Diao Tingyue, Liu Kang, Zhou Lue, Wang Qiuhong, Lyu Junrui, Zhu Ziwei, Chen Fuchao, Qin Wengang, Yang Handong, Wang Chaolong, Zhang Xiaomin, Wu Tangchun
Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan, China.
School of Public Health, Guangzhou Medical University, Guangzhou, China.
Clin Epigenetics. 2025 May 29;17(1):87. doi: 10.1186/s13148-025-01898-w.
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.
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.
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.
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.
睡眠是一种生物必需行为,对健康至关重要。然而,睡眠模式(整合睡眠决定因素)与DNA甲基化年龄加速(DNAm AA)之间的关联尚不清楚。我们旨在研究睡眠模式与DNAm AA之间的关联。
这项横断面和前瞻性队列研究使用了2013年至2018年12月31日收集的东风-同济队列的数据。睡眠模式通过睡眠评分(范围为0至4,分数越高表明睡眠模式越健康)来反映,睡眠评分由就寝时间、睡眠时间、睡眠质量和午睡情况来表征。DNAm AA通过PhenoAge加速(PhenoAgeAccel)、GrimAge加速(GrimAgeAccel)、达尼丁PACE(DunedinPACE)和DNAm死亡风险评分(DNAm MS)进行估计。线性回归模型用于估计睡眠模式与DNAm AA之间横断面关联的β值和95%置信区间(CIs)。中介模型用于评估DNAm AA在前瞻性队列中睡眠模式与全因死亡率之间关联中的中介作用。
在3566名参与者(平均年龄65.5岁)中,426名参与者在平均5.4年的随访期间死亡。较高的睡眠评分与较低的DNAm AA呈剂量反应关系。睡眠评分每增加1分,与显著更低的PhenoAgeAccel(β = -0.208;95% CI -0.369至-0.047)、GrimAgeAccel(β = -0.107;95% CI -0.207至-0.007)、DunedinPACE(β = -0.008;95% CI -0.012至-0.004)和DNAm MS(β = -0.019;95% CI -0.030至-0.008)相关。实际年龄改变了较高睡眠评分与较低PhenoAgeAccel(交互作用p = 0.031)和DunedinPACE(交互作用p = 0.027)之间的关联,在老年人中观察到更强的关联。此外,较慢的DunedinPACE介导了较高睡眠评分与较低全因死亡风险之间6.2%(95% CI 0.8%至11.5%)的关联。
在这项队列研究中,睡眠评分较高的个体DNAm AA较慢,尤其是在老年人中。较慢的DunedinPACE部分解释了较高睡眠评分与较低全因死亡风险之间的关联。这些发现表明,采用健康的睡眠模式可能促进健康衰老,并进一步有助于预防过早死亡,突出了睡眠模式作为衰老临床管理潜在工具的价值。