Identification and validation of plasma metabolomics reveal potential biomarkers for coronary heart disease

Abstract

Coronary heart disease (CHD) is a prevalent and chronic life-threatening disease. However, there is no reliable way for early diagnosis and prevention of CHD so far. The precise molecular pathological mechanism of CHD remains obscure. Therefore, developing novel biomarkers is urgently needed. In order to evaluate the potential of untargeted plasma metabolomics in biomarker discovery for characterizing CHD, plasma metabolites from patients newly diagnosed with CHD and controls were profiled using liquid chromatography quadrupole time-of-flight mass spectrometry. Differential metabolites were identified using both univariate and multivariate statistical analyses. Metabolites with significant changes were subjected to binary logistic regression analysis, and a CHD prediction model was established. A total of 28 differential plasma metabolites were identified, of which the concentrations of 11 increased significantly and those of 17 decreased significantly in patients with CHD compared with controls. The altered metabolic pathways included reduced phospholipid metabolism, increased monoglyceride metabolism, and abnormal fatty acid metabolism. Furthermore, binary logistic regression showed that nine metabolites could be used as potential plasma biomarkers for the diagnosis of CHD. The prediction model based on these nine metabolites was then tested with an independent cohort of samples (area under the curve = 0.929). Our plasma metabolomics study not only yielded fundamental insights into dysregulated metabolism in CHD but also presented a combinatorial biomarker that might support the clinical diagnosis of CHD.