The cervicovaginal microbiome of pregnant people living with HIV on antiretroviral therapy in the Democratic Republic of Congo: A Pilot Study and Global Meta-analysis
Bibliographic record
Abstract
Abstract Recent studies are revealing that a suboptimal cervicovaginal microbiome (CVMB), including enrichment of anaerobic bacteria associated with multiple female genital disorders, and adverse pregnancy and birth outcomes in pregnant people. Problematically, however, the majority of the available data to date are biased towards highly developed, Global North countries, leaving underrepresented populations like the Democratic Republic of Congo (DRC) poorly characterised. Here, we investigate the CVMB from a cohort of 82 pregnant people living with HIV (PLWH) on antiretroviral therapy (ART) from the DRC. Specifically, we explore the associations between the CVMB via 16S rRNA gene sequencing and maternal peripheral immune factors. Additionally, we compare the CVMB of PLWH-ART from DRC to publicly available CVMB data (5 studies, 1861 samples) in a meta-analysis to elucidate the impact of HIV on the CVMB. Combined, these analyses revealed differences in community structure and predicted function of the microbiota between PLWH-ART and pregnant people without HIV (PWoH). Taxonomically, the CVMB of DRC PLWH-ART were enriched for Lactobacillus iners- dominated CVMBs (53%) or a diverse, polymicrobial CVMB, i.e., bacterial vaginosis (BV) (43%). Functional predictions made from these taxa suggested that protein-coupled receptors, amino sugar and nucleotide sugar metabolism, fatty acid metabolism, and polycyclic aromatic hydrocarbon degradation pathways were differentially abundant between communities. Correlation with host plasma immune factors revealed putative links between some CVMB metrics (e.g., alpha diversity and species abundance) that have been linked to adverse pregnancy and birth outcomes. Importance HIV remains prevalent in sub-Saharan Africa, where it has been linked to adverse birth outcomes. . Suboptimal CVMBs have shown similar links. This pilot study fills critical gaps in understanding how HIV interacts with the pregnant CVMB in populations underrepresented in microbiome research, like the Democratic Republic of Congo. We identified maternal systemic immune factors associated with suboptimal CVMBs that have been linked to poor birth outcomes. In a global meta-analysis, we found significant taxonomic and functional difference in the CVMBs between pregnant people living with and without HIV, revealing potential biomarkers that for increased risks for adverse birth outcomes. These findings provide crucial insights into CVMB features that may influence pregnancy health in pregnant people living with HIV, guiding future research and tailored interventions to support safer pregnancies in the DRC and similar populations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".