Prediction and pathogenesis of gallstone disease based on clinical metabolomics

Abstract

Abstract Gallstone is a common disease of biliary system at present. At present, our research on its pathogenesis is still at a single analysis stage. In this study, we collected peripheral serum samples from patients with gallstones and non-biliary diseases, obtained the difference of metabolites in the peripheral blood of both sides through omics technology, and established a clinical risk prediction model for gallstones based on the clinical information of patients. The weighted gene co-expression network analysis was applied to find the metabolite set with high correlation with the pathogenesis of gallstone, and the KEGG enrichment analysis was used to find the relevant enrichment pathway, so as to obtain the metabolic pathway related to the pathogenesis of gallstone. Among them, Pantothenate and CoA biosynthesis, Linoleic acid metabolism path, Citrate cycle (TCA cycle), Glyoxylate and dicarboxylate metabolism are screened that they set with high correlation with the pathogenesis of gallstone. We found in combination with other studies that these highly correlated pathways increase the incidence of gallstones by up-regulating cholesterol synthesis raw materials, reducing cholesterol breakdown, and affecting glucose and lipid metabolism. Therefore, blocking or inhibiting the related pathways or metabolites of GSD formation has guiding significance for the clinical prevention and treatment of this disease.