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Record W4409378282 · doi:10.1038/s41514-025-00217-0

Relationship between physical activity and DNA methylation-predicted epigenetic clocks

2025· article· en· W4409378282 on OpenAlexaff
Yanwei You, Yuquan Chen, Hao Ding, Qiyu Liu, Rui Wang, Kailin Xu, Qingyuan Wang, Danijela Gašević, Xindong Ma

Bibliographic record

Venuenpj Aging · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsCentre for Global Health Research
Fundersnot available
KeywordsEpigeneticsDNA methylationMethylationDNABiologyComputational biologyGeneticsGeneGene expression

Abstract

fetched live from OpenAlex

This study investigates the relationship between physical activity (PA) levels and DNA methylation (DNAm)-predicted epigenetic clocks in a U.S. population sample (n = 948, mean age 62, 49% female). Eight epigenetic clocks were analyzed, revealing that higher PA levels were significantly associated with younger biological ages across all indicators, with the strongest effects observed for SkinBloodAge and LinAge. Multivariable linear regression models, adjusted for sociodemographic and lifestyle factors, highlighted the potential of PA to reduce biological ageing. Subgroup analyses indicated that these associations were more pronounced among non-Hispanic whites, individuals with a BMI of 25-30, and former smokers, suggesting that the impact of PA varies across different groups. These findings emphasize the role of PA in slowing biological ageing and reducing age-related health risks. Promoting regular PA, especially among older adults and those with higher BMI, could improve well-being and lifespan, highlighting PA as a modifiable factor in healthy ageing and age-related disease prevention.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.313
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations51
Published2025
Admission routes1
Has abstractyes

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