Bioinformatics Research Center, North Carolina State University, Raleigh, 27606, USA.
Department of Population Health and Pathobiology, North Carolina State University, Raleigh, 27607, USA.
BMC Biol. 2023 Sep 25;21(1):199. doi: 10.1186/s12915-023-01702-2.
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 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.
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.
高通量测序测量阴道微生物组产生了阴道微生物组与早产（PTB；妊娠 37 周前分娩）之间有趣的潜在关系。然而，研究结果不一致。
在这里，我们对以前发表的 12 个孕妇队列的数据集进行了综合分析，这些队列的阴道微生物组通过 16S rRNA 基因测序进行了测量。在我们的分析中，2039 名女性中有 586 名早产。这些数据集在早产的定义、研究人群的特征和测序方法上存在很大差异。尽管如此，一小部分类群在所有队列的测量微生物组中占绝大多数。我们使用机器学习（ML）模型根据阴道微生物组的组成来预测 PTB，发现预测准确率较低（0.28-0.79）。当在一个数据集上训练的 ML 模型预测另一个数据集的 PTB 时，预测准确率通常较低。无论是在数据集内还是跨数据集，早期早产（<32 周，<34 周）比晚期早产（34-37 周）更能从阴道微生物组中预测。综合差异丰度分析显示，L. crispatus 与 PTB 之间存在显著的负相关，这在几乎所有研究中都是一致的。大多数（25 个中的 18 个）属的存在与 PTB 的风险增加相关，L. iners、Prevotella 和 Gardnerella 显示出特别一致和显著的相关性。一些研究之间的差异可以归因于特定的方法差异，但大多数研究之间与阴道微生物组和早产之间的关系的差异并非如此。
我们认为，未来关于阴道微生物组和 PTB 的研究将受益于关注更早的早产，并改进报告影响阴道微生物组和/或分娩结果的具体患者元数据。
