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

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 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.

Keywords

• Microbiology
• Reproductive biology