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Genomic biomarkers for chronic kidney disease

  • Wenjun Ju
    Correspondence
    Reprint requests: Wenjun Ju, University of Michigan, 1560A MSRB II, 1150 W. Medical Center Drive, Ann Arbor, MI 48109-0680.
    Affiliations
    Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Mich

    Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich
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  • Shahaan Smith
    Affiliations
    Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Mich
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  • Matthias Kretzler
    Affiliations
    Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, Mich

    Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Mich
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Published:February 10, 2012DOI:https://doi.org/10.1016/j.trsl.2012.01.020
      Chronic kidney disease (CKD) remains a major challenge in nephrology and for public health care, affecting 14% to 15% of the adult US population and consuming significant health care resources. In the next 20 years, the number of patients with end stage renal disease is projected to increase by 50%. Ideal biomarkers that allow early identification of CKD patients at high risk of progression are urgently needed for early and targeted treatment to improve patient care. Recent success of integrating molecular approaches for personalized management of neoplastic diseases, including diagnosis, staging, prognosis, treatment selection, and monitoring, has strongly encouraged kidney researchers to pursue molecular definitions of patients with kidney disease. Challenges for molecular marker identification in CKD are a high degree of cellular heterogeneity of the kidney and the paucity of human tissue availability for molecular studies. Despite these limitations, potential molecular biomarker candidates have been uncovered at multiple levels along the genome––phenome continuum. Here we will review the identification and validation of potential genomic biomarker candidates of CKD and CKD progression in clinical studies. The challenges in predicting CKD progression, as well as the promises and opportunities resulting from a molecular definition of CKD will be discussed.

      Abbreviations:

      CKD (chronic kidney disease), ADPKD (autosomal dominant polycystic kidney disease), FFPE (formalin-fixed, paraffin-embedded), GFR (glomerular filtration rate), GWAS (genome-wide-association studies), KDOQI (Kidney Disease Outcomes Quality Initiative), NF-кB (nuclear factor-кB), qRT-PCR (quantitative real time PCR), PBMC (peripheral blood mononuclear cell), SNPs (single nucleotide polymorphisms)
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