Bai Shunjie, Xie Jing, Bai Huili, Tian Tian, Zou Tao, Chen Jian-Jun
Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Department of Endocrinology, the Fourth People's Hospital of Chongqing, Chongqing University Central Hospital, Chongqing, People's Republic of China.
J Inflamm Res. 2021 Aug 6;14:3755-3766. doi: 10.2147/JIR.S324922. eCollection 2021.
Although many works have been conducted to explore the biomarkers for diagnosing major depressive disorder (MDD), the widely accepted biomarkers are still not identified. Thus, the combined application of serum metabolomics and fecal microbial communities was used to identify gut microbiota-derived inflammation-related serum metabolites as potential biomarkers for MDD.
MDD patients and healthy controls (HCs) were included in this study. Both serum samples and fecal samples were collected. The liquid chromatography mass spectrometry (LC-MS) was used to detect the metabolites in serum samples, and the 16S rRNA gene sequencing was used to analyze the gut microbiota compositions in fecal samples.
Totally, 60 MDD patients and 60 HCs were recruited. The 24 differential serum metabolites were identified, and 10 of these were inflammation-related metabolites. Three significantly affected inflammation-related pathways were identified using differential metabolites. The 17 differential genera were identified, and 14 of these genera belonged to phyla Firmicutes. Four significantly affected inflammation-related pathways were identified using differential genera. Five inflammation-related metabolites (LysoPC(16:0), deoxycholic acid, docosahexaenoic acid, taurocholic acid and LysoPC(20:0)) were identified as potential biomarkers. These potential biomarkers had significant correlations with genera belonged to phyla Firmicutes. The panel consisting of these biomarkers could effectively distinguish MDD patients from HCs with an area under the curve (AUC) of 0.95 in training set and 0.92 in testing set.
These findings suggested that the disturbance of phyla Firmicutes might be involved in the onset of depression by regulating host's inflammatory response, and these potential biomarkers could be useful for future investigating the objective methods for diagnosing MDD.
尽管已经开展了许多研究来探索用于诊断重度抑郁症（MDD）的生物标志物，但尚未确定被广泛接受的生物标志物。因此，本研究采用血清代谢组学和粪便微生物群落的联合应用，以鉴定肠道微生物群衍生的炎症相关血清代谢物作为MDD的潜在生物标志物。
本研究纳入了MDD患者和健康对照（HCs）。收集了血清样本和粪便样本。采用液相色谱-质谱联用（LC-MS）检测血清样本中的代谢物，并采用16S rRNA基因测序分析粪便样本中的肠道微生物群组成。
共招募了60例MDD患者和60例HCs。鉴定出24种差异血清代谢物，其中10种为炎症相关代谢物。利用差异代谢物鉴定出3条显著受影响的炎症相关通路。鉴定出17个差异菌属，其中14个菌属属于厚壁菌门。利用差异菌属鉴定出4条显著受影响的炎症相关通路。鉴定出5种炎症相关代谢物（溶血磷脂酰胆碱（16:0）、脱氧胆酸、二十二碳六烯酸、牛磺胆酸和溶血磷脂酰胆碱（20:0））作为潜在生物标志物。这些潜在生物标志物与厚壁菌门的菌属具有显著相关性。由这些生物标志物组成的检测 panel 在训练集中的曲线下面积（AUC）为0.95，在测试集中为0.92，能够有效地区分MDD患者和HCs。
这些发现表明，厚壁菌门的紊乱可能通过调节宿主的炎症反应参与抑郁症的发病，这些潜在生物标志物可能有助于未来研究诊断MDD的客观方法。
