Habitual late sleep initiation is associated with increased incidence of type 2 diabetes mellitus in Korean adults: the Korean Genome and Epidemiology Study

Attention to Innate Circadian Rhythm and the Impact of Its Disruption on Diabetes

Novel strategies are required to reduce the risk of developing diabetes and/or clinical outcomes and complications of diabetes. In this regard, the role of the circadian system may be a potential candidate for the prevention of diabetes. We reviewed evidence from animal, clinical, and epidemiological studies linking the circadian system to various aspects of the pathophysiology and clinical outcomes of diabetes. The circadian clock governs genetic, metabolic, hormonal, and behavioral signals in anticipation of cyclic 24-hour events through interactions between a "central clock" in the suprachiasmatic nucleus and "peripheral clocks" in the whole body. Currently, circadian rhythmicity in humans can be subjectively or objectively assessed by measuring melatonin and glucocorticoid levels, core body temperature, peripheral blood, oral mucosa, hair follicles, rest-activity cycles, sleep diaries, and circadian chronotypes. In this review, we summarized various circadian misalignments, such as altered light-dark, sleep-wake, rest-activity, fasting-feeding, shift work, evening chronotype, and social jetlag, as well as mutations in clock genes that could contribute to the development of diabetes and poor glycemic status in patients with diabetes. Targeting critical components of the circadian system could deliver potential candidates for the treatment and prevention of type 2 diabetes mellitus in the future.

The Relationship between Breakfast and Sleep and Cardiovascular Risk Factors

Despite extensive research on the individual effects of breakfast and sleep on health outcomes, there has been limited investigation into their combined effects. We aimed to evaluate the relationship between breakfast-eating behavior and sleep timing on cardiovascular disease (CVD) risk factors. A total of 16,121 participants (6744 men and 9377 women) aged 19 years or older were selected from the Korea National Health and Nutrition Examination Surveys (2016-2018, 2021). We classified participants into four groups: early sleep + regular breakfast eaters (group 1), late sleep + regular breakfast eaters (group 2), early sleep + infrequent breakfast eaters (group 3), and late sleep + infrequent breakfast eaters (group 4). In men, group 4 had a lower prevalence of obesity than group 1 (OR 0.78, 95%CI 0.62-0.97), and groups 2, 3, and 4 had a higher prevalence of metabolic syndrome (MetS) than group 1 (OR 1.43, 1.62, and 1.47, respectively). In women, group 4 had a lower prevalence of dyslipidemia than group 1 (OR 0.59, 95%CI 0.44-0.80), and group 2 had a higher prevalence of MetS than group 1 (OR 1.24, 95%CI 1.03-1.50). The combination of skipping breakfast and late sleep timing was associated with the higher prevalence of MetS particularly in men. Moreover, the relationship between breakfast and sleep timing on CVD risk factors differed by sex and age group.

Optimal cutoffs of sleep timing and sleep duration for cardiovascular risk factors

AIM

We aimed to establish the optimal cutoffs of sleep timing and duration to assess obesity, hypertension (HTN), diabetes mellitus (DM), dyslipidemia (DL), and metabolic syndrome (MetS) using data from the Korea National Health and Nutrition Examination Surveys.

METHODS

In this cross-sectional study, data from 18,677 participants (8,107 men and 10,570 women) aged 19 or over were used. A receiver operating characteristic (ROC) curve adjusted for potential confounding variables was constructed to calculate the cutoff of sleep-related variables (bedtime, mid-sleep on free days corrected for sleep debt on workdays (MSFsc), and sleep duration) for assessing cardiovascular disease (CVD) risk factors according to sex.

RESULTS

Bedtime between 9:00 PM to 0:30 AM for men and 10:00 PM to 11:00 PM for women is appropriate for assessing obesity, HTN, DM, DL, and MetS. The cutoff range was 9:00 PM to 11:00 PM for men ≥65 years and 9:00 PM to 12:00 AM for women ≥65 years, which was slightly earlier than that for participants <65 years. The optimal MSFsc cutoff points were established between 12:00 AM to 3:00 AM and sleep durations around 6 h were associated with the optimal cutoffs for assessing CVD risk factors.

CONCLUSIONS

Bedtime between 10:00 PM to 11:00 PM, early MSFsc, and short sleep durations were appropriate for assessing CVD risk factors.

The associations between sleep timing and night sleep duration with dyslipidemia in a rural population: The Henan Rural Cohort Study

Evidence linking sleep timing and night sleep duration to dyslipidemia was limited and inconclusive, especially among low- and middle-income adults. The aims were to evaluate the associations between sleep timing, night sleep duration and dyslipidemia in a rural population. Based on the Henan Rural Cohort Study, a total of 37 164 participants were included. The Pittsburgh Sleep Quality Index was used to collect sleep information. Logistic regression and restrictive cubic splines were conducted to explore the associations. Of the 37 164 enrolled participants, 13881 suffered from dyslipidemia. Compared to the reference groups, people who went to sleep after 23:00 or woke up after 7:30 had higher prevalence of dyslipidemia, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs)were 1.30 (1.20-1.41) and 1.34 (1.19-1.50). The adjusted OR (95%CI) of participants in the Late-sleep/Late-rise category compared to the Early-sleep/Early-rise category was 1.55 (1.08-1.23). Compared to the reference (7~≤8 h), the adjusted OR (95%CI) was 1.11 (1.03-1.20) for longer (>9 h) night sleep duration. Moreover, the combined effects of sleep duration (>9 h) with sleep time (22:00~) (OR = 1.46, 95%CI: 1.16-1.84), sleep duration (>9 h) with wake-up time (≥7:30) (OR = 1.28, 95%CI: 1.08-1.51), and sleep duration (>9 h) with the Late-sleep/Late-rise category (OR = 1.41, 95%CI: 1.14-1.75) increased the prevalence of dyslipidemia. Accordingly, our results indicate that delayed sleep timing and longer night sleep duration had independent and joint effects on higher risks of dyslipidemia in rural population.

Sleep Duration and the Risk of Type 2 Diabetes: A Community-Based Cohort Study with a 16-Year Follow-up

BACKGRUOUND

We aimed to investigate the moderating effects of obesity, age, and sex on the association between sleep duration and the development of diabetes in Asians.

METHODS

We analyzed data from a cohort of the Korean Genome and Epidemiology Study conducted from 2001 to 2020. After excluding shift workers and those with diabetes at baseline, 7,407 participants were stratified into three groups according to sleep duration: ≤5 hours/night, >5 to 7 hours/night (reference), and >7 hours/night. The Cox proportional hazards analyses were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident type 2 diabetes mellitus (T2DM). Subgroup analyses were performed according to obesity, age, and sex.

RESULTS

During 16 years of follow-up, 2,024 cases of T2DM were identified. Individuals who slept ≤5 h/night had a higher risk of incident diabetes than the reference group (HR, 1.17; 95% CI, 1.02 to 1.33). The subgroup analysis observed a valid interaction with sleep duration only for obesity. A higher risk of T2DM was observed in the ≤5 hours/night group in non-obese individuals, men, and those aged <60 years, and in the >7 hours/night group in obese individuals (HRs were 1.34 [95% CI, 1.11 to 1.61], 1.22 [95% CI, 1 to 1.49], and 1.18 [95% CI, 1.01 to 1.39], respectively).

CONCLUSION

This study confirmed the effect of sleep deprivation on the risk of T2DM throughout the 16-year follow-up period. This impact was confined to non-obese or young individuals and men. We observed a significant interaction between sleep duration and obesity.

Association of Sleep Patterns with Type 2 Diabetes Mellitus: A Cross-Sectional Study Based on Latent Class Analysis

Sleep duration, sleep quality and circadian rhythm disruption indicated by sleep chronotype are associated with type 2 diabetes. Sleep involves multiple dimensions that are closely interrelated. However, the sleep patterns of the population, and whether these sleep patterns are significantly associated with type 2 diabetes, are unknown when considering more sleep dimensions. Our objective was to explore the latent classes of sleep patterns in the population and identify sleep patterns associated with type 2 diabetes. Latent class analysis was used to explore the best latent classes of sleep patterns based on eleven sleep dimensions of the study population. Logistic regression was used to identify sleep patterns associated with type 2 diabetes. A total of 1200 participants were included in the study. There were three classes of sleep patterns in the study population: "circadian disruption with daytime dysfunction" (class 1), "poor sleep status with daytime sleepiness" (class 2), and "favorable sleep status" (class 3). After controlling for all confounding factors, people in class 2 have significantly higher prevalence of type 2 diabetes than those in class 3 (OR: 2.24, 95% CI 1.26-4.00). Sleep problems have aggregated characteristics. People with sleep patterns involving more or worse sleep problems have higher significantly prevalence of T2DM.

Adherence to a Healthy Sleep Pattern and Incidence of Cardiometabolic Multimorbidity Among Hypertensive Patients: A Prospective Study of UK Biobank

Study Objectives

To investigate whether a healthy sleep pattern would reduce the risk of cardiometabolic multimorbidity (CMM) among hypertensives.

Methods

This is a prospective cohort analysis from the UK Biobank. A total of 69 524 hypertensives without a history of diabetes mellitus, coronary heart disease, or stroke at baseline were enrolled. Five dimensions of healthy sleep at baseline including early chronotype, sleep 7–8 h/d, free of insomnia, no snoring, and no frequent excessive daytime sleepiness were used to generate a healthy sleep score ranging from 0 to 5 (one point was given for each dimension of healthy sleep). A higher score indicated a healthier sleep pattern. We set five groups corresponding to the healthy sleep score of 5, 4, 3, 2, and 0–1, respectively. The primary outcome was the incidence of overall CMM among enrolled hypertensives. We assessed the adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) by Fine-Gray subdistribution hazard models.

Results

We found the full-adjusted HR (95% CI) for overall CMM was 0.93 (0.91–0.95) for a 1-point increase in the healthy sleep score. Compared to hypertensives with a healthy sleep score of 0–1, those with a score of 5 had a 27% lower risk of overall CMM, and 37%, 23%, and 20% lower risks of diabetes mellitus, coronary heart disease, and stroke, respectively, after adjusting for sociodemographic characteristic, lifestyle, and clinical factors.

Conclusions

Our results indicated that a healthy sleep pattern was associated with lower risks of CMM outcomes among hypertensives.

Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis

Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation-maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.

Association of sleep duration with risk of type 2 diabetes mellitus in a rural Chinese population: a nested case-control study

PURPOSE

To investigate the association of sleep duration with type 2 diabetes mellitus (T2DM) in a rural Chinese population.

METHODS

A 1:1 matched nested case-control study was performed based on a cohort that had been established in rural communities in Henan Province, China. T2DM patients and healthy controls (550 pairs) were included in this study.

RESULTS

Abnormal sleep duration significantly increased the risk of T2DM with an approximate U-shaped association (sleep duration ≤ 6 h, OR = 1.742, 95% CI = 1.007-3.011, P = 0.047; sleep duration 8-9 h, OR = 1.462, 95% CI = 1.038-2.060, P = 0.030) compared with participants with a night sleep duration of 7-8 h, after adjusting for multiple confounders. When stratified by gender, only women were sensitive to shorter sleep duration (OR = 2.483, 95% CI = 1.149-5.366, P = 0.021). Abnormal sleep duration (too short or too long) had adverse effects on homeostasis model assessment (HOMA) and blood metabolites, and the effect was more noticeable in people with longer sleep durations.

CONCLUSION

In a rural Chinese population, both too short and too long sleep duration increased the risk of T2DM. Especially women with less sleep duration have a higher risk of T2DM. Abnormal sleep also affects the HOMA index and metabolites; the relationship between HOMA-IR, total cholesterol, and LDL-Cholesterol with sleep duration was U-shaped, while fasting plasma glucose, body mass index, waist circumference, and triglyceride levels increased significantly only with longer sleep duration.

Associations of midpoint of sleep and night sleep duration with type 2 diabetes mellitus in Chinese rural population: the Henan rural cohort study

Background

The study aimed to investigate the independent and combined effects of midpoint of sleep and night sleep duration on type 2 diabetes mellitus (T2DM) in areas with limited resources.

Methods

A total of 37,276 participants (14,456 men and 22,820 women) were derived from the Henan Rural Cohort Study. Sleep information was assessed based on the Pittsburgh Sleep Quality Index. Logistic regression models and restricted cubic splines were used to estimate the relationship of the midpoint of sleep and night sleep duration with T2DM.

Results

Of the 37,276 included participants, 3580 subjects suffered from T2DM. The mean midpoint of sleep among the Early, Intermediate and Late groups were 1:05 AM ±23 min, 1:56 AM ±14 min, and 2:57 AM ±34 min, respectively. Compared to the Intermediate group, adjusted odds ratios (ORs) and 95% confidence interval (CI) of T2DM were 1.13 (1.04–1.22) and 1.14 (1.03–1.26) in the Early group and the Late group. Adjusted OR (95% CI) for T2DM compared with the reference (7- h) was 1.28 (1.08–1.51) for longer (≥ 10 h) night sleep duration. The combination of late midpoint of sleep and night sleep duration (≥ 9 h) increased 38% (95% CI 10–74%) prevalence of T2DM. These associations were more obvious in women than men.

Conclusions

Late and early midpoint of sleep and long night sleep duration were all associated with higher prevalence of T2DM. Meanwhile, midpoint of sleep and night sleep duration might have combined effects on the prevalence of T2DM, which provided potential health implications for T2DM prevention, especially in rural women.

Trial registration

The Henan Rural Cohort Study has been registered at Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). Date of registration: 2015-07-06.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-10833-6.

A Novel Risk Score for Type 2 Diabetes Containing Sleep Duration: A 7-Year Prospective Cohort Study among Chinese Participants

BACKGROUND

Sleep duration is associated with type 2 diabetes (T2D). However, few T2D risk scores include sleep duration. We aimed to develop T2D scores containing sleep duration and to estimate the additive value of sleep duration.

METHODS

We used data from 43,404 adults without T2D in the Beijing Health Management Cohort study. The participants were surveyed approximately every 2 years from 2007/2008 to 2014/2015. Sleep duration was calculated from the self-reported usual time of going to bed and waking up at baseline. Logistic regression was employed to construct the risk scores. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to estimate the additional value of sleep duration.

RESULTS

After a median follow-up of 6.8 years, we recorded 2623 (6.04%) new cases of T2D. Shorter (both 6-8 h/night and <6 h/night) sleep durations were associated with an increased risk of T2D (odds ratio (OR) = 1.43, 95% confidence interval (CI) = 1.30-1.59; OR = 1.98, 95%CI = 1.63-2.41, respectively) compared with a sleep duration of >8 h/night in the adjusted model. Seven variables, including age, education, waist-hip ratio, body mass index, parental history of diabetes, fasting plasma glucose, and sleep duration, were selected to form the comprehensive score; the C-index was 0.74 (95% CI: 0.71-0.76) for the test set. The IDI and NRI values for sleep duration were 0.017 (95% CI: 0.012-0.022) and 0.619 (95% CI: 0.518-0.695), respectively, suggesting good improvement in the predictive ability of the comprehensive nomogram. The decision curves showed that women and individuals older than 50 had more net benefit.

CONCLUSIONS

The performance of T2D risk scores developed in the study could be improved by containing the shorter estimated sleep duration, particularly in women and individuals older than 50.