ArticleAtherosclerosis2025
Evaluation of a machine learning-based metabolic marker for coronary artery disease in the UK Biobank.
Article in Atherosclerosis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024.Frontiers in medicine · 2025Pooled it
- Advanced detection of coronary artery disease using a GAN-transformer model on plasma cytokine profiles.Scientific reports · 2026Article
- Development of Age- and Sex-Specific Metabolomics-Based Biological Ageing Clocks for 10-Year Mortality Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AI-driven identification of nutrition-modulated biomarkers and drug targets for cardiovascular therapeutic mechanisms.Frontiers in pharmacology · 2026Review
- Effect of changes in the triglyceride-glucose index on atherogenic lipid profiles in coronary artery disease patients receiving lipid-lowering therapy: a prospective cohort study.Cardiovascular diabetology · 2025Article
- Advances in Metabolomics: A Comprehensive Review of Type 2 Diabetes and Cardiovascular Disease Interactions.International journal of molecular sciences · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
BACKGROUND AND
aimsAn in silico quantitative score of coronary artery disease (ISCAD), built using machine learning and clinical data from electronic health records, has been shown to result in gradations of risk of subclinical atherosclerosis, coronary artery disease (CAD) sequelae, and mortality. Large-scale metabolite biomarker profiling provides increased portability and objectivity in machine learning for disease prediction and gradation. However, these models have not been fully leveraged. We evaluated a quantitative score of CAD derived from probabilities of a machine learning model trained on metabolomic data.
methodsWe developed a CAD-predictive learning model using metabolic data from 93,642 individuals from the UK Biobank (median [IQR] age, 57 [14] years; 39,796 [42 %] male; 5640 [6 %] with diagnosed CAD), and assessed its probabilities as a quantitative metabolic risk score for CAD (M-CAD; range 0 [lowest probability] to 1 [highest probability]) in participants of the UK Biobank. The relationship of M-CAD with arterial stiffness index, ejection fraction, CAD sequelae, and mortality was assessed.
resultsThe model predicted CAD with an area under the receiver-operating-characteristic curve of 0.712. Arterial Stiffness Index increased by 0.19 and ejection fraction decreased by 0.2 % per 0.1 increase in M-CAD. Both incident and recurrent myocardial infarction increased stepwise over M-CAD quartiles (odds ratio (OR) 15.3 [4.2 %] and 12.5 [0.2 %]) in top quartiles as compared to the first quartile of incident and recurrent MI respectively). Likewise, the hazard ratio and prevalence of all-cause mortality, CVD-associated mortality, and CAD-associated mortality increased stepwise over M-CAD deciles (2.98 [14 %], 9.34 [4.3 %], 26.7 [2.7 %] in the top deciles as compared to the first decile of all-cause, CVD, and CAD mortality respectively).
conclusionsMetabolic-based machine learning can be used to build a quantitative risk score for CAD that is associated with atherosclerotic burden, CAD sequelae and mortality.
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