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Increased richness and diversity of the vaginal microbiota and spontaneous preterm birth.

ABSTRACT: BACKGROUND:The bacterial community present in the female lower genital tract plays an important role in maternal and neonatal health. Imbalances in this microbiota have been associated with negative reproductive outcomes, such as spontaneous preterm birth (sPTB), but the mechanisms underlying the association between a disturbed microbiota and sPTB remain poorly understood. An intrauterine infection ascending from the vagina is thought to be an important contributor to the onset of preterm labour. Our objective was to characterize the vaginal microbiota of pregnant women who had sPTB (n?=?46) and compare to those of pregnant women who delivered at term (n?=?170). Vaginal swabs were collected from women at 11-16 weeks of gestational age. Microbiota profiles were created by PCR amplification and pyrosequencing of the cpn60 universal target region. RESULTS:Profiles clustered into seven community state types: I (Lactobacillus crispatus dominated), II (Lactobacillus gasseri dominated), III (Lactobacillus iners dominated), IVA (Gardnerella vaginalis subgroup B or mix of species), IVC (G. vaginalis subgroup A dominated), IVD (G. vaginalis subgroup C dominated) and V (Lactobacillus jensenii dominated). The microbiota of women who experienced preterm birth (

SUBMITTER: Freitas AC

PROVIDER: S-EPMC6022438 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Increased richness and diversity of the vaginal microbiota and spontaneous preterm birth.

Freitas Aline C AC Bocking Alan A Hill Janet E JE Money Deborah M DM

Microbiome 20180628 1

<h4>Background</h4>The bacterial community present in the female lower genital tract plays an important role in maternal and neonatal health. Imbalances in this microbiota have been associated with negative reproductive outcomes, such as spontaneous preterm birth (sPTB), but the mechanisms underlying the association between a disturbed microbiota and sPTB remain poorly understood. An intrauterine infection ascending from the vagina is thought to be an important contributor to the onset of preter ...[more]

PMID: 29954448

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Project description:ObjectiveThe effect of vaginal microbiota on spontaneous preterm birth (sPTB) has not been fully addressed, and few studies have explored the associations between vaginal taxa and sPTB in the gestational diabetes mellitus (GDM) and non-GDM groups, respectively. Study Design. To minimize external interference, a total of 41 pregnant women with sPTB and 308 controls (pregnant women without sPTB) from same regain were enrolled in this case-cohort study. Controls were randomly selected at baseline. With the exception of GDM, other characteristics were not significantly different between the two groups. Vaginal swabs were collected at early second trimester. Using 16S amplicon sequencing, the main bioinformatics analysis was performed on the platform of QIIME 2. Vaginal microbiota traits of the sPTB group were compared with controls. Finally, the effects of binary taxa on sPTB in the GDM group and the non-GDM group were analyzed, respectively.ResultsThe proportion of GDM in the sPTB (19.51%) was higher than the controls (7.47%, P = 0.018). The vaginal microbiota of pregnant women with sPTB exhibited higher alpha diversity metrics (observed features, P = 0.001; Faith's phylogenetic diversity, P = 0.013) and different beta diversity metrics (unweighted UniFrac, P = 0.006; Jaccard's distance, P = 0.004), compared with controls. The presence of Lactobacillus paragasseri/gasseri (aOR: 3.12, 95% CI: 1.24-7.84), Streptococcus (aOR: 3.58, 95% CI: 1.68-7.65), or Proteobacteria (aOR: 3.39, 95% CI: 1.55-7.39) was associated with an increased risk of sPTB in the non-GDM group (P < 0.05). However, the relative abundance of novel L. mulieris (a new species of the L. delbrueckii group) was associated with a decreased risk of sPTB (false discovery rate, 0.10) in all pregnant women.ConclusionGDM may modify the association of vaginal taxa with sPTB, suggesting that maternal GDM should be considered when using vaginal taxa to identify pregnant women at high risk of sPTB.

Project description:Previous studies have investigated the associations between the vaginal microbiome and preterm birth, with the aim of determining whether differences in community patterns meaningfully alter risk and could therefore be the target of intervention. We report on vaginal microbial analysis of a nested case-control subset of the Pregnancy, Infection, and Nutrition (PIN) Study, including 464 White women (375 term birth and 89 spontaneous preterm birth, sPTB) and 360 Black women (276 term birth and 84 sPTB). We found that the microbiome of Black women has higher alpha-diversity, higher abundance of Lactobacillus iners, and lower abundance of Lactobacillus crispatus. However, among women who douche, there were no significant differences in microbiome by race. The sPTB-associated microbiome exhibited a lower abundance of L. crispatus, while alpha diversity and L. iners were not significantly associated with sPTB. For each order of magnitude increase in the normalized relative abundance of L. crispatus, multivariable adjusted odds of sPTB decreased by approximately 20% (odds ratio, 0.81; 95% confidence interval, 0.70, 0.94). When we considered the impact of douching, associations between the microbiome and sPTB were limited to women who do not douche. We also observed strong intercorrelations between a range of maternal factors, including poverty, education, marital status, age, douching, and race, with microbiome effect sizes in the range of 1.8 to 5.2% in univariate models. Therefore, race may simply be a proxy for other socially driven factors that differentiate microbiome community structures. Future work will continue to refine reliable microbial biomarkers for preterm birth across diverse cohorts. IMPORTANCE Approximately 10% of all pregnancies in the United States end in preterm birth, and over 14% of pregnancies end in preterm birth among Black women. Knowledge on the associations between vaginal microbiome and preterm birth is important for understanding the potential cause and assessing risk of preterm birth. Our study is one of the largest studies performed to date to investigate the associations between vaginal microbiome and spontaneous preterm birth (sPTB), with stratified design for Black and White women. We found that the vaginal microbiome was different between Black and White women. The vaginal microbiome was associated with sPTB, and a lower abundance of L. crispatus increased the risk of sPTB independent of racial differences in microbial community structures. Furthermore, we also found that vaginal douching obscured the associations between vaginal microbiome, race, and preterm birth, suggesting that vaginal douching is an important factor to consider in future studies.

Project description:INTRODUCTION:Bacterial vaginosis (BV) and vaginal microbiota disruption during pregnancy are associated with increased risk of spontaneous preterm birth (SPTB), but clinical trials of BV treatment during pregnancy have shown little or no benefit. An alternative hypothesis is that vaginal bacteria present around conception may lead to SPTB by compromising the protective effects of cervical mucus, colonising the endometrial surface before fetal membrane development, and causing low-level inflammation in the decidua, placenta and fetal membranes. This protocol describes a prospective case-cohort study addressing this hypothesis. METHODS AND ANALYSIS:HIV-seronegative Kenyan women with fertility intent are followed from preconception through pregnancy, delivery and early postpartum. Participants provide monthly vaginal specimens during the preconception period for vaginal microbiota assessment. Estimated date of delivery is determined by last menstrual period and first trimester obstetrical ultrasound. After delivery, a swab is collected from between the fetal membranes. Placenta and umbilical cord samples are collected for histopathology. Broad-range 16S rRNA gene PCR and deep sequencing of preconception vaginal specimens will assess species richness and diversity in women with SPTB versus term delivery. Concentrations of key bacterial species will be compared using quantitative PCR (qPCR). Taxon-directed qPCR will also be used to quantify bacteria from fetal membrane samples and evaluate the association between bacterial concentrations and histopathological evidence of inflammation in the fetal membranes, placenta and umbilical cord. ETHICS AND DISSEMINATION:This study was approved by ethics committees at Kenyatta National Hospital and the University of Washington. Results will be disseminated to clinicians at study sites and partner institutions, presented at conferences and published in peer-reviewed journals. The findings of this study could shift the paradigm for thinking about the mechanisms linking vaginal microbiota and prematurity by focusing attention on the preconception vaginal microbiota as a mediator of SPTB.

Project description:A major challenge in preventing preterm birth (PTB) is identifying women at greatest risk. This pilot study prospectively examined the differences in vaginal microbiota and metabolite profiles of women who delivered prematurely compared to their term counterparts in a cohort of asymptomatic (studied at 20-22, n = 80; and 26-28 weeks, n = 41) and symptomatic women (studied at 24-36 weeks, n = 37). Using 16S rRNA sequencing, the vaginal microbiota from cervicovaginal fluid samples was characterized into five Community State Types (CST) dominated by Lactobacillus spp.: CSTI (Lactobacillus crispatus), CSTII (Lactobacillus gasseri), CSTIII (Lactobacillus iners), CSTV (Lactobacillus jensenii); and mixed anaerobes-CSTIV. This was then related to the vaginal metabolite profile and pH determined by 1H-Nuclear Magnetic Resonance spectroscopy and pH indicator paper, respectively. At 20-22 weeks, the term-delivered women (TDW) indicated a proportion of CSTI-dominated microbiota >2-fold higher compared to the preterm-delivered women (PTDW) (40.3 vs. 16.7%, P = 0.0002), and a slightly higher proportion at 26-28 weeks (20.7 vs. 16.7%, P = 0.03). CSTV was >2-fold higher in the PTDW compared to TDW at 20-22 (22.2 vs. 9.7%, P = 0.0002) and 26-28 weeks (25.0 vs. 10.3%, P = 0.03). Furthermore, at 26-28 weeks no PTDW had a CSTII-dominated microbiome, in contrast to 28% of TDW (P < 0.0001). CSTI-dominated samples showed higher lactate levels than CSTV at 20-22 weeks (P < 0.01), and 26-28 weeks (P < 0.05), while CSTII-dominated samples indicated raised succinate levels over CSTV at 26-28 weeks (P < 0.05). These were supported by Principal coordinates analysis, which revealed strong clustering of metabolites according to CST. In addition, the CSTI-dominated samples had an average pH of 3.8, which was lower than those of CSTII-4.4, and CSTV-4.2 (P < 0.05). Elevated vaginal lactate and succinate were associated with predominance of CSTI and II over CSTV in women who delivered at term compared with their preterm counterparts. This suggests that L. jensenii-dominance and decreased lactate and/or succinate could increase the risk of PTB, while L. crispatus/gasseri may confer some protection against inflammation-associated PTB and highlight the need for further study in this area.

Project description:An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case-control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level, and PTB history), white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed. The subjects were randomly divided into a training (n = 101) and a test set (n = 49) in a two-to-one ratio. When training ML models using selected markers, five-fold cross-validation was performed on the training set. A univariate analysis was performed to select markers using seven statistical tests, including the Wilcoxon rank-sum test. Using the selected markers, including Lactobacillus spp., Gardnerella vaginalis, Ureaplasma parvum, Atopobium vaginae, Prevotella timonensis, and Peptoniphilus grossensis, machine learning models (logistic regression, random forest, extreme gradient boosting, support vector machine, and GUIDE) were used to build prediction models. The test area under the curve of the logistic regression model was 0.72 when it was trained with the 17 selected markers. When analyzed by combining white blood cell count and cervical length with the seven vaginal microbiome markers, the random forest model showed the highest test area under the curve of 0.84. The GUIDE, the single tree model, provided a more reasonable biological interpretation, using the 10 selected markers (A. vaginae, G. vaginalis, Lactobacillus crispatus, Lactobacillus fornicalis, Lactobacillus gasseri, Lactobacillus iners, Lactobacillus jensenii, Peptoniphilus grossensis, P. timonensis, and U. parvum), and the covariates produced a tree with a test area under the curve of 0.77. It was confirmed that the association with preterm birth increased when P. timonensis and U. parvum increased (AUC = 0.77), which could also be explained by the fact that as the number of Peptoniphilus lacrimalis increased, the association with preterm birth was high (AUC = 0.77). Our study demonstrates that several candidate bacteria could be used as potential predictors for preterm birth, and that the predictive rate can be increased through a machine learning model employing a combination of cervical length and white blood cell count information.

Dataset Information

Increased richness and diversity of the vaginal microbiota and spontaneous preterm birth.

Publications

Increased richness and diversity of the vaginal microbiota and spontaneous preterm birth.

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