Gut microbiota variations in depression and anxiety: a systematic review
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate the characteristics of gut microbiota in depression and anxiety through a systematic review. METHODS: Articles were searched in the PubMed, Embase, and PsycINFO databases from their inception to February 12th, 2023. Case-control studies on the characteristics of gut microbiota in depression and anxiety were included. Methodological quality assessment of included studies was performed using the Newcastle-Ottawa Scale (NOS). A qualitative synthesis was conducted to assess bacterial diversity (α- and β-diversity) and taxa abundance differences at the phylum, family, and genus levels. RESULTS: A total of 24 articles were included in the systematic review, 20 studies were conducted in China. Our results showed that the findings of the α- and β-diversity assessments were inconsistent for both depression and anxiety. In gut microbiota composition, we found that depression and anxiety were characterized by an enrichment of pro-inflammatory bacteria and a depletion of anti-inflammatory SCFAs-producing bacteria. Specifically, Actinobacteria, Proteobacteria, Rikenellaceae, Porphyromonadaceae and Bifidobacteriaceae were more abundant in the depression group, as well as Firmicutes, Prevotellacea and Ruminococcaceae in lower abundance. In the anxiety group, the abundance of Firmicutes, Lachnospira, Faecalibacterium, Sutterella, and Butyricicoccus was lower, while the abundance of Bacteroidetes, Enterobacteriaceae, and Fusobacterium was increased. CONCLUSIONS: The systematic review found that depression and anxiety might be characterized by an enrichment of pro-inflammatory bacteria and the depletion of anti-inflammatory SCFAs-producing bacteria. However, there were conflicting reports on the abundance of bacteria due to confounders such as diet and psychotropic medications. Further studies are strongly suggested. CLINICAL TRIAL NUMBER: Not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".