Evidence map›Paper›PMID 40442824›Full record

ArticleClinical epigenetics2025

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

Tingyue Diao, Kang Liu, Lue Zhou, Qiuhong Wang, Junrui Lyu, Ziwei Zhu, Fuchao Chen, Wengang Qin, Handong Yang, Chaolong Wang and 2 more

Abstract read
In one paragraph

Article in Clinical epigenetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Tingyue DiaoDepartment 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.
Kang LiuDepartment 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.
Lue ZhouDepartment 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.
Qiuhong WangDepartment 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.
Junrui LyuDepartment 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.
Ziwei ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Fuchao ChenHubei Clinical Research Center of Hypertension, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China.
Wengang QinHubei Clinical Research Center of Hypertension, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China.
Handong YangHubei Clinical Research Center of Hypertension, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China.
Chaolong WangDepartment of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Xiaomin ZhangDepartment 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.
Tangchun WuDepartment 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. wut@mails.tjmu.edu.cn.

Funding

National Natural Science Foundation of China 81930092National Natural Science Foundation of China 82021005National Natural Science Foundation of China 82192903
6 · The paper itself

Abstract

backgroundSleep 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.

methodsThis 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.

resultsAmong 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.

conclusionIn 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.

Indexed as

AgingDNA MethylationSleepAgedChinaCross-Sectional StudiesEast Asian PeopleFemaleHumansMaleMiddle AgedProspective StudiesSleep QualityAgingAll-cause mortalityDNA methylation age accelerationEpigenetic clocksSleep pattern

Identifiers

PMID40442824
PMCPMC12123996

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.