Protein biomarkers have been identified across many age-related morbidities. However, characterising epigenetic influences could further inform disease predictions. Here, we leverage epigenome-wide data to study links between the DNAm signatures of the circulating proteome and incident diseases. Using data from four cohorts, we trained and tested epigenetic scores (EpiScores) for 953 plasma proteins, identifying 109 scores that explained between 1% and 58% of the variance in protein levels after adjusting for known protein quantitative trait loci (pQTL) genetic effects. By projecting these EpiScores into an independent sample, (Generation Scotland; n=9,537) and relating them to incident morbidities over a follow-up of 14 years, we uncovered 137 EpiScore - disease associations. These associations were largely independent of immune cell proportions, common lifestyle and health factors and biological aging. Notably, we found that our diabetes-associated EpiScores highlighted previous top biomarker associations from proteome-wide assessments of diabetes. These EpiScores for protein levels can therefore be a valuable resource for disease prediction and risk stratification.
GrantsBavarian State Ministry of Health and Care Age UK Medical Research Council and Biotechnology and Biological Sciences Research Council Australian Research Council Australian Research Council Fellowship Scottish Funding Council Chief Scientist Office of the Scottish Government Health Directorates Medical Research Council Biotechnology and Biological Sciences Research Council Munich Center of Health Sciences German Federal Ministry of Education and Research Qatar National Research Fund Qatar Foundation MRC Human Genetics Unit Health Data Research UK Alzheimer's Research UK NIH HHS Dementias Platform UK