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

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

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Chronic kidney disease(CKD)is an increasingly prevalent medical condition associated with high mortal-ity and cardiovascular complications.The intricate interplay between kidney dysfunction and subsequent metabolic disturbances may provide insights into the underlying mechanisms driving CKD onset and pro-gression.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 quan-tification of thousands of metabolites.We collected plasma samples from 21 healthy controls and 62 CKD patients,categorized into different stages(22 in stages 1-3,20 in stage 4,and 20 in stage 5).Us-ing LC-MS-based WT-Met approach,we were able to effectively annotate and quantify a total of 1431 metabolites from the plasma samples.Focusing on the 539 endogenous metabolites,we identified 399 significantly 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 demon-strates 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.

Widely-targeted metabolomicsMachine-learningChronic kidney diseaseBiomarkerMass spectrometry

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

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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

School of Nursing,Wuhan University,Wuhan 430071,China

Metware Biotechnology Co.,Ltd.,Wuhan 430075,China

Department of Nephrology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China

Key Laboratory of Vascular Aging,Ministry of Education,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan 430030,China

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

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2024

10.1016/j.cclet.2024.109627

中国化学快报(英文版)
中国化学会

中国化学快报(英文版)

CSTPCD
影响因子：0.771
ISSN：1001-8417
年,卷(期)：2024.35(11)