Bedtime, sleep pattern, and incident cardiovascular disease in middle-aged and older Chinese adults: The dongfeng-tongji cohort study

Sleep patterns and DNA methylation age acceleration in middle-aged and older Chinese adults

BACKGROUND

Sleep is a biological necessity and fundamental to health. However, the associations of sleep patterns (integrating sleep determinants) with DNA methylation age acceleration (DNAm AA) remain unknown. We aimed to investigate the associations of sleep patterns with DNAm AA.

METHODS

This cross-sectional and prospective cohort study used data from the Dongfeng-Tongji cohort collected from 2013 to December 31, 2018. Sleep patterns were reflected by sleep scores (range 0-4, with higher scores indicating healthier sleep patterns) characterized by bedtime, sleep duration, sleep quality, and midday napping. DNAm AA was estimated by PhenoAge acceleration (PhenoAgeAccel), GrimAge acceleration (GrimAgeAccel), DunedinPACE, and DNAm mortality risk score (DNAm MS). Linear regression models were used to estimate β and 95% confidence intervals (CIs) for the cross-sectional associations between sleep patterns and DNAm AA. Mediation models were applied to assess the mediating role of DNAm AA in the associations between sleep patterns and all-cause mortality in a prospective cohort.

RESULTS

Among 3566 participants (mean age 65.5 years), 426 participants died during a mean 5.4-year follow-up. A higher sleep score was associated with lower DNAm AA in a dose-response manner. Each 1-point increase in sleep score was associated with significantly lower PhenoAgeAccel (β = - 0.208; 95% CI - 0.369 to - 0.047), GrimAgeAccel (β = - 0.107; 95% CI - 0.207 to - 0.007), DunedinPACE (β = - 0.008; 95% CI - 0.012 to - 0.004), and DNAm MS (β = - 0.019; 95% CI - 0.030 to - 0.008). Chronological age modified the associations between higher sleep scores and lower PhenoAgeAccel (p for interaction = 0.031) and DunedinPACE (p for interaction = 0.027), with stronger associations observed in older adults. Moreover, a slower DunedinPACE mediated 6.2% (95% CI 0.8% to 11.5%) of the association between a higher sleep score and a lower all-cause mortality risk.

CONCLUSION

In this cohort study, individuals with a higher sleep score had a slower DNAm AA, particularly in older adults. A slower DunedinPACE partially explained the association between higher sleep scores and lower all-cause mortality risk. These findings suggest that adopting healthy sleep patterns may promote healthy aging and further benefit premature mortality prevention, highlighting the value of sleep patterns as a potential tool for clinical management in aging.

Bidirectional predictors between baseline and recovery sleep measures and cardiovascular measures during sleep deprivation and psychological stress

For the first time, we investigated bidirectional predictors between baseline and recovery sleep and cardiovascular (CV) measures during total sleep deprivation (TSD) and psychological stress in a five-day experiment with 32 healthy adults (27-53y; 14 females). CV measures were collected in the morning after two baseline nights (B1, B2) and during TSD morning (TSD AM) and evening following psychological stress (TSD PM). Actigraphy assessed sleep during B2 before TSD and the first recovery night (R1) after TSD. Higher B2 wake after sleep onset (WASO) predicted lower TSD PM stroke volume and higher TSD PM systemic vascular resistance index (SVRI), with greater B2 percent sleep predicting inverse relationships, explaining 12.8%-15.9% of the TSD CV variance. Also, higher B2 WASO predicted higher B2 AM SVRI. Furthermore, longer TSD left ventricular ejection time predicted later R1 sleep offset, longer sleep duration, and higher WASO; by contrast, higher TSD AM and TSD PM heart rate predicted earlier R1 sleep offset. TSD CV indices explained 14.8%-24.9% of the R1 sleep variance. Notably, females showed significant predictive bidirectional relationships. Our novel results demonstrate that baseline sleep predicts CV metrics during TSD and psychological stress, and that these metrics predict recovery sleep, underscoring crucial relationships, mechanisms, and biomarkers between sleep and cardiovascular health.

Association of sleep timing, sleep duration, and sleep latency with metabolic syndrome in middle-aged adults in Korea: A cross-sectional and longitudinal study

OBJECTIVE

Sleep is a potential risk factor for metabolic syndrome. We investigated the associations of various sleep characteristics with the status and incidence of metabolic syndrome in middle-aged Koreans.

METHODS

Using data from a community-based Korean Medicine Daejeon Citizen Cohort study on participants aged 30-50years, cross-sectional (n = 1984) and longitudinal (n = 1216, median follow-up: 2.1years) analyses were performed. To study the association of metabolic syndrome and five components with various sleep characteristics, measured using the Pittsburgh Sleep Quality Index, we used Poisson and logistic regression and Cox proportional hazard regression analyses, adjusting for covariates.

RESULTS

Of 1984 participants, 66%, 19%, and 15% belonged to the non-metabolic syndrome, pre-metabolic syndrome, and metabolic syndrome groups, respectively. After covariate adjustments, the pre-metabolic syndrome group was associated with late mid-sleep time (≥5:00; prevalence ratios 1.61, 95% confidence interval 1.01-2.54) and late bedtime (≥2:00; prevalence ratios 1.55, 95% confidence interval 1.03-2.34), and the metabolic syndrome group was associated with long sleep latency (prevalence ratios 1.33, 95% confidence interval 1.03-1.73), poor sleep quality (prevalence ratios 1.38, 95% confidence interval 1.07-1.78), and early wake time (<6:00; prevalence ratios 1.29, 95% confidence interval 1.01-1.63). Longitudinal analysis of participants without metabolic syndrome at baseline indicated a significant increase in metabolic syndrome risk associated with very short sleep duration (<6 hours; hazard ratio 1.72, 95% confidence interval 1.06-2.79), long sleep latency (>30 minutes; hazard ratio 1.86, 95% confidence interval 1.1-3.12), and early wake time (<6:00 o'clock; hazard ratio 1.73, 95% confidence interval 1.01-2.97).

CONCLUSION

Sleep characteristics, such as short duration, long latency, and early wake time, were associated with an increased risk of metabolic syndrome in middle-aged adults.

Estimation of physiological aging based on routine clinical biomarkers: a prospective cohort study in elderly Chinese and the UK Biobank

BACKGROUND

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

METHODS

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

RESULTS

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]). In both DFTJ and UKB, PAI calibrated better than PhenoAge when comparing the predicted and observed survival probabilities. Furthermore, ΔPAI outperformed any single biomarker to predict incident risks of eight age-related chronic diseases.

CONCLUSIONS

Our results highlight the potential of PAI and ΔPAI as integrative biomarkers to evaluate aging acceleration and facilitate the development of targeted intervention strategies for healthy aging.

Association between self-reported napping and risk of cardiovascular disease and all-cause mortality: A meta-analysis of cohort studies

OBJECTIVES

This meta-analysis aims to assess the association between adult nap duration and risk of all-cause mortality and cardiovascular diseases (CVD).

METHODS

PubMed, Cochrane Library, Embase and Web of Science databases were searched to identify eligible studies. The quality of observational studies was assessed using the Newcastle-Ottawa Scale. We performed all statistical analyses using Stata software version 14.0. For the meta-analysis, we calculated hazard ratio (HR) and their corresponding 95% confidence intervals (CIs). To assess publication bias, we used a funnel plot and Egger's test.

RESULTS

A total of 21 studies involving 371,306 participants revealed varying methodological quality, from moderate to high. Those who indulged in daytime naps faced a significantly higher mortality risk than non-nappers (HR: 1.28; 95% CI: 1.18-1.38; I2 = 38.8%; P<0.001). Napping for less than 1 hour showed no significant association with mortality (HR: 1.00; 95% CI: 0.90-1.11; I2 = 62.6%; P = 0.971). However, napping for 1 hour or more correlated with a 1.22-fold increased risk of mortality (HR: 1.22; 95% CI: 1.12-1.33; I2 = 40.0%; P<0.001). The risk of CVD associated with napping was 1.18 times higher than that of non-nappers (HR: 1.18; 95% CI: 1.02-1.38; I2 = 87.9%; P = 0.031). Napping for less than 1 hour did not significantly impact CVD risk (HR: 1.03; 95% CI: 0.87-1.12; I2 = 86.4%; P = 0.721). However, napping for 1 hour or more was linked to a 1.37-fold increased risk of CVD (HR: 1.37; 95% CI: 1.09-1.71; I2 = 68.3%; P = 0.007).

CONCLUSIONS

Our meta-analysis indicates that taking a nap increases the risk of overall mortality and CVD mortality. It highlights that the long duration time of the nap can serve as a risk factor for evaluating both overall mortality and cardiovascular mortality.

Changes in sleep score and leisure-time physical activity, their combination, and all-cause mortality in middle-aged and older Chinese adults: The Dongfeng-Tongji cohort study

OBJECTIVES

To prospectively investigate the associations of longitudinal changes in sleep score and LTPA and their combination with all-cause mortality.

METHODS

Among 12,543 participants (mean age: 66.1 years) from the Dongfeng-Tongji cohort, we calculated sleep score (range, 0-4, integrating bedtime, sleep duration, sleep quality, and midday napping, higher score indicating healthier sleep) and LTPA at baseline (2008-2010) and the first follow-up (2013) surveys and their 5-year changes (defining stable sleep score as no change and stable LTPA as change within 150 min/week). We prospectively documented deaths from the first follow-up survey (2013) through December 31, 2018.

RESULTS

During a mean 5.5-year follow-up, 792 deaths occurred. The 5-year changes in sleep score and LTPA were inversely associated with all-cause mortality risk, regardless of their initial values. When assessing 5-year changes in sleep score and LTPA jointly, compared with the stable sleep score-stable LTPA group, the decreased sleep score-decreased LTPA group had a 40 % (5-85 %) higher all-cause mortality risk, whereas the increased sleep score-increased LTPA group had a 34 % (9-52 %) lower risk. The direction of the joint association was mainly driven by sleep score change. Participants maintaining sleep scores ≥ 3 and LTPA ≥ 150 min/week over 5 years had a 44 % (28-56 %) lower all-cause mortality risk.

CONCLUSIONS

Promoting sleep hygiene and LTPA together may benefit efforts in reducing mortality risk, with particular attention to monitoring long-term sleep health.

Changes in Sleep Patterns, Genetic Susceptibility, and Incident Cardiovascular Disease in China

Importance

The associations of changes in sleep patterns with incident cardiovascular disease (CVD) are not fully elucidated, and whether these associations are modified by genetic susceptibility remains unknown.

Objectives

To investigate the associations of 5-year changes in sleep patterns with incident CVD and whether genetic susceptibility modifies these associations.

Design, Setting, and Participants

This prospective cohort study of the Dongfeng-Tongji cohort was conducted from 2008 to 2018 in China. Eligible participants included those with complete sleep information at baseline survey (2008-2010) and the first follow-up survey (2013); participants who had no CVD or cancer in 2013 were prospectively assessed until 2018. Statistical analysis was performed in November 2023.

Exposures

Five-year changes in sleep patterns (determined by bedtime, sleep duration, sleep quality, and midday napping) between 2008 and 2013, and polygenic risk scores (PRS) for coronary heart disease (CHD) and stroke.

Main Outcomes and Measures

Incident CVD, CHD, and stroke were identified from 2013 to 2018. Cox proportional hazards regression models were applied to estimate hazard ratios (HRs) and 95% CIs.

Results

Among 15 306 individuals (mean [SD] age, 65.8 [7.4] years; 8858 [57.9%] female and 6448 male [42.1%]), 5474 (35.78%) had persistent unfavorable sleep patterns and 3946 (25.8%) had persistent favorable sleep patterns. A total of 3669 incident CVD cases were documented, including 2986 CHD cases and 683 stroke cases, over a mean (SD) follow-up of 4.9 (1.5) years. Compared with those with persistent unfavorable sleep patterns, individuals with persistent favorable sleep patterns over 5 years had lower risks of incident CVD (HR, 0.80; 95% CI, 0.73-0.87), CHD (HR, 0.84; 95% CI, 0.76-0.92), and stroke (HR, 0.66; 95% CI, 0.54-0.82) in the subsequent 5-year period. No significant effect modification by PRS was observed for sleep pattern change and CHD or stroke risk. However, sleep pattern changes and PRS were jointly associated with the CHD and stroke risk in a dose-dependent manner, with the lowest risk being among those with persistent favorable sleep patterns combined with low PRS (HR for CHD, 0.65; 95% CI, 0.52-0.82 and HR for stroke, 0.48; 95% CI, 0.29-0.79).

Conclusions and Relevance

In this cohort study of middle-aged and older Chinese adults, individuals with persistent favorable sleep patterns had a lower CVD risk, even among those with higher genetic risk. These findings highlight the importance of maintaining favorable sleep patterns for CVD prevention.

Association of sleep characteristics with cardiovascular disease risk in adults over 40 years of age: a cross-sectional survey

Background

The relationship between sleep characteristics and cardiovascular disease (CVD) risk has yet to reach a consistent conclusion, and more research needs to be carried out. This study aimed to explore the relationship between snoring, daytime sleepiness, bedtime, sleep duration, and high-risk sleep patterns with CVD risk.

Methods

Data from the National Health and Nutrition Examination Survey (NHANES) 2015-2018 were collected and analyzed. Multivariable logistic regression was used to evaluate the relationship between snoring, daytime sleepiness, bedtime, sleep duration, high-risk sleep patterns, and CVD risk. Stratified analysis and interaction tests were carried out according to hypertension, diabetes and age.

Results

The final analysis contained 6,830 participants, including 1,001 with CVD. Multivariable logistic regression suggested that the relationship between snoring [OR = 7.37,95%CI = (6.06,8.96)], daytime sleepiness [OR = 11.21,95%CI = (9.60,13.08)], sleep duration shorter than 7 h [OR = 9.50,95%CI = (7.65,11.79)] or longer than 8 h [OR = 6.61,95%CI = (5.33,8.19)], bedtime after 0:00 [OR = 13.20,95%CI = (9.78,17.80)] compared to 22:00-22:59, high-risk sleep patterns [OR = 47.73,95%CI = (36.73,62.04)] and CVD risk were statistically significant. Hypertension and diabetes interacted with high-risk sleep patterns, but age did not.

Conclusions

Snoring, daytime sleepiness, excessive or short sleep duration, inappropriate bedtime, and high-risk sleep patterns composed of these factors are associated with the CVD risk. High-risk sleep patterns have a more significant impact on patients with hypertension and diabetes.