Statistical Analysis
The compound Finder Software (Thermo Science, USA) was used for compound component extraction and data preprocessing in samples, including analysis such as baseline filtering, peak identification and integration, retention time correction, peak alignment, and mass fragment attribution. The edited data matrix was imported into Simca-P software (version 11.0) for multivariate statistical analysis after Centralization and Pareto scaling. The dissimilarities in the metabolome dataset among the groups were analyzed using principal component analysis (PCA) and partial least squares discrimination analysis (PLS-DA). The VVC and Control groups were looked for differentially expressed metabolites according to the variable importance in projection (VIP) value (>1) of the PLS-DA model, statistical significance was observed at P value < 0:05. Receiver Operating Characteristics (ROC) analysis was used to identify the metabolites that distinguished the patients in the VVC group from control group and the strength of the discriminators were measured with the Area Under the Curve (AUC) values. AUC values above 0.8 were considered as good and above 0.9 were considered as excellent discriminators. Furthermore, we used the human metabolome database (HMDB, https://hmdb.ca) and our internal standard metabolite library to identify and analyze the metabolites. Additionally, the Kyoto Encyclopedia of Genes and Genomes (KEGG) online database and the ingenuity pathway analysis (IPA) server were applied to understand networks of the metabolic pathways between differential metabolites.