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.
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)。
作者简介
Yao-Hua Gu contributed equally to this work;Yu Chen contributed equally to this work;Qing Li contributed equally to this work;Corresponding authors:Tang Tang.E-mail addresses:tangtang@metware.cn;Corresponding authors:Fan He.E-mail addresses:fhe@tjh.tjmu.edu.cn;Corresponding authors:Bi-Feng Yuan.E-mail addresses:bfyuan@whu.edu.cn。
