High-throughput sequencing measurements of the vaginal microbiome have yielded intriguing potential relationships between the vaginal microbiome and preterm birth (PTB; live birth prior to 37 weeks of gestation). However, results across studies have been inconsistent. Here we perform an integrated analysis of previously published datasets from 12 cohorts of pregnant women whose vaginal microbiomes were measured by 16S rRNA gene sequencing. Of 1926 women included in our analysis, 568 went on to deliver prematurely. Substantial variation between these datasets existed in their definition of preterm birth, characteristics of the study populations, and sequencing methodology. Nevertheless, a small group of taxa comprised a vast majority of the measured microbiome in all cohorts. We trained machine learning (ML) models to predict PTB from the composition of the vaginal microbiome, finding low to modest predictive accuracy (0.28-0.79). Predictive accuracy was typically lower when ML models trained in one dataset predicted PTB in another dataset. Earlier preterm birth (<32 weeks, <34 weeks) was more predictable from the vaginal microbiome than late preterm birth (34 - 37 weeks), both within and across datasets. Integrated differential abundance analysis revealed a highly significant negative association between L. crispatus and PTB that was consistent across almost all studies. The presence of the majority (18 out of 25) of genera was associated with a higher risk of PTB, with L. iners, Prevotella, and Gardnerella showing particularly consistent and significant associations. Some example discrepancies between studies could be attributed to specific methodological differences, but not most study-to-study variations in the relationship between the vaginal microbiome and preterm birth. We believe future studies of the vaginal microbiome and PTB will benefit from a focus on earlier preterm births, and improved reporting of specific patient metadata shown to influence the vaginal microbiome and/or birth outcomes.

The authors have declared no competing interest.

NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES (Award number: R35GM133745)

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ENA/SRA or dbGap with reference numbers summarized in the supplementary table S2. The processed data are available in the first author's github by request.