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Precision medicine breakthrough: Multi-omics integration elevates CAD risk prediction
Qiang Su1, Qiu-Yan Li2, Chen-Kai Hu3
1Department of Cardiology, Jiangbin Hospital of Guangxi Zhuang Autonomous Region, No. 85 Hedi Road, Nanning, Guangxi, 530021, China.
Insights
Integrating genomic, proteomic, and metabolomic data significantly improves coronary artery disease (CAD) risk prediction. This multi-omics approach enhances precision medicine by identifying high-risk individuals and potential drug targets.
Area of Science:
- Genomics
- Proteomics
- Metabolomics
- Cardiovascular Disease Research
- Precision Medicine
Background:
- Coronary artery disease (CAD) is a leading global cause of mortality.
- Conventional risk stratification methods for CAD have limitations.
- Multi-omics data integration offers potential for improved CAD risk prediction.
Purpose of the Study:
- To comprehensively integrate genomic, proteomic, and metabolomic data for enhanced CAD risk prediction.
- To develop and validate a multi-omics model for stratifying CAD risk.
- To explore the translational potential of identified biological pathways and drug targets.
Main Methods:
- Integration of large-scale genomic, proteomic, and metabolomic data from UK Biobank.
- Application of similarity network fusion and elastic net regularization for multi-omics integration.
- Utilized a late integration (stacking) strategy for prediction modeling and employed rigorous internal validation techniques.
Main Results:
- The integrated multi-omics model demonstrated superior CAD prediction accuracy (C-statistic: 0.798) compared to clinical factors alone (0.741) or genomics alone (0.704).
- Network analysis identified four key biological modules associated with CAD: vascular dysfunction, lipid metabolism, inflammatory response, and cardiac remodeling.
- High-risk individuals identified by the model showed significantly increased odds of CAD and higher 10-year incidence.
Conclusions:
- Multi-omics data integration substantially improves coronary artery disease risk prediction.
- The developed model enables precision medicine strategies through effective reclassification of intermediate-risk individuals.
- This study provides a framework for the clinical translation of multi-omics risk assessment for CAD.
Background:
Coronary artery disease (CAD) remains the leading cause of global mortality. Multi-omics integration offers unprecedented opportunities for precision risk stratification beyond conventional approaches. However, comprehensive integration of genomic, proteomic, and metabolomic data for CAD prediction remains underexplored.
Methods:
We integrated genomic (n = 442,574), proteomic (n = 54,219 with 1472 proteins), and metabolomic (n = 121,249 with 249 metabolites) data from UK Biobank. Multi-omics integration employed similarity network fusion combined with elastic net regularization in the 23,776 participants with complete multi-omics data, with a late integration (stacking) strategy applied for prediction modeling across the full cohort. Internal validation encompassed temporal split (2006-2008 vs. 2009-2010), geographical split (England vs. Scotland/Wales), 10-fold cross-validation, and bootstrap resampling (1000 iterations).
Results:
Among 442,574 participants (34,241 CAD cases, 7.74%), we identified 241 independent genomic loci and integrated 1472 proteins with 249 metabolites. Network analysis revealed four major biological modules: vascular dysfunction (37.8% variance), lipid metabolism (25.4%), inflammatory response (19.2%), and cardiac remodeling (13.6%). The integrated multi-omics model achieved superior discrimination (C-statistic: 0.798, 95% CI: 0.793-0.803) compared to clinical factors alone (0.741) and genomics-only approaches (0.704, P < 0.001), with net reclassification improvement of 10.34% (95% CI: 8.92-11.77%). Temporal validation demonstrated robust transportability (C-statistic: 0.791) with consistent calibration (Hosmer-Lemeshow χ²=8.73, P = 0.366). Individuals in the highest risk quintile exhibited 4.52-fold increased CAD odds (95% CI: 4.28-4.78) with 10-year incidence of 11.24% compared to 2.51% in the lowest quintile. Drug target analysis identified 8 proteins targeted by approved cardiovascular drugs and 5 additional targets in Phase II/III clinical trials, supporting translational potential.
Conclusions:
Multi-omics integration substantially enhances CAD risk prediction with robust internal validation, enabling targeted precision medicine strategies through successful intermediate-risk reclassification. These findings establish a framework for clinical translation of multi-omics risk assessment.
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