A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder

作者：Yuzhen Zheng, Duan Zeng, Ying Tian, Siyuan Li, Shen He, Huafang Li · 发表于：Frontiers in Psychiatry · 年份：2025 · DOI：10.3389/fpsyt.2025.1610520 · 被引用次数：5 · 研究领域：Metabolomics and Mass Spectrometry Studies、Tryptophan and brain disorders、Immune cells in cancer

Background: Despite the high prevalence of major depressive disorder (MDD), current diagnostic methods rely on subjective clinical assessments, highlighting the need for biomarkers. This study aimed to investigate plasma metabolite signatures in patients with MDD compared with healthy controls (HC) and to identify diagnostic biomarkers associated with depressive features. Methods: A total of 99 patients with MDD and 50 HC were included in this study from a study cohort. Targeted plasma metabolomics was employed to quantify metabolites across diverse biochemical classes. Weighted gene co-expression network analysis (WGCNA) was performed to construct metabolite networks and identify modules and metabolites associated with depressive features. Diagnostic models were developed based on the identified hub metabolites, using six supervised machine-learning algorithms. Model interpretability was enhanced through the application of the SHapley Additive exPlanations (SHAP) algorithm. Results: Pathways such as biosynthesis of phenylalanine, tyrosine and tryptophan, glutathione metabolism, and arginine and proline metabolism were significantly enriched in the comparison of metabolic profiles between the MDD and HC groups. Seven hub metabolites were identified as the biomarker signatures that effectively discriminate the MDD and HC groups. Among these metabolites, one sphingomyelin (SM (OH) C16:1), one hexosylceramide (HexCer(d18:1/24:1)), one phosphatidylcholine (PC aa C40:6), and one c...