| Title: | Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to ASCVD Prediction. |
| Journal: | AMIA Joint Summits on Translational Science Proceedings |
| Published: | 1 Jun 2026 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/42317817/ |
Publication 20649
WARNING: the interactive features of this website use CSS3, which your browser does not support. To use the full features of this website, please update your browser.
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.</p>
2 Authors
- Yonghyun Nam
- Dokyoon Kim
1 Application
| Application ID | Title |
|---|---|
| 32133 | Integration of multi-organ imaging phenotypes, clinical phenotypes, and genomic data |
Enabling scientific discoveries that improve human health