Non-<i>Lactobacillus</i>-Dominant and Polymicrobial Vaginal Microbiomes Are More Common in Younger South African Women and Predictive of Increased Risk of Human Immunodeficiency Virus Acquisition
Bibliographic record
Abstract
BACKGROUND: Adolescent girls and young women aged 15‒24 years in sub-Saharan Africa are at disproportionate risk of human immunodeficiency virus (HIV) infection. Given the known association between vaginal microbial dysbiosis and HIV susceptibility, we performed an age-stratified analysis of the vaginal microbiome in South African women and compared this to their risk of HIV acquisition. METHODS: Vaginal microbiome data were generated by mass spectrometry-based proteomic analysis of cervicovaginal lavages collected from participants (n = 688) in the Centre for the AIDS Programme of Research in South Africa (CAPRISA) 004 trial. Participants were grouped by age (18-19 years, n = 93; 20-24 years, n = 326; 25-41 years, n = 269). RESULTS: Four microbiome types were identified based on predominant taxa, including Lactobacillus crispatus (CST-LC, 12.2%), Lactobacillus iners (CST-LI, 43.6%), Gardnerella vaginalis (CST-GV, 26.6%), or polymicrobial (CST-PM, 15.1%). Women aged 18-19 and 20-24 years had increased CST-PM and a non-Lactobacillus-dominant microbiome compared to those 25-41 years (odds ratio [OR], 3.14 [95% confidence interval {CI}, 1.12-7.87], P = .017; OR, 2.81 [95% CI, 1.07-7.09], P = .038, respectively; and OR, 1.65 [95% CI, 1.02-2.65], P = .028; OR, 1.40 [95% CI, 1.01-1.95], P = .030, respectively). The HIV incidence rate of women with CST-PM microbiome was 7.19-fold higher compared to women with CST-LC (hazard ratio [HR], 7.19 [95% CI, 2.11-24.5], P = .00162), which was also consistent in women aged 20-24 years (HR, 4.90 [95% CI, 1.10-21.9], P = .0375). CONCLUSIONS: Younger women were more likely to have a higher-risk polymicrobial microbiome suggesting that vaginal microbiota are contributing to increased HIV-1 susceptibility in this group. CLINICAL TRIALS REGISTRATION: NCT00441298.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".