Abstract
INTRODUCTION: Accurate prediction of 10-year major adverse cardiovascular events (MACE) is critical for effective disease prevention and management. Although the SCORE2 model introduced sex-specific algorithms, opportunities remain to further refine prediction. OBJECTIVES: To evaluate whether adding sex-specific proteomic profiles to the SCORE2 model enhances 10-year MACE risk prediction in the large UK Biobank (UKB) cohort. METHODS: Data from 47,382 UKB participants, aged 40 to 69 years without prior cardiovascular disease or diabetes, were utilized. Proteomic profiling of plasma samples was conducted using the Olink Explore 3072 platform, measuring 2,923 unique proteins, of which 2,085 could be used. Sex-specific Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for biomarker selection. Model performance was assessed by changes in Harrell's C-index (a measure of discrimination), net reclassification index (NRI), and integrated discrimination index (IDI). RESULTS: During 10-year follow-up, 2,163 participants experienced MACE. Overall, 18 proteins were selected by LASSO regression, with 5 of them identified in both sexes, 7 only in males, and 6 only in females. Incorporating these proteins significantly improved the C-index of the SCORE2 model from 0.713 to 0.778 (P < 0.001) in the total population. The improvement was greater in males (C-index increase from 0.684 to 0.771; Δ = +0.087) than in females (from 0.720 to 0.769; Δ = +0.049). The WAP four-disulfide core domain protein (WFDC2) and the growth/differentiation factor 15 (GDF15) were the proteins contributing the strongest C-index increase in both sexes, even more than the N-terminal prohormone of brain natriuretic peptide (NTproBNP). CONCLUSION: The derived sex-specific 10-year MACE risk prediction models, combining 12 protein concentrations among men and 11 protein concentrations among women with the SCORE2 model, significantly improved the discriminative abilities of the SCORE2 model. This study shows the potential of sex-specific proteomic profiles for enhanced cardiovascular risk stratification and personalized prevention strategies.