Development of a predictive framework for ovarian reserve decline based on pelvic microbiota dysbiosis.

BACKGROUND

Diminished ovarian reserve (DOR) is increasingly recognized as a multifactorial condition, not solely related to aging. Emerging evidence suggests that environmental and biological factors, including the pelvic microbiota, may influence ovarian function across different age groups. In this study, we examined the association between pelvic microbiota dysbiosis and DOR, with the broader goal of identifying early microbiota-based markers to support predictive diagnosis, preventive strategies, and personalized reproductive care.

METHODS

Ascitic fluid samples were collected from women with normal ovarian reserve and those diagnosed with DOR. Microbial profiling was performed using 16S ribosomal RNA (rRNA) gene sequencing to compare the composition and diversity of the pelvic microbiota between the two groups. A multivariable predictive model was constructed by combining key microbial genera with clinical indicators such as body mass index (BMI), aiming to support early risk estimation of DOR.

RESULTS

Microbial analysis revealed a significantly higher abundance of in the DOR group compared to controls, suggesting its potential role as a microbial marker of diminished ovarian reserve. The predictive model integrating microbial and clinical data demonstrated moderate accuracy, with an area under the curve (AUC) of 0.88 ± 0.16.

CONCLUSIONS

Women with a BMI ≥ 24.0 face an increased risk of ovarian function decline. If pelvic microbiota profiling further reveals dysbiosis, particularly enrichment, early microbial screening and individualized probiotic treatment with or may be warranted. This strategy embodies the core principles of predictive, preventive, and personalized medicine (PPPM/3PM).