Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality

Abstract

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

Keywords: DNA methylation; aging; biological aging; epigenetic clocks; geroscience.

Fig. 1.

R² decomposition from OLS regression for five measures of epigenetic age acceleration: PC-trained clocks and DunedinPACE (N = 3,581)^{1 1}All models are adjusted for demographic covariates, SES, childhood SES and financial hardship, previous depression, behavioral variables, and cell-type proportions.

Fig. 2.

R² decomposition from OLS regression (or pseudo R² from logistic regression) for health outcomes: PC-trained clock for GrimAge age acceleration (N = 3,140 for ADL/IADL difficulties, multimorbidity; N = 3,581 for cognitive dysfunction, mortality)^{1 1}All models are adjusted for demographic covariates, SES, childhood SES and financial hardship, previous depression, behavioral variables, and cell-type proportions.