Metabolomics-Driven Insights into Biomarkers for Poor Ovarian Response: A Narrative Review

Poor ovarian response (POR) remains a significant challenge in the field of assisted reproductive technology (ART), as the quantity and quality of oocytes retrieved directly influence embryo implantation, clinical pregnancy, and live birth rates. Metabolomics has become a valuable tool for elucidati...

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Main Authors: Anastasios Potiris, Sofoklis Stavros, Eleni Alyfanti, Nikolaos Machairiotis, Eirini Drakaki, Athanasios Zikopoulos, Efthalia Moustakli, Charikleia Skentou, Peter Drakakis, Ekaterini Domali
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Biomedicines
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Online Access:https://www.mdpi.com/2227-9059/13/1/214
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Summary:Poor ovarian response (POR) remains a significant challenge in the field of assisted reproductive technology (ART), as the quantity and quality of oocytes retrieved directly influence embryo implantation, clinical pregnancy, and live birth rates. Metabolomics has become a valuable tool for elucidating the molecular mechanisms underlying diminished ovarian reserve (DOR) and POR. This review aims to synthesize findings from metabolomic studies examining metabolite expression patterns in serum and follicular fluid samples from women with POR. A literature search was performed using the Medline/PubMed and Scopus databases, employing keywords related to metabolomics and POR. In total, nine studies met the inclusion criteria for this review. These studies identified several metabolites with differential expression in serum and follicular fluid samples between women with normal ovarian response and those with POR. Although the metabolomic profiles varied significantly among studies, consistent alterations in prostaglandin related metabolites were observed in two of the nine studies reviewed. These findings suggest that, pending further validation, these metabolites may serve as potential biomarkers for ovarian response. Metabolomics has significantly advanced our understanding of the mechanisms underlying ovarian function and holds promise for identifying effective biomarkers that could improve the prediction and management of POR.
ISSN:2227-9059