Characterization of the Vaginal Microbiome in Women of Reproductive Age From 5 Regions in Brazil
Bibliographic record
Abstract
BACKGROUND: Composition of the vaginal microbiome is strongly related to a woman's reproductive health and risk of sexually transmitted infections. Ethnoracial, behavioral, and environmental factors can influence microbiome. The Brazilian population is unique in terms of miscegenation of ethnic groups and behavioral characteristics across different regions. We aimed to characterize the vaginal microbiome of women from 5 geographical regions of Brazil. METHODS: We sequenced V3-V4 regions of 16S rRNA gene in vaginal samples of 609 reproductive-aged women. We performed logistic regression analyses to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between sociodemographic and behavioral factors with Lactobacillus-depleted microbiome (community state type [CST] IV). RESULTS: Vaginal samples were grouped into 5 CST: CST I (L. crispatus predominant, 30.5%), CST II (L. gasseri predominant, 4.4%), CST III (Lactobacillus iners predominant, 36.5%), CST IV (Lactobacillus-depleted, 27.4%), and CST V (L. jensenii predominant, 1.2%). Several factors were independently associated with CST IV, such as smoking (OR, 1.80; 95% CI, 1.02-3.18), number of partners (OR, 2.11; 95% CI, 1.20-3.70), and vaginal douching (OR, 2.24; 95% CI, 1.34-3.74). A protective effect was observed for milk/dairy intake (OR, 0.47; 95% CI, 0.27-0.82) and sitz bathing (OR, 0.43; 95% CI, 0.19-0.98). CONCLUSIONS: Nearly two thirds of Brazilian women may be at an increased risk for adverse outcomes associated with a vaginal microbiota characterized by the depletion of Lactobacillus or dominance by L. iners, whose protective role has been widely questioned. Several factors related to sexual behavior and intimate hygiene were associated with CST IV.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".