Epigenetic age acceleration and overall mortality among cancer survivors

Project Summary

Aging is a biological process that increases our risk for chronic diseases. One way to measure aging effects is through DNA methylation, an epigenetic change that influences gene expression without altering the genetic code. Disruptions in methylation patterns are linked to cardiovascular, neurodegenerative diseases, and cancer. Epigenetic clocks estimate biological age using these patterns. The gap between biological and chronological age is called epigenetic age acceleration (EAA), a marker of faster aging. Higher EAA is linked to greater disease risk, poor health, and lower survival. Current clocks have limitations in predicting risk and outcomes accurately. This project aims to validate a deep learning model to estimate epigenetic age. Using CLSA data, we will evaluate how well EAA distinguishes between individuals with and without chronic diseases and examine its link to survival. This may help develop more personalized approaches to chronic disease care and improve outcomes.