Development of an Accurate Biological Aging Clock Using Machine Learning Models and DNA Methylation Patterns in Skeletal Muscle

Objective: A leading challenge in aging research is measuring age accurately, as monitoring healthy individuals over decades to assess the effects of interventions for the aging process is time and cost prohibitive. The goal of this work was to develop an accurate epigenetic skeletal muscle-based aging clock using high-dimensional DNA methylation patterns to calculate chronological age. Method: Genome-wide DNA methylation values from skeletal muscle tissue samples from 47 individuals with 200,000 methylation sites (CpGs) per sample were used for the study. Feature selection was used to narrow down CpGs. An Elastic Net regression model with a Leave-One-Sample-Out Cross-Validation (LO-SAMPLE-OCV) was used to evaluate feature relevance and model performance. I then implemented an ensemble approach where predictions from the LO-SAMPLE-OCV process served as inputs to another model using ElasticNet regression. This two-step approach leverages the strengths of ElasticNet for both feature selection and meta-learning, aiming to enhance predictive accuracy while also capturing the complex relationships of high-dimensional data. Results & Conclusion: The model outperformed the Voisin et. al. method against the performance metrics of median absolute age differences (6.5 years using the Voisin method versus 1.9 years and 2.31 years using the LO-SAMPLE-OCV and ensemble methods respectively) and mean age differences (1.9 years using the Voisin method versus 0.26 years and -0.17 years using the LO-SAMPLE-OCV and ensemble methods respectively). Impact: This tool can be used to accurately predict and understand aging to develop interventions for the prevention, early detection, diagnosis, and treatment of aging-related diseases, including cancer.