Genetic and metabolomic approaches for coronary heart disease risk prediction

Abstract

The prediction of coronary heart disease (CHD) risk is currently based on traditional risk factors (TRFs) like age, sex, lipid levels, blood pressure. Here we investigated, using the CAREMA cohort, whether this prediction can potentially be improved by applying a metabolomics approach and by including information on single nucleotide polymorphisms (SNPs) previously reported to be relevant for cardiovascular disease risk. Based on the results described in this thesis it can be concluded that a metabolomics profile generated by 1H-NMR spectroscopy when combined with age and sex could mark individuals at high risk for cardiovascular disease. The performance of that method was comparable to a score based on TRFs. It was also found that a weighted genetic risk score based 29 SNPs associated with CHD marginally improved CHD risk prediction. In the Leiden Longevity Study it was examined if comprehensive lipid profiles could mark metabolic health in middle-aged individuals. We found that male metabolic health was characterized by LDL particle size and female metabolic health was characterized by triglyceride levels. Finally it was observed that an increased delta-5 desaturase activity was protective against CHD. This effect was mainly observed in AA carriers of a SNP located in FADS1

LASSO-regressionCase-cohortGenetic-risk-scoreGWASLipidsLongevityDesaturasesCox-regressionMetabolites1H-NMR