Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine
Xi-Ru Zhang1,2, Wen-Fang Zhong2, Rui-Yan Liu2
1Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, Guangdong, China.
Insights
Integrating biomarkers with traditional factors significantly improves cardiovascular disease prediction. A new Biomarker Risk Score (BRS) effectively stratifies individuals, aiding personalized prevention strategies for major adverse cardiovascular events (MACE).
Area of Science:
- Biomedical research
- Cardiovascular epidemiology
- Biomarker discovery
Background:
- Cardiovascular diseases (CVD) are a leading global cause of death.
- Traditional risk models have limitations in predicting new-onset major adverse cardiovascular events (MACE).
- Need for improved risk stratification using integrated data.
Purpose of the Study:
- To enhance prediction and risk stratification of MACE.
- To integrate traditional risk factors with biochemical and metabolomic biomarkers.
- To develop and validate a Biomarker Risk Score (BRS).
Main Methods:
- Analysis of UK Biobank data (229,352 participants).
- Biomarker selection using AUC, JMIM, and correlation analyses.
- Cox proportional hazards models and CatBoost/SHAP for threshold determination and BRS calculation.
Main Results:
- Combined predictors significantly outperformed traditional models (C-index > 0.75 for most outcomes, 0.822 for CVD mortality).
- Key predictors identified: cystatin C, HbA1c, GlycA, GGT; protective effects from IGF-1, DHA.
- BRS effectively stratified individuals into risk groups, with high-risk individuals showing a 2.76-fold increased risk for CVD death.
Conclusions:
- Integration of biomarkers and traditional factors improves MACE prediction and risk stratification.
- The BRS is a promising tool for identifying high-risk individuals.
- Potential to support personalized CVD prevention and management.
Background:
Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major adverse cardiovascular events (MACE). This study aimed to improve prediction and risk stratification by integrating traditional risk factors with biochemical and metabolomic biomarkers.
Methods:
We analyzed data from 229,352 participants in the UK Biobank (median age 58.0 years; 45.4% male) who were free of baseline MACE. Biomarker selection was conducted using area under the curve (AUC), minimal joint mutual information maximization (JMIM), and correlation analyses, while Cox proportional hazards models were employed to evaluate the predictive performance of combined traditional risk factors and biomarkers. Optimal binary thresholds were determined utilizing CatBoost and SHAP, leading to the calculation of a Biomarker Risk Score (BRS) for each participant. Multivariable Cox models were conducted to assess the associations of each concerned biomarker and BRS with new-onset endpoints.
Results:
The combination of PANEL + All Biochemistry + Cor0.95 of Nonov Met predictors demonstrated significantly improved discriminative performance compared to traditional models, such as Age + Sex and ASCVD, across all endpoints. Although the prediction for hemorrhagic stroke was suboptimal (C-index = 0.699), C-index values for other outcomes surpassed 0.75, with the highest value (0.822) recorded for CVD-related mortality. Key predictors of new-onset MACE included cystatin C, HbA1c, GlycA, and GGT, while IGF-1 and DHA exhibited potential protective effects. The BRS stratified individuals into low-, intermediate-, and high-risk groups, with the strongest effect observed for CVD death, where the high-risk group had a relative risk of 2.76 (95% CI 2.48-3.07) compared to the low-risk group.
Conclusion:
Integrating traditional risk factors and biomarkers improves prediction and risk stratification of new-onset MACE. The BRS shows promise as a tool for identifying high-risk individuals, with the potential to support personalized CVD prevention and management strategies.
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