DNA methylation GrimAge strongly predicts lifespan and healthspan

By A. Lu, Austin Quach, James G. Wilson, A. Reiner, A. Aviv, K. Raj, Lifang Hou, Andrea A. Baccarelli, Yun Li, J. Stewart and 4 more. Aging, 2019

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It was unknown whether plasma protein levels can be estimated based on DNA methylation (DNAm) levels, and if so, how the resulting surrogates can be consolidated into a powerful predictor of lifespan. We present here, seven DNAm-based estimators of plasma proteins including those of plasminogen activator inhibitor 1 (PAI-1) and growth differentiation factor 15. The resulting predictor of lifespan, DNAm GrimAge (in units of years), is a composite biomarker based on the seven DNAm surrogates and a DNAm-based estimator of smoking pack-years. Adjusting DNAm GrimAge for chronological age generated novel measure of epigenetic age acceleration, AgeAccelGrim. 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.

Subfields: Molecular Biology, Pediatrics, Perinatology and Child Health, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/39df53d7fdd4243312459c18435601d370d369cd

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Citations in the corpus

DunedinPACE, a DNA methylation biomarker of the pace of aging (2021, methodology): “We computed the methylation clocks proposed by Levine et al. and Lu et al. (Levine et al., 2018; Lu et al., 2019) using the methylation data provided by the individual studies and the Horvath Lab’s webtool (http://dnamage.genetics.ucla.edu/new).”
Measuring biological age using omics data (2022, methodology): “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 47,48,50,64–72 .”
Measuring biological aging in humans: A quest (2019, background): “…uses a “phenotypic age” (PhenoAge) index for reference and/or is tuned to cardiovascular risk factors, including smoking (GrimAge), and is strongly predictive of mortality and a cadre of age-related adverse health outcomes, including disability and dementia (Levine et al., 2018; Lu et al., 2019).”