ArticleThe journals of gerontology. Series A, Biological sciences and medical sciences2018
Measures of Biologic Age in a Community Sample Predict Mortality and Age-Related Disease: The Framingham Offspring Study.
Article in The journals of gerontology. Series A, Biological sciences and medical sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Post-traumatic stress disorder moderates the association between BrainAge acceleration and GrimAge acceleration.Neurobiology of aging · 2026Pooled it
- Socioeconomic Inequalities and Molecular Risk for Aging in Young Adulthood.American journal of epidemiology · 2023Pooled it
- Trial
- Epigenetic Age Acceleration Reflects Long-Term Cardiovascular Health.Circulation research · 2021Trial
- Longitudinal digital phenotyping of activity rhythms and biological aging using commercial wearables.Nature communications · 2026Article
- Development and Validation of the AI-HeartAge Model in Framingham and UK Biobank.Hypertension (Dallas, Tex. : 1979) · 2026Article
- Early-life determinants of cardiometabolic outcomes and accelerated biological ageing in Colombia.Aging · 2026Article
- Association of phenotypic age acceleration with the risk of all-cause and cardiovascular disease-related mortality in patients with hypertension.Blood pressure monitoring · 2025Article
- EpInflammAge: Epigenetic-Inflammatory Clock for Disease-Associated Biological Aging Based on Deep Learning.International journal of molecular sciences · 2025Article
- The association between dietary live microbes intake and PhenoAge in US non-diabetes adults and the mediating effect of insulin resistance: NHANES 2007-2018.Aging clinical and experimental research · 2025Article
- The long-term associations of perinatal obesogenic environment with offspring biological aging.American journal of epidemiology · 2025Article
- Development and validation of deep learning- and ensemble learning-based biological ages in the NHANES study.Frontiers in aging neuroscience · 2025Article
- Inflammaging Markers in the Extremely Cold Climate: A Case Study of Yakutian Population.International journal of molecular sciences · 2024Article
- Clinical biomarker-based biological age predicts deaths in Brazilian adults: the ELSA-Brasil study.GeroScience · 2024Article
- Childhood Maltreatment and Biological Aging in Middle Adulthood: The Role of Psychiatric Symptoms.Biological psychiatry global open science · 2024Article
- Association Between Phenotypic Age and the Risk of Mortality in Patients With Heart Failure: A Retrospective Cohort Study.Clinical cardiology · 2024Article
- Circadian rhythm analysis using wearable-based accelerometry as a digital biomarker of aging and healthspan.NPJ digital medicine · 2024Article
- A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study.Journal of the American Heart Association · 2024Article
- Educational Mobility, Pace of Aging, and Lifespan Among Participants in the Framingham Heart Study.JAMA network open · 2024Article
- Validation of biomarkers of aging.Nature medicine · 2024Review
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8 authors.
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Abstract
Background: We tested the association of biologic age (BA) measures constructed from different types of biomarkers with mortality and disease in a community-based sample. Methods: In Framingham Offspring participants at Exams 7 (1998-2001, mean age 62 ± 10) and 8 (2005-2008, mean age 67 ± 9), we used the Klemera-Doubal method to estimate clinical BA and inflammatory BA and computed the difference (∆age) between BA and CA. Clinical ∆age was computed at Exam 2 (1979-1983, mean age 45 ± 10). At Exam 8, we computed measures of intrinsic and extrinsic epigenetic age. Participants were followed through 2014 for outcomes. Cox proportional hazards models tested the association of each BA estimate with each outcome adjusting for covariates. Results: Sample sizes ranged from 2532 to 3417 participants. In multivariable models, each 1-year increase in clinical ∆age at Exam 2 (hazard ratio [HR] = 1.04-1.06, p < 2 × 10-16) and clinical ∆age and inflammatory ∆age at Exam 7 significantly increased the hazards of mortality and incident cardiovascular disease (HR = 1.01-1.05, p < 2 × 10-7), whereas inflammatory ∆age increased the hazards of cancer (HR = 1.01, p < .05). At Exam 8, increased clinical ∆age, inflammatory ∆age, and extrinsic epigenetic age all significantly increased the hazard of mortality (HR = 1.03-1.05, all p < .05); clinical ∆age and inflammatory ∆age increased cardiovascular disease risk (HR = 1.04-1.05, all p < .01); and clinical ∆age increased cancer risk (HR = 1.03, p < .01) when all three BA measures were included in the model. Intrinsic epigenetic age was not significantly associated with any outcome. Conclusions: Our findings suggest BA measures may be complementary in predicting risk for mortality and age-related disease.
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