Sex-specific prediction of major cardiovascular events in apparently healthy individuals with multi-omics data

Background While multi-omics approaches incorporating polygenic risk scores (PRS), metabolomics, and proteomics have shown promise in improving cardiovascular disease (CVD) risk prediction, their added value beyond sex-specific CVD risk factor-based models remains underexplored. Purpose We aimed to assess whether integrating multi-omics biomarkers into the SCORE2 model improves the prediction of major adverse cardiovascular events (MACE) in apparently healthy individuals. Methods This study included 26,841 UK Biobank participants, aged 40-69 years, and without CVD and diabetes. Multi-omics biomarkers were fitted in sex-specific models including the variables of SCORE2 and 9 metabolites, 12 proteins, and a PRS of about 1 million SNPs in males, as well as 7 metabolites, 11 proteins, and a PRS of about 1 million SNPs in females. The model’s performance of SCORE2 and the multi-omics extended model were compared with Harrell’s C-index and the net reclassification index (NRI). Results Over a 10-year follow-up, 1,226 MACE events occurred. Integrating multi-omics biomarkers into SCORE2 significantly improved the predictive performance (C-index: 0.714 to 0.774, p<0.001; NRI: 20.7%). In males, the C-index improved from 0.670 to 0.736 (ΔC-index = +0.066, p<0.001; NRI = 17.1%), while in females, it increased from 0.724 to 0.786 (ΔC-index = +0.062, p<0.001; NRI = 22.5%). However, full multi-omics measurements may not be needed because the combination of proteomics and PRS in males (C-index: 0.736) and proteomics and metabolomics in females (C-index: 0.787) had comparable model performance. Conclusion Integrating multi-omics biomarkers significantly improves CVD risk prediction beyond traditional models, with different optimal biomarker combinations in males and females.