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. 2024 Aug 17:17:5521-5531.
doi: 10.2147/JIR.S461621. eCollection 2024.

Metabolic Assessment in Non-Dialysis Patients with Chronic Kidney Disease

Hao Hong #  1 , Suya Zhou #  2 , Junyao Zheng  3 , Haimin Shi  3 , Yue Chen  4 , Ming Li  3
Affiliations

Metabolic Assessment in Non-Dialysis Patients with Chronic Kidney Disease

Hao Hong et al. J Inflamm Res. .

Abstract

Purpose: The aim of this study was to investigate the changes of different metabolites in the body fluids of non-dialysis patients with chronic kidney disease (CKD) using a metabolomics approach. The goal was to identify early biomarkers of CKD progression through metabolic pathway analysis.

Patients and methods: Plasma samples from 47 patients with stages 1-4 CKD not requiring dialysis and 30 healthy controls were analyzed by liquid chromatography-mass spectrometry (LC-MS). Using multivariate data analysis, specifically a partially orthogonal least squares discriminant analysis model (OPLS-DA), we investigated metabolic differences between different stages of CKD. The sensitivity and specificity of the analysis were evaluated using the Area Under Curve (AUC) method. Furthermore, the metabolic pathways were analyzed using the Met PA database.

Results: Plasma samples from CKD patients and controls were successfully differentiated using an OPLS-DA model. Initially, twenty-five compounds were identified as potential plasma metabolic markers for distinguishing CKD patients from healthy controls. Among these, six compounds (ADMA, D-Ornithine, Kynurenine, Kynurenic acid, 5-Hydroxyindoleacetic acid, and Gluconic acid) were found to be associated with CKD progression It has been found to be associated with the progression of CKD. Changes in metabolic pathways associated with CKD progression include arginine and ornithine metabolism, tryptophan metabolism, and the pentose phosphate pathway.

Conclusion: By analyzing the metabolic pathways of different metabolites, we have identified the significant impact of CKD progression. The main metabolic pathways involved are Arginine and Ornithine metabolism, Tryptophan metabolism, and Pentose phosphate pathway. ADMA, D-Ornithine, L-Kynurenine, Kynurenic acid, 5-Hydroxyindoleacetic acid, and Gluconic acid could serve as potential early biomarkers for CKD progression. These findings have important implications for the early intervention and treatment of CKD, as well as for further research into the underlying mechanisms of its pathogenesis.

Keywords: arginine and ornithine metabolism; biomarkers; pentose phosphate pathway; tryptophan metabolism.

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Conflict of interest statement

All authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Base peak chromatogram of CKD patients and control group obtained with LC-MS positive (A), negative (B)ionization mode.
Figure 2
Figure 2
PCA models built on QC data obtained with LC-MS analysis in (A) positive ionization mode, (B) negative ionization mode; OPLS-DA models built on plasma data obtained with LC-MS analysis in (C) positive ionization mode, (D) negative ionization mode, respectively.
Figure 3
Figure 3
Heatmap of discriminating metabolites between CKD patients with stage 1 to stage 4 and controls with positive (A), negative (B) ionization mode.
Figure 4
Figure 4
Differences in normalized intensity of selected metabolites between 6 compared groups.
Figure 5
Figure 5
ROC curve of 6 metabolites between 6 between control group and CKD patients.
Figure 6
Figure 6
Pathway identification between CKD patients and controls.
Figure 7
Figure 7
Heatmap of correlation analysis between 6 discriminating metabolites and various clinical parameters.
See this image and copyright information in PMC

References

    1. Hill NR, Fatoba ST, Oke JL, et al. Global prevalence of chronic kidney disease – a systematic review and meta-analysis. PLoS One. 2016;11(7):e0158765. doi:10.1371/journal.pone.0158765 - DOI - PMC - PubMed
    1. Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate, ann. Intern Med. 2009;150:604. - PMC - PubMed
    1. Zhang ZH, Chen H, Vaziri ND, et al. Metabolomic signatures of chronic kidney disease of diverse etiologies in the rats and humans. J Proteome Res. 2016;15(10):3802–3812. doi:10.1021/acs.jproteome.6b00583 - DOI - PubMed
    1. Andrassy KM. Comments on ‘KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease’. Kidney Int. 2013;84(3):622–623. doi:10.1038/ki.2013.243 - DOI - PubMed
    1. Chen J, Hamm LL, Mohler ER, et al. Interrelationship of multiple endothelial dysfunction biomarkers with chronic kidney disease. PLoS One. 2015;10(7):e0132047. doi:10.1371/journal.pone.0132047 - DOI - PMC - PubMed

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