Gut Microbiota in Psychiatric Disorders: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This systematic review sought to comprehensively summarize gut microbiota research in psychiatric disorders following PRISMA guidelines. METHODS: Literature searches were performed on databases using keywords involving gut microbiota and psychiatric disorders. Articles in English with human participants up until February 13, 2020, were reviewed. Risk of bias was assessed using a modified Newcastle-Ottawa Scale for microbiota studies. RESULTS: Sixty-nine of 4231 identified studies met the inclusion criteria for extraction. In most studies, gut microbiota composition differed between individuals with psychiatric disorders and healthy controls; however, limited consistency was observed in the taxonomic profiles. At the genus level, the most replicated findings were higher abundance of Bifidobacterium and lower abundance of Roseburia and Faecalibacterium among patients with psychiatric disorders. CONCLUSIONS: Gut bacteria that produce short-chain fatty acids, such as Roseburia and Faecalibacterium, could be less abundant in patients with psychiatric disorders, whereas commensal genera, for example, Bifidobacterium, might be more abundant compared with healthy controls. However, most included studies were hampered by methodological shortcomings including small sample size, unclear diagnostics, failure to address confounding factors, and inadequate bioinformatic processing, which might contribute to inconsistent results. Based on our findings, we provide recommendations to improve quality and comparability of future microbiota studies in psychiatry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 source (direct Gemma or distilled Codex), 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".