Abstract
BACKGROUND: Cholecystolithiasis is defined as a disease caused by complex and changeable factors. Advanced age, female sex, and a hypercaloric diet rich in carbohydrates and poor in fiber, together with obesity and genetic factors, are the main factors that may predispose people to choledocholithiasis. However, serum biomarkers for the rapid diagnosis of choledocholithiasis remain unclear. AIMS: This study was designed to explore the pathogenesis of cholecystolithiasis and identify the possible metabolic and lipidomic biomarkers for the diagnosis of the disease. METHODS: Using UHPLC-MS/MS and GC-MS, we detected the serum of 28 cholecystolithiasis patients and 19 controls. Statistical analysis of multiple variables included Principal Component Analysis (PCA). Visualization of differential metabolites was performed using volcano plots. The screened differential metabolites were further analyzed using clustering heatmaps. The quality of the model was assessed using random forests. RESULTS: In this study, dramatically altered lipid homeostasis was detected in cholecystolithiasis group. In addition, the levels of short-chain fatty acids and amino acids were noticeably changed in patients with cholecystolithiasis. They detected higher levels of FFA.18.1, FFA.20.1, LPC16.0, and LPC20.1, but lower levels of 1-Methyl-L-histidine and 4-Hydroxyproline. In addition, glycine and L-Tyrosine were higher in choledocholithiasis group. Analyses of metabolic serum in affected patients have the potential to develop an integrated metabolite-based biomarker model that can facilitate the early diagnosis and treatment of the disease. CONCLUSION: Our results highlight the value of integrating lipid, amino acid, and short-chain fatty acid to explore the pathophysiology of cholecystolithiasis disease, and consequently, improve clinical decision-making.