Proteomic signatures for sudden cardiac death and related intermediate phenotypes.

Abstract

BACKGROUND: Novel markers for sudden cardiac death (SCD) are needed. OBJECTIVE: This study aimed to explore whether a protein risk score derived from a large-scale proteomics dataset improves risk prediction of SCD in the general population. METHODS: A total of 52,705 individuals with 1459 unique plasma protein measurements were included from the UK Biobank Pharma Proteomics Project. A protein risk score was developed using lasso-penalized Cox regression on 40,722 participants enrolled at the English centers and validated on 11,983 participants enrolled at the remaining centers. RESULTS: The protein risk score formula developed from the derivation set comprised 64 unique plasma proteins including latent-transforming growth factor beta-binding protein 2, protein tyrosine phosphatase receptor sigma, and spondin-1. In the test set, a per standard deviation increase in protein risk score was associated with a hazard ratio of 2.60 (95% confidence interval [CI] 2.12-3.18) for SCD. Adding a protein risk score to SCD clinical risk factors resulted in a concordance index increase of 0.063 (95% CI 0.037-0.105) for SCD. For ventricular arrhythmia-mediated SCDs, an increase in concordance index when a protein risk score was added to SCD clinical risk factors was 0.070 (95% CI 0.010-0.188). A protein risk score added to SCD clinical risk factors resulted in a risk reclassification of 16.9% (95% CI 9.0-24.7) at a 10-year risk threshold of 5%. A protein risk score was significantly associated with intermediate phenotypes of SCD including corrected QT prolongation, an increase in left ventricular mean myocardial thickness, and a decrease in left ventricular global longitudinal strain. CONCLUSION: A protein risk score derived from a single plasma sample significantly improved risk prediction of SCD and related intermediate phenotypes.