Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics

Abstract Background and Aims To evaluate the potential of improved prediction of the 10-year risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes by adding metabolomic biomarkers to the SCORE2-Diabetes model. Methods Data from 10,257 and 1,039 patients with type 2 diabetes 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-Diabetes model was primarily evaluated with Harrell’s C-index. Results Seven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly ( P =0.034) from 0.660 to 0.680 in the total sample. In external validation with ESTHER, the C-index increase was higher (+0.041) and remained statistically significant ( P =0.015). Conclusions Incorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine.