Bolun Zhang,1,2 Shaochong Lin,1,2 Sidong Wang,2,3 Weiyu Chen,4 Yushu Chen,4 Dandan Cao,2 Qingzhi Liu,2 Yuanqing Yao1 

1Department of Medical College, Nankai University, Tianjin, People’s Republic of China; 2Center for Reproductive Medicine, Shenzhen Hospital, The University of Hong Kong, Shenzhen, People’s Republic of China; 3Department of Obstetrics and Gynaecology, Li ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, People’s Republic of China; 4Hangzhou Veritas Genetics Medical Institute Co., Ltd., Hangzhou, People’s Republic of China

Correspondence: Yuanqing Yao, Department of Medical College, Nankai University, Tianjin, People’s Republic of China, Email yaoyq_hkuszh@126.com

Purpose: Emerging evidence suggests that an abnormal endometrial microbiota may be a potential factor contributing to recurrent pregnancy loss (RPL). This study aimed to characterize the endometrial microbiota in patients with RPL and to explore its association with miscarriage.
Patients and Methods: Based on specific inclusion and exclusion criteria, EndoMetrial Microbiome Assay (EMMA) data from women attending clinics were collected and categorized into RPL and control groups according to their miscarriage history. Species diversity analysis, differential microbiota analysis, and machine learning methods were employed to identify key microbial genera associated with RPL. Microbial network analysis was then performed to further characterize the endometrial microbiome in patients with RPL.
Results: No significant differences in α-diversity were observed between the RPL and control groups across multiple indices (all P > 0.05); however, β-diversity differed significantly (Euclidean distance, P = 0.039). Regarding species composition, the control group showed a significantly higher abundance of Lactobacillus, whereas the RPL group had increased levels of pathogenic bacteria, including Gardnerella, Staphylococcus, and Streptococcus. Machine learning identified three key genera associated with RPL: Streptococcus, Chryseobacterium, and Fusobacterium. Microbial network analysis further revealed the fragility of the endometrial microbial community in patients with RPL.
Conclusion: These findings offer novel insights into the mechanisms of endometrial microenvironmental changes in patients with RPL and highlight potential microbial biomarkers and therapeutic targets for future clinical applications.

Keywords: recurrent pregnancy loss, endometrial microbiota, machine learning, microbial biomarkers