Purpose: Endometriosis (EMS) is a chronic gynecological disorder with unclear pathogenesis. While the vaginal and gut microbiomes are known to influence EMS, few studies have analyzed both microbiomes integrally. This study aims to characterize the vaginal and gut microbiome profiles in EMS patients and evaluate their diagnostic potential.
Patients and Methods: We conducted metagenomic sequencing on 22 paired vaginal and fecal samples from EMS patients and controls. Microbial composition, diversity, and metabolic pathways were analyzed. Machine learning models were employed to assess the predictive performance of microbiome features in EMS diagnosis.
Results: EMS patients exhibited pronounced shifts in the vaginal microbiome, characterized by reduced Lactobacillus and increased Bifidobacterium and Gardnerella, which correlated with elevated luteinizing hormone (LH) and follicle-stimulating hormone (FSH) levels. The gut microbiome displayed decreased diversity, with a depletion of beneficial taxa such as Ruminococcus and Prevotella, alongside an enrichment of Dialister. Metabolic pathways in both microbial communities were significantly altered. Machine learning analyses demonstrated that gut microbiome features outperformed both vaginal microbiome and hormonal indices in predicting EMS, highlighting their strong diagnostic potential.
Conclusion: This study underscores the pivotal role of the gut microbiota in EMS and elucidates the complex interplay between microbial dysbiosis and disease pathogenesis. Our findings indicate that gut microbiome signatures may serve as superior diagnostic biomarkers for EMS, thereby paving the way for microbiome-based diagnostic and therapeutic strategies.
