Evidence map›Paper›PMID 36978155›Full record

ArticleClinical epigenetics2023

Healthy lifestyle, DNA methylation age acceleration, and incident risk of coronary heart disease.

Jiahui Si, Lu Chen, Canqing Yu, Yu Guo, Dianjianyi Sun, Yuanjie Pang, Iona Y Millwood, Robin G Walters, Ling Yang, Yiping Chen and 10 more

Open access · goldFull text read
In one paragraph

Article in Clinical epigenetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
18.6field-weighted citation impact, top 1% of its field
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

25 citing papers in PubMed, 32 citations in OpenAlex.

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  7. [Effects of Multiple Lifestyle Factors on Cardiovascular Biological Aging and Their Relative Contributions].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2025
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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

20 authors at 6 institutions in 3 countries.

Jiahui SiInstitute of Medical Technology, Health Science Center of Peking University, Beijing, China.
Lu ChenDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
Canqing YuDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
Yu GuoFuwai Hospital Chinese Academy of Medical Sciences, Beijing, China.
Dianjianyi SunDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
Yuanjie PangDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China.
Iona Y MillwoodMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Robin G WaltersMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Ling YangMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Yiping ChenMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Huaidong DuMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Shixian FengNCDs Prevention and Control Department, Henan CDC, Zhengzhou, Henan, China.
Xiaoming YangClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Daniel AveryClinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Oxford, UK.
Junshi ChenChina National Center for Food Safety Risk Assessment, Beijing, China.
Zhengming ChenMedical Research Council Population Health Research Unit at the University of Oxford, Oxford, UK.
Liming LiangDepartments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Liming LiDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China. lmlee@vip.163.com.
Jun LvDepartment of Epidemiology and Biostatistics, School of Public Health, Peking University Health Science Center, 38 Xueyuan Road, Beijing, 100191, China. lvjun@bjmu.edu.cn.
China Kadoorie Biobank Collaborative Group
University of Oxford · GBPeking University · CNChina National Center for Food Safety Risk Assessment · CNHarvard University · USChinese Academy of Medical Sciences & Peking Union Medical College · CNHenan Provincial Center for Disease Control and Prevention · CN

Funding

Cancer Research UK 29186Medical Research Council MC_PC_14135Medical Research Council MC_UU_00017/1Wellcome Trust 212946/Z/18/Z
6 · The paper itself

Abstract

backgroundDNA methylation clocks emerged as a tool to determine biological aging and have been related to mortality and age-related diseases. Little is known about the association of DNA methylation age (DNAm age) with coronary heart disease (CHD), especially in the Asian population.

resultsMethylation level of baseline blood leukocyte DNA was measured by Infinium Methylation EPIC BeadChip for 491 incident CHD cases and 489 controls in the prospective China Kadoorie Biobank. We calculated the methylation age using a prediction model developed among Chinese. The correlation between chronological age and DNAm age was 0.90. DNA methylation age acceleration (Δage) was defined as the residual of regressing DNA methylation age on the chronological age. After adjustment for multiple risk factors of CHD and cell type proportion, compared with participants in the bottom quartile of Δage, the OR (95% CI) for CHD was 1.84 (1.17, 2.89) for participants in the top quartile. One SD increment in Δage was associated with 30% increased risk of CHD (OR = 1.30; 95% CI 1.09, 1.56; Ptrend = 0.003). The average number of cigarette equivalents consumed per day and waist-to-hip ratio were positively associated with Δage; red meat consumption was negatively associated with Δage, characterized by accelerated aging in those who never or rarely consumed red meat (all P < 0.05). Further mediation analysis revealed that 10%, 5% and 18% of the CHD risk related to smoking, waist-to-hip ratio and never or rarely red meat consumption was mediated through methylation aging, respectively (all P for mediation effect < 0.05).

conclusionsWe first identified the association between DNAm age acceleration and incident CHD in the Asian population, and provided evidence that unfavorable lifestyle-induced epigenetic aging may play an important part in the underlying pathway to CHD.

Indexed as

Coronary DiseaseDNA MethylationAgingEpigenesis, GeneticHealthy LifestyleHumansProspective StudiesCardiovascular healthCoronary artery diseaseEpigenetic age

Identifiers

PMID36978155
PMCPMC10045869
OpenAlexW4361218710

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read28
Read underepoch 390

Registered trials

None linked

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 Socratic graph.