Oral Methods of Microbiota Manipulation for Depression Symptoms: A Systematic Review: Méthodes orales de manipulation du microbiote pour traiter les symptômes de dépression : Une revue systématique
Bibliographic record
Abstract
ObjectiveThe effectiveness of current treatment options for depressive symptoms has been widely investigated with acknowledgment that some patients were either not adequately responding to treatment, finding the existing treatment intolerable, or otherwise prefer alternative options. There is increasing interest in microbiota modulation as an alternate form of depression treatment, with a growing number of trials and reviews on the subject published in the last five years. This systematic review aimed to analyze all completed randomized control trials (RCTs) that assessed depression symptoms in adults not using antidepressants, before and after oral methods of microbiota manipulation.MethodAll completed parallel-arm RCTs that assessed depression symptoms in adult participants before and after oral methods of microbiota manipulation were retrieved from four databases, MEDLINE, Embase, PsycINFO, and Cochrane Central Register of Controlled Trials. Data on study and intervention characteristics as well as RCT conclusions were collected independently and in duplicate, and each study's findings were summarized individually. Risk of bias was completed.ResultsWe included 66 RCTs in our review, 34 of which concluded significant differences between the intervention and control group in depressive symptom using different interventions and measures. Of the 66 trials, 54 used probiotic interventions, seven used prebiotic, eight used synbiotic and two used oral fecal microbiota transplantation. Wide variation was observed in studies' design, intervention composition and consumption methods across all 66 RCTs. No statistical synthesis or meta-analyses were possible due to the wide variety of interventions, measures and outcomes.ConclusionsThe heterogeneity of the existing RCTs did not allow for concrete conclusions on whether oral microbiota manipulation interventions are viable alternative treatment options for adults experiencing depression symptoms. We encourage the development of standardized guidelines for the design and reporting of microbiota studies in depression for the possibility of future intervention efficacy testing.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".