Abstract
Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide, and accurately predicting individual risk is critical for prevention. Here we aimed to integrate unmodifiable risk factors, such as age and genetics, with modifiable risk factors, such as clinical and biometric measurements, into a meta-prediction framework that produces actionable and personalized risk estimates. In the initial development of the model, ~2,000 predictive features were considered, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: first, a prevalent CAD cohort used to train predictive models for cross-sectional prediction at baseline and prospective estimation of contributing risk factor levels and diagnoses (baseline models) and, second, an incident CAD cohort using, in part, these baseline models as meta-features to train a final CAD incident risk prediction model. The resultant 10-year incident CAD risk model, composed of 15 derived meta-features with multiple embedded polygenic risk scores, achieves an area under the curve of 0.84. In an independent test cohort from the All of Us research program, this model achieved an area under the curve of 0.81 for predicting 10-year incident CAD risk, outperforming standard clinical scores and previously developed integrative models. Moreover, this framework enables the generation of individualized risk reduction profiles by quantifying the potential impact of standard clinical interventions. Notably, genetic risk influences the extent to which these interventions reduce overall CAD risk, allowing for tailored prevention strategies.
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Data availability
All data are made available from the UKBB55 (https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access) and All of Us research program74 (https://workbench.researchallofus.org/login) to researchers from universities and other institutions with genuine research inquiries following institutional review board and biobank approval. This research has been conducted using the UKBB resource under application number 41999 and the All of Us v7 Curated Data Repository (R2022Q4R9 and C2022Q4R9 versions).
Code availability
The machine learning code used to generate the meta-predictions is available via GitHub at http://github.com/TorkamaniLab/CAD_meta_prediction.
Change history
19 August 2025
A Correction to this paper has been published: https://doi.org/10.1038/s41591-025-03925-y
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Acknowledgements
We thank J. C. Ducom, L. Dong and the Scripps High-Performance Computing service for their support. Thanks to E. Topol for his comments on this paper. This work is supported by R01HG010881 to A.T. as well as grant UM1TR004407. We recognize that some of the factors labeled unmodifiable in this paper may be modifiable in some circumstances.
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Concept and design: A.T. Acquisition, analysis or interpretation of data: A.T., S.-F.C., S.E.L., H.J.S., C.H., J.-F.C. and N.E.W. Drafting of the paper: A.T., S.-F.C., J.-B.P. and A.K. Critical revision of the paper for important intellectual content: A.T., J.-B.P. and E.D.M.
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Extended data
Extended Data Fig. 1 Feature importance and SHAP summary for 10-year prospective CAD risk prediction in the UK Biobank.
This composite figure provides two views of the importance of the final 50 features used in prospective 10-year CAD meta-prediction for the incident CAD cohort (n = 33,419). The left panel displays a bar plot ranking the features in order of importance as quantified by the mean absolute SHAP value. The length of each bar represents the magnitude of a feature’s importance. The right panel presents a SHAP summary plot with each point representing the feature’s SHAP value at an individual level. Color coding represents the feature value (red for high, blue for low). Positive values correspond to the prediction contribution to the positive class, and negative values correspond to the prediction contribution to the negative class. Abbreviations: Dx: Diagnosis; MUF: modifiable and unmodifiable factors; UF: unmodifiable factors.
Extended Data Fig. 2 Evaluating the calibration and predictive value of feature categories for the meta-prediction model in the UK Biobank.
This figure evaluates the performance of the meta-prediction model compared to existing clinical risk scores within the test set of the incident CAD cohort (n = 33,419), highlighting both the model’s calibration and the predictive contribution of distinct feature categories. a. Calibration plot showing the predicted vs. observed 10-year CAD risk by decile. Brier’s scores are provided as a summary calibration measure. Conventional risk scores were re-calibrated using the same approach within the UK Biobank training cohort. b. Predictive performance of individual feature categories and their combinations in the meta-prediction model. The left panel illustrates the Area Under the Receiver Operating Characteristic (AUROC) for various models. The right panel shows the Area Under the Precision-Recall Curve (AUPRC). The models compared include the full meta-prediction model, partial models restricted to a single feature class and feature class combination, including 15 meta-features; 22 polygenic risk scores (PRSs) and 13 modifiable risk factors (MFs); 13 MFs; sex and age with 12 PRSs; 12 PRSs; and sex and age alone. Abbreviation: MF: Modifiable factors.
Extended Data Fig. 3 Comparative performance of CAD risk prediction models in the UK Biobank.
This figure compares the predictive performance of our final perspective 10-year CAD meta-prediction model to established clinical risk scores, including PCE, QRISK3, PREVENT, and previous polygenic score benchmarks including GPSCAD, metaGRSCAD, Aragam2022, Tcheandjieu2022, and GPSMult, as well as ML models including ML4HEN-COX and UKBCRP using the incident CAD cohort (n = 33,419). For each model, the left panel depicts a scatter plot illustrating the incidence of CAD events across percentile bins of predicted risk, showcasing the predictive density of each model at 10-years of follow-up, while the right panel displays the 10-year cumulative risk trajectories for each risk prediction model, highlighting the ability of each model to stratify risk across time. Shaded regions in the right panel represent 95% confidence intervals (CI). In all cases, meta-prediction significantly outperforms prior approaches.
Extended Data Fig. 4 SHAP summary plots for meta-features in the final model in the UK Biobank.
This figure presents a comprehensive collection of SHAP summary plots for each of the 35 meta-features integrated into the meta-prediction for the incident CAD cohort (n = 33,419). Each subplot provides insight into the contribution of individual baseline features towards the prediction of each meta-feature. The color coding indicates each feature value (red = high, blue = low), with the SHAP value on the y-axis reflecting the impact on the model’s output (positive = contribution to the positive class, negative = contribution to the negative class). Abbreviations: Dx: Diagnosis; MUF: modifiable and unmodifiable factors; UF: unmodifiable factors.
Extended Data Fig. 5 External validation of the streamlined meta-prediction.
Comparative test accuracy for the streamlined meta-prediction model in UKBB (AUROC = 0.81) versus AoU external validation with bootstrapping 95% confidence interval (CI) as the shaded part. a. Tested on the larger AoU validation cohort (AUROC = 0.81), further stratified by self-reported European (EUR), Africna (AFR) and Hispanic (HIS) groups; AoU-EHR (AUROC = 0.81), AoU-AFR (AUROC = 0.79), and AoU-HIS (AUROC = 0.84) respectively. b. And tested in the AoU sub-cohort with complete phenotypes (AUROC = 0.78). Additional conditions tested in AoU include PCE (AUC = 0.72), QRISK3 (AUC = 0.73), and PREVENT (AUC = 0.73). Abbreviations: AoU: All of Us research program; AUC: area under curve; UKBB: UK Biobank.
Extended Data Fig. 6 SHAP explanation of streamlined meta-prediction in UK Biobank and All of Us research program.
Both panels display the total 50 features contributing to the streamlined meta-prediction. The vertical axis orders each feature by its overall importance to risk prediction. Each point represents a participant and is color-coded according to the feature’s direction of contribution to the individuals’ risk prediction (red increased risk, blue decreased risk). The value associated with each point on the x-axis represents the magnitude of its contribution to the individuals’ risk prediction. The left panel presents the results from the test set of UK Biobank (n = 33,419). The right panel presents the results from the external validation set of All of Us research program (n = 198,424). Abbreviations: Dx: Diagnosis; MUF: modifiable and unmodifiable factors; UF: unmodifiable factors.
Extended Data Fig. 7 Overview of generalizable genetic meta-prediction model in the UK Biobank.
Using the incident CAD cohort (n = 33,419), we demonstrated a. Calibration plot of generalizable genetic meta-prediction model for predicted vs observed risk by decile within the test set of the incident CAD cohort (Brier’s score of generalizable genetic meta-prediction: 0.073). b. Cumulative risk curve of CAD (%) development over the 10-year follow-up period stratified by percentile of predicted risk. c. Incidence rates of CAD observed across the test cohort, stratified by percentile of predicted risk. Data are presented as mean values ± SD. d. and e. Comparative test accuracy (n = 33,419) for the generalizable genetic meta-prediction model (AUROC = 0.80; AUPRC = 0.28) versus other standard clinical and research risk scores, including PCE (AUROC = 0.73; AUPRC = 0.20), QRISK3 (AUROC = 0.74; AUPRC = 0.22), PREVENT (AUROC = 0.72; AUPRC = 0.19) and GPSCAD (AUROC = 0.73; AUPRC = 0.20). Abbreviations: AUC: Area under curve; CAD: coronary artery disease; EHR: electric health records; PCE: pool cohort equations.
Extended Data Fig. 8 Feature importance and SHAP summary for 10-year prospective CAD risk prediction of generalizable genetic model in the UK Biobank.
This composite figure provides two views of the importance of the final 50 features used in prospective 10-year CAD meta-prediction of generalizable genetic model for the incident CAD cohort (n = 33,419). The left panel displays a bar plot ranking the features in order of importance as quantified by the mean absolute SHAP value. The length of each bar represents the magnitude of a feature’s importance. The right panel presents a SHAP summary plot with each point representing the feature’s SHAP value at an individual level. Color coding represents the feature value (red for high, blue for low). Positive values correspond to the prediction contribution to the positive class, and negative values correspond to the prediction contribution to the negative class. Abbreviations: Dx: Diagnosis; MUF: modifiable and unmodifiable factors; UF: unmodifiable factors.
Extended Data Fig. 9 Feature distribution pre- and post-imputation in the UK Biobank.
This figure demonstrates the preservation of variable distributions pre- and post- imputation, stratified by sex. For numeric features, density plots are presented, with bolded edges representing the distribution pre-imputation and light edges post-imputation. For categorical features, two sets of stacked histograms are presented side by side for comparison: on the left are the original distributions pre-imputation, and on the right are the distributions post-imputation of missing data.
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Chen, SF., Lee, S.E., Sadaei, H.J. et al. Meta-prediction of coronary artery disease risk. Nat Med 31, 2277–2288 (2025). https://doi.org/10.1038/s41591-025-03648-0
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DOI: https://doi.org/10.1038/s41591-025-03648-0
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