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Quantifying the Independent and Combined Contributions of Clinical, Genetic, Proteomic, and Metabolomic Signals to
Yonghyun Nam1, Dokyoon Kim1,2,3
1Division of Informatics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
Insights
Predicting atherosclerotic cardiovascular disease (ASCVD) risk improves with multi-modal data. Proteomics offers the strongest signal, enhancing precision prevention strategies beyond clinical factors and polygenic risk scores (PRS).
Area of Science:
- Cardiovascular Disease Research
- Genetics and Genomics
- Proteomics and Metabolomics
Background:
- Current atherosclerotic cardiovascular disease (ASCVD) risk prediction models using clinical factors and polygenic risk scores (PRS) inadequately capture near-term risk dynamics.
- Dynamic biological processes are crucial for accurate, short-term ASCVD risk assessment.
Purpose of the Study:
- To evaluate the independent and combined predictive contributions of clinical, genetic, proteomic, and metabolomic data for ASCVD risk.
- To develop and assess multi-modal risk scores for improved ASCVD risk stratification.
Main Methods:
- Utilized UK Biobank data (n=15,787) with matched genetics, proteomics, metabolomics, and clinical information.
- Constructed modality-specific risk scores: PRS, proteomics (ProRS), metabolomics (MetRS), and clinical factors.
- Integrated scores using Cox models to assess predictive performance.
Main Results:
- Proteomics (ProRS) demonstrated the strongest independent predictive contribution to ASCVD risk.
- Polygenic risk scores (PRS) provided orthogonal inherited risk information.
- Multi-modal integration significantly improved discrimination compared to single-modality models, with ProRS-PRS stratification identifying distinct risk subgroups.
Conclusions:
- Multi-modal risk modeling, especially integrating proteomics, enhances ASCVD risk stratification.
- Proteomics-integrated approaches are vital for advancing precision prevention strategies in cardiovascular disease.
- Combined genetic and proteomic data identify unique biological and clinical risk profiles.
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.
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