HC-95164, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the National Heart, Lung, and Blood Institute and by grants UL1-TR-000040 and UL1-TR-001079 from the National Center for Research Resources. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Conflict of interest statement. None declared.
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