Human Papilloma Virus Infection and Vaginal Microbiome Profiles in Pre-menopausal Women: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Cervical cancer is a common cancer among women worldwide, especially in low- and middle-income countries. Persistent infection with high-risk human papillomavirus (HR-HPV) largely drives the development of cervical cancer. While Lactobacillus-dominant communities are considered protective, dysbiosis, marked by reduced Lactobacilli and increased anaerobic diversity (community state type (CST)-IV), may promote viral persistence. This study aims to assess HR-HPV prevalence and compare vaginal microbiome profiles in women with suspected HPV infection and healthy controls. Material and methods: Vaginal swabs were collected from pre-menopausal women with clinically suspected HPV infection and healthy controls. Samples underwent HR-HPV detection using the Truenat® HPV-HR RT-PCR (real-time polymerase chain reaction) assay (Molbio Diagnostics Limited, Goa, India). Vaginal microbiome profiling was performed using 16S rRNA gene amplicon sequencing on the GridION platform (Oxford Nanopore Technologies plc, Oxford, United Kingdom). Taxonomic classification was carried out using the EPI2ME 16S workflow with Kraken2 (Johns Hopkins University, Baltimore, Maryland, United States), and CSTs were assigned using the VALENCIA (VAginaL community state typE Nearest CentroId clAssifier) algorithm. Statistical analyses and microbial community comparisons were performed using MicrobiomeAnalyst (Xia Lab, Sainte-Anne-de-Bellevue, Quebec, Canada). Results: A total of 86 clinically suspected HPV cases and 63 healthy pre-menopausal controls were enrolled. Overall, high-risk HPV (HPV 16/31 and HPV-18/45) was detected in 11.6% (10/86) of cases, with no positivity in the control group. Vaginal microbiome profiling revealed significantly higher alpha diversity in women with inflammatory cytology compared to healthy controls, and increased Shannon and Simpson diversity indices in HPV-positive and inflammatory groups. Beta diversity analysis showed distinct microbial clustering between all groups. Taxonomic analysis demonstrated a predominance of Lactobacillus spp. in healthy individuals, particularly Lactobacillus crispatus and Lactobacillus iners, whereas disease groups showed increased abundance of Pseudomonas, Rheinheimera, and Agrobacterium. CST-I was more common in healthy controls (7/17, 41.2%), while CST-IV-linked to dysbiosis-was predominant among suspected cases (9/21, 47.6%). Linear discriminant analysis effect size (LEfSe) analysis identified Lactobacillus, Anaerococcus, and Dialister as key genera in healthy individuals, whereas Pseudomonas, Rhizobium, and Rheinheimera were enriched in HPV-positive and inflammatory smear groups, highlighting potential microbial biomarkers of vaginal dysbiosis. Conclusion: These findings underscore the importance of vaginal microbiome composition in cervical health and support further investigation into microbial biomarkers for early detection and targeted interventions in HPV-associated disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".