Autor: |
Flaviani F; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine.; Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust., Hezelgrave NL; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., Kanno T; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine.; Institute of Pharmaceutical Science, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, and., Prosdocimi EM; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom., Chin-Smith E; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., Ridout AE; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., von Maydell DK; Institute of Pharmaceutical Science, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, and., Mistry V; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., Wade WG; Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, United Kingdom., Shennan AH; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., Dimitrakopoulou K; Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy's and St. Thomas' NHS Foundation Trust., Seed PT; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine., Mason AJ; Institute of Pharmaceutical Science, School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, and., Tribe RM; Department of Women and Children's Health, School of Life Course Sciences, Faculty of Life Sciences and Medicine. |
Abstrakt: |
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 Ca2+) associated with risk of sPTB < 37 weeks (AUC 0.752). A combination of glucose, aspartate, Ca2+, 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. |