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

医学 脂类学 接收机工作特性 痛风 高尿酸血症 内科学 生物标志物 尿酸 无症状的 代谢组学 色谱法 胃肠病学 生物信息学 生物化学 化学 生物
作者
Shijia Liu,Yingzhuo Wang,Huanhuan Liu,Tingting Xu,Ma-Jie Wang,Jiawei Lu,Yang Guo,Wenjun Chen,Mengying Ke,Guisheng Zhou,Yan Lü,Peidong Chen,Wei Zhou
出处
期刊:Rheumatology [Oxford University Press]
卷期号:61 (6): 2644-2651 被引量:6
标识
DOI:10.1093/rheumatology/keab743
摘要

This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom.Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (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.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 diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, 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 the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the 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 it was 84.03% in external validation set.We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients.
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