Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to ASCVD Prediction.

Authors

Yonghyun Nam, Dokyoon Kim

Published in

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. Volume 2026. Pages 343-352. Epub Jun 01, 2026.

Abstract

Atherosclerotic cardiovascular disease (ASCVD) risk prediction based on clinical factors and polygenic risk scores (PRS) does not fully capture dynamic biological processes related to near-term risk. Using UK Biobank participants with matched genetics, plasma proteomics, metabolomics, and clinical data (n=15,787; 1,731 incident ASCVD events), we evaluated the independent and combined contributions of clinical, genetic, proteomic, and metabolomic signals within a unified modeling framework. Modality-specific risk scores were constructed for PRS, proteomics (ProRS), metabolomics (MetRS), and clinical factors, then integrated using Cox models. Proteomics provided the strongest independent predictive contribution, PRS contributed orthogonal inherited risk information, and metabolomics provided intermediate prognostic value. Multi-modal integration improved discrimination compared with single-modality models, and joint ProRS-PRS stratification identified biologically and clinically distinct risk subgroups. These findings support multi-modal risk modeling, particularly proteomics-integrated approaches, for improving ASCVD risk stratification and precision prevention.

PMID:
42317817
Bibliographic data and abstract were imported from PubMed on 19 Jun 2026.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.