4.2 Article

1 H NMR-based metabolomics study for identifying urinary biomarkers and perturbed metabolic pathways associated with severity of IgA nephropathy: a pilot study

Journal

MAGNETIC RESONANCE IN CHEMISTRY
Volume 55, Issue 8, Pages 693-699

Publisher

WILEY
DOI: 10.1002/mrc.4573

Keywords

IgA nephropathy; metabolite biomarker; nuclear magnetic resonance; urine biomarker; metabolomics

Funding

  1. Chronic Kidney Disease Research Center
  2. Urology and Nephroloy Research Center at Shahid Beheshti University of Medical Sciences, Tehran, Iran

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The severity of IgA nephropathy (IgAN), the most common primary glomerulonephritis, is judged on the basis of histologic and clinical features. A limited number of studies have considered molecular signature of IgAN for this issue, and no reliable biomarkers have been presented non-invasively for use in patient evaluations. This study aims to identify metabolite markers excreted in the urine and impaired pathways that are associated with a known marker of severity (proteinuria) to predict mild and severe stages of IgAN. Urine samples were analysed using nuclear magnetic resonance from biopsy-proven IgAN patients at mild and severe stages. Multivariate statistical analysis and pathway analysis were performed. The most changed metabolites were acetoacetate, hypotaurine, homocysteine, L-kynurenine and phenylalanine. Nine metabolites were positively correlated with proteinuria, including mesaconic acid, trans-cinnamic acid, fumaric acid, 5-thymidylic acid, anthranilic acid, indole, deoxyguanosine triphosphate, 13-cis-retinoic acid and nicotinamide riboside, while three metabolites were negatively correlated with proteinuria including acetoacetate, hypotaurine and hexanal. 'Phenylalanine metabolism' was the most significant pathway which was impaired in severe stage in comparison to mild stage of IgAN. This study indicates that nuclear magnetic resonance is a versatile technique that is capable of detecting metabolite biomarkers in combination with advanced multivariate statistical analysis. Copyright (C) 2017 John Wiley & Sons, Ltd.

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