Metabolomic Analysis of Follicular Fluid in Normal-Weight Patients with Polycystic Ovary Syndrome

Abstract

Background: This study aimed to examine the differential variations in the metabolic composition of follicular fluid (FF) among normal-weight patients with polycystic ovary syndrome (PCOS) and controls and to identify potential biomarkers that may offer insights into the early identification and management of these patients.

Methods: We collected FF samples from 45 normal-weight women with PCOS and 36 normal-weight controls without PCOS who were undergoing in vitro fertilization-embryo transfer. An untargeted metabolomic study of collected FF from infertile women was performed using high-performance liquid chromatography-tandem spectrometry (LC-MS). The tendency of the two groups to separate was demonstrated through multivariate analysis. Univariate analysis and variable importance in projection were used to screen out differential metabolites. Metabolic pathway analysis was conducted using the Kyoto Encyclopedia of Genes and Genomes (KEGG), and a diagnostic model was established using the random forest algorithm.

Results: The metabolomics analysis revealed an increase in the expression of 23 metabolites and a decrease in that of 10 metabolites in the FF of normal-weight women with PCOS. According to the KEGG pathway analysis, these differential metabolites primarily participated in the metabolism of glycerophospholipids and the biosynthesis of steroid hormones. Based on the biomarker combination of the top 10 metabolites, the area under the curve value was 0.805. The concentrations of prostaglandin E2 in the FF of individuals with PCOS exhibited an inverse association with the proportion of high-quality embryos (p < 0.05).

Conclusions: Our research identified a distinct metabolic profile of the FF from normal-weight women with PCOS. The results offer a broader comprehension of the pathogenesis and advancement of PCOS, and the detected differential metabolites could be potential biomarkers and targets for the treatment of PCOS.

Keywords: LC–MS; follicular fluid; metabolomics; polycystic ovary syndrome; reproductive.

Figure 1

Separation trends between the normal-weight PCOS group (red circles) and the control group (blue circles) were described using OPLS-DA score scatter plots with permutation tests. (A) OPLS-DA score scatter plot of positive ion mode in HILIC; (B) permutation test of positive ion mode in HILIC; (C,D) negative ion mode in HILIC; (E,F) positive ion mode in RPLC; (G,H) negative ion mode in RPLC.

Figure 2

Identification of differential metabolites between the two groups. (A) Volcano plots show differential metabolites in different modes, where red dots represent upregulated metabolites and blue dots represent downregulated metabolites; (B) hierarchical clustering heatmap of differential metabolites.

Figure 3

(A) KEGG pathway enrichment analysis of differential metabolites. The blue represents the p value, which means the metabolic pathway was significantly influenced. The size of the point represents the number of relevant metabolites involved in this pathway. (B) Correlation network diagram of differential metabolites based on r > 0.6 (or r < −0.6) and p value < 0.05.

Figure 4

Spearman correlation coefficients of clinical indicators and differential metabolites. The correlation coefficient (r) value is shown by the color of the point on the map while the p value is indicated by its size.

Figure 5

Development and testing of a diagnostic model based on 10 metabolites. (A) The ranking of classification importance of the top 10 metabolites. (B) The ROC curve and AUC value of the combined diagnostic model of metabolites; the ROC curves for the top 5, 10, 15, 20, and all metabolites are presented individually by blue, red, green, purple, and yellow curves. (C) Heatmap of the top 10 differential metabolites.