Effect of probiotic-derived metabolites on hormonal and metabolic profiles in women with polycystic ovary syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Polycystic ovary syndrome (PCOS) is a common endocrine-metabolic disorder linked to insulin resistance and hyperandrogenism. Gut microbiota-derived metabolites, including short-chain fatty acids (SCFAs), indoles, and bile acids, influence endocrine and metabolic pathways. Yet, no systematic review has specifically examined metabolite-targeted interventions in PCOS. Objective: To assess the effects of probiotic-derived metabolite interventions on hormonal and metabolic outcomes in women with PCOS. Methods: Following PRISMA 2020 and a PROSPERO-registered protocol (CRD42025543210), we searched MEDLINE, Embase, Web of Science, Scopus, Cochrane CENTRAL, and two Chinese databases to May 2025 without language restrictions. Eligible studies were randomized or quasi-randomized controlled trials ≥8 weeks. Two reviewers independently screened, extracted data, and assessed risk of bias (RoB 2). Pooled analyses used random-effects models, and evidence certainty was appraised with GRADE. Results: (1), and an SCFA blend (1). Interventions significantly reduced total testosterone (MD -0.19 ng/mL, 95% CI -0.30 to -0.08), LH/FSH ratio (SMD -0.46; 95% CI -0.66 to -0.26), fasting insulin (MD -2.4 µIU/mL; 95% CI -3.9 to -0.9), and HOMA-IR (MD -0.49; 95% CI -0.78 to -0.19). HDL-C increased modestly (MD + 3.2 mg/dL; 95% CI + 0.7 to +5.6). Evidence certainty was moderate for insulin-related outcomes and low for sex-hormone outcomes. Conclusion: STargeting gut-derived metabolites, particularly with sodium butyrate and multi-strain synbiotics, improves hormonal and metabolic markers in PCOS. Larger multicenter RCTs with metabolomic confirmation are warranted to establish clinical translation. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42025543210.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".