Objectives 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. Methods 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. 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 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. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients.

中文翻译：

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血清脂质组学揭示了无症状高尿酸血症和痛风患者的不同代谢特征

目的 本研究旨在使用脂质组学表征无症状高尿酸血症 (HUA) 和痛风患者的全身脂质谱，并从中寻找潜在的潜在病理机制。方法 以南京中医药大学附属医院为中心 1（发现和内部验证集）和苏州市中医院为中心 2（外部验证集）收集血清，包括 88 名正常人、157 名 HUA 和 183 名痛风患者. 通过超高效液相色谱加 Q-Exactive 质谱 (UHPLC-Q Exactive MS) 进行脂质组学。差异代谢物通过正交偏最小二乘判别分析模式中投影≥1的变量重要性和调整后的错误发现率P≤0.05来识别。通过逻辑回归和接受者操作特征 (ROC) 分析发现生物标志物。结果在发现集中，分别发现了正常受试者与 HUA 和正常受试者与痛风的总共 245 和 150 种代谢物。受干扰的代谢物包括甘油二酯、甘油三酯 (TAG)、磷脂酰胆碱、磷脂酰乙醇胺、磷脂酰肌醇等。我们还发现了 HUA 与痛风的 116 种差异代谢物。其中，TAG 18:1-20:0-22:1和TAG 14:0-16:0-16:1的biomarker panel能够很好地区分HUA和痛风。受试者工作特征 ROC 曲线下面积为 0.8288，敏感性为 82%，特异性为 78%，95% CI 0.747, 0.9106。在内部验证集中，TAG 18:1-20:0-22:1 和 TAG 14:0-16:0-16 的预测准确率：HUA和痛风的1个panel区分达到74.38%，而在外部验证集中为84.03%。结论 我们确定了具有预测和诊断 HUA 和痛风患者潜力的血清生物标志物组。