| Title: | Meta-prediction of coronary artery disease risk |
| Journal: | Nature Medicine |
| Published: | 16 Apr 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40240837/ |
| DOI: | https://doi.org/10.1038/s41591-025-03648-0 |
Publication 15811
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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.</p>
9 Keywords
- Aged
- Coronary Artery Disease
- Female
- Humans
- Male
- Middle Aged
- Risk Assessment
- Risk Factors
- United Kingdom
10 Authors
- Shang-Fu Chen
- Sang Eun Lee
- Hossein Javedani Sadaei
- Jun-Bean Park
- Ahmed Khattab
- Jie-Fu Chen
- Corneliu Henegar
- Nathan E. Wineinger
- Evan D. Muse
- Ali Torkamani
1 Application
| Application ID | Title |
|---|---|
| 41999 | Identification of Genomic Risk Factors and Prediction of Cardiovascular Disease Risk through Deep Learning |
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