Zhang Ying, Su Jiayu, Zhou Ke, Wang Shuangshuang, Zhang Jingwei, Zhang Tiannan, Liu Shijia, Lu Yan
Department of Pharmacy, The Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu 210029, P.R. China.
School of Life Science and Technology, China Pharmaceutical University, Nanjing, Jiangsu 211198, P.R. China.
Exp Ther Med. 2024 Sep 16;28(5):429. doi: 10.3892/etm.2024.12717. eCollection 2024 Nov.
Gout is a heterogeneous disease caused by the deposition of monosodium urate crystals in joints, but its pathogenesis is currently poorly understood. The discovery of novel biomarkers is necessary for the early detection and diagnosis of gout. The present study aimed to characterize the metabolic profile of patients with gout using metabolomics, and to uncover the underlying pathological mechanisms leading to gout development. Serum samples were collected from 49 healthy participants and 47 patients with gout. Using ultra-high-performance liquid chromatography Orbitrap Exploris mass spectrometer non-target metabolomics technology, with a variable importance in the projection >1 and a false discovery rate adjusted P<0.05 was used, while a biomarker panel was screened using receiver operating characteristic (ROC) analysis. The potential differentially expressed markers related to gout were identified by ROC analysis, and the erythrocyte sedimentation rate, uric acid, alanine transaminase, aspartate aminotransferase, creatinine, triglyceride, total cholesterol, high-density lipoprotein and low-density lipoprotein levels were significantly different in the group of patients with gout compared with those in healthy individuals. A total of 186 differentially expressed metabolites were identified, with 156 differential metabolites upregulated and 30 downregulated in the patients with gout compared with healthy individuals. Pathway analysis demonstrated that D-glutamine and D-glutamate metabolism may serve key roles in gout. Compared with healthy people, the indolelactic acid (ILA) level of patients with gout was significantly higher. ILA may serve as a potential biomarker for the diagnosis of gout and could be used to detect or predict gout progression in the future.
痛风是一种由单钠尿酸盐晶体在关节中沉积引起的异质性疾病，但其发病机制目前尚不清楚。发现新型生物标志物对于痛风的早期检测和诊断至关重要。本研究旨在利用代谢组学对痛风患者的代谢谱进行表征，并揭示导致痛风发生的潜在病理机制。收集了49名健康参与者和47名痛风患者的血清样本。使用超高效液相色谱Orbitrap Exploris质谱仪的非靶向代谢组学技术，采用投影变量重要性>1且错误发现率调整后P<0.05的标准，同时使用受试者工作特征（ROC）分析筛选生物标志物组。通过ROC分析确定了与痛风相关的潜在差异表达标志物，痛风患者组的红细胞沉降率、尿酸、丙氨酸转氨酶、天冬氨酸转氨酶、肌酐、甘油三酯、总胆固醇、高密度脂蛋白和低密度脂蛋白水平与健康个体相比有显著差异。共鉴定出186种差异表达代谢物，与健康个体相比，痛风患者中有156种差异代谢物上调，30种下调。通路分析表明，D-谷氨酰胺和D-谷氨酸代谢可能在痛风中起关键作用。与健康人相比，痛风患者的吲哚乳酸（ILA）水平显著更高。ILA可能作为痛风诊断的潜在生物标志物，未来可用于检测或预测痛风进展。
