Frontiers in Psychiatry · 2024 · 10 citations · 39 references
Life SciencesMicrobiologyPsychiatryMedical MicrobiologyMicrobial EcologyMicrobiome ResearchGut MicrobiomeGut MicrobiotaMicrobial Community ProfilingMicrobiome DiversityMicrobiome DysbiosisAmplicon SequencingEffect SizeFecal Microbiome AnalysisIntestinal Microbiota EcologyIntestinal Microbiota EffectsMatched Control GroupMicrobial Dysbiosis AssessmentMicrobiome-based Risk Stratification
Introduction Early detection of depression is important for preventing depression-related suicides and reducing the risk of recurrence. This study explored the association between depression and intestinal microbiota and developed a depression risk-estimation method based on this. Methods The intestinal microbiota of Japanese patients with depression (33 males and 35 females) and disease-free controls (246 males and 384 females) in their 20’s to 60’s were compared by sex using 16S rRNA gene amplicon sequencing. A depression-risk estimation method was developed using structural equation modeling. Results Intestinal bacteria taxa that differed between depression and control groups were identified based on effect size (absolute value greater than 0.2). Neglecta was more abundant, while Coprobacter , Butyricimonas , Clostridium _XlVb, and Romboutsia were less abundant in the male depression group compared to the male control group. In the female depression group, Massilimicrobiota , Merdimonas , and Sellimonas were more abundant, whereas Dorea and Agathobacter were less abundant compared to the female control group. Several of the intestinal bacterial taxa that were less abundant in depression were associated with butyrate or hydrogen production. Using these depression-associated intestinal bacteria as indicators, risk-estimation models using structural equation modeling for depression were developed. In the risk-estimation models for males and females, the areas under the receiver operating characteristic curve were 0.72 and 0.70, respectively, indicating that these models can distinguish between individuals with and without depression. Conclusions This study provides insights into depression etiology and aids in its early detection and treatment.
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