Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome
Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome
Abstract Spontaneous preterm birth (sPTB) is a leading cause of maternal and neonatal morbidity and mortality, yet both its prevention and early risk stratification are limited. The vaginal microbiome has been associated with PTB risk, possibly via metabolic or other interactions with its host. Here, we performed untargeted metabolomics on 232 vaginal samples, in which we have previously profiled the microbiota using 16S rRNA gene sequencing. Samples were collected at 20-24 weeks of gestation from women with singleton pregnancies, of which 80 delivered spontaneously before 37 weeks of gestation. We find that the vaginal metabolome correlates with the microbiome and separates into six clusters, three of which are associated with spontaneous preterm birth (sPTB) in Black women. Furthermore, while we identify five metabolites that associate with sPTB, another five associate with sPTB only when stratifying by race. We identify multiple microbial correlations with metabolites associated with sPTB, including intriguing correlations between vaginal bacteria that are considered sub-optimal and metabolites that were enriched in women who delivered at term. We propose that several sPTB-associated metabolites may be exogenous, and investigate another using metabolic models. Notably, we use machine learning models to predict sPTB risk using metabolite levels, weeks to months in advance, with high accuracy. We show that these predictions are more accurate than microbiome-based and maternal covariates-based models. Altogether, our results demonstrate the potential of vaginal metabolites as early biomarkers of sPTB and highlight exogenous exposures as potential risk factors for prematurity.
Liu Martin C.、Anton Lauren、Gerson Kristin D.、Lee Harry H.、Liao Jingqiu、Kindschuh William F.、Elovitz Michal A.、Levy Maayan、Korem Tal、Gajer Pawel、Ravel Jacques、Baldini Federico
Program for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center||Department of Biomedical Informatics, Columbia University Irving Medical CenterDepartment of Obstetrics and Gynecology, Maternal and Child Health Research Center, Perelman School of Medicine, University of PennsylvaniaDepartment of Obstetrics and Gynecology, Maternal and Child Health Research Center, Perelman School of Medicine, University of PennsylvaniaProgram for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical CenterProgram for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical CenterProgram for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical CenterDepartment of Obstetrics and Gynecology, Maternal and Child Health Research Center, Perelman School of Medicine, University of PennsylvaniaDepartment of Microbiology, University of Pennsylvania Perelman School of MedicineProgram for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center||Department of Obstetrics and Gynecology, Columbia University Irving Medical Center||CIFAR Azrieli Global Scholars programInstitute for Genome Sciences, University of Maryland School of MedicineInstitute for Genome Sciences, University of Maryland School of Medicine||Department of Microbiology and Immunology, University of Maryland School of MedicineProgram for Mathematical Genomics, Department of Systems Biology, Columbia University Irving Medical Center
妇产科学医学研究方法基础医学
Liu Martin C.,Anton Lauren,Gerson Kristin D.,Lee Harry H.,Liao Jingqiu,Kindschuh William F.,Elovitz Michal A.,Levy Maayan,Korem Tal,Gajer Pawel,Ravel Jacques,Baldini Federico.Preterm birth is associated with xenobiotics and predicted by the vaginal metabolome[EB/OL].(2025-03-28)[2025-04-28].https://www.biorxiv.org/content/10.1101/2021.06.14.448190.点此复制
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