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Updated: Jun 21, 2026
04:49
A Modified Technique for Inducing Polycystic Ovary Syndrome in Mice
Published on: July 5, 2024
A Risk Score for Polycystic Ovary Syndrome Based on Meta-Analysis and Machine Learning of Gut Microbiota Signatures
Panwang Huang1,2,3, Ye Li1,3, Fan-Sheng Kong1,3
1Department of Paediatrics, Affiliated Hospital of Jiangnan University, Wuxi, 214122, Jiangsu, China.
Reproductive Sciences (Thousand Oaks, Calif.)
|June 19, 2026
Summary
Polycystic Ovary Syndrome (PCOS) involves gut microbiota changes. A 20-genus microbial signature and risk score show potential for PCOS diagnosis, identifying key bacterial alterations.
Area of Science:
- Microbiome research
- Endocrinology
- Metabolic disorders
Background:
- Polycystic Ovary Syndrome (PCOS) is a common endocrine and metabolic disorder in reproductive-aged women.
- Emerging evidence points to a significant role of the gut microbiota in PCOS pathogenesis.
Purpose of the Study:
- To comprehensively evaluate gut microbiota alterations in PCOS.
- To identify microbial biomarkers for PCOS diagnosis through integrated analysis.
Main Methods:
- Systematic literature search of PubMed, Web of Science, and Embase for 16S rRNA gene sequencing studies.
- Meta-analysis of 10 PCOS cohorts (858 individuals) at the genus level.
- Machine learning (LASSO) to identify a 20-genus microbial signature and construct a risk score.
Main Results:
- Significant decreases in Subdoligranulum, NK4A214_group, and Collinsella; increase in Bacteroides in PCOS cohorts.
- A 20-genus microbial signature achieved an AUC of 0.835 for PCOS diagnosis prediction.
- Negativibacillus and Lachnospiraceae_UCG_010 identified as potential driver microbes in PCOS.
Conclusions:
- Key gut microbiota alterations are highlighted across PCOS cohorts.
- The identified microbial signature and LASSO-based risk model offer novel insights.
- This approach presents a potential tool for PCOS diagnosis.
