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Zhang, L.* ; Omarov, M.* ; Xu, L.* ; Das, B.* ; Luo, H. ; Hauck, S.M. ; Petrera, A. ; Yu, Z.* ; Goonewardena, S.N.* ; Zeggini, E. ; Peters, A. ; Dichgans, M.* ; Murthy, V.L.* ; Thorand, B. ; Georgakis, M.K.*

Proteomic signatures as biomarkers of atherosclerosis burden.

Cardiovasc. Res. 122, 1589–1608 (2026)
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AIMS: Atherosclerosis is currently evaluated by imaging, but scalable circulating biomarkers to detect its presence and quantify overall burden are lacking. We sought to define plasma proteomic signatures that reflect the systemic burden of atherosclerosis. METHODS AND RESULTS: In UK Biobank, we trained machine-learning models on plasma proteomics in a nested, propensity-score-matched case-control sample (1,666 cases; 1,666 controls; Olink Explore 3072, 2,920 proteins) to derive four AtheroBurden signatures: one based on the entire proteome (all 2,920 proteins) and three biologically informed subsets-genetically anchored (n = 402), atherogenesis-related (n = 680), and artery-enriched (n = 248). We then computed individual-level signatures in 41,200 disease-free participants and validated their performance through 1) correlation with carotid plaque burden via ultrasound (n = 1,712); 2) prospective major adverse cardiovascular events (MACE) prediction over 13.7-year follow-up using multivariable Cox models and incremental prediction over Systematic COronary Risk Evaluation version 2 (SCORE2) (ΔC-index; net reclassification improvement [NRI]); as well as in the external Cooperative Health Research in the Region of Augsburg (KORA) S4 (n = 1,361) and Age1 (n = 796) cohorts; and 3) longitudinal trajectories analysis in 1,210 participants with three serial proteomic measurements.All four AtheroBurden signatures robustly discriminated prevalent atherosclerotic disease (ROC-AUC up to 0.91, 95% CI: 0.89-0.93) with their levels significantly increasing with the number of clinically affected vascular beds. We found significant correlations with carotid ultrasound-measured plaque burden. In prospective analysis, each signature exhibited strong associations with incident MACE (HR per SD increase in whole proteome signature: 1.75, 95% CI: 1.68-1.83; HR for Q4 vs. Q1: 3.18, 95% CI: 2.8-3.61), providing significant improvements in risk discrimination (ΔC-index: +0.044; p < 0.0001) and reclassification (NRI: 0.12 (95% CI: 0.09-0.15) at a 10% risk threshold) beyond SCORE2. These results were replicated in the KORA S4 and KORA-Age1 cohorts, where the signatures were associated with risk of future myocardial infarction and stroke. Furthermore, longitudinal analyses demonstrated steeper annual increases in proteomic signature among individuals with higher burden of vascular risk factors and individuals who experienced MACE over follow-up. CONCLUSIONS: Plasma proteomic signatures effectively capture atherosclerotic burden and improve cardiovascular risk prediction in asymptomatic individuals. They may complement existing risk stratification by serving as a scalable and accessible blood-based screening tool to identify individuals more likely to have subclinical atherosclerosis and may benefit from confirmatory imaging and earlier prevention strategies.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Atherosclerosis ; Cardiovascular Disease ; Machine Learning ; Proteomics ; Risk Prediction; Coronary-artery-disease; Intima-media Thickness; Cardiovascular Risk; Uk Biobank; Plasma; Associations; Prediction; Genetics; Survival; Profile
ISSN (print) / ISBN 0008-6363
e-ISSN 1755-3245
Journal Cardiovascular Research
Quellenangaben Volume: 122, Issue: 12, Pages: 1589–1608 Article Number: , Supplement: ,
Publisher Oxford University Press
Publishing Place Great Clarendon St, Oxford Ox2 6dp, England
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Core Facility Metabolomics and Proteomics (CF-MPC)
Institute of Translational Genetics (ITG)
Grants US National Human Genome Research Institute
China Scholarship Council
VA MERIT
Melvyn Rubenfire Professorship in Preventive Cardiology
Hertie Foundation
Fritz-Thyssen Foundation
National Institute of Diabetes and Digestive and Kidney Diseases
National Heart, Lung, and Blood Institute
Taubman Medical Research Institute
Vascular Dementia Research Foundation
National Institute on Aging
Munich Cluster for Systems Neurology
German Research Foundation