The SCORE2-Pro subgroup-stratified plasma proteomic model significantly improved myocardial infarction risk prediction compared to the clinical model, increasing AUC from 0.638 to 0.699 in females.
Cohort (n=51,010)
Yes
Does a clinical subgroup-stratified plasma proteomic signature (SCORE2-Pro) improve risk prediction for myocardial infarction in adults compared to traditional clinical models?
Subgroup-specific plasma proteomic signatures significantly improve myocardial infarction risk prediction and reclassification beyond traditional clinical models.
Effect estimate: AUC +0.061
Absolute Event Rate: 0.699% vs 0.638%
p-value: p=1.51x10-3
Abstract Background Myocardial infarction (MI) remains a leading cause of global mortality, with risk varying substantially across demographic and clinical subgroups. Although SCORE2 is widely implemented for cardiovascular risk stratification, the extent to which clinical subgroup specific plasma proteomics can further refine personalized MI risk prediction remains uncertain. Methods SCORE2-Pro, a clinical subgroup-stratified plasma proteome prediction model was built stratified by sex, age, smoking status, non-high-density lipoprotein (non-HDL) cholesterol, and systolic blood pressure. In 51,010 UK Biobank participants (aged 40–69 years; 54.9% female) without MI at baseline, 70% were used for model development, and the remaining 30% for an internal hold-out validation. We used light gradient boosting machine classifiers and Cox proportional hazards models to identify top-predictive protein combinations and stratification strategies for MI. Results The SCORE2-Pro model revealed distinct and highly effective protein panels for each subgroup. Compared with the clinical model, SCORE2-Pro remarkably enhanced predictive performance across demographic and clinical subgroups. A 9-protein model in females improved AUC by + 0.061 ( P = 1.51 × 10 −3 ), with a net reclassification index (NRI) of + 0.125 ( P = 2.69 × 10 −6 ). Similarly, a 7-protein model for middle-aged subpopulation demonstrated an AUC improvement of + 0.036 ( P = 0.033) with an NRI of + 0.127 ( P = 8.09 × 10 −11 ). Notably, in high-risk populations, SCORE2-Pro model significantly improved reclassification performance compared with the clinical model (NRI: + 0.121, P = 0.020). Conclusions By adopting a clinical subgroup stratification approach using factors derived from SCORE2, we identified subgroup-specific proteomic signatures for MI that considerably improve predictive accuracy and reclassification beyond traditional clinical models.
Tong et al. (Mon,) conducted a cohort in Myocardial infarction (n=51,010). SCORE2-Pro (subgroup-specific proteomic model) vs. Clinical SCORE2 model was evaluated on Area under the curve (AUC) for myocardial infarction prediction in females (AUC +0.061, p=1.51x10-3). The SCORE2-Pro subgroup-stratified plasma proteomic model significantly improved myocardial infarction risk prediction compared to the clinical model, increasing AUC from 0.638 to 0.699 in females.