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Background: Coronary stenosis severity determines ischemic symptoms and adverse outcomes. The metabolomic analysis of human fluids can provide an insight into the pathogenesis of complex disease. Thus, this study aims to investigate the metabolomic and lipidomic biomarkers of coronary artery disease (CAD) severity and to develop diagnostic models for distinguishing individuals at an increased risk of atherosclerotic burden and plaque instability. Methods: Widely targeted metabolomic and lipidomic analyses of plasma in 1,435 CAD patients from three independent centers were performed. These patients were classified as stable coronary artery disease (SCAD), unstable angina (UA), and myocardial infarction (MI). Associations between CAD stages and metabolic conditions were assessed by multivariable-adjusted logistic regression. Furthermore, the least absolute shrinkage and selection operator logistic-based classifiers were used to identify biomarkers and to develop prediagnostic models for discriminating the diverse CAD stages. Results: On the basis of weighted correlation network analysis, 10 co-clustering metabolite modules significantly (p p Conclusion: Differences in metabolic profiles of diverse CAD subtypes provided a new approach for the risk stratification of unstable plaque and the pathogenesis decipherment of CAD progression.
Chen et al. (Wed,) studied this question.