The effect of interventions targeting gut microbiota on depressive symptoms: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Despite their popularity, the efficacy of interventions targeting gut microbiota to improve depressive symptoms is unknown. Our objective is to summarize the effect of microbiome-targeting interventions on depressive symptoms. METHODS: We conducted a systematic review and meta-analysis. We searched MEDLINE, Embase, PsycINFO, Database of Abstracts of Reviews of Effects, Cochrane Database of Systematic Reviews and the Cochrane Controlled Register of Trials from inception to Mar. 5, 2021. We included studies that evaluated probiotic, prebiotic, synbiotic, paraprobiotic or fecal microbiota transplant interventions in an adult population (age ≥ 18 yr) with an inactive or placebo comparator (defined by the absence of active intervention). Studies must have measured depressive symptoms with a validated scale, and used a randomized controlled trial study design. We conducted a random effects meta-analysis of change scores, using standardized mean difference as the measure of effect. RESULTS: Sixty-two studies formed the final data set, with 50 included in the meta-analysis. Probiotic, prebiotic, and synbiotic interventions on depressive symptoms showed statistically significant benefits. In the single studies evaluating each of fecal microbiota transplant and paraprobiotic interventions, neither showed a statistically significant benefit. INTERPRETATION: Despite promising findings of benefit of probiotic, prebiotic and synbiotic interventions for depressive symptoms in study populations, there is not yet strong enough evidence to favour inclusion of these interventions in treatment guidelines for depression. Critical questions about species administered, dosage and timing relative to other antidepressant medications remain to be answered. STUDY REGISTRATION: PROSPERO no. 143178.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".