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基于血清代谢组学鉴定慢性肾脏病的血清生物标志物。

Identification of serum biomarkers for chronic kidney disease using serum metabolomics.

机构信息

Department of Nephrology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

Department of Infectious Diseases, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.

出版信息

Ren Fail. 2024 Dec;46(2):2409346. doi: 10.1080/0886022X.2024.2409346. Epub 2024 Oct 8.

DOI:10.1080/0886022X.2024.2409346
PMID:39378112
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11463012/
Abstract

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.

摘要

本研究旨在通过研究血清代谢组学来鉴定慢性肾脏病（CKD）的生物标志物。收集了 194 名非透析 CKD 患者和 317 名健康对照者（HC）的血清样本。采用超高效液相色谱-串联质谱（UPLC-MS）进行非靶向代谢组学分析。在单独的 HC 和 CKD 患者组中建立和验证随机森林模型。与健康对照组（HC）相比，慢性肾脏病（CKD）患者的血清代谢组图谱存在显著差异。鉴定出 314 种差异代谢物，其中 179 种在 CKD 患者中上调，135 种下调。KEGG 富集分析显示了几个关键途径，包括精氨酸生物合成、苯丙氨酸代谢、亚油酸代谢和嘌呤代谢。该分类器的诊断效能较高，在训练集和验证集中的曲线下面积为 1，在交叉验证集中为 0.9435。本研究全面深入地研究了非透析 CKD 患者的血清代谢，强调了异常生物代谢在 CKD 发病机制中的潜在作用。探索代谢物可能为 CKD 的管理提供新的可能性。

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