Cardiovascular risk prediction using metabolomic biomarkers and polygenic risk scores: A cohort study and modelling analyses

Background and AimsMetabolomic biomarker scores and polygenic risk scores (PRS) have shown promise for improving cardiovascular disease (CVD) prediction, but have not yet been evaluated in the context of current prediction models (SCORE2) and ESC recommendations for 10-year prediction of fatal and non-fatal CVD. MethodsMetabolomics biomarker scores were constructed and compared to PRS and SCORE2 in 297,463 UK Biobank participants (8,919 incident CVD cases) aged 40-69 without previous CVD, diabetes, or lipid-lowering treatment. Improvement in risk discrimination when added to SCORE2 was assessed using Harrels C-index. Improvement in risk stratification following ESC guideline risk thresholds was assessed using categorical net reclassification. Population modelling was subsequently applied to estimate the impact on CVD prevention if applied at scale. ResultsRisk discrimination provided by SCORE2 (C-index: 0.719) was similarly improved by addition of metabolomic biomarker scores ({Delta}C-index: 0.010 [0.009-0.012]) and PRSs ({Delta}C-index 0.009; [0.008-0.011]). Addition of both metabolomic biomarker scores and PRSs to SCORE2 yielded the largest improvement risk discrimination, with {Delta}C-index 0.018 (0.016-0.020). Concomitant improvements in risk stratification were observed in categorical net reclassification index, with net case reclassification of 11.99% (10.98-12.99%). Modelling metabolic biomarker scores and PRSs for targeted risk-reclassification increased the number of CVD events prevented per 100,000 screened from 209 to 368 ({Delta}CVDprevented: 160 [151-169]) while essentially maintaining the number of statins prescribed per CVD event prevented. ConclusionsCombining NMR scores and PRSs with SCORE2 enhanced prediction of first-onset CVD and could have substantial population health benefit if applied at scale.