{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T14:53:33Z","timestamp":1788274413164,"version":"build-2803163510"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Poor ovarian response (POR) is a pathological condition characterized by inadequate ovarian response to gonadotropin stimulation in patients undergoing <jats:italic>in vitro<\/jats:italic> fertilization and embryo transfer. It represents a primary cause of failure in many assisted reproductive technology treatments. Utilizing non-targeted metabolomics technology applied to follicular fluid, this research aims to elucidate the metabolic characteristics associated with POR, explore the underlying molecular mechanisms, and identify potential biomarkers. By analyzing metabolic factors that influence oocyte quality, we aspire to provide insights for the early detection and intervention of patients with POR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this research, 60 follicular fluid samples were collected for a non-targeted metabolomic study, including 30 samples from POR patients and 30 from women with normal ovarian reserve. The orthogonal partial least squares discriminant analysis model was employed to discern separation trends between the two groups. Pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Additionally, random forest and logistic regression models were utilized to identify biomarkers indicative of POR within the follicular fluid.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on data from the Human Metabolome Database, our metabolomic analysis identified 40 differential metabolites associated with POR, including 18 up-regulated and 22 down-regulated metabolites. KEGG pathway analysis revealed that these metabolites predominantly participate in glycerophospholipid metabolism, choline metabolism in cancer, autophagy processes. Notably, perillyl aldehyde emerged as a potential biomarker for POR.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study represents the first comprehensive examination of metabolic alterations in follicular fluid among patients with POR using non-targeted metabolomics technology. We have identified significant metabolic changes within the follicular fluid of individuals affected by POR which may offer valuable insights into therapeutic strategies for managing this condition as well as improving outcomes in assisted reproductive technologies.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1547550","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T04:13:14Z","timestamp":1747023194000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Non-targeted metabolomic analysis of follicular fluid in infertile individuals with poor ovarian 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