Citation: 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. Metabolome profiling by widely-targeted metabolomics and biomarker panel selection using machine-learning for patients in different stages of chronic kidney disease[J]. Chinese Chemical Letters, ;2024, 35(11): 109627. doi: 10.1016/j.cclet.2024.109627 shu

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

Yao-Hua Gu ^{a, b, 1} ,
Yu Chen ^{c, 1} ,
Qing Li ^{d, 1} ,
Neng-Bin Xie ^a ,
Xue Xing ^d ,
Jun Xiong ^a ,
Min Hu ^a ,
Tian-Zhou Li ^a ,
Ke-Yu Yuan ^a ,
Yu Liu ^e ,
Tang Tang ^{c, *,} ,
Fan He ^{d, e, *,} ,
Bi-Feng Yuan ^{a, f, *,}

a.
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
b.
School of Nursing, Wuhan University, Wuhan 430071, China
c.
Metware Biotechnology Co., Ltd., Wuhan 430075, China
d.
Department of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
e.
Key Laboratory of Vascular Aging, Ministry of Education, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
f.
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

tangtang@metware.cn

fhe@tjh.tjmu.edu.cn

bfyuan@whu.edu.cn

Received Date: 31 October 2023
Revised Date: 8 January 2024
Accepted Date: 3 February 2024
Available Online: 9 February 2024

Figures(5)

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 62 CKD 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 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 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.
- Widely-targeted metabolomics,
- Machine-learning,
- Chronic kidney disease,
- Biomarker,
- Mass spectrometry
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