Enhancing the performance and interpretability of epigenetic clocks

Epigenetic clocks based on DNA methylation (DNAm) accurately predict age, but their biological underpinnings remain unclear. One primary mechanism by which DNAm might influence gene regulation is by modulating transcription factor binding activity. This study investigates the regulatory potential of predictive CpGs in established epigenetic clocks. Our analysis reveals that generally most CpGs used by epigenetic clocks do not overlap known transcription factor binding sites (TFBS), indicating that clock accuracy is not primarily driven by changes in TF binding dynamics. However, analysis of CpGs within TFBSs identifies key transcription factors potentially involved in aging, including ZBED1, NFE2, and CEBPB, which are enriched for age-associated CpGs, while RELA, IKZF1, and STAT3 significantly protected against methylation changes. Leveraging TFBS-associated and age-correlated CpGs, combined with noise-stabilizing feature engineering steps, we developed an alternative TFMethyl Clock model that provides competitive predictions of chronological age. Age-predictive CpGs selected by our model enrich for target genes involved in interleukin-1β production and fatty-acid metabolism, while being enriched at TFBSs of NR2C2. Furthermore, approximately three-fourths of these target genes exhibit significant age-related changes, suggesting deeper insights into possible methylation-driven aging processes. Our findings demonstrate that incorporating regulatory information into epigenetic clocks may provide mechanistic insights into the aging process while improving the interpretability and predictive power.