Dysregulations of amino acid metabolism and lipid metabolism in urine of children and adolescents with major depressive disorder: a case-control study

• Published in: Psychopharmacology Vol. 241; no. 8; pp. 1691 - 1703
• Main Authors: Jiang, Yuanliang, Cai, Yuping, Teng, Teng, Wang, Xiaolin, Yin, Bangmin, Li, Xuemei, Yu, Ying, Liu, Xueer, Wang, Jie, Wu, Hongyan, He, Yuqian, Zhu, Zheng-Jiang, Zhou, Xinyu
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2024; Springer Nature B.V
• Subjects: Adolescents; Amino acids; Biomedical and Life Sciences; Biomedicine; Children; Lipid metabolism; Lipids; Liquid chromatography; Mass spectroscopy; Mental depression; Mental disorders; Metabolism; Metabolites; Metabolomics; Neurosciences; Original Investigation; Pharmacology/Toxicology; Psychiatry; Teenagers; Urine; Urine; Metabolomics; Biomarker; Children and adolescents; Major depressive disorder
• ISSN: 0033-3158; 1432-2072; 1432-2072
• DOI: 10.1007/s00213-024-06590-0

Rationale The mechanisms underlying major depressive disorder (MDD) in children and adolescents are unclear. Metabolomics has been utilized to capture metabolic signatures of various psychiatric disorders; however, urinary metabolic profile of MDD in children and adolescents has not been studied. Objectives We analyzed urinary metabolites in children and adolescents with MDD to identify potential biomarkers and metabolic signatures. Methods Here, liquid chromatography-mass spectrometry was used to profile metabolites in urine samples from 192 subjects, comprising 80 individuals with antidepressant-naïve MDD (AN-MDD), 37 with antidepressant-treated MDD (AT-MDD) and 75 healthy controls (HC). We performed orthogonal partial least squares discriminant analysis to identify differential metabolites and employed logistic regression and receiver operating characteristic analysis to establish a diagnostic panel. Results In total, 143 and 71 differential metabolites were identified in AN-MDD and AT-MDD, respectively. These were primarily linked to lipid metabolism, molecular transport, and small molecule biochemistry. AN-MDD additionally exhibited dysregulated amino acid metabolism. Compared to HC, a diagnostic panel of seven metabolites displayed area under the receiver operating characteristic curves of 0.792 for AN-MDD, 0.828 for AT-MDD, and 0.799 for all MDD. Furthermore, the urinary metabolic profiles of children and adolescents with MDD significantly differed from those of adult MDD. Conclusions Our research suggests dysregulated amino acid metabolism and lipid metabolism in the urine of children and adolescents with MDD, similar to results in plasma metabolomics studies. This contributes to the comprehension of mechanisms underlying children and adolescents with MDD.