Abstract

This study aimed to explore the association between vaginal microbiota and infertility. We searched a range of electronic databases for appropriate articles, including PubMed, Web of Science, Embase, Chinese National Knowledge Infrastructure (CNKI), and Wanfang, from inception to 8th September 2019. Identified articles were then screened using strict inclusion and exclusion criteria. By referring to Tamarelle's method, we divided vaginal microbiota into two categories: low-Lactobacillus vaginal microbiota (LL-VMB) and high-Lactobacillus vaginal microbiota (HL-VMB). Patients were defined as HL-VMB if they had a Nugent score of 0-3, a negative Amesel/Spiegel's test, or if the vaginal community status was dominated by either L. crispatus, L. iners, L. gasseri and L. jensenii via 16S rRNA sequencing. Otherwise, cases were regarded as LL-VMB. Statistical analyses were performed with STATA 13.0 statistical software. Effect estimates are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Fifteen articles were included in our final analysis. The HL-VMB was negatively related to infertility; a fixed model showed that the pooled OR was 0.83 (95% CI 0.77-0.90). There was no significant publication bias, as determined by Begg's test (P = 0.488) and Egger's test (P = 0.652). Using a random effect model, the pooled OR for intermediate bacterial vaginitis (BV) and infertility was 1.39 (95% CI 1.10-1.76) and the pooled OR for positive BV was 1.72 (95% CI 1.10-2.69). Subgroup and sensitivity analyses further demonstrated that the associations identified were stable. However, the acquired evidence was not sufficient to make inferences with regards to the mechanisms underlying these relationships. This systematic review and meta-analysis identified a negative correlation between HL-VMB and female infertility. However, due to a variety of limitations, the evidence acquired does not allow us to identify the specific mechanisms underlying this association. Further high-quality studies are needed to verify the causal relationship and explore the molecular mechanisms involved.

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