• Login
    View Item 
    •   DSpace Home
    • School of Medicine
    • Journal Papers in PubMed
    • View Item
    •   DSpace Home
    • School of Medicine
    • Journal Papers in PubMed
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

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

    Thumbnail
    Date
    2017-Aug
    Author
    Shiva Kalantari
    Mohsen Nafar
    Shiva Samavat
    Mahmoud Parvin
    Metadata
    Show full item record
    Abstract
    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 © 2017 John Wiley & Sons, Ltd.
    DOI
    http://dx.doi.org/10.1002/mrc.4573
    Collections
    • Journal Papers in PubMed

    Contact Us | Send Feedback
     

     

    Browse

    All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    Contact Us | Send Feedback