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Sleep and Breathing

, Volume 20, Issue 2, pp 569–574 | Cite as

Shorter sleep duration is associated with poorer glycemic control in type 2 diabetes patients with untreated sleep-disordered breathing

  • Nantaporn Siwasaranond
  • Hataikarn Nimitphong
  • Sunee Saetung
  • Naricha Chirakalwasan
  • Boonsong Ongphiphadhanakul
  • Sirimon Reutrakul
Sleep Breathing Physiology and Disorders • Original Article

Abstract

Purpose

The purpose of this study is to explore the impact of sleep duration on glycemic control in type 2 diabetes patients with untreated sleep-disordered breathing (SDB).

Methods

Ninety type 2 diabetes patients participated in the study. SDB was diagnosed using an overnight in-home monitoring device (WatchPAT200). Sleep duration was recorded by wrist actigraphy for 7 days. Medical records were reviewed for hemoglobin A1c (HbA1c) values.

Results

Seventy-one patients (78.8 %) were diagnosed with SDB [apnea-hypopnea index (AHI) ≥ 5]. In patients with SDB, there was no significant relationship between AHI and glycemic control. In addition, oxygen desaturation index, minimum oxygen saturation, and time spent below oxygen saturation of 90 % were not significantly correlated with glycemic control. Sleep duration, however, was inversely correlated with HbA1c (r = −0.264, p 0.026). Multiple regression analysis adjusting for age, sex, body mass index, insulin use, diabetes duration, and AHI revealed that sleep duration was significantly associated with HbA1c (p = 0.005). Each hour reduction in sleep duration was associated with a 4.8 % increase in HbA1c of its original value (95 % CI 1.5–8.0).

Conclusion

In type 2 diabetes patients with untreated SDB, shorter sleep duration was independently associated with poorer glycemic control. Sleep duration optimization may lead to improved glycemic control in this population.

Keywords

Sleep-disordered breathing Obstructive sleep apnea Sleep duration Diabetes Glycemic control Short sleep duration 

Introduction

One of the common types of sleep-disordered breathing (SDB) is obstructive sleep apnea (OSA). OSA is a complex sleep disorder characterized by repetitive episodes of upper airway closures or partial collapse during sleep, resulting in intermittent hypoxia, fragmented sleep with low amounts of slow wave sleep, and generally reduced total sleep time. The gold standard diagnostic test is overnight laboratory polysomnography (PSG), which allows for the quantification of episodes of apnea and hypopnea per hour of sleep, yielding an apnea-hypopnea index (AHI). A diagnosis of OSA is made when the AHI ≥ 5 [1]. OSA is well-recognized as a risk factor for insulin resistance and type 2 diabetes, independent of the degree of obesity [2], and is very prevalent (56–84 %) in patients with type 2 diabetes [3]. Increasing severity of OSA has been shown to correlate with poorer glycemic control, although the results were not entirely consistent [4, 5].

Short sleep duration has been associated with increased diabetes risk in multiple prospective observational studies [6]. In patients with type 2 diabetes, there was a suggestion of a U-shaped relationship between self-report sleep duration and glycemic control [7, 8]. One of the largest studies, conducted in 4870 Japanese participants, revealed that shorter or longer sleep duration was associated with higher hemoglobin A1c (HbA1c) levels than those sleeping 6.5–7.4 h [7]. Some studies only found an association between either short [9] or long [10] sleep duration with glycemic control. The main limitation is that sleep duration was mostly self-reported. Only a few studies have examined sleep duration using objective measurements, such as actigraphy. These studies were relatively small (20–47 participants), and none have found an association between sleep duration and glycemic control in patients with type 2 diabetes [11, 12, 13].

Since OSA is not an uncommon disorder and short sleep duration (<6 h) is prevalent in a modern society [14], it is expected that some people will have both conditions. Recent data emerged which suggested that this combination may be more detrimental to health than one alone. For example, a combination of OSA and short sleep (<6 h) as assessed by PSG in 1499 individuals was associated with a much higher risk of having hypertension [odds ratio (OR) 4.37] compared to those with OSA but normal sleep duration (OR 2.51) [15]. In addition, self-reported short sleep duration (<5 h) and OSA (as assessed by a portable home sleep study) was associated with a significant risk of having visceral obesity as measured by computed tomography (OR 4.40) in 838 participants of the Korean Genome and Epidemiology Study when compared to those sleeping ≥7 h and without OSA [16]. Another study in 136 Japanese participants with and without metabolic syndrome, matched by age and body mass index, revealed that the severity of OSA (as measured by type 3 portable monitor) was comparable between groups [17]. However, participants with metabolic syndrome had significantly shorter sleep duration (5.8 vs 6.1 h) as measured by actigraphy.

To date, there has not been a study examining an impact of this combination in patients with type 2 diabetes. Therefore, the purpose of this study was to explore the role of sleep duration on glycemic control in type 2 diabetes patients with untreated SDB, especially OSA. In addition, as sleep quality has been shown to impact glycemic control in some studies [3], we also explore this sleep variable in the current study.

Methods

Adults with type 2 diabetes who were being followed in the endocrinology clinic at Faculty of Medicine Ramathibodi Hospital, Mahidol University, were invited to participate. Exclusion criteria included having previously diagnosed OSA, being currently pregnant, or performing shift work. Additionally, patients with a history of congestive heart failure or low ejection fraction, chronic obstructive pulmonary disease, end-stage renal disease, or severe chronic liver disease such as cirrhosis, stroke, permanent pacemaker placement, and use of certain medications (opioids/narcotics, alpha adrenergic blockers, clonidine, methyldopa, nitroglycerin) were also excluded in order to obtain valid results from the SDB diagnostic method utilized (see subsequent sections). All participants gave written informed consent. The protocol was approved by the Institutional Review Board, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand.

Assessment of diabetes history and glycemic control

After obtaining informed consent, weight and neck circumference (cm) was measured. Research personnel interviewed participants about their diabetes history and management. Height, age, current medications, and most recent HbA1c values (within 3 months) were extracted from patient medical records. HbA1c is an index of glycemic control over the preceding 90 days. Body mass index (BMI) was calculated as weight (kg)/height (m)2. Fasting plasma glucose was obtained in the morning after no calories were consumed past midnight. This was performed, in most cases, upon the return of the Actiwatch (see in the following paragraphs) or within 1 week of the sleep assessments.

Assessment of SDB

SDB was diagnosed using an FDA-approved portable diagnostic device, WatchPAT 200 (Itamar Medical, Israel), which has been validated against PSG in populations with and without diabetes [18, 19]. This non-invasive device is shaped similar to a large watch, worn on the non-dominant wrist immediately before bedtime and removed upon awakening in the morning. The device has two probes connecting to the participants’ fingers to measure changes in peripheral arterial tone (PAT) and oxygen saturation, along with built-in actigraphy to measure sleep time. The severity of SDB is assessed by PAT apnea-hypopnea index (AHI) which is automatically generated by the software, using changes in the peripheral arterial tonometry. SDB is considered present if AHI ≥ 5. SDB is considered mild if AHI ≥ 5 but <15, moderate if AHI ≥ 15–30, and severe if AHI > 30. Even though the device cannot differentiate obstructive from central apnea events, we excluded patients with certain conditions which may pose a higher risk for central apnea as described in the exclusion criteria. Therefore, we expected that the AHI values obtained mostly represent OSA.

Other sleep parameters included oxygen desaturation index (ODI) which is the average number of times per hour of sleep time that the oxygen saturation drops by 3 % or more. Minimum O2 is the lowest oxygen saturation value over the recording period. T90 is the percentage of total sleep time in which the oxygen saturation remains below 90 %.

Because this device relies on changes in peripheral arterial tone, use of certain medications which could affect the arterial tone, including alpha-blockers and short-acting nitrates, was not allowed according to the device’s operation manual. These were described in the exclusion criteria.

Sleep duration, time in bed, and sleep efficiency measurement

Participants wore an Actiwatch 2 wrist activity monitor (Philips Respironics, Bend, Oregon, U.S.A.) for 7 consecutive days. These monitors use highly sensitive omnidirectional accelerometers to count the number of wrist movements in 30-s epochs. The software scores each 30-s epoch as sleep or wake based on a threshold of activity counts that is estimated using activity within the epoch being scored as well as the epochs 2 min before and after that epoch. Bedtime and wake time are set by the researcher using the event markers, a sleep log data, as well as an in-person review of sleep timing with the participants upon return of the watch. Sleep duration was defined as the amount of actual sleep obtained at night, time in bed was defined as the time interval between bedtime and wake time, and sleep efficiency (a marker of sleep quality) as percentage of time in bed spent sleeping. These variables were calculated using Actiware 6.0 software, supplied by the manufacturer. For each participant, the mean across all available nights was used. For 95 % of participants in these analyses, at least 6 days of actigraphy recording were available and the remaining 5 % had 3 to 5 days of actigraphy recording.

For most participants, actigraphy recording was performed right before SDB assessment. For some, the order was reversed but both assessments were performed consecutively.

Statistical analysis

All study data were checked for normality and presence of potential violations of statistical assumptions. Continuous variables which were normally distributed were expressed as mean ± SD, and those without a normal distribution were expressed as median (interquartile range). Categorical data were expressed as frequencies/percentages.

HbA1c, fasting plasma glucose (FPG), diabetes duration, AHI, ODI, minimum O2, and T90 were not normally distributed; therefore, the natural logarithm (Ln) transformation of these variables was used in the analyses. To determine the factors associated with glycemic control, Pearson correlations were used to explore the associations between the natural logarithm of HbA1c or FPG and continuous demographic and sleep variables. Unpaired independent sample t tests or ANOVA were used to analyze differences in the natural logarithm of HbA1c and FPG among demographic and sleep parameter groups.

To determine whether sleep duration was independently associated with glycemic control in those with SDB, a hierarchical multiple regression was performed to assess the association between sleep duration and HbA1c, controlling for demographic and OSA severity (AHI). Demographic variables [age, sex (male reference), BMI, diabetes duration, and insulin use] were entered in the first step. AHI was entered in the second step. In the final step, sleep duration was entered. Collinearity analysis demonstrated no collinearity among the variables. Similar analyses were performed to determine whether time in bed or sleep efficiency was independently associated with glycemic control.

Results

Of the 90 participants, 71 participants (78.8 %) met the diagnosis criteria of SDB (AHI ≥5) and were included in the analyses. Of these, 36 (50.7 %), 23 (32.3 %), and 12 (16.9 %) had mild, moderate, and severe SDB, respectively, with an overall median AHI of 14.8. Baseline characteristics and objective sleep parameters of patients with SDB are shown in Table 1. On average, participants were obese (BMI ≥ 25 kg/m2 using the Asian cutoff [20]) with a median HbA1c of 7.2 %, and one third were using insulin. Average sleep duration was 5.97 h, average time in bed was 7.40 h, and average sleep efficiency was 80.6 %.
Table 1

Baseline demographic and sleep characteristics of participants

 

N = 71

Demographic and glycemic characteristics

 Age

55.2 ± 11.9

 Male (n) (%)

31 (43.7)

 BMI (kg/m2)

28.8 ± 4.9

 Neck circumference (cm)

37.7 ± 3.5

 Diabetes duration (years)

10 (5–19)

 Insulin use (n) (%)

25 (35.2)

 HbA1c (%)

7.2 (6.7–8.1)

 Fasting plasma glucose (mg/dL)

133 (111–155)

Objective sleep measurements

 AHI

14.8 (9.6–25.5)

 ODI

9.2 (4.9–17.5)

 Minimum O2 (%)

85 (80–88)

 T90 (%)

0.5 (0.1–1.5)

 Sleep duration (h)

5.97 ± 1.09

 Time in bed (h)

7.40 ± 1.23

 Sleep efficiency (%)

80.6 ± 7.0

Data are expressed in mean ± SD or median (IQR) unless otherwise noted

Association between demographic and sleep parameters with glycemic control

Pearson correlations between demographic and sleep parameters with glycemic control are shown in Table 2. Additionally, there were no differences in HbA1c levels between the sexes [male 7.2 % (6.6–8.1) vs female 7.3 % (6.7–8.1), p = 0.456]. Those who required insulin had significantly higher HbA1c compared to those who were not using insulin [7.8 % (7.3–8.8) vs 7.1 % (6.6–7.7), p = 0.003]. FPG levels were not different between sexes (p = 0.23) or insulin use (p = 0.38).
Table 2

Correlation (r) between demographics and sleep characteristics and glycemic control (natural log of HbA1c and FPG) in participants with SDB

 

HbA1c

FPG

 

r value

p value

r value

p value

Demographics

 Age

−0.157

0.190

−0.188

0.117

 BMI

0.180

0.133

0.064

0.595

 Neck circumference

0.017

0.886

0.138

0.250

 Ln years diabetes duration

−0.052

0.667

−0.093

0.442

Objective sleep measurements

 Ln AHI

0.182

0.130

0.118

0.327

 Ln ODI

0.189

0.114

0.143

0.233

 Ln minimum O2

−0.073

0.547

−0.041

0.737

 Ln T90

0.128

0.286

0.101

0.403

 Sleep duration

−0.264

0.026

−0.100

0.405

 Time in bed

−0.202

0.090

−0.008

0.944

 Sleep efficiency

−0.160

0.182

−0.224

0.060

Severity of SDB as expressed by AHI was not significantly correlated with HbA1c or FPG. In addition, no significant association between HbA1c and SDB severity groups was found [7.2 % (6.6–8.1), 7.2 % (6.6–7.8), and 7.7 % (7.3–8.5) for mild, moderate, and severe OSA, respectively, p = 0.667]. Similarly, other SDB parameters (ODI, minimum O2, and T90) were not significantly correlated with HbA1c or FPG. Sleep duration as measured by actigraphy, however, was negatively correlated with HbA1c, while time in bed showed borderline association. Sleep duration and time in bed were not associated with FPG. In addition, there was no association between sleep efficiency and HbA1c, but it was marginally associated with FPG.

The hierarchical multiple regression assessing the association between sleep duration and HbA1c, controlling for relevant demographic and SDB severity, is presented in Table 3. Demographic variables explained 14.7 % of the variance in HbA1c. AHI was added in the second step. Inclusion of this variable did not improve the explanatory power of the model (∆R 2 = 0.011, p = 0.363). Sleep duration was added in the final model and was significantly associated with HbA1c (unstandardized coefficient, B = −0.048, p = 0.005). Each hour decrease in sleep duration was associated with an increase in HbA1c of 4.8 % (95 % CI 1.5–8.0 %) of its original value. Further, this model explained an additional 9.7 % of the variance in HbA1c (∆R 2 = 0.097, p = 0.005, total adjusted R 2 = 0.240), which indicated that sleep duration contributed significantly to the model’s explanation of the variance of HbA1c above and beyond demographic and OSA severity.
Table 3

Hierarchical regression analysis with natural log of HbA1c as the outcome in participants with SDB

 

Model 1

Model 2

Model 3

Variable

B

p value

B

p value

B

p value

Age

−0.002

0.319

−0.003

0.230

−0.003

0.145

Sex (reference: male)

0.023

0.558

0.032

0.429

0.044

0.249

BMI

0.006

0.209

0.005

0.361

0.002

0.684

Ln years diabetes duration

0.007

0.787

0.009

0.751

0.007

0.770

Insulin use

0.107

0.012

0.097

0.027

0.119

0.005

Ln AHI

  

0.029

0.363

0.025

0.407

Sleep duration (h)

    

−0.048

0.005

Adjusted R 2

0.147

 

0.145

 

0.240

 

∆R2

 

0.011

0.363

0.097

0.005

B unstandardized coefficient

Similar regression analysis with time in bed revealed that time in bed was also significantly associated with HbA1c (B = −0.037, p = 0.011). This was expected as time in bed and sleep duration were highly correlated (r = 0.887, p < 0.001). Sleep efficiency, however, was not independently associated with HbA1c (B = −0.003, p = 0.318) or FPG (B = −007, p = 0.132).

Discussion

To our knowledge, this study is the first to explore the role of habitual sleep duration on glycemic control in type 2 diabetes patients with untreated SDB. We found that sleep duration in this patient group was independently associated with HbA1c, after adjusting for demographics and SDB severity. Each hour decrease in sleep duration was associated with a 4.8 % increase in HbA1c of its original values. For example, if sleep duration is reduced by 2 h, HbA1c of 7 % is expected to increase to 7.7 %. This magnitude of difference pars with the efficacy of some of currently available diabetes medications [21]. While there was a trend of increasing HbA1c with increasing severity of SDB in this group, this was not statistically significant. It is possible that this was due to the relatively small number of participants with severe SDB. As time in bed was closely related to sleep duration, this was also found to be associated with glycemic control in a similar fashion. We, however, did not find an association between sleep efficiency and glycemic control. The results of this study support the detrimental impact of shorter sleep duration on glycemic control in patients with type 2 diabetes with untreated SDB.

While there are no mechanistic studies exploring the effect of the combination of SDB, specifically OSA, and short sleep on glucose metabolism, each component has been examined separately. In one experimental study, 13 healthy volunteers were subjected to 5 h of intermittent hypoxia while awake, resulting in an average of 24.3 desaturation events per hour [22], equivalent to moderate OSA. Insulin sensitivity and glucose effectiveness, as assessed by intravenous glucose tolerance test (IVGTT), were reduced by 17 and 31 %, respectively, without simultaneous increase in insulin secretion. A causative role of partial sleep restriction in promoting alterations in glucose metabolism was established in several well-controlled, experimental studies in healthy human subjects involving sleep restriction to 4 to 5.5 h per night for 5 to 14 nights and assessments of glucose metabolism by IVGTT tolerance test or euglycemic-hyperinsulinemic clamp. These have confirmed a reduction of insulin sensitivity ranging from 18 to 24 %, in response to sleep restriction without simultaneous increase in insulin levels. This resulted in impaired glucose tolerance and increased risk of diabetes [23, 24, 25, 26]. Factors likely contributing to the increase in insulin resistance include decreased brain glucose utilization during waking hours, increased activity of hypothalamic pituitary adrenal axis and sympathetic nervous system, increased proinflammatory cytokines, and abnormal adipocyte functions resulting from sleep deprivation [3]. Therefore, it is likely that the combination of OSA and sleep deprivation may be more detrimental to glucose metabolism than each condition alone, although this has not been studied specifically in type 2 diabetes.

Intervention studies with continuous positive airway pressure (CPAP) to improve insulin resistance or glycemic control have yielded conflicting results [2]. These may be due to differences in study designs or duration of nightly CPAP use, as well as different measures of glucose metabolism. A recent meta-analysis of six randomized control trials in patients with type 2 diabetes (total 128 participants) revealed that CPAP use did not result in lower HbA1c levels, but the index of insulin resistance improved significantly [27]. The data on the impact of sleep extension on glucose metabolism have recently become available in small experimental studies in healthy volunteers. A short-term home sleep extension for 6 weeks in 16 non-obese healthy adults who were habitual short sleepers found a significant positive correlation between changes in total sleep time and indices of insulin sensitivity at the end of the experiment [28]. In another study, three nights of “catch-up” sleep in 19 men with chronic sleep restriction during the work days resulted in improved insulin sensitivity [29]. As our results support the role of sleep duration on glycemic control in type 2 diabetes patients with OSA, it is possible that, in order to maximize glycemic improvement in this population, the intervention may need to focus both on treating OSA as well as sleep duration optimization.

Our study’s strength is that we utilized objective sleep measurements for both SDB and sleep duration. However, the limitation is that the number of the participants is relatively small. The cross-sectional nature of the study did not prove a causal relationship. In addition, there were no participants with long sleep duration (longest 8.1 h) so we could not explore the impact of long sleep duration on glucose metabolism. The participants overall had relatively well-controlled diabetes, which may not be a true representation of the patient population. This may be due to a selection bias of participants who were interested in research and able to understand the WatchPAT and Actiwatch 2 instructions for home use. In addition, the WatchPAT cannot differentiate between central and obstructive events. Efforts were made to enroll participants who were not at risk for central apnea, but we could not exclude the possibility that some may have had a component of central apnea as well. As the device relies partly on an intact autonomic nervous system, it is possible that our patients with diabetes may have had occult autonomic dysfunction, which would have resulted in an underestimation of obstructive events. None of our patients, however, had overt autonomic dysfunction. Lastly, we did not formally assess insomnia, restless leg syndrome, or periodic limb movements during sleep, although none of the participants had these diagnoses. There were four participants who were using benzodiazepines as needed. Excluding these participants did not change the results (sleep duration independently predicted HbA1c, B −0.049, p 0.005).

In summary, in type 2 diabetes patients with untreated SDB, shorter sleep duration was independently associated with poorer glycemic control. Further research is needed to confirm this finding. Whether optimization of sleep duration in addition to CPAP treatment will improve glycemic control beyond CPAP treatment alone should be explored.

Notes

Acknowledgments

The study received grant support from Mahidol University.

Conflict of interest

S.R. received speaker honoraria from Sanofi Aventis and research grant from Merck. All other authors disclosed no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Nantaporn Siwasaranond
    • 1
  • Hataikarn Nimitphong
    • 1
  • Sunee Saetung
    • 1
  • Naricha Chirakalwasan
    • 2
    • 3
  • Boonsong Ongphiphadhanakul
    • 1
  • Sirimon Reutrakul
    • 1
  1. 1.Section of Endocrinology, Department of Medicine, Faculty of Medicine Ramathibodi HospitalMahidol UniversityBangkokThailand
  2. 2.Division of Pulmonary and Critical Care Medicine, Department of Medicine, Faculty of MedicineChulalongkorn UniversityBangkokThailand
  3. 3.Excellence Center for Sleep Disorders, King Chulalongkorn Memorial HospitalThai Red Cross SocietyBangkokThailand

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