Literature DB >> 32861687

A specific bacterial DNA signature in the vagina of Australian women in midpregnancy predicts high risk of spontaneous preterm birth (the Predict1000 study).

Matthew S Payne1, John P Newnham2, Dorota A Doherty2, Lucy L Furfaro3, Narisha L Pendal3, Diane E Loh4, Jeffrey A Keelan2.   

Abstract

BACKGROUND: Intrauterine infection accounts for a quarter of the cases of spontaneous preterm birth; however, at present, it is not possible to efficiently identify pregnant women at risk to deliver preventative treatments.
OBJECTIVE: This study aimed to establish a vaginal microbial DNA test for Australian women in midpregnancy that will identify those at increased risk of spontaneous preterm birth. STUDY
DESIGN: A total of 1000 women with singleton pregnancies were recruited in Perth, Australia. Midvaginal swabs were collected between 12 and 23 weeks' gestation. DNA was extracted for the detection of 23 risk-related microbial DNA targets by quantitative polymerase chain reaction. Obstetrical history, pregnancy outcome, and demographics were recorded.
RESULTS: After excluding 64 women owing to losses to follow-up and insufficient sample for microbial analyses, the final cohort consisted of 936 women of predominantly white race (74.3%). The overall preterm birth rate was 12.6% (118 births); the spontaneous preterm birth rate at <37 weeks' gestation was 6.2% (2.9% at ≤34 weeks' gestation), whereas the preterm premature rupture of the membranes rate was 4.2%. No single individual microbial target predicted increased spontaneous preterm birth risk. Conversely, women who subsequently delivered at term had higher amounts of Lactobacillus crispatus, Lactobacillus gasseri, or Lactobacillus jensenii DNA in their vaginal swabs (13.8% spontaneous preterm birth vs 31.2% term; P=.005). In the remaining women, a specific microbial DNA signature was identified that was strongly predictive of spontaneous preterm birth risk, consisting of DNA from Gardnerella vaginalis (clade 4), Lactobacillus iners, and Ureaplasma parvum (serovars 3 and 6). Risk prediction was improved if Fusobacterium nucleatum detection was included in the test algorithm. The final algorithm, which we called the Gardnerella Lactobacillus Ureaplasma (GLU) test, was able to detect women at risk of spontaneous preterm birth at <37 and ≤34 weeks' gestation, with sensitivities of 37.9% and 44.4%, respectively, and likelihood ratios (plus or minus) of 2.22 per 0.75 and 2.52 per 0.67, respectively. Preterm premature rupture of the membranes was more than twice as common in GLU-positive women. Adjusting for maternal demographics, ethnicity, and clinical history did not improve prediction. Only a history of spontaneous preterm birth was more effective at predicting spontaneous preterm birth than a GLU-positive result (odds ratio, 3.6).
CONCLUSION: We have identified a vaginal bacterial DNA signature that identifies women with a singleton pregnancy who are at increased risk of spontaneous preterm birth and may benefit from targeted antimicrobial therapy.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Fusobacterium spp; Gardnerella spp; Lactobacillus spp; Ureaplasma spp; diagnostic test; genotype; preterm birth; preterm premature rupture of the membranes; real-time polymerase chain reaction; vagina

Mesh:

Substances:

Year:  2020        PMID: 32861687     DOI: 10.1016/j.ajog.2020.08.034

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  7 in total

1.  Racism and perinatal health inequities research: where we have been and where we should go.

Authors:  Irene E Headen; Michal A Elovitz; Ashley N Battarbee; Jamie O Lo; Michelle P Debbink
Journal:  Am J Obstet Gynecol       Date:  2022-05-18       Impact factor: 10.693

2.  Large-scale characterisation of the pregnancy vaginal microbiome and sialidase activity in a low-risk Chinese population.

Authors:  Sherrianne Ng; Muxuan Chen; Samit Kundu; Xuefei Wang; Zuyi Zhou; Zhongdaixi Zheng; Wei Qing; Huafang Sheng; Yan Wang; Yan He; Phillip R Bennett; David A MacIntyre; Hongwei Zhou
Journal:  NPJ Biofilms Microbiomes       Date:  2021-12-20       Impact factor: 7.290

Review 3.  Contribution of Lactobacillus iners to Vaginal Health and Diseases: A Systematic Review.

Authors:  Nengneng Zheng; Renyong Guo; Jinxi Wang; Wei Zhou; Zongxin Ling
Journal:  Front Cell Infect Microbiol       Date:  2021-11-22       Impact factor: 5.293

Review 4.  The reproductive tract microbiota in pregnancy.

Authors:  Karen Grewal; David A MacIntyre; Phillip R Bennett
Journal:  Biosci Rep       Date:  2021-09-30       Impact factor: 3.840

5.  Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model.

Authors:  Sunwha Park; Jeongsup Moon; Nayeon Kang; Young-Han Kim; Young-Ah You; Eunjin Kwon; AbuZar Ansari; Young Min Hur; Taesung Park; Young Ju Kim
Journal:  Front Microbiol       Date:  2022-08-02       Impact factor: 6.064

6.  Spontaneous preterm labor can be predicted and prevented.

Authors:  R Romero
Journal:  Ultrasound Obstet Gynecol       Date:  2021-01       Impact factor: 8.678

7.  Cervicovaginal microbiota and metabolome predict preterm birth risk in an ethnically diverse cohort.

Authors:  Flavia Flaviani; Natasha L Hezelgrave; Tokuwa Kanno; Erica M Prosdocimi; Evonne Chin-Smith; Alexandra E Ridout; Djuna K von Maydell; Vikash Mistry; William G Wade; Andrew H Shennan; Konstantina Dimitrakopoulou; Paul T Seed; A James Mason; Rachel M Tribe
Journal:  JCI Insight       Date:  2021-08-23
  7 in total

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