ArticleResearch square2025
Proteomic Signatures as Biomarkers of Atherosclerosis Burden.
Article in Research square, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Atherosclerosis progresses silently over decades before manifesting clinically as myocardial infarction or stroke. Currently, no circulating biomarker reliably quantifies the burden of atherosclerosis beyond imaging techniques. Here, we sought to define plasma proteomic signatures that reflect the systemic burden of atherosclerosis. Using CatBoost machine learning applied to plasma proteomes (Olink Explore 3072; 2,920 proteins) from 44,788 UK Biobank participants, we derived four proteomic signatures which robustly discriminated individuals with known atherosclerotic disease from propensity score-matched controls (ROC-AUC up to 0.92, 95% CI: 0.90-0.94 in the test set). Each signature was based on distinct protein sets: the whole proteome (WholeProteome; n = 2920), 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). Among 41,200 individuals without atherosclerosis at baseline, 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 increased with the number of clinically affected vascular beds, correlated with carotid ultrasound-measured plaque burden, and predicted future myocardial infarction and stroke in the external KORA S4 (n=1,361) and KORA-Age1 (n=796) cohorts with a median follow-up period of 15.1 and 6.8 years, respectively. Longitudinal analyses across three serial assessments showed that all signatures followed distinct trajectories, with significantly steeper annual increases among individuals with a higher burden of vascular risk factors. These findings demonstrate that proteomic signatures effectively capture atherosclerotic burden and improve cardiovascular risk prediction in asymptomatic individuals. Plasma proteomics may serve as a scalable and accessible alternative to imaging for identifying subclinical atherosclerosis, thereby supporting prevention strategies for cardiovascular disease.
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