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Journal Article
Research Support, Non-U.S. Gov't
Urinary peptide profiling to differentiate between minimal change disease and focal segmental glomerulosclerosis.
PloS One 2014
BACKGROUND: Minimal change disease (MCD) and primary focal segmental glomerulosclerosis (FSGS) are the main causes of primary idiopathic nephrotic syndrome in children and adults, with diagnosis being essential for the appropriate choice of therapy and requiring renal biopsy. However, the presence of only normal glomeruli on renal biopsy of FSGS patients may lead to the misclassification of these patients as having MCD. The aim of this study was to (i) compare the peptide profile of MCD and FSGS patients with that of a group of healthy subjects, (ii) generate and validate a class prediction model to classify MCD and FSGS patients and (ii) identify candidate biomarkers of these glomerular entities by analysis of the urinary peptidome.
METHODS: The urinary peptide profile was analyzed by magnetic bead-based technology combined with MALDI-TOF mass spectrometry in 44 patients diagnosed of MCD (n = 22) and FSGS (n = 22). The resulting spectra were compiled and analyzed using ClinProTools software.
RESULTS: A class prediction model was developed to differentiate MCD and FSGS patients. The validation of this model correctly classified 81.8% (9/11) of MCD patients and 72.7% (8/11) of FSGS patients. Moreover, the signal with m/z 1913.60, identified as a fragment of uromodulin, and the signal with m/z 2392.54, identified as a fragment of alpha-1-antitrypsin, showed higher and lower peak areas, respectively, in FSGS patients compared with MCD patients.
CONCLUSIONS: The simple, non-invasive technique described in the present study may be a useful tool to help clinicians by confirming diagnoses achieved by renal biopsy, thereby reducing misdiagnoses and avoiding the implementation of inappropriate therapies.
METHODS: The urinary peptide profile was analyzed by magnetic bead-based technology combined with MALDI-TOF mass spectrometry in 44 patients diagnosed of MCD (n = 22) and FSGS (n = 22). The resulting spectra were compiled and analyzed using ClinProTools software.
RESULTS: A class prediction model was developed to differentiate MCD and FSGS patients. The validation of this model correctly classified 81.8% (9/11) of MCD patients and 72.7% (8/11) of FSGS patients. Moreover, the signal with m/z 1913.60, identified as a fragment of uromodulin, and the signal with m/z 2392.54, identified as a fragment of alpha-1-antitrypsin, showed higher and lower peak areas, respectively, in FSGS patients compared with MCD patients.
CONCLUSIONS: The simple, non-invasive technique described in the present study may be a useful tool to help clinicians by confirming diagnoses achieved by renal biopsy, thereby reducing misdiagnoses and avoiding the implementation of inappropriate therapies.
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