Integrated metabolomics and machine learning for coronary artery disease severity stratification: identification of plasma metabolic signatures
In brief
Metabolite models classify coronary disease severity with discrimination scores above 0.95
Among 617 participants, plasma metabolite patterns differed between people with coronary artery disease and controls; caffeine levels fell and oxylipins rose as disease severity increased. Models combining metabolomic and clinical data distinguished severity groups with area under the receiver operating characteristic curve values above 0.95. These findings suggest potential for blood-based stratification, but independent validation is needed before clinical use.
- Journal
- Acta biochimica et biophysica Sinica (Q2)
- Published
- 30 September 2026
- Study design
- Case-control study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Ting Hu, Xian Ding, Feifei Han, Zhuoling An
- PMID
- 42848466
- DOI
- 10.3724/abbs.2026163
Why clinicians should know about it
- Picked for Biochemistry (medical) (top studies of the week, 11 October 2026): Plasma metabolomics identifies CAD severity signatures
Abstract
Coronary artery disease (CAD) remains a leading cause of global mortality and morbidity. Current diagnostic methods, such as coronary angiography, are invasive and resource intensive. Metabolomics offers a powerful approach to identify disease-related metabolic signatures. This study aims to characterize plasma metabolic alterations in CAD patients and develop a biomarker panel for severity stratification. A total of 617 participants, including 297 healthy controls and 320 CAD patients, are classified by coronary angiography into mild (< 70% stenosis) and severe (≥ 70% stenosis) groups and further stratified by clinical presentation into asymptomatic, angina, and myocardial infarction subgroups. Untargeted metabolomics is performed on plasma samples. Differential metabolites are identified via statistical analysis, and machine learning models are developed to predict CAD severity. A total of 253 significantly altered metabolites are identified between CAD patients and controls, which are primarily involved in lipid metabolism, caffeine metabolism, amino acid metabolism, and energy pathways. Key metabolites such as caffeine show stepwise decreases with increasing CAD severity, while oxylipins exhibit progressive increases. Weighted gene co-expression network analysis (WGCNA) identifies 30 metabolic modules, with modules significantly correlated with CAD severity. Machine learning models integrating clinical and metabolomic data achieve high diagnostic accuracy, with area under the receiver operating characteristic curve (AUC) values > 0.95 for CAD stratification. Therefore, this study comprehensively characterizes plasma metabolic alterations in CAD and identifies potential biomarker panels for clinical stratification. The integration of metabolomics and machine learning shows promise for the early detection and personalized management of CAD.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.