Identification of serum biomarkers for chronic kidney disease using serum metabolomics

This study aimed to identify biomarkers for chronic kidney disease (CKD) by studying serum metabolomics. Serum samples were collected from 194 non-dialysis CKD patients and 317 healthy controls (HC). Using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS), untargeted metabolomics analysis was conducted. A random forest model was developed and validated in separate sets of HC and CKD patients. The serum metabolomic profiles of patients with chronic kidney disease (CKD) exhibited significant differences compared to healthy controls (HC). A total of 314 metabolites were identified as significantly different, with 179 being upregulated and 135 being downregulated in CKD patients. KEGG enrichment analysis revealed several key pathways, including arginine biosynthesis, phenylalanine metabolism, linoleic acid metabolism, and purine metabolism. The diagnostic efficacy of the classifier was high, with an area under the curve of 1 in the training and validation sets and 0.9435 in the cross-validation set. This study provides comprehensive insights into serum metabolism in non-dialysis CKD patients, highlighting the potential involvement of abnormal biological metabolism in CKD pathogenesis. Exploring metabolites may offer new possibilities for the management of CKD.

本研究旨在通过血清代谢组学分析，鉴定慢性肾脏病（chronic kidney disease, CKD）的生物标志物。本研究收集了194例非透析慢性肾脏病患者与317例健康对照（healthy controls, HC）的血清样本，采用超高效液相色谱-串联质谱（ultra-high-performance liquid chromatography-tandem mass spectrometry, UPLC-MS）技术开展非靶向代谢组学分析。构建随机森林模型，并在独立的健康对照与慢性肾脏病患者队列中进行验证。慢性肾脏病患者的血清代谢组学特征与健康对照相比存在显著差异。共鉴定出314种差异显著的代谢物，其中慢性肾脏病患者体内179种代谢物表达上调、135种表达下调。京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析显示，存在多条关键代谢通路，包括精氨酸生物合成、苯丙氨酸代谢、亚油酸代谢及嘌呤代谢。该分类器的诊断效能优异，训练集与验证集的曲线下面积（area under the curve, AUC）为1，交叉验证集的曲线下面积为0.9435。本研究全面解析了非透析慢性肾脏病患者的血清代谢特征，揭示了异常生物学代谢可能参与慢性肾脏病的发病机制。对代谢物的探索可为慢性肾脏病的临床管理提供新的思路。