Potential biomarkers for clinical outcomes of IVF cycles in women with/without PCOS: Searching with metabolomics.

Abstract

BackgroundPolycystic ovary syndrome (PCOS) is a heterogeneous endocrinological and metabolic disorder which is the common cause of female infertility. The dysmetabolism displayed in it has not been completely ascertained. Metabonomics may shed light on understanding many small molecule endogenous metabolites and their associated metabolic pathways.ObjectiveTo analyze the different metabolites and related metabolic pathways in follicular fluid and embryo culture fluid of PCOS and non-PCOS groups. Finding markers predictable for clinical outcomes of in vitro fertilization-embryo transfer (IVF-ET) treatment.Population and sample60 women who underwent IVF-ET were selected, including 30 with PCOS and 30 with the fallopian tubal issues only. We collected the first tube follicular fluid (FF) of all patients at the time of oocyte pick up and the waste embryo culture medium (ECM) after D3 high-quality embryo transplant.MethodsAll samples were performed nontargeted Ultra High Performance Liquid Chromatography-Mass Spectrometry (UHPLC-QE-MS) analysis. Related metabolic pathways were screened by KEGG annotation. To search potential indicators, the logistic regression was made combined with clinical data.Mean outcome measuresPredictive performance of markers of clinical outcomes (pregnancy rate, delivery rate, live birth rate, miscarriage rate) of assisted reproductive technology (ART).ResultsComparing the PCOS group against the non-PCOS group, we found 11 significantly different metabolites in the FF and 56 in the ECM. There are a total of 11 kinds of biomarkers associated with clinical outcomes. Androsterone sulfate, Glycerophosphocholine, and Elaidic carnitine seem robust to predict the abortion rate of the PCOS group, with an AUC of 0.941, 0.933, 0.933, respectively. The glycerol phospholipid metabolic pathway is enriched in both the follicular fluid and embryo culture fluid.ConclusionsThe differential metabolites were mainly a variety of lipids. Some of them can predict clinical outcomes to a certain extent.