Uterine microbiome signatures associated with endometriosis
Machine learning models using subtle uterine microbiome features from the proliferative phase showed modest diagnostic potential for endometriosis, though no robust individual microbial biomarkers were identified.
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This study analyzed uterine microbiomes from 266 tissue samples collected during the proliferative or secretory menstrual phases, using 16S rRNA gene sequencing at genus and sub-genus levels to test associations with endometriosis status. Variable Lactobacillus abundance was observed across individuals, Prevotella was borderline-enriched in proliferative-phase patients, and only a small number of differentially abundant sub-genus taxa showed changes that did not remain significant after FDR correction. Because individual biomarkers were not robust, the authors built a feature set integrating weakly differential taxa, algorithmically selected taxa via machine learning, and a functional dysbiosis score; a classifier trained on proliferative-phase data achieved moderate performance (AUC = 0.70), whereas secretory-phase models performed worse (AUC = 0.58). This paper is centrally about endometriosis — specifically, uterine microbiome signatures and how they differ by menstrual cycle phase to support endometriosis classification.
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