Cervicovaginal microbiota, women's health, and reproductive outcomes

Kroon, Samuel J.; Ravel, Jacques; Huston, Wilhelmina M.

doi:10.1016/j.fertnstert.2018.06.036

Cited by 180 publications

(163 citation statements)

References 124 publications

Supporting

Mentioning

147

Contrasting

Unclassified

Order By: Relevance

“…displayed a similar predictive ability for the disease as the classifier based on POF-associated genetic variants (which had an AUC of 0.841, sensitivity of 71.4%, specificity of 100%, and cut-off rate of 43.2%), implying that the microbial signature that we identified could represent a powerful tool for the prediction of POF. Our results of the changes in relative abundance of particular genera in the context of POF confirm that species of the dominant vaginal genus, Their dominance in the vaginal niche indicate that Lactobacillus are the dominant facultative anaerobes of the genital tract, supported by their presence in most women[31,32].In women with higher levels of basal FSH and lower levels of basal E2, there were fewer Lactobacillus in the vagina than the control group. Eade et al evaluated the presence of Lactobacillus spp in confluent monolayers of endocervical, ectocervical, and vaginal epithelial cells and they found that the majority of Lactobacillus caused a significant decrease in the expression of AMPs, although…”

supporting

confidence: 75%

Changes in the vaginal microbiota associated with primary ovarian failure

Wang

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

BackgroundPrimary ovary failure (POF) is defined as follicular failure in women of reproductive age. Although many factors are speculated to contribute to the occurrence of POF, the exact aetiology is unclear.Alterations in the microbiome of women with POF are poorly studied; MethodsThis study investigated the vaginal microbiota of 22 patients with POF and 29 healthy individuals.High-throughput Illumina Miseq sequencing targeting the V3-V4 region of the 16S ribosomal RNA (rRNA) gene was used to evaluate the relationships between vaginal flora and clinical characteristics of POF. ResultsDifferent from before, we found the diversity and richness of the vaginal flora of patients with POF was significantly different to that of healthy controls. Comparison of the flora of patients POF with that of menopausal women revealed that the relative abundance of Lactobacillus was significantly reduced in the latter, and women with reduced relative abundance of Lactobacillus in microbiota community decreased the pregnancy success rate at term. This study confirms that vaginal microbiota dysbiosis occurs in patients with POF. Additionally, the vaginal microbiota is closely related to clinical characteristics of POF and the inclusion of a disease verification model could reveal more accurate information related to the composition of the microbiota and its functions. ConclusionThe result suggests the present study enabled identification of microbiota associated with POF, further investigations of differences in the microbiota in the context of POF will improve our understanding of the pathogenesis of the disease which involve modification of the vaginal microbiota.

show abstract

supporting

confidence: 75%

Changes in the vaginal microbiota associated with primary ovarian failure

Wang

Han

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Up to now, the research on the distribution of female reproductive flora is mainly focuses on vaginal microbiota or limited to small-scale exploratory on the single site [22][23][24]. Although the previous study has found the intrauterine bacterial colonization in EMS patients by 16S rRNA gene sequencing analysis, the study did not establish a control group [15].…”

Section: Discussionmentioning

confidence: 98%

Microbiota composition and distribution along the female reproductive tract of women with endometriosis

Wei

Zhang

Tang

et al. 2020

Ann Clin Microbiol Antimicrob

View full text Add to dashboard Cite

Endometriosis (EMS) is a multifactorial disease that affects 10%-15% women of reproductive age and is associated with chronic pelvic pain and infertility. The pathogenesis of EMS has not been consistently explained until now. In this study, we involved 36 endometriosis patients and 14 control subjects who performed laparoscopic surgery due to gynecological benign tumor. The samples from lower third of vagina (CL), posterior vaginal fornix (CU), cervical mucus (CV), endometrium (ET) and peritoneal fluid (PF), were collected and sequenced by 16S rRNA amplicon. The continuous change of the microbiota distribution was identified along the reproductive tract. The flora in lower reproductive tract (CL, CU) were dominated by Lactobacillus. Significant difference of the community diversity began showing in the CV of EMS patients and gradually increased upward the reproductive tract. It indicates the microbiota in cervical samples is expected to be an indicator for the risk of EMS. This study also highlights the decreasing of Lactobacillus in vaginal flora and the increasing of signature Operational Taxonomic Units (OTUs) in transaction zone (CV) and upper reproductive tract (ET, PF) of EMS patients, which reflect the alteration of microbial community associated with EMS, participation of specific colonized bacteria in the EMS pathogenesis and relationship between microbiota and development of disease.

show abstract

“…These include uterine factors 70 (eg, intrauterine adhesions, 73,74 uterine myomas, 75 and endometrial polyps 76 ), endometriosis, 77 ovarian function, 78 oviduct obstruction, 79,80 maternal diseases such as diabetes mellitus, 81 immune disorders, 82,83 and the uterine microbiota. 84,85 Because these factors cannot be detected by the AI classifier from an image of the blastocyst, the accuracy, sensitivity, and specificity for live birth cannot reach 1. These clinical characteristics of the blastocyst prevent the accuracy of predicting live birth by any means from reaching close to 1.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Miyagi

Habara

Hirata

et al. 2019

Reprod Medicine & Biology

View full text Add to dashboard Cite

Purpose To identify artificial intelligence (AI) classifiers in images of blastocysts to predict the probability of achieving a live birth in patients classified by age. Results are compared to those obtained by conventional embryo (CE) evaluation. Methods A total of 5691 blastocysts were retrospectively enrolled. Images captured 115 hours after insemination (or 139 hours if not yet large enough) were classified according to maternal age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years. The classifiers for each category and a classifier for all ages were related to convolutional neural networks associated with deep learning. Then, the live birth functions predicted by the AI and the multivariate logistic model functions predicted by CE were tested. The feasibility of the AI was investigated. Results The accuracies of AI/CE for predicting live birth were 0.64/0.61, 0.71/0.70, 0.78/0.77, 0.81/0.83, 0.88/0.94, and 0.72/0.74 for the age categories <35, 35‐37, 38‐39, 40‐41, and ≥42 years and all ages, respectively. The sum value of the sensitivity and specificity revealed that AI performed better than CE ( P = 0.01). Conclusions AI classifiers categorized by age can predict the probability of live birth from an image of the blastocyst and produced better results than were achieved using CE.

show abstract

Cervicovaginal microbiota, women's health, and reproductive outcomes

Cited by 180 publications

References 124 publications

Changes in the vaginal microbiota associated with primary ovarian failure

Changes in the vaginal microbiota associated with primary ovarian failure

Microbiota composition and distribution along the female reproductive tract of women with endometriosis

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

Contact Info

Product

Resources

About