Comprehensive Metabolomics Identified the Prominent Role of Glycerophospholipid Metabolism in Coronary Artery Disease Progression.

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 < 0.05) changed at different CAD stages and showed apparent correlation with CAD severity indicators. Moreover, cross-comparisons within CAD patients characterized that a total of 72 and 88 metabolites/lipid species significantly associated with UA (vs. SCAD) and MI (vs. UA), respectively. The disturbed pathways included glycerophospholipid metabolism, and cysteine and methionine metabolism. Furthermore, models incorporating metabolic and lipidomic profiles with traditional risk factors were constructed. The combined model that incorporated 11 metabolites/lipid species and four traditional risk factors represented better discrimination of UA and MI (C-statistic = 0.823, 95% CI, 0.783-0.863) compared with the model involving risk factors alone (C-statistic = 0.758, 95% CI, 0.712-0.810). The combined model was successfully used in discriminating UA and MI patients (p < 0.001) in a three-center validation cohort.

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.