Meta-analysis reveals the vaginal microbiome is a better predictor of earlier than later preterm birth

被引:4
|
作者
Huang, Caizhi [1 ]
Gin, Craig [2 ]
Fettweis, Jennifer [3 ]
Foxman, Betsy [4 ]
Gelaye, Bizu [5 ]
MacIntyre, David A. [6 ]
Subramaniam, Akila [7 ]
Fraser, William [8 ,9 ]
Tabatabaei, Negar [8 ,9 ,10 ]
Callahan, Benjamin [1 ,2 ]
机构
[1] North Carolina State Univ, Bioinformat Res Ctr, Raleigh, NC 27606 USA
[2] North Carolina State Univ, Dept Populat Hlth & Pathobiol, Raleigh, NC 27607 USA
[3] Virginia Commonwealth Univ, Dept Obstet & Gynecol, Richmond, VA 23284 USA
[4] Univ Michigan, Thomas Francis Sch Publ Hlth, Raleigh, NC 27606 USA
[5] Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
[6] Imperial Coll London, March Dimes Prematur Res Ctr, Dept Metab Digest & Reprod, London SW7 2AZ, England
[7] Univ Alabama Birmingham, Obstet & Gynecol & Maternal Fetal Med, Birmingham, AL 35294 USA
[8] Univ Sherbrooke, Dept Obstet, Sherbrooke, PQ J1K 2R1, Canada
[9] Univ Sherbrooke, Dept Gynecol, Sherbrooke, PQ J1K 2R1, Canada
[10] Univ Illinois, Coll Med, Dept Pharmacol & Regenerat Med, Chicago, IL 60612 USA
关键词
Meta-analysis; Machine learning; Preterm birth; Vaginal microbiome; FLORA; RISK; DNA;
D O I
10.1186/s12915-023-01702-2
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background 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.Results 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 2039 women included in our analysis, 586 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.Conclusions 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.
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页数:16
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