Potential value of oral Mogibacterium in major depressive disorder

Major depressive disorder (MDD) is characterized by substantial clinical heterogeneity. Oral microbiota can provide real-time information relevant to the early identification of disease risk and the prediction of therapeutic outcomes. The present study aimed to characterize the specific profiles of oral microbiota in the buccal mucosa of patients with MDD and to explore potential mechanisms linking oral microbiota to the pathophysiology of MDD.

A total of 38 patients with MDD and 30 healthy controls (HCs) were enrolled. All MDD patients received standard antidepressant treatment and were followed up at two time points (2 weeks and 6 weeks). Neuropsychological assessments were administered, and 16S rRNA sequencing was employed to determine the abundance of oral bacteria.

(1) Significant differences in the diversity of oral microbiota from the buccal mucosa were observed between the MDD and HC groups. (2) The relative abundances of the oral genera Aggregatibacter, Lautropia, Peptostreptococcus, and Mogibacterium in the buccal mucosa were significantly altered in MDD patients compared to HCs. (3) The abundance of Mogibacterium was significantly correlated with scores on the 24-item Hamilton Depression Scale (HAMD-24) and the Self-Rating Depression Scale, as well as with serum levels of brain-derived neurotrophic factor (BDNF), nerve growth factor, and vascular endothelial growth factor (VEGF) in MDD patients. (4) BDNF and VEGF mediated the relationship between the relative abundance of oral Mogibacterium and HAMD-24 scores in MDD patients. (5) In MDD patients, the baseline relative abundance of oral Mogibacterium prior to treatment was significantly correlated with the rate of change in HAMD-24 scores following 2 and 6 weeks of antidepressant treatment.

Oral Mogibacterium dysbiosis may contribute to the underlying pathophysiology of MDD, potentially via its influence on neuroplasticity. This oral bacterium may serve as a potential biomarker for diagnosing MDD and predicting responses to antidepressant treatment.