Vaginal Microbiome Dysbiosis and First-Trimester Bleeding: A Systematic Review and Meta-Analysis of Maternal Outcomes
Bibliographic record
Abstract
Abstract Background: First-trimester bleeding (FTB) affects up to one-quarter of pregnancies and is a key predictor of miscarriage and other adverse obstetric outcomes. Emerging evidence suggests that alterations in the vaginal microbiome may contribute to early gestational instability; however, pathogen-specific risks during the first trimester remain incompletely defined. Objective: This study aimed to systematically evaluate the association between vaginal dysbiosis and FTB, miscarriage, and related maternal outcomes. Materials and Methods: A systematic review and meta-analysis were conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. PubMed, Scopus, Embase, Web of Science, and Google Scholar were searched for studies published between 2000 and 2025 that assessed vaginal or cervical microbiota in early pregnancy (≤14 weeks) and reported associations with FTB or adverse pregnancy outcomes. Random-effects meta-analyses were performed to estimate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Risk of bias was assessed using the Newcastle–Ottawa Scale, and publication bias was evaluated using funnel plots and Egger’s test. Results: Thirty-three studies encompassing 12,487 women were included. Overall, vaginal dysbiosis was associated with an increased risk of FTB and early adverse outcomes (pooled OR = 1.48; 95% CI = 0.86–2.55), with substantial heterogeneity ( I 2 =90.2%). Pathogen-specific analyses demonstrated strong associations for Ureaplasma species (OR = 3.18; 95% CI = 2.15–4.70) and Gardnerella vaginalis/Atopobium vaginae (OR = 2.74; 95% CI = 1.58–3.98). Lactobacillus -dominant microbiota showed a protective effect (OR = 0.54; 95% CI = 0.32–0.91). Regional analyses indicated higher risks in Asian and African cohorts. No significant overall publication bias was detected. Conclusions: Vaginal dysbiosis, particularly involving anaerobic and mycoplasmal pathogenesis associated with an increased likelihood of FTB and early pregnancy loss, while Lactobacillus dominance appears protective. These findings support the potential role of early pregnancy microbiome assessment as a biomarker for obstetric risk and highlight the need for prospective interventional studies targeting microbiome modulation in early gestation.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".