Wang Xuan, Cao Di, Zhang Hanlin, Chen Wei, Sun Jiaxin, Hu Huimin
Department of Dermatology, Lianyungang Municipal Oriental Hospital, Lianyungang, China.
Department of Dermatology, The First People's Hospital of Lianyungang, Lianyungang, China.
Front Psychiatry. 2025 Feb 18;16:1539596. doi: 10.3389/fpsyt.2025.1539596. eCollection 2025.
Major depressive disorder (MDD) is highly heterogeneous, which provides a significant challenge in the management of this disorder. However, the pathogenesis of major depressive disorder is not fully understood. Studies have shown that depression is highly correlated with gut flora. The objective of this study was to explore the potential of microbial biomarkers in the diagnosis of major depressive disorder.
In this study, we used a metagenomic approach to analyze the composition and differences of gut bacterial communities in 36 patients with major depressive disorder and 36 healthy individuals. We then applied a Support Vector Machine Recursive Feature Elimination (SVM-RFE) machine learning model to find potential microbial markers.
Our results showed that the alpha diversity of the intestinal flora did not differ significantly in major depressive disorder compared to healthy populations. However, the beta diversity was significantly altered. Machine learning identified 8 MDD-specific bacterial biomarkers, with Alistipes, Dysosmobacter, Actinomyces, Ruthenibacterium, and Thomasclavelia being significantly enriched, while Faecalibacterium, Pseudobutyrivibrio, and Roseburia were significantly reduced, demonstrating superior diagnostic accuracy (area under the curve, AUC = 0.919). In addition, the gut bacteria performed satisfactorily in the validation cohort with an AUC of 0.800 (95% CI: 0.6334-0.9143).
This study reveals the complex relationship between gut microbiota and major depressive disorder and provides a scientific basis for the development of a microbiota-based diagnostic tool for depression.
重度抑郁症（MDD）具有高度异质性，这给该疾病的管理带来了重大挑战。然而，重度抑郁症的发病机制尚未完全明确。研究表明，抑郁症与肠道菌群高度相关。本研究的目的是探索微生物生物标志物在重度抑郁症诊断中的潜力。
在本研究中，我们采用宏基因组学方法分析了36例重度抑郁症患者和36名健康个体的肠道细菌群落组成及差异。然后应用支持向量机递归特征消除（SVM-RFE）机器学习模型来寻找潜在的微生物标志物。
我们的结果显示，与健康人群相比，重度抑郁症患者肠道菌群的α多样性无显著差异。然而，β多样性发生了显著改变。机器学习识别出8种MDD特异性细菌生物标志物，其中阿利斯杆菌属、异常杆菌属、放线菌属、鲁特尼杆菌属和托马斯拉韦菌属显著富集，而粪杆菌属、假丁酸弧菌属和罗斯氏菌属显著减少，显示出卓越的诊断准确性（曲线下面积，AUC = 0.919）。此外，肠道细菌在验证队列中的表现令人满意，AUC为0.800（95%CI：0.6334 - 0.9143）。
本研究揭示了肠道微生物群与重度抑郁症之间的复杂关系，并为开发基于微生物群的抑郁症诊断工具提供了科学依据。
