Serum proteomic profiling of carotid atherosclerosis: a population outcome study

Abstract

Abstract Background Circulating proteins reflecting atherosclerosis may improve cardiovascular (CV) disease prediction. We applied feature selection and unsupervised clustering on proteomic data to identify proteins associated with carotid atherosclerosis and construct a protein-based classification system for prediction of CV events. Methods 491 community-dwelling participants (mean age, 58±11 years; 51% women) underwent carotid ultrasonography and proteomic profiling (CVD II panel, Olink Proteomics). Subsequently, CV outcome was collected (median follow-up time: 10.2 years). We applied partial least squares (PLS) to identify proteins linked to carotid intima-media thickness (cIMT). Next, we assessed the association between future CV events and protein-based phenogroups derived by unsupervised clustering (Gaussian Mixture modelling) based on proteins influential in PLS. Results PLS marked 19 proteins as important, which were all associated with cIMT in multivariable-adjusted linear regression. 8 of the 19 proteins were excluded from the clustering due to high collinearity. Based on the 11 remaining proteins, the clustering algorithm subdivided the cohort into two phenogroups. Compared to the first (n=177), participants in the second phenogroup (n=314) presented: i) a more unfavourable lipid profile with higher total cholesterol and triglycerides and lower HDL cholesterol (P≤0.014); ii) higher cIMT (P=0.0020); and iii) a significantly higher risk for future CV events (multivariable-adjusted hazard ratio (95% CI) versus cluster 1: 2.05 (1.26 to 3.52); P=0.0093). The protein-based phenogrouping supplemented the ACC/AHA 10-year ASCVD risk score for prediction of a first CV event. Conclusions Phenogrouping based on a focused set of proteins related to carotid atherosclerosis identified individuals at high risk for future CV events. This approach may complement current CV risk stratification strategies.Summary of workflow and findingsCV event prediction