DNA methylation GrimAge strongly predicts lifespan and healthspan
Open accessAbstract
.Using large scale validation data from thousands of individuals, we demonstrate that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death (Cox regression P=2.0E-75), time-to-coronary heart disease (Cox P=6.2E-24), time-to-cancer (P= 1.3E-12), its strong relationship with computed tomography data for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). AgeAccelGrim is strongly associated with a host of age-related conditions including comorbidity count (P=3.45E-17). Similarly, age-adjusted DNAm PAI-1 levels are associated with lifespan (P=5.4E-28), comorbidity count (P= 7.3E-56) and type 2 diabetes (P=2.0E-26). These DNAm-based biomarkers show the expected relationship with lifestyle factors including healthy diet and educational attainment.Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.
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Individuals with positive age gaps, termed age acceleration, are at greater risk of mortality and some diseases of ageing such as heart disease, metabolic syndrome and certain cancers.
“These hypotheses have been supported by observations that individuals with positive age gaps, termed age acceleration, are at greater risk of mortality and some diseases of ageing such as heart disease, metabolic syndrome and certain cancers46–50.”
From Measuring biological age using omics data (2022) · cites this paper as [48]
By design, second-generation clocks specifically designed to associate with mortality risk have robust associations with mortality risk, and some show associations with heart disease risk, physical functioning and several blood chemistry markers of health.
“By design, all these clocks have robust associations with mortality risk, and some show associations with heart disease risk, physical functioning (balance, grip strength, walking speed) and several blood chemistry markers of health.”
From Measuring biological age using omics data (2022) · cites this paper as [48]
The DNAm GrimAge clock shows consistently stronger associations with various measures of age-related dysfunction, including heart disease, frailty, and cognitive and physical decline, compared to other clocks.
“The DNAm GrimAge clock shows consistently stronger associations with various measures of age-related dysfunction, including heart disease, frailty, and cognitive and physical decline, compared to other clocks47,48,50,64–72.”
From Measuring biological age using omics data (2022) · cites this paper as [48]
The GrimAge clock trained methylation clocks directly on clinical protein markers and smoking pack-years, whose outputs were combined into a Cox proportional hazards model with age and sex to predict mortality.
“The GrimAge clock48 took a similar approach but trained methylation clocks directly on clinical protein markers and smoking pack-years, whose outputs were then combined into a Cox proportional hazards model with age and sex to predict mortality.”
From Measuring biological age using omics data (2022) · cites this paper as [48]
Both PhenoAge and GrimAge models trained estimators of plasma proteins and metabolites with known disease biology.
“Interestingly, both models trained estimators of plasma proteins and metabolites with known disease biology, again pointing to the plasma as fertile ground for future study.”
From Measuring biological age using omics data (2022) · cites this paper as [48]
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“…Hannum et al., 2013; Horvath, 2013); two second-generation clocks trained to estimate biological age and predict health span and mortality (PhenoAge and GrimAge; Levine et al., 2018; Lu et al., 2019); and a third-generation clock estimating the pace of aging (DunedinPACE; Belsky et al., 2022).”
- Authors
- Lu, A. T., Quach, A., Wilson, J. G., Reiner, A. P., Aviv, A., Raj, K., Hou, L., Baccarelli, A. A., Li, Y., Stewart, J. D., Whitsel, E. A., Assimes, T. L., Ferrucci, L., & Horvath, S.
- Affiliations
- University of California, Los Angeles (US)
- University of Mississippi Medical Center (US)
- Fred Hutch Cancer Center (US)
- Rutgers, The State University of New Jersey (US)
- Public Health England (GB)
- Northwestern University (US)
- Columbia University (US)
- University of North Carolina at Chapel Hill (US)
- VA Palo Alto Health Care System (US)
- National Institutes of Health (US)
- Corresponding author
- Assimes, Themistocles L.
- Horvath, Steve
- ORCID
- Venue
- Aging
- Publisher
- Impact Journals, LLC
- Published
- 2019-01-21
- Volume
- 11
- Issue
- 2
- Pages
- 303-327
- ISSN
- 1945-4589 (ISSN-L)
- PMC
- PMC6366976
- OpenAlex
- W2912654909
- Cited by
- 2,540
- References
- 62
- Citations by year
- 2026: 481 · 2025: 514 · 2024: 487 · 2023: 365 · 2022: 317 · 2021: 189 · 2020: 134 · 2019: 52
- FWCI
- 57.31
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- cc-by
- Locations
- Aging (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Rutgers University Community Repository (Rutgers University) (submittedVersion)
- Funders
- National Institutes of Health (n01hc25195-41-0-1)
- National Institute on Aging (R01AG029451, U01AG060908)
- National Heart, Lung, and Blood Institute (HHSN268201300050C, HHSN268201300046C, HHSN268201600018C, HHSN268201500001I, HHSN268201600004C, HHSN268201300048C, HHSN268201500001C, HHSN268201600001C, HHSN268201300049C, N01-HC-025195, R01HL116446, HHSN268201300047C, HHSN268201600003C, HHSN268201600002C)
- National Institute of General Medical Sciences (U54GM115428)
- National Institute of Environmental Health Sciences (R01ES020836)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Aging › Genetics, Aging, and Longevity in Model Organisms
- Health Sciences › Medicine › Physiology › Telomeres, Telomerase, and Senescence
- Keywords
- epigenetic clocks, DNA methylation, lifetime prediction, healthspan, time to death, coronary artery disease, cancer risk, hepatic steatosis, visceral fat, age at menopause, comorbidities, plasminogen activator inhibitor-1, type 2 diabetes, anti-aging, lifestyle factors, educational attainment, diet
- MeSH terms
- Adipose Tissue, Aging, Blood Proteins, Diet, Education, Female, Humans, Life Style, Longevity, Longitudinal Studies, Male, Predictive Value of Tests, Tomography, X-Ray Computed, Reproducibility of Results, Biomarkers, Fatty Acids, Omega-3, Databases, Factual, DNA Methylation, Dietary Supplements
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