Plasma metabolomics disentangles T2DM- and coronary artery disease (CAD)-specific dysmetabolism and identifies potential biomarkers for CAD risk escalation in diabetic patients
Authors' conclusion
These findings disentangle T2DM- and CAD-specific metabolic disturbances and identify escalation/de-escalation features of CAD risk in diabetic patients, which are potential candidates for future risk stratification pending validation.
Study context
- Population
- Healthy controls and T2DM patients with or without CAD
- Participants
- 123 / 50 / 155
- Exposure
- Healthy controls (HCs) vs T2DM without CAD vs T2DM with CAD
- HCsn = 123reference
- T2DMn = 50
- T2DM_CADn = 155
- Outcome
- Plasma metabolomic profiles
Methodological detailsuntargeted plasma metabolomics, machine learning models
- Untargeted plasma metabolomics
- Machine learning models
Main finding
As reported in the abstract
- Distinct metabolic patterns across the three cohorts
- T2DM-specific metabolites enriched in carbohydrates and certain lipid species (glucose and lipid metabolism disturbances)
- CAD-specific metabolites predominantly lipids and organic acids, involving amino acid and fatty acid metabolic pathways
- Several metabolites changed progressively from HCs through T2DM to T2DM_CAD; others showed opposing trends, suggesting compensatory or protective adaptations
- Machine learning models integrating key metabolites with clinical parameters effectively distinguished study groups, with promising performance for CAD risk assessment in T2DM
Observational data: associations, not effects.
Original publication
Plasma metabolomics disentangles T2DM- and CAD-specific dysmetabolism and identifies potential biomarkers for CAD risk escalation in diabetic patients. Cardiovasc Diabetol. Published 19 November 2025. doi:10.
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