ArticleClinical epigenetics2025
Associations between five indicators of epigenetic age acceleration and all-cause and cause-specific mortality among US adults aged 50 years and older.
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 4 papers.
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Who cites it
4 citing papers in PubMed.
- Age-dependent prognostic value of biological age for metastasis and survival in gastrointestinal cancer.Scientific reports · 2026Article
- Biological age acceleration predicts mortality and heart failure after acute myocardial infarction in older adults.Journal of geriatric cardiology : JGC · 2026Article
- The role of health behaviors in links between stress and epigenetic aging.Brain, behavior, & immunity - health · 2026Article
- Urinary organophosphate metabolites, DNA methylation aging, and heart disease mortality in middle-aged and older adults: an exploratory cohort study withFrontiers in public health · 2026Article
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Abstract
backgroundAlthough DNA methylation age estimators (DNAmAges) are reliable tools for predicting aging, their effectiveness in predicting mortality risk has not been fully validated. This study compared the predictive utility of five different DNAmAges (HorvathAge, HannumAge, PhenoAgeAge, GrimAge and GrimAge2) for all-cause and cause-specific mortality among adults aged ≥ 50 years.
methodsWe screened 1966 participants adults aged ≥ 50 from the National Health and Nutrition Examination Survey (1999-2002) and linked them to the National Death Index to obtain cause and status of death. We used weighted Cox proportional hazards models to examine the associations between epigenetic age acceleration (EAA) measured by different DNAmAges and all-cause and cause-specific mortality in the general population, adjusting for various covariates including age, smoking status and chronic diseases. We used restricted cubic splines to explore nonlinear associations. Finally, stratified analyses were performed to assess the relationship between DNA age estimators and stratification variables.
resultsThe multivariable adjustment model showed that EAA measured by HorvathAge (AAHorvathAge), HannumAge (AAHannumAge), PhenoAge (AAPhenoAge), GrimAge (AAGrimAge) and GrimAge2 (AAGrimAge) were significantly associated with the risk of death, among which AAGrimAge and AAGrimAge2 had stronger statistical correlation and the correlation pattern was positively correlated. Specifically, each 5-year increase in AAGrimAge was associated with a 44% increased risk of all-cause death, a 33% increased risk of cardiovascular death and a 54% increased risk of non-cardiovascular death. And each 5-year increase in AAGrimAge2 was associated with a 40% increased risk of all-cause death, a 33% increased risk of cardiovascular death and a 47% increased risk of non-cardiovascular death. In contrast, AAHorvathAge, AAHannumAge and AAPhenoAge showed a J-shaped correlation with the risk of all-cause mortality and non-cardiovascular mortality, with the inflection points of all-cause mortality and non-cardiovascular mortality occurring at AAHorvathAge of 2.29 and 2.8, AAHannumAge of 3.07 and 2.97, and AAPhenoAge of - 7.65 and 7.04, respectively. No interaction was found between DNAmAges and stratification variables.
conclusionsAAGrimAge and AAGrimAge2 outperformed AAHorvathAge, AAHannumAge and AAPhenoAge in predicting mortality risk, and the association pattern was positive.
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