Non-targeted Metabolomics Profiling of Plasma Samples From Patients With Major Depressive Disorder

• Published in: Frontiers in psychiatry Vol. 12; p. 810302
• Main Authors: Wu, Zhonghao, Yu, Heming, Tian, Yu, Wang, Yue, He, Yong, Lan, Tianlan, Li, Yan, Bai, Mengge, Chen, Xiangyu, Chen, Zhi, Ji, Ping, Zhang, Hongmei, Jin, Xin, Song, Jinlin, Cheng, Ke, Xie, Peng
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 21.02.2022
• Subjects: biomarkers; LC-MS/MS; major depressive disorder; non-targeted metabolomics; plasma; Psychiatry; receiver operating characteristic curve; LC-MS/MS; major depressive disorder; biomarkers; non-targeted metabolomics; diagnosis; plasma; receiver operating characteristic curve
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2021.810302

Major depressive disorder (MDD) is a neuropsychiatric disorder caused by multiple factors. Although there are clear guidelines for the diagnosis of MDD, the direct and objective diagnostic methods remain inadequate thus far. This study aims to discover peripheral biomarkers in patients with MDD and promote the diagnosis of MDD. Plasma samples of healthy controls (HCs, = 52) and patients with MDD ( = 38) were collected, and then, metabolism analysis was performed using ultrahigh-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). Heatmap analysis was performed to identify the different metabolites. Meanwhile, receiver operating characteristic (ROC) curves of these differential metabolites were generated. Six differential metabolites were found by LC-MS/MS analysis. Three of these were increased, including L-aspartic acid (Asp), diethanolamine, and alanine. Three were decreased, including O-acetyl-L-carnitine (LAC), cystine, and fumarate. In addition, LAC, Asp, fumarate, and alanine showed large areas under the curve (AUCs) by ROC analysis. The study explored differences in peripheral blood between depressed patients and HCs. These results indicated that differential metabolites with large AUCs may have the potential to be promising biomarkers for the diagnosis of MDD.