Quantifying the interactions among maternal race, vaginal metabolites, and microbes in preterm birth

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Project Summary/Abstract Despite years of investigation into its causes and potential biomarkers, the rate of pregnancies ending preterm in the United States has remained around 10% in the overall population and 15% in Black women. Two thirds of preterm births occur spontaneously and are not initiated by a medical intervention. As spontaneous preterm birth is a leading cause of neonatal morbidity and mortality, and is associated with maternal complications, it both reflects and drives significant racial disparities in reproductive health. We and others have shown that both vaginal metabolites and microbes are associated with spontaneous preterm birth, but also that these associations vary across races. Therefore, in order to identify biomarkers that will enable early diagnosis of preterm birth and develop strategies for its prevention in diverse populations, we must fully understand how maternal race interacts with these associations. Understanding the role of race in spontaneous preterm birth will require identifying the social and environmental variables that explain its impact on microbial and metabolite risk factors. Previous studies on the influence of race on the associations between vaginal metabolites, microbes, and preterm birth suffered from small sample sizes, limited clinical data, and a lack of longitudinal data. They also relied on 16S rRNA gene sequencing, which measures only the composition of the microbiome, and does not account for strain differences or quantify functional genetic elements. The objective of this proposal is to study the interactions among maternal race, vaginal metabolite levels, vaginal microbes, and spontaneous preterm birth. I will perform a paired analysis of vaginal metabolites and vaginal metagenomic sequencing data, which profiles the entire genomic content of the microbiome. The data I will analyze originates from the nuMoM2b cohort, a large, extensively characterized, and racially-diverse cohort of pregnant women, in which microbiome samples were collected at multiple timepoints. My central hypothesis is that maternal race has significant interactions with the associations among vaginal metabolites, vaginal microbes, and preterm birth. To understand these interactions, I will identify associations between vaginal metabolites and functional elements of vaginal microbes with spontaneous preterm birth, and compare them between Black and white women. I will also identify microbiome dynamics using longitudinal data and mathematical models of microbial interactions and growth rates. I will then identify the social and environmental factors that explain the influence of race on associations among vaginal microbes, metabolites, and preterm birth. This project will identify preterm birth risk factors that will be useful in diverse populations, will raise additional hypotheses regarding potential mechanisms underlying spontaneous preterm birth in both Black and white women, and will lay a groundwork for future research on preterm birth treatment and prevention.