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

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article numbere149257
Number of pages16
JournalJCI Insight
Volume6
Issue number16
Early online date13 Jul 2021
DOIs
E-pub ahead of print13 Jul 2021
Published23 Aug 2021

Bibliographical note

Funding Information: The authors thank the CLRN midwifery staff, particularly Jenny Carter, Judy Filmer, Falak Diab, Vicky Robinson, Giorgia DallaValle, and Debbie Finucane for help with recruitment and collection of samples and clinical data. Further thanks to the maternity NIHR CRN recruiting teams at Poole Hospital (Stephanie Grigsby), West Middlesex Hospital, Twickenham (Amy Barker), and St. Mary’s Hospital, Manchester. NMR experiments were carried out using the facilities of the Centre for Biomolecular Spectroscopy, King’s College London, using equipment acquired with a Multi-user Equipment Grant from the Wellcome Trust and an Infrastructure Grant from the British Heart Foundation. We thank Andrew Atkinson for his assistance in performing these experiments. Funding was provided from Tommy’s Charity (no. 1060508); NIHR Biomedical Research Centre (BRC) based at Guy’s and St. Thomas’ National Health Service Foundation Trust, and the Rosetrees Trust (charity no. 298582) (M303-CD1). NH was funded by a NIHR Doctoral Research Fellowship (DRF-2013-06-171). TK was supported by the Wellcome Trust Institutional Strategic Support Fund (ISSF3), via the King’s Together Fund. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. Funding Information: funding from Evolve BioSystems Inc. (ISRCTN11690200) and collaborates with Mirvie Inc. WGW receives research funding from GSK, is a consultant for Symrise AG, and holds patents relating to probiotics for oral care. DVM is a coauthor on a patent application related to Alzheimer’s Disease (patent no. 23168P). Funding Information: The authors thank the CLRN midwifery staff, particularly Jenny Carter, Judy Filmer, Falak Diab, Vicky Robinson, Giorgia DallaValle, and Debbie Finucane for help with recruitment and collection of samples and clinical data. Further thanks to the maternity NIHR CRN recruiting teams at Poole Hospital (Stephanie Grigsby), West Middlesex Hospital, Twickenham (Amy Barker), and St. Mary's Hospital, Manchester. NMR experiments were carried out using the facilities of the Centre for Biomolecular Spectroscopy, King's College London, using equipment acquired with a Multi-user Equipment Grant from the Wellcome Trust and an Infrastructure Grant from the British Heart Foundation. We thank Andrew Atkinson for his assistance in performing these experiments. Funding was provided from Tommy's Charity (no. 1060508); NIHR Biomedical Research Centre (BRC) based at Guy's and St. Thomas' National Health Service Foundation Trust, and the Rosetrees Trust (charity no. 298582) (M303-CD1). NH was funded by a NIHR Doctoral Research Fellowship (DRF-2013-06-171). TK was supported by the Wellcome Trust Institutional Strategic Support Fund (ISSF3), via the King's Together Fund. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care. Publisher Copyright: Copyright: © 2021, Flaviani et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

King's Authors

Abstract

The syndrome of spontaneous preterm birth (sPTB) presents a challenge to mechanistic understanding, effective risk stratification, and clinical management. Individual associations between sPTB, self-reported ethnic ancestry, vaginal microbiota, metabolome, and innate immune response are known but not fully understood, and knowledge has yet to impact clinical practice. Here, we used multi-data type integration and composite statistical models to gain insight into sPTB risk by exploring the cervicovaginal environment of an ethnically heterogenous pregnant population (n = 346 women; n = 60 sPTB < 37 weeks' gestation, including n = 27 sPTB < 34 weeks). Analysis of cervicovaginal samples (10-15 +6 weeks) identified potentially novel interactions between risk of sPTB and microbiota, metabolite, and maternal host defense molecules. Statistical modeling identified a composite of metabolites (leucine, tyrosine, aspartate, lactate, betaine, acetate, and Ca 2+) associated with risk of sPTB < 37 weeks (AUC 0.752). A combination of glucose, aspartate, Ca 2+, Lactobacillus crispatus, and L. acidophilus relative abundance identified risk of early sPTB < 34 weeks (AUC 0.758), improved by stratification by ethnicity (AUC 0.835). Increased relative abundance of L. acidophilus appeared protective against sPTB < 34 weeks. By using cervicovaginal fluid samples, we demonstrate the potential of multi-data type integration for developing composite models toward understanding the contribution of the vaginal environment to risk of sPTB.

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