Leveraging DNA methylation to create Epigenetic Biomarker Proxies that inform clinical care: A new framework for Precision Medicine

The lack of accurate, cost-effective, and clinically relevant biomarkers remains a major barrier to incorporating omic data into clinical practice. Previous studies have shown that DNA methylation algorithms have utility as surrogate measures for selected proteins and metabolites. We expand upon this work by creating DNAm surrogates, termed epigenetic biomarker proxies (EBPs), across clinical laboratories, the metabolome, and the proteome. After screening >2,500 biomarkers, we trained and tested 1,694 EBP models and assessed their incident relationship with 12 chronic diseases and mortality, followed up to 15 years. We observe broad clinical relevance: 1) there are 1,292 and 4,863 FDR significant incident and prevalent associations, respectively; 2) most of these associations are replicated when looking at the lab-based counterpart, and > 62% of the shared associations have higher odds and hazard ratios to disease outcomes than their respective observed measurements; 3) EBPs of current clinical biochemistries detect deviations from normal with high sensitivity and specificity. Longitudinal EBPs also demonstrate significant changes corresponding to the changes observed in lab-based counterparts. Using two cohorts and > 30,000 individuals, we found that EBPs validate across healthy and sick populations. While further study is needed, these findings highlight the potential of implementing EBPs in a simple, low-cost, high-yield framework that benefits clinical medicine.