Biomarker discovery and metabolic profiling in serum of cardiovascular disease patients with untargeted metabolomics and machine learning

作者：Xia Shen, Shuyuan Guo, Ningning Liang, Mingming Zhao, Wang Chun, Zi Li, Dewen Yan, Lemin Zheng, Huiyong Yin · 发表于：Clinical and Translational Medicine · 年份：2024 · DOI：10.1002/ctm2.1722 · 被引用次数：12 · 研究领域：Metabolomics and Mass Spectrometry Studies、Traditional Chinese Medicine Studies、Liver Disease Diagnosis and Treatment

Dear Editor, Here, we employed a nontargeted metabolomics to examine serum metabolic profiles in a Chinese cohort of 243 patients with coronary heart disease (CHD) and myocardial infarction (MI), and identified 48 and 46 differential metabolites to distinguish CHD and MI from control, respectively. Employing statistical and Least Absolute Shrinkage and Selection Operator (LASSO) methodology, we built a model based on three polar metabolites, arginine, hypoxanthine and acetylcarnitine, to discriminate CHD from MI with an area under the curve (AUC) of .92 and .88 in the training and test set, respectively. Metabolomics emerges as an enabling technique to identify circulating metabolites as potential disease biomarkers, including cardiovascular diseases (CVD).1 Although dysregulation of lipid metabolism has been implicated in CVD, yet a comprehensive analysis of their underlying metabolomic profiles and disease-specific metabolic biomarkers is lacking, especially for polar metabolites.2 In a large cohort of 10741samples, Zeller et al. found five phosphatidylcholines (PCs) were negatively correlated with CHD,3 while Wittenbecher et al. found a significant correlation of Ceramide 16:0 and PC 32:0 in heart failure.4 Furthermore, combining metabolomics with machine learning algorithms holds enormous promise to build better diagnostic models for various human diseases.5, 6 In this study, we collected serum samples from 73 MI patients, 83 CHD patients and 87 controls and identified a ...