Journal Article

DZNE-2026-00915

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Proteomic signatures as biomarkers of atherosclerosis burden.

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.DZNE* ; Murthy, V. L. ; Thorand, B. ; Georgakis, M. K.

2026
Oxford University Press Oxford

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Please use a persistent id in citations: doi:10.1093/cvr/cvag140

Abstract: 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.In UK Biobank, we trained machine-learning models on plasma proteomics in a nested, propensity score-matched case-control sample (1666 cases; 1666 controls; Olink Explore 3072, 2920 proteins) to derive four AtheroBurden signatures: one based on the entire proteome (all 2920 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 (i) correlation with carotid plaque burden via ultrasound (n = 1712); (ii) prospective major adverse cardiovascular events (MACEs) 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 = 1361) and Age1 (n = 796) cohorts; and (iii) longitudinal trajectories analysis in 1210 participants with 3 serial proteomic measurements. All four AtheroBurden signatures robustly discriminated prevalent atherosclerotic disease (receiver operating characteristic area under curve 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 [hazard ratio (HR) per standard deviation 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.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.

Keyword(s): Humans (MeSH) ; Proteomics: methods (MeSH) ; Biomarkers: blood (MeSH) ; Carotid Artery Diseases: blood (MeSH) ; Carotid Artery Diseases: diagnostic imaging (MeSH) ; Carotid Artery Diseases: genetics (MeSH) ; Carotid Artery Diseases: epidemiology (MeSH) ; Plaque, Atherosclerotic (MeSH) ; Predictive Value of Tests (MeSH) ; Prognosis (MeSH) ; Risk Assessment (MeSH) ; UK Biobank (MeSH) ; Female (MeSH) ; Male (MeSH) ; Middle Aged (MeSH) ; Aged (MeSH) ; Case-Control Studies (MeSH) ; Machine Learning (MeSH) ; Time Factors (MeSH) ; Blood Proteins: analysis (MeSH) ; Risk Factors (MeSH) ; Reproducibility of Results (MeSH) ; Prospective Studies (MeSH) ; Biological Specimen Banks (MeSH) ; Atherosclerosis ; Cardiovascular disease ; Machine learning ; Proteomics ; Risk prediction ; Biomarkers ; Blood Proteins

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Contributing Institute(s):

1. Vascular Cognitive Impairment & Post-Stroke Dementia (AG Dichgans)

Research Program(s):

1. 353 - Clinical and Health Care Research (POF4-353) (POF4-353)

Appears in the scientific report 2026

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Database coverage:
; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; Nationallizenz ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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