Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2007 Mar;18(3):913-22.
doi: 10.1681/ASN.2006070767. Epub 2007 Feb 14.

Urine biomarkers predict the cause of glomerular disease

Affiliations

Urine biomarkers predict the cause of glomerular disease

Sanju A Varghese et al. J Am Soc Nephrol. 2007 Mar.

Abstract

Diagnosis of the type of glomerular disease that causes the nephrotic syndrome is necessary for appropriate treatment and typically requires a renal biopsy. The goal of this study was to identify candidate protein biomarkers to diagnose glomerular diseases. Proteomic methods and informatic analysis were used to identify patterns of urine proteins that are characteristic of the diseases. Urine proteins were separated by two-dimensional electrophoresis in 32 patients with FSGS, lupus nephritis, membranous nephropathy, or diabetic nephropathy. Protein abundances from 16 patients were used to train an artificial neural network to create a prediction algorithm. The remaining 16 patients were used as an external validation set to test the accuracy of the prediction algorithm. In the validation set, the model predicted the presence of the diseases with sensitivities between 75 and 86% and specificities from 92 to 67%. The probability of obtaining these results in the novel set by chance is 5 x 10(-8). Twenty-one gel spots were most important for the differentiation of the diseases. The spots were cut from the gel, and 20 were identified by mass spectrometry as charge forms of 11 plasma proteins: Orosomucoid, transferrin, alpha-1 microglobulin, zinc alpha-2 glycoprotein, alpha-1 antitrypsin, complement factor B, haptoglobin, transthyretin, plasma retinol binding protein, albumin, and hemopexin. These data show that diseases that cause nephrotic syndrome change glomerular protein permeability in characteristic patterns. The fingerprint of urine protein charge forms identifies the glomerular disease. The identified proteins are candidate biomarkers that can be tested in assays that are more amenable to clinical testing.

PubMed Disclaimer

Figures

Figure 1
Figure 1
Proteins that are necessary for diagnosis of the cause of the nephrotic syndrome. Representative gel from a patient with the nephrotic syndrome. Proteins were separated in two dimensions by isoelectric point and molecular weight. The 21 numbered spots provided the most sensitivity to the analysis of the cause of the glomerular disease by artificial neural network (ANN). Numbers correspond to protein identifications in Table 3.
Figure 2
Figure 2
Unsupervised cluster analysis of protein expression in patients with glomerular diseases. Patterns of clustering did not occur on the basis of collection order, disease, race, age, or serum creatinine of patients. The colored boxes represent disease, race, age, and serum creatinine values. The creatinine values are color coded for those above or below the median value. Numbers in the line above the disease represent the sequential order in which the samples were collected.
Figure 3
Figure 3
Sensitivity and specificity of biomarkers to predict four glomerular diseases. Calculations were made for patients in the set of patients who were not used to train the ANN. Sensitivity for membranous nephropathy is not reported because only one patient was tested. The legend shows the number of true positives/patients with the disease for the sensitivity bars and the number of true negatives/number of patients without the disease for specificity bars.
Figure 4
Figure 4
Relationship of number of spots included in the analysis to the sensitivity and total accuracy for the assay. An ANN was trained for each sequential removal of spots from the data set. Sensitivity was calculated as the percentage of true-positive diagnoses from 16. Total accuracy was calculated as the percentage of correct test from all 64 possible.

References

    1. US Renal Data System. USRDS 2004 Annual Data Report: Atlas of End-Stage Renal Disease in the United States. Bethesda: National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases; 2004.
    1. Brenner BM, Hostetter TH, Humes HD. Glomerular perm-selectivity: Barrier function based on discrimination of molecular size and charge. Am J Physiol. 1978;234:F455–F460. - PubMed
    1. Brenner BM, Hostetter TH, Humes HD. Molecular basis of proteinuria of glomerular origin. N Engl J Med. 1978;298:826–833. - PubMed
    1. Caulfield JP, Farquhar MG. The permeability of glomerular capillaries to graded dextrans. Identification of the basement membrane as the primary filtration barrier. J Cell Biol. 1974;63:883–903. - PMC - PubMed
    1. Eiro M, Katoh T, Watanabe T. Risk factors for bleeding complications in percutaneous renal biopsy. Clin Exp Nephrol. 2005;9:40–45. - PubMed

Publication types

MeSH terms