Researchers have long known that atherosclerosis progresses silently over many years before manifesting clinically as myocardial infarction or stroke. As of now, no circulating biomarker reliably quantifies atherosclerotic burden beyond imaging techniques.
An international team composed of scientists mainly from Boston and Munich sought to define plasma proteomic signatures that systematically capture the burden of atherosclerosis. Speaking at the recent ASHG Conference in Boston, they described how they used CatBoost machine learning applied to plasma proteomes (Olink Explore 3072; 2,920 proteins) from 44,788 UK Biobank participants. The investigators derived four distinct proteomic signatures based on biologically-informed protein sets: the whole proteome (WholeProteome; n=2,920), proteins associated with genetic predisposition to atherosclerosis (Genetic; n=402), those implicated in atherogenesis (Mechanistic; n=680), and proteins enriched in arterial tissue (Arterial; n=248).
Robustly discriminated individuals
These signatures robustly discriminated individuals with known atherosclerotic disease from propensity score-matched controls (ROC-AUC up to 0.91, 95% CI: 0.89–0.93), according to the scientists. Among 41,200 individuals without baseline atherosclerosis, all four signatures were strongly associated with future major adverse cardiovascular events over a median follow-up of 13.7 years (HR per SD increase in WholeProteome signature: 1.70, 95% CI: 1.64–1.77), providing significant improvements in risk discrimination (ΔC-index: +0.036; p <0.0001) and reclassification (Net Reclassification Index: 0.085–0.135 at a 10% risk threshold) beyond SCORE2.
Signature levels correlated with the number of clinically affected vascular beds and carotid ultrasound-measured plaque burden, and successfully predicted future cardiovascular events in external KORA S4 (n=1,361) and KORA-Age1 (n=796) cohorts. Longitudinal analyses using linear mixed-effects models revealed signature trajectories with steeper annual increases among individuals with higher baseline risk factors or subsequent MACE events, and consistent associations with polygenic risk score for coronary artery disease, reflecting their capacity to capture genetically-influenced atherosclerotic burden.
These findings demonstrate that proteomic signatures effectively capture atherosclerotic burden and improve cardiovascular risk prediction in asymptomatic individuals, the scientists noted. Plasma proteomics may serve as a scalable and accessible alternative to imaging for identifying subclinical atherosclerosis, thereby supporting prevention strategies for cardiovascular disease, they added.