Metabolomics on depression: A comparison of clinical and animal research

代谢组学 重性抑郁障碍 巴比妥酸 医学 萧条(经济学) 生物信息学 内科学 生物 精神科 心情 受体 NMDA受体 宏观经济学 经济
作者
Yibo Wang,Xiaoyan Cai,Yuchen Ma,Yang Yang,Chen‐Wei Pan,Xiaohong Zhu,Chaofu Ke
出处
期刊:Journal of Affective Disorders [Elsevier]
卷期号:349: 559-568
标识
DOI:10.1016/j.jad.2024.01.053
摘要

Depression is a major cause of suicide and mortality worldwide. This study aims to conduct a systematic review to identify metabolic biomarkers and pathways for major depressive disorder (MDD), a prevalent subtype of clinical depression. We searched for metabolomics studies on depression published between January 2000 and January 2023 in the PubMed and Web of Science databases. The reported metabolic biomarkers were systematically evaluated and compared. Pathway analysis was implemented using MetaboAnalyst 5.0. We included 26 clinical studies on major depressive disorder and 78 metabolomics studies on depressive-like animal models. A total of 59 and 86 high-frequency metabolites were reported consistently in two-thirds of clinical and murine studies, respectively. In the comparison between murine and clinical studies, we identified 9 consistently changed metabolites (tryptophan, tyrosine, phenylalanine, methionine, fumarate, valine, deoxycholic acid, pyruvate, kynurenic acid) in the blood, 1 consistently altered metabolite (indoxyl sulfate) in the urine and 14 disturbed metabolic pathways in both types of studies. These metabolic dysregulations and pathways are mainly implicated in enhanced inflammation, impaired neuroprotection, reduced energy metabolism, increased oxidative stress damage and disturbed apoptosis, laying solid molecular foundations for MDD. Due to unavailability of original data like effect-size results in many metabolomics studies, a meta-analysis cannot be conducted, and confounding factors cannot be fully ruled out. This systematic review delineated metabolic biomarkers and pathways related to depression in the murine and clinical samples, providing opportunities for early diagnosis of MDD and the development of novel diagnostic targets.
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