YANG Zhu-lin, TIAN Lin-yun, WU Hao, LI Hui-min, GU Ying, REN Da-bing, ZHUANG Ke, ZHAO Yan, ZHANG Hong, YI Lun-zhao. Blood Metabolomics Reveals the Metabolic Disorder in Disease Progression for Patients with Ischemic Heart Disease[J]. Journal of Chinese Mass Spectrometry Society, 2024, 45(2): 256-268. DOI: 10.7538/zpxb.2023.0068
Citation: YANG Zhu-lin, TIAN Lin-yun, WU Hao, LI Hui-min, GU Ying, REN Da-bing, ZHUANG Ke, ZHAO Yan, ZHANG Hong, YI Lun-zhao. Blood Metabolomics Reveals the Metabolic Disorder in Disease Progression for Patients with Ischemic Heart Disease[J]. Journal of Chinese Mass Spectrometry Society, 2024, 45(2): 256-268. DOI: 10.7538/zpxb.2023.0068

Blood Metabolomics Reveals the Metabolic Disorder in Disease Progression for Patients with Ischemic Heart Disease

  • Ischemic heart disease is one of the main causes of death worldwide. In this study, the plasma samples of 47 patients with angina, 51 patients with myocardial infarction, and 80 patients with heart failure were collected. Ultra-performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) and chemometrics were used to analyze the plasma metabolic disorder in disease progression for patients with ischemic heart disease. Chromatographic separation was performed on an ACE3 C18 column (150 mm×3.0 mm×3 μm), and MS analysis was carried out by electrospray ionization (ESI) source at positive and negative modes combined with full scanning. In total, 97 endogenous metabolites were identified and quantitative analyzed. Principal component analysis and partial least square-discriminant analysis were applied in combination with variable importance projection (VIP) and t-test to screen differentially characteristic metabolites. Metabolite concentration was analyzed using SPSS and the metabolites with significant differences (P<0.05) were found between different processes of diseases. Discriminant analysis using partial least squares-discriminant analysis (PLS-DA) was conducted on various disease processes. Classification models were established for angina, myocardial infarction, and heart failure patients. The variables with VIP>1 were selected using Variable Importance in Projection screening. Finally, disease-specific metabolites were identified using VIP>1 and t-test P<0.05. Accordingly, 28 and 32 characteristic metabolites were selected to distinguish between patients with angina and myocardial infarction, and with myocardial infarction and heart failure, respectively. The metabolic pathway analysis revealed disorders in both amino acid metabolism and the tricarboxylic acid cycle during the progression from angina to myocardial infarction, and from myocardial infarction to heart failure. In the progression from myocardial infarction to heart failure, there is a significant disturbance in lipid metabolism, including glycerophospholipid metabolism and fatty acid biosynthesis. Based on the results of 1 receiver operating character (ROC) analysis, several indicators including glycocholic acid, fumaric acid, palmitic acid, troponin, high-density lipoprotein, alanine aminotransferase, and aspartate aminotransferase, were found to have diagnostic significance for distinguishing between angina and myocardial infarction, with respective area under curve (AUC) values of 0.737 6, 0.831 9, 0.827 7, 0.938 7, 0.646 0, 0.704 5 and 0.758 9. When these indicators were combined, the AUC was increased to 1.000 0. Similarly, citrulline, citric acid, stearic acid, glycerophospholine, and NT-proBNP were found to have diagnostic significance for distinguishing between myocardial infarction and heart failure, with respective AUC values of 0.619 4, 0.748 7, 0.878 4, 0.636 0 and 0.812 1. When these indicators were combined, the AUC was increased to 0.957 0. At present, there have been some advances in understanding the metabolic characteristics of patients with angina or heart failure based on metabolomics, but there is limited research on the metabolic characteristics of patients with ischemic heart disease. The findings of this study can provide important metabolic targets for precise diagnosis of ischemic heart disease and drug and nutritional interventions during disease development.
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