Efficacy of Gut Microbiome-Targeted Interventions on Mental Health Symptoms in Women Across Key Hormonal Life Stages: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Women are disproportionately affected by depression and generalized anxiety disorder compared to men throughout their lives. Hormonal changes during the menstrual cycle, pregnancy, postpartum, and menopause are often associated with mood disturbances. Evidence suggests that modulating the gut microbiome through gut-targeted interventions may offer a novel therapeutic approach for various mental health conditions. Objective: This systematic review and meta-analysis aimed to synthesize evidence from randomized controlled trials (RCTs) on the efficacy of gut microbiome-targeted interventions in improving mental health symptoms in women during key hormonal transitions. Methods: A systematic search was conducted from inception to August 2025 across Embase, MEDLINE (PubMed), Web of Science, PsycINFO, CINAHL, Scopus, FSTA, CENTRAL, the WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov. Two reviewers independently screened, extracted data, and assessed study quality. Methodological quality was evaluated using Cochrane’s risk-of-bias tool (RoB 2.0). Statistical analyses were performed with Comprehensive Meta-Analysis software (version 4). Results: Eleven RCTs were included, of which eight were used in the meta-analyses. Gut microbiome-targeted interventions significantly reduced depressive symptoms (Standardized Mean Difference (SMD) = −0.848; 95% Confidence Interval (CI): −1.470 to −0.226; p = 0.008) and anxiety symptoms (SMD = −0.997; 95% CI: −1.684 to −0.311; p = 0.004) versus controls. Heterogeneity was high (depression: Cochran’s Q = 87.1, I2 = 92%, τ2 = 0.729; anxiety: Q = 35.3, I2 = 89%, τ2 = 0.535), but sensitivity analyses confirmed robustness. Meta-regressions indicated that treatment duration was not a significant moderator (depression: p = 0.12; anxiety: p = 0.28). Conclusions: Gut-targeted interventions significantly reduced symptoms of both depression and anxiety, highlighting their potential as complementary therapeutic strategies for managing mood disorders in women across hormonal life stages. However, high heterogeneity limits the ability to determine optimal standardized clinical recommendations, highlighting the need for further research to guide clinical applications and inform individualized approaches to treatment.
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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 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".