Epigenetic contributions to clinical risk prediction of cardiovascular disease

Abstract

AbstractBackground and AimsCardiovascular disease (CVD) is among the leading causes of death worldwide. Discovery of new omics biomarkers could help to improve risk stratification algorithms and expand our understanding of molecular pathways contributing to the disease. Here, ASSIGN – a cardiovascular risk prediction tool recommended for use in Scotland – was examined in tandem with epigenetic and proteomic features in risk prediction models in ý12,657 participants from the Generation Scotland cohort.MethodsPreviously generated DNA methylation-derived epigenetic scores (EpiScores) for 109 protein levels were considered, in addition to both measured levels and an EpiScore for cardiac troponin I (cTnI). The associations between individual protein EpiScores and the CVD risk were examined using Cox regression (ncasesý1,274; ncontrolsý11,383) and visualised in a tailored R application. Splitting the cohort into independent training (n=6,880) and test (n=3,659) subsets, a composite CVD EpiScore was then developed.ResultsSixty-five protein EpiScores were associated with incident CVD independently of ASSIGN and the measured concentration of cTnI (P<0.05), over a follow up of up to 16 years of electronic health record linkage. The most significant EpiScores were for proteins involved in metabolic, immune response and tissue development/regeneration pathways. A composite CVD EpiScore (based on 45 protein EpiScores) was a significant predictor of CVD risk independent of ASSIGN and the concentration of cTnI (Hazard Ratio HR=1.32, P=3.7×10-3, 0.3% increase in C-statistic).ConclusionsEpiScores for circulating protein levels are associated with CVD risk independent of traditional risk factors and may increase our understanding of the aetiology of the disease.Graphical abstractStructural graphical abstractASSIGN – a cardiovascular risk prediction tool recommended for use in Scotland – was examined in tandem with epigenetic and proteomic features in risk prediction models in ζ12,657 participants from the Generation Scotland cohort. Cox regression was used to model the association between individual predictors and CVD hospitalisation events ascertained over 16 years of follow-up. Finally, a composite protein EpiScore was developed (based on the protein EpiScores) and its predictive performance was tested. CVD – Cardiovascular Disease, EpiScore – Epigenetic Score, Cox PH – Cox Proportional Hazards Regression, DNAm – DNA methylation.