Plasma Metabolomics Profiling in Maintenance Hemodialysis Patients Based on Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry

• Published in: Kidney diseases Vol. 6; no. 2; pp. 125 - 134
• Main Authors: Chen, Yu, Wen, Ping, Yang, Junwei, Niu, Jianying
• Format: Journal Article
• Language: English
• Published: Basel, Switzerland S. Karger AG 01.03.2020
• Subjects: Analysis; Chronic kidney failure; Development and progression; Ethylenediaminetetraacetic acid; Hemodialysis; Liquid chromatography; Metabolites; Organic acids; Research Article; Spectrum analysis; Type 2 diabetes; China; Metabolic profiling; Liquid chromatography quadrupole time-of-flight mass spectrometry; End-stage renal disease
• ISSN: 2296-9381; 2296-9357
• DOI: 10.1159/000505156

Background: Key pathogenetic mechanisms underlying renal disease progression are unaffected by current treatment. Metabolite profiling has significantly contributed to a deeper understanding of the biochemical metabolic networks and pathways in disease, but the biochemical details in maintenance hemodialysis (MHD) patients remain largely undefined. Methods: The metabolic fingerprinting of plasma samples from 19 MHD patients and 12 healthy controls was characterized using liquid chromatography quadrupole time-of-flight mass spectrometry. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were applied to analyze the metabolic data. Results: The plasma metabolite profile distinguished the MHD patients from the healthy controls successfully by using both PCA and OPLS-DA models. Sixty-three metabolites were identified as the key metabolites to discriminate the MHD patients from healthy controls, involving several metabolic pathways (all p < 0.05). An increase in plasma levels of D-glucose, hippuric acid, androsterone glucuronide, indolelactic acid, and a reduction in plasma levels of glycerophosphocholine, serotonin, L-lactic acid, phytosphingosine, and several lysophosphatidylcholine were observed in MHD patients compared to healthy subjects. Metabolomics analysis combined with KEGG pathway enrichment analysis revealed that non-alcoholic fatty liver disease, choline metabolism in cancer, the forkhead box O signaling pathway, and the hypoxia-inducible factor-1 signaling pathway in MHD patients were significantly changed (p < 0.05). Conclusion: The identification of a novel signaling pathway and key metabolite markers in MHD patients provides insights into potential pathogenesis and valuable pharmacological targets for end-stage renal disease.