Harnessing Large-Scale Multi-Omics Data for Risk Prediction and Deep Phenotyping of Valvular Heart Diseases in the
Zhihao Jiang1, Yang Liu1, Mingyu Song1
1Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.
Insights
Developing a multi-omics risk model improves valvular heart disease (VHD) prediction. Proteomic data significantly enhances models, identifying key proteins and modifiable risk factors like blood pressure for VHD.
Area of Science:
- Cardiovascular Medicine
- Genomics
- Proteomics
- Machine Learning
Background:
- Valvular heart disease (VHD) mechanisms and risk factors are not fully understood.
- Accurate prediction models are needed for early diagnosis and intervention.
Purpose of the Study:
- To develop and validate a multi-omics risk prediction model for VHD.
- To identify biological mechanisms underlying VHD development.
- To explore potential biomarkers and therapeutic targets.
Main Methods:
- Utilized UK Biobank data for Cox proportional hazards and machine learning models (XGBoost, LightGBM).
- Incorporated clinical, genomic, proteomic, and metabolomic data for prediction.
- Performed cluster analysis, functional enrichment, Mendelian randomization, and Bayesian colocalization.
Main Results:
- Clinical Cox models achieved strong predictive performance (C-index 0.75-0.81).
- Proteomic data incorporation significantly enhanced prediction (C-index > 0.81).
- A simplified 10-year model with four proteins showed sustained performance (C-index 0.75-0.82).
- Identified blood pressure and lipids as key modifiable risks.
- Linked VHD to protease inhibition, AVS to fibrosis/matrix metabolism, MVR to immune-inflammation.
- Suggests causal roles for CNTN5, CD8A in AVS/MVR, and IGFBP7 in AVS.
Conclusions:
- Multi-omics data, particularly proteomics, can significantly improve VHD risk prediction.
- Identified specific pathways and genetic factors associated with VHD subtypes.
- Findings support the development of novel diagnostic biomarkers and precision therapies for VHD.
Abstract:
The risk profile of valvular heart disease (VHD) and its underlying mechanisms remain poorly understood. This study aimed to develop and validate a multi-omics-based risk prediction model, and to elucidate potential biological mechanisms. Using data from the UK Biobank, Cox proportional hazards and machine learning models (XGBoost and LightGBM) were evaluated for predicting VHD and its subtypes (aortic valve stenosis, AVS; aortic valve regurgitation, AVR; mitral valve regurgitation, MVR). Cox models based on key clinical factors showed the best predictive performance (C-index of 0.75-0.81), which was further enhanced by incorporating proteomic data (all C-index > 0.81) but not by genomic or metabolomic data. Notably, a simplified 10-year model comprising only four top proteins maintained favorable performance (C-index of 0.75-0.82). Cluster analysis identified blood pressure and lipid levels as leading modifiable risk factors for VHD onset. Functional enrichment analysis revealed that VHD is primarily associated with protease inhibition, AVS with fibrotic and matrix metabolic pathways, and MVR with immune-inflammatory activation. Mendelian randomization and Bayesian colocalization analyses suggested causal associations between CNTN5 and CD8A with risks of AVS and MVR, whilst IGFBP7 showed a reverse-direction association with AVS. These findings highlight promising avenues for early diagnostic biomarkers and potential precision-targeted therapies.
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