How genetics and epigenetics can improve cardiovascular risk prediction: examples from the Moli-sani and Moli-family studies

Abstract

Abstract One of the goals of personalized medicine (PM) is to use the ever-growing understanding of biology to provide a higher level of precision in disease prevention and patient care. The simultaneous study of the risk markers and epigenetic factors may have the desired discriminatory accuracy to distinguish between high-risk and low-risk subjects. The presentation will illustrate how genetics and epigenetics can improve cardiovascular risk (CVD) prediction in Italian adults from the general population. A subcohort of 1160 subjects (55.3±11.7 years; men 47.7%) was randomly selected from the Moli-sani cohort study (N = 24,325, yrs:2005-2010) and compared in a nested case-cohort design with incident CVD events (N = 592 validated myocardial infarctions or strokes), identified in a median follow-up of 9.2 years. DNA samples were used to genotype 14 tag SNPs in the genes NMU (encoding for Neuromedin U, a hypothalamic peptide), NMUR1 and NMUR2 (receptors) and NMS (Neuromedin S) and to quantify methylation levels at 17 CpG sites of two NMU regions through pyrosequencing. ELISA tests on circulating NMU levels are on-going. The associations between variants and incident CVD events using classical CVD risk factors as covariates and prediction metrics using standard risk alogrithms as reference were calculated. Four SNPs in NMU and NMUR1 genes and 6 NMU CpG sites showed a statistically significant association with events (p from 0.037 to < 0.0001), independently from classical CVD risk factors. Combined epigenetic and genetic variants in genes from the NMU pathway are associated with increased CVD risk and could have a potential clinical utility when added to current CVD risk algorithms for CVD prediction.