OBJECTIVE: Gout is the most common inflammatory arthritis and the worldwide incidence is increasing. By revealing the metabolic alterations in serum and urine of gout patients, the first aim of our study was to discover novel molecular biomarkers allowing for early diagnosis. We also aimed to investigate the underlying pathogenic pathways. METHODS: Serum and urine samples from gout patients (n = 30) and age-matched healthy controls (n = 30) were analysed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to screen the differential metabolites and construct a diagnostic model. Next, the model was verified and optimized in the second validation cohort (n = 100). The pathways were illustrated to understand the underlying pathogenesis of gout. RESULTS: In general, serum metabolomics demonstrated a clearer distinction than urine metabolomics. In the discovery cohort, 40 differential serum metabolites were identified that could distinguish gout patients from healthy controls. Among them, eight serum metabolites were verified in the validation cohort. Through regression analysis, the final model consisted of three serum metabolites-pyroglutamic acid, 2-methylbutyryl carnitine and Phe-Phe-that presented optimal diagnostic power. The three proposed metabolites produced an area under the curve of 0.956 (95% CI 0.911, 1.000). Additionally, the proposed metabolic pathways were primarily involved in purine metabolism, branched-chain amino acids (BCAAs) metabolism, the tricarboxylic acid cycle, synthesis and degradation of ketone bodies, bile secretion and arachidonic acid metabolism. CONCLUSION: The metabolomics signatures could serve as an efficient tool for early diagnosis and provide novel insights into the pathogenesis of gout.
OBJECTIVE:Gout is the most common inflammatory arthritis and the worldwide incidence is increasing. By revealing the metabolic alterations in serum and urine of goutpatients, the first aim of our study was to discover novel molecular biomarkers allowing for early diagnosis. We also aimed to investigate the underlying pathogenic pathways. METHODS: Serum and urine samples from goutpatients (n = 30) and age-matched healthy controls (n = 30) were analysed by ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to screen the differential metabolites and construct a diagnostic model. Next, the model was verified and optimized in the second validation cohort (n = 100). The pathways were illustrated to understand the underlying pathogenesis of gout. RESULTS: In general, serum metabolomics demonstrated a clearer distinction than urine metabolomics. In the discovery cohort, 40 differential serum metabolites were identified that could distinguish goutpatients from healthy controls. Among them, eight serum metabolites were verified in the validation cohort. Through regression analysis, the final model consisted of three serum metabolites-pyroglutamic acid, 2-methylbutyryl carnitine and Phe-Phe-that presented optimal diagnostic power. The three proposed metabolites produced an area under the curve of 0.956 (95% CI 0.911, 1.000). Additionally, the proposed metabolic pathways were primarily involved in purine metabolism, branched-chain amino acids (BCAAs) metabolism, the tricarboxylic acid cycle, synthesis and degradation of ketone bodies, bile secretion and arachidonic acid metabolism. CONCLUSION: The metabolomics signatures could serve as an efficient tool for early diagnosis and provide novel insights into the pathogenesis of gout.