Background: Epigenetic clocks have been extensively investigated in individuals of European ancestry and may be suboptimal in East Asians. We developed a novel epigenetic clock (KoMethylNet) using neural network analysis of DNA methylation (DNAm) data from the Korean population to predict chronological ages.
Methods: DNAm data (367,785 CpG sites) from 2,747 participants (Infinium Human Methylation 450 K BeadChip: N = 397; Infinium MethylationEPIC BeadChip: N = 2,350) in the Korean Genome and Epidemiology Study (KoGES) were used to train the neural network on chronological ages. SHapley Additive exPlanation analysis was used to select the optimal number of CpG sites. KoMethylNet-epigenetic age acceleration (EAA)-phenotype analysis was conducted with linear regression, to identify aging-related phenotypes in the Korean population.
Results: KoMethylNet, which uses 300 CpG sites, achieved a mean absolute error (MAE) of 2.82 years, a mean squared error (MSE) of 12.68 years, and a Pearson's correlation coefficient (R) of 0.90 with chronological age. In the external validation using healthy Korean individuals, KoMethylNet achieved the highest performance (MAE = 2.74, MSE = 12.29, R = 0.94). Seven phenotypes, including diabetes-related traits (diabetes, HbA1c, and urine glucose), were positively associated with KoMethylNet-EAA.
Conclusions: We developed a neural network-based DNAm aging clock using Korean population data that enables precise age prediction and offers potential opportunities for advancing aging-related research in Korea.
Keywords: DNA methylation aging clock; Deep learning; Epigenetic age acceleration.
© 2025. The Author(s).