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Disease Markers
Volume 19 (2004), Issue 4-5, Pages 169-183

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

Que N. Van,1 John R. Klose,1 David A. Lucas,1 DaRue A. Prieto,1 Brian Luke,2 Jack Collins,2 Stanley K. Burt,2 Gwendolyn N. Chmurny,1 Haleem J. Issaq,1 Thomas P. Conrads,1 Timothy D. Veenstra,1 and Susan K. Keay3,4

1Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick, Inc., NCI Frederick, Frederick, MD, USA
2Advanced Biomedical Computer Center, SAIC-Frederick Inc., NCI-Frederick, Frederick, MD, USA
3Division of Infectious Diseases, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
4Research Service, VA Maryland Health Care System, Baltimore, MD 21201, USA

Received 13 July 2004; Accepted 13 July 2004

Copyright © 2004 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%.