Serum lipidomics reveals distinct metabolic profiles for asymptomatic hyperuricemic and gout patients.

OBJECTIVES This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and find potential underlying pathological mechanisms therefrom. METHODS Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as center 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as center 2 (external validation set) including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by UHPLC-Q Exactive MS. Differential metabolites were identifed by both variable importance in the projection ≥ 1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted p ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. RESULTS In the discovery set, a total of 245 and 150 metabolites respectively were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included DAG, TAG, PC, PE, PI, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under ROC curve was 0.8288, the sensitivity was 82%, the specificity was 78% at a 95% confidence interval from 0.747-0.9106. In internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while 84.03% in external validation set. CONCLUSION We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients.