Julia Andrade Pessoa Morales1, Camila Marconi1,2, Mariam El-Zein3, Jacques Ravel4, Gabriel Victor da Silva Pinto1, Rosana Silveira1, Moises Diogode Lima5, Newton Sergio de Carvalho2, Rosane Ribeiro Figueiredo Alves6, Cristina Maria Garcia de Lima Parada7, Sandra Helena Morais Leite8, Luisa L Villa9, Eduardo L Franco3, Marcia Guimarães da Silva1. 1. Department of Pathology, Botucatu Medical School, Sao Paulo State University, Botucatu, São Paulo, Brazil. 2. Department of Basic Pathology, Federal University of Paraná, Curitiba, Paraná, Brazil. 3. Division of Cancer Epidemiology, McGill University, Montréal, Québec, Canada. 4. Institute of Genomic Science, University of Maryland School of Medicine, Baltimore, Maryland, USA. 5. Department of Gynecology and Obstetrics, Federal University of Paraiba, João Pessoa, Paraiba, Brazil. 6. Department of Gynecology and Obstetrics, Federal University of Goias, Goiania, Goias, Brazil. 7. Department of Nursing, Botucatu Medical School, Sao Paulo State University, Botucatu, São Paulo, Brazil. 8. Department of Gynecology and Obstetrics, State University of Para, Belem, Pará, Brazil. 9. Center for Translational Investigation in Oncology, Cancer Institute of São Paulo State, Medical School, São Paulo State University, São Paulo, Brazil.
Abstract
BACKGROUND: Interplay between vaginal microbiome and human papillomavirus (HPV) remains unclear, partly due to heterogeneity of microbiota. METHODS: We used data from 546 women enrolled in a cross-sectional study in 5 Brazil. We genotyped vaginal samples for HPV and sequenced V3-V4 region of 16S rRNA gene for vaginal microbiome analysis. We used stepwise logistic regression to construct 2 linear scores to predict high-risk HPV (hrHPV) positivity: one based exclusively on presence of individual bacterial taxa (microbiome-based [MB] score) and the other exclusively on participants' sociodemographic, behavioral, and clinical (SBC) characteristics. MB score combined coefficients of 30 (of 116) species. SBC score retained 6 of 25 candidate variables. We constructed receiver operating characteristic curves for scores as hrHPV correlates and compared areas under the curve (AUC) and 95% confidence intervals (CI). RESULTS: Overall, prevalence of hrHPV was 15.8%, and 26.2% had a Lactobacillus-depleted microbiome. AUCs were 0.8022 (95% CI, .7517-.8527) for MB score and 0.7027 (95% CI, .6419-.7636) for SBC score (P = .0163). CONCLUSIONS: The proposed MB score is strongly correlated with hrHPV positivity-exceeding the predictive value of behavioral variables-suggesting its potential as an indicator of infection and possible value for clinical risk stratification.
BACKGROUND: Interplay between vaginal microbiome and human papillomavirus (HPV) remains unclear, partly due to heterogeneity of microbiota. METHODS: We used data from 546 women enrolled in a cross-sectional study in 5 Brazil. We genotyped vaginal samples for HPV and sequenced V3-V4 region of 16S rRNA gene for vaginal microbiome analysis. We used stepwise logistic regression to construct 2 linear scores to predict high-risk HPV (hrHPV) positivity: one based exclusively on presence of individual bacterial taxa (microbiome-based [MB] score) and the other exclusively on participants' sociodemographic, behavioral, and clinical (SBC) characteristics. MB score combined coefficients of 30 (of 116) species. SBC score retained 6 of 25 candidate variables. We constructed receiver operating characteristic curves for scores as hrHPV correlates and compared areas under the curve (AUC) and 95% confidence intervals (CI). RESULTS: Overall, prevalence of hrHPV was 15.8%, and 26.2% had a Lactobacillus-depleted microbiome. AUCs were 0.8022 (95% CI, .7517-.8527) for MB score and 0.7027 (95% CI, .6419-.7636) for SBC score (P = .0163). CONCLUSIONS: The proposed MB score is strongly correlated with hrHPV positivity-exceeding the predictive value of behavioral variables-suggesting its potential as an indicator of infection and possible value for clinical risk stratification.