Assessing the utility of epigenetic clocks for health prediction in South Korean

Abstract

Epigenetic clocks have been developed to track both chronological age and biological age, which is defined by physiological biomarkers and the risk of adverse health outcomes. Epigenetic age acceleration (EAA) has been found to predict various diseases, aging-related factors, and mortality. However, epigenetic clocks have predominantly been developed with individuals of European or Hispanic ancestry, and their association with health outcomes and environmental factors has not been sufficiently assessed in East Asian populations. Here, we investigated nine epigenetic clocks: five trained on chronological age (first-generation) and four on biological age (second-generation), using DNA methylation data from blood samples of South Koreans. EAAs of second-generation epigenetic clocks reflected the risk of chronic diseases (type 2 diabetes and hypertension), levels of health-related blood markers (alanine aminotransferase, aspartate aminotransferase, high density lipoprotein, triglyceride, and high sensitivity C-reactive protein), and lung functions (percentage of predicted FEV1 and percentage of predicted FVC), while EAAs of first generation clocks did not. Using follow-up data, we also found that EAAs of second-generation clocks were associated with the time to onset risks of chronic diseases. Health behavior factors (drinking, smoking, exercise, body mass index, and waist-hip ratio), socioeconomic status (income level and educational attainment), and psychosocial status were associated with EAAs of second-generation clocks, while only smoking status was associated with EAAs of first-generation clocks. We conducted validation analyses in an independent South Korean cohort and replicated the association of EAAs with health outcomes and environmental factors. Age acceleration of epigenetic clocks is influenced by various environmental factors and can serve as an effective predictor of health in South Korea.

Keywords: aging; epigenetic clock; healthcare; lifestyle; methylation.

FIGURE 1

Correlation matrix of epigenetic age accelerations. Positive correlations are denoted by red shades, with lighter shades signifying weaker correlation values.

FIGURE 2

Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and health outcomes in KARE. Odds ratios (ORs) with 95% confidence intervals (CIs) or beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].

FIGURE 3

Forest plots for epigenetic age accelerations (EAAs) of second-generation clocks and environmental factors in KARE. Beta values with standard errors (SEs) were displayed along with their corresponding p-values. All regression models adjusted for age, sex, and 10 principal components, with all EAAs scaled to a mean of 0 and a standard deviation of 1. Significant results were highlighted with a blue background based on Bonferroni-corrected p-values [0.05/(4 × 9) = 1.39E-03].