DNA methylation GrimAge strongly predicts lifespan and healthspan

Abstract

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.

New Targeted Approaches for Epigenetic Age Predictions

“…Epigenetic clocks raise hopes as a biomarker in forensic medicine, to determine donor age of an unknown specimen or of a person with allegedly unknown age (Horvath and Raj 2018). On the other hand, accelerated epigenetic aging has been shown to be associated with shorter life expectancy (Marioni et al 2015;Lin et al 2016;Zhang et al 2017;Lu et al 2019;Lund et al 2019), and it is liable to be affected by environmental exposure, gender, specific mutations and diseases (Fiorito et al 2019;Jeffries et al 2019;Martin-Herranz et al 2019). Therefore, epigenetic clocks seem to reflect aspects of biological age, which opens perspectives as a surrogate for intervention studies.…”

Aging Biomarkers: From Functional Tests to Multi‐Omics Approaches

“…The most recently presented “GrimAge” clock further elaborates the “phenotypic age” concept, combining clinical biomarkers with DNA methylation levels. [ 110 ]…”

“…These estimators, or aging clocks, measured the deviation of predicted age from actual age (biological age acceleration), which was found to be correlated with many age‐related diseases and conditions. Now, first‐generation metrics of aging are being replaced by second‐generation metrics that have appeared in the last several years [ 109,110 ] . Second‐generation metrics of aging integrate single type of omics data with clinically relevant biomarkers and, as a result, have greater predictive power and better reflect changes in individual health.…”

Next‐generation disease modeling with direct conversion: a new path to old neurons

“…Further, biological or epigenetic age can be well measured using the DNA methylation profile exploiting a growing number of epigenetic clock algorithms [134][135][136]. The original epigenetic clock used the methylation values of 353 specific CpG loci to calculate the age of a human sample, and Huh et al have employed this tool to demonstrate for the first time that iNs from adult donors are indeed (epigenetically) adult cells, while iPSCs and their derivatives are typically rejuvenated into prenatal epigenetic ages [134,137,138].…”