Population aging presents major health, social, economic, and political challenges. Aging is characterized by functional decline and increased disease risk. Recent advances in DNA methylation (DNAm) analysis have enabled more accurate estimates of biological age (BA), with accelerated epigenetic aging linked to unhealthy aging and higher mortality risk. We estimated DNAm-based BA using two-wave longitudinal data from 894 participants aged 50–75 years at baseline in the German ESTHER cohort, with a mean follow-up duration of 8.1 years. Cross-sectional correlations between chronological age (CA) and BA estimates based on five established epigenetic clocks were assessed. Average BA trajectories were modeled using linear regression. Multivariable linear regression was applied to identify potential baseline determinants of BA, and Cox proportional hazards models and restricted cubic splines (RCS) analyses were used to evaluate associations between BA dynamics and all-cause mortality. BAs were correlated with baseline characteristics, including CA and sex. Longitudinally, BA increased at a slower rate than CA, and changes in BA were only weakly correlated with baseline CA. Smoking, physical activity, and alcohol consumption were identified as major determinants of individual BA trajectories. Furthermore, the rate of change in BA was significantly associated with all-cause mortality, with up to a 28% increased risk per standard deviation increase in BA slope. Our findings demonstrate strong correlations between BA and CA and highlight the influence of lifestyle factors on BA trajectories and mortality risk in older adults. We also emphasize the presence of sex-specific patterns in BA trajectories, underscoring the need for stratified approaches in aging research.
Yin et al. (Thu,) studied this question.