Metabolomics data improve 10-year cardiovascular risk prediction with the SCORE2 algorithm for the general population without cardiovascular disease or diabetes

Abstract Background and Aims The value of metabolomic biomarkers for cardiovascular risk prediction is unclear. This study aimed to evaluate the potential of improved prediction of the 10-year risk of major adverse cardiovascular events (MACE) in large population-based cohorts by adding metabolomic biomarkers to the novel SCORE2 model, which was introduced in 2021 for the European population without previous cardiovascular disease or diabetes. Methods Data from 187,039 and 5,578 participants from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. LASSO regression with bootstrapping was used to identify metabolites in sex-specific analyses and the predictive performance of metabolites added to the SCORE2 model was primarily evaluated with Harrell's C-index. Results Thirteen metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, six male- and four female-specific metabolites) in the UKB derivation set. In internal validation with the UKB, adding the selected metabolites to the SCORE2 model increased the C-index statistically significantly (P<0.001) from 0.691 to 0.710. In external validation with ESTHER, the C-index increase was similar (from 0.673 to 0.688, P=0.042). The inflammation biomarker, glycoprotein acetyls, contributed the most to the increased C-index in both men and women. Conclusions The integration of metabolomic biomarkers into the SCORE2 model markedly improves the prediction of 10-year cardiovascular risk. With recent advancements in reducing costs and standardizing processes, NMR metabolomics holds considerable promise for implementation in clinical practice.