Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease  

作　　者：Yao-Hua Gu Yu Chen Qing Li Neng-Bin Xie Xue Xing Jun Xiong Min Hu Tian-Zhou Li Ke-Yu Yuan Yu Liu Tang Tang Fan He Bi-Feng Yuan 

机构地区：[1]Department of Occupational and Environmental Health,School of Public Health,Research Center of Public Health,Renmin Hospital of Wuhan University,Wuhan University,Wuhan 430071,China [2]School of Nursing,Wuhan University,Wuhan 430071,China [3]Metware Biotechnology Co.,Ltd.,Wuhan 430075,China [4]Department of Nephrology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China [5]Key Laboratory of Vascular Aging,Ministry of Education,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China [6]Department of Radiation and Medical Oncology,Cancer Precision Diagnosis and Treatment and Translational Medicine Hubei Engineering Research Center,Zhongnan Hospital of Wuhan University,Wuhan Research Center for Infectious Diseases and Cancer,Chinese Academy of Medical Sciences,Wuhan 430071,China

出　　处：《Chinese Chemical Letters》2024年第11期266-272,共7页中国化学快报（英文版）

基　　金：supported by the National Key R&D Program of China(Nos.2022YFC3400700,2022YFA0806600);the Key Research and Development Project of Hubei Province(No.2023BCB094);the Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University(No.JCRCGW-2022-008);the Key Laboratory of Hubei Province(No.2021KFY005)。

摘　　要：Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortality and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and progression.Herein,we proposed a large-scale plasma metabolite identification and quantification system that combines the strengths of targeted and untargeted metabolomics technologies,i.e.,widely-targeted metabolomics(WT-Met)approach.WT-Met method enables large-scale identification and accurate quantification of thousands of metabolites.We collected plasma samples from 21 healthy controls and 62CKD patients,categorized into different stages(22 in stages 1-3,20 in stage 4,and 20 in stage 5).Using LC-MS-based WT-Met approach,we were able to effectively annotate and quantify a total of 1431metabolites from the plasma samples.Focusing on the 539 endogenous metabolites,we identified 399significantly altered metabolites and depicted their changing patterns from healthy controls to end-stage CKD.Furthermore,we employed machine-learning to identify the optimal combination of metabolites for predicting different stages of CKD.We generated a multiclass classifier consisting of 7 metabolites by machine-learning,which exhibited an average AUC of 0.99 for the test set.In general,amino acids,nucleotides,organic acids,and their metabolites emerged as the most significantly altered metabolites.However,their patterns of change varied across different stages of CKD.The 7-metabolite panel demonstrates promising potential as biomarker candidates for CKD.Further exploration of these metabolites can provide valuable insights into their roles in the etiology and progression of CKD.

关 键 词：Widely-targeted metabolomics MACHINE-LEARNING Chronic kidney disease BIOMARKER Mass spectrometry 

分 类 号：R692[医药卫生—泌尿科学]