Epigenetic aging signatures and age prediction in human skeletal muscle

Abstract

Aging causes progressive molecular and cellular changes that impair skeletal muscle function. DNA methylation is a key epigenetic regulator of this process, but its role in skeletal muscle, especially in Asian populations and postmortem samples, remains underexplored. We analyzed DNA methylation profiles from 103 pectoralis major muscle samples from autopsies of South Korean individuals (18–85 years) using the Infinium EPIC array. Targeted validation and age prediction modeling were performed with Next-Generation Sequencing (NGS) and Single Base Extension (SBE). We identified 20 age-associated CpG markers linked to genes involved in muscle structure, metabolism, and stress response. Machine learning models built on these CpG sites showed high prediction accuracy, with mean absolute errors of 5.537 years in sequencing and 3.797 years in extension platforms, and strong correlation with chronological age.

This study introduces the skeletal muscle epigenetic clocks in an Asian population using postmortem skeletal muscle tissue. These novel prediction models, based on 20 common CpG markers using SBE and NGS platforms, provide a robust framework for forensic applications and enable population-tailored epigenetic profiling. Beyond predictive utility, the identified age-associated methylation signatures offer valuable insights into the molecular pathways of muscle aging and hold promise as biomarkers for translational research and age-related clinical interventions.