Identification of a plasma proteomic signature associated with sudden cardiac death risk in the UK biobank

Background Sudden cardiac death (SCD) remains difficult to predict in the general population, and proteomics may offer additional prognostic information. We aimed to develop and evaluate a plasma proteomic signature for SCD risk prediction in the UK Biobank. Methods We analyzed prospective data from 52,826 UK Biobank participants, including 408 incident SCD cases. A proteomic signature was derived using repeated LASSO-Cox regression. Its biological relevance was explored by GO enrichment and PPI analyses. Predictive performance was assessed by discrimination, reclassification, calibration, and decision curve analysis. Results A 71-protein signature was identified. Each 1-SD increase in the proteomic signature was associated with a higher risk of incident SCD (HR 3.25, 95% CI 2.94–3.58). Although non-proportional hazards were detected, the time-dependent association remained positive throughout follow-up. The proteomic-signature model showed substantially better discrimination than the clinical model (AUC 0.871 vs. 0.770), whereas the combined model provided only minimal additional improvement (AUC 0.872). Compared with the clinical model alone, the combined model improved reclassification (IDI 0.033, 95% CI 0.022–0.050; continuous NRI 0.403, 95% CI 0.351–0.459; both p < 0.001). Calibration was good, and net clinical benefit was mainly observed at relatively low threshold probabilities. Sensitivity analyses supported the robustness of the findings. Conclusion The proteomic signature was independently associated with incident SCD and improved risk stratification beyond the clinical model. However, its clinical utility should be interpreted cautiously, and external validation is required before clinical application.