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dc.contributor.authorNoyes, N.
dc.contributor.authorCho, K.-C.
dc.contributor.authorRavel, J.
dc.date.accessioned2019-05-17T12:53:07Z
dc.date.available2019-05-17T12:53:07Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85041119431&doi=10.1371%2fjournal.pone.0191625&partnerID=40&md5=887a2aaf368bbff40bbb0a3465bd9bec
dc.identifier.urihttp://hdl.handle.net/10713/9119
dc.description.abstractThe vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., Eggerthella, Gardnerella, Dialister, Sneathia and Ruminococcaceae), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including L. jensenii, Corynebacteria, and Proteobacteria. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants' sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research. Copyright 2018 Noyes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.en_US
dc.description.urihttps://dx.doi.org/10.1371/journal.pone.0191625en_US
dc.language.isoen_USen_US
dc.publisherPublic Library of Scienceen_US
dc.relation.ispartofPLoS ONE
dc.subjectvaginal microbiomeen_US
dc.subjectsexual habitsen_US
dc.subjectmenstrual hygieneen_US
dc.subject.meshVaginosis, Bacterialen_US
dc.subject.meshDemographyen_US
dc.subject.meshBayes Theoremen_US
dc.titleAssociations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0191625
dc.identifier.pmid29364944


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