Comprehensive Analysis of Vaginal and Gut Microbiome Alterations in Endometriosis Patients

Zhao Y, Xiaoyu Hu, Li C, Huang J, Guo K, Pan Q, Yu Z
In: International Journal of Women's Health, Vol Volume 17, Iss Issue 1, Pp 5775-5786 (2025) · 2025 · W7117943201
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Metagenomic sequencing of paired vaginal and fecal samples reveals that gut microbiome features outperform vaginal profiles and hormonal indices in predicting endometriosis, highlighting their diagnostic potential.

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This study utilized metagenomic sequencing on paired vaginal and fecal samples from endometriosis patients and controls to characterize microbial profiles and assess their diagnostic utility. The analysis revealed significant shifts in the vaginal microbiome, including reduced Lactobacillus and increased Gardnerella, which correlated with elevated LH and FSH levels, while the gut microbiome showed decreased diversity and specific taxonomic depletion. Machine learning models indicated that gut microbiome features were superior to both vaginal microbiome data and hormonal indices in predicting endometriosis, highlighting strong diagnostic potential. This paper is centrally about endometriosis — specifically investigating the role of gut and vaginal dysbiosis as diagnostic biomarkers for the disease.

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Abstract

Yiming Zhao,1,2,* Xinyu Hu,1,* Chunyan Li,2 Jing Huang,3 Ke Guo,4 Qiong Pan,1 Zheng Yu2 1Department of Obstetrics and Gynecology, the Third Xiangya Hospital of Central South University, Changsha, People’s Republic of China; 2Human Microbiome and Health Group, Department of Microbiology, Xiangya School of Basic Medical Sciences, Central South University, Changsha, Hunan, People’s Republic of China; 3Department of Parasitology, Xiangya School of Basic Medical Sciences, Central South University, Changsha, Hunan, People’s Republic of China; 4Department of Neurology, the Third Xiangya Hospital of Central South University, Changsha, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiong Pan, Email [email protected] Zheng Yu, Email [email protected]: 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.Keywords: vaginal microbiome, gut microbiome, endometriosis, metagenomic sequencing
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International Journal of Women's Health (Dec 2025) Comprehensive Analysis of Vaginal and Gut Microbiome Alterations in Endometriosis Patients Abstract Yiming Zhao,1,2,* Xinyu Hu,1,* Chunyan Li,2 Jing Huang,3 Ke Guo,4 Qiong Pan,1 Zheng Yu2 1Department of Obstetrics and Gynecology, the Third Xiangya Hospital of Central South University, Changsha, People’s Republic of China; 2Human Microbiome and Health Group, Department of Microbiology, Xiangya School of Basic Medical Sciences, Central South University, Changsha, Hunan, People’s Republic of China; 3Department of Parasitology, Xiangya School of Basic Medical Sciences, Central South University, Changsha, Hunan, People’s Republic of China; 4Department of Neurology, the Third Xiangya Hospital of Central South University, Changsha, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qiong Pan, Email [email protected] Zheng Yu, Email [email protected]: 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.Keywords: vaginal microbiome, gut microbiome, endometriosis, metagenomic sequencing

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