Vaginal microbiome dysbiosis influences partner's sperm motility

Limited knowledge exists regarding the sperm microbiota and his role in fertility. Recent studies have shown a relationship between seminal microbiome and sperm quality. Vaginal microbiome analysis is being introduced in assisted reproduction clinics as a promising diagnostic tool. However, its resu...

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Veröffentlicht in:Reproductive biomedicine online 2024-05, Vol.48, p.104044, Article 104044
Hauptverfasser: Pay, A., Turienzo, A., García, E.M., Ortiz, J.A., Lledó, B., Hortal, M., Cascales, A.M., Morales, R., Bernabeu, A., Bernabeu, R.
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Sprache:eng
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Zusammenfassung:Limited knowledge exists regarding the sperm microbiota and his role in fertility. Recent studies have shown a relationship between seminal microbiome and sperm quality. Vaginal microbiome analysis is being introduced in assisted reproduction clinics as a promising diagnostic tool. However, its results are biased despite the evident reciprocal influence between partners' genital tracts. Therefore, the aim of this study was to analyse the influence of the vaginal microbiome on their partners' semen quality. A retrospective study was performed (January 2020-November 2023). The study population was composed by sixty-four couples attending our fertility clinic whose vaginal microbiome was analysed. Forty-five female patients had normal microbiome and nineteen, dysbiotic microbiome. Simultaneously, the seminal parameters (volume, concentration, motility and morphology) of their male partners were examined. The vaginal analysis involved mass sequencing of the V3V4 region of 16S rRNA. The bioinformatic analysis of vaginal microbiome was performed using QIIME2 and the statistical analysis, with R Software (v.4.0.3). We observed a significantly lower progressive motility rate in couples with an altered vaginal microbiome (34.51 vs. 23.74, p=0.023). Additionally, although sperm concentration and morphology were lower in couples with dysbiotic vaginal microbiomes (37.19 vs. 25.63 mill/mL and 4.40 vs. 3.42% normal sperm forms, respectively), they did not reach statistical significance. Considering the relative abundance of each bacteria, a multivariate linear regression model was performed to predict the sperm motility. The best predictive model, which minimized the AIC value (Akaike's information criterion), included as predictors the relative abundance of Lactobacillus, Alkalibacillus, Streptococcus, Propionibacterium, Chitinophaga, Escherichia, Enterococcus, Xenophylus and explains 37.6% (R2) of the variability. Among these genera, the levels of Lactobacillus (β=0.32; CI 0.17-0.48; p
ISSN:1472-6483
DOI:10.1016/j.rbmo.2024.104044