Pediatric nephrourological diseases are associated with functional alterations frequently related to inflammatory states. A feedback loop adjusts urinary system function while forcing adaptation to internal and external influences during disease development and as a result of treatment. We hypothesized that nephrourological dysfunction would alter the urine metabolite pattern in children in a defined manner. To characterize the metabolite patterns associated with nephrouropathies, a proton nuclear magnetic resonance (1H NMR)-based metabonomic analysis was performed on urine samples obtained from twenty-one children affected by nephrouropathies and 19 healthy controls. Urine samples were analyzed with a 400 MHz Varian spectrometer and multivariate statistical techniques were applied for data interpretation. Linear discriminant analysis-based classification of the spectral data demonstrated high accuracy (95 per cent) in the separation of the two groups of samples. The urine metabolite profiles were shown to correlate with nephrourological disorders in our model. In conclusion, 1H NMR-based metabonomic analysis of urine appears to be a promising, non-invasive approach to investigate and monitor pediatric nephrourological diseases.

1H NMR-based metabolic profiling of urine from children with nephrouropathies

ATZORI, LUIGI;BARBERINI, LUIGI;LOCCI, EMANUELA;CESARE MARINCOLA, FLAMINIA;SCANO, PAOLA;FANOS, VASSILIOS
2010

Abstract

Pediatric nephrourological diseases are associated with functional alterations frequently related to inflammatory states. A feedback loop adjusts urinary system function while forcing adaptation to internal and external influences during disease development and as a result of treatment. We hypothesized that nephrourological dysfunction would alter the urine metabolite pattern in children in a defined manner. To characterize the metabolite patterns associated with nephrouropathies, a proton nuclear magnetic resonance (1H NMR)-based metabonomic analysis was performed on urine samples obtained from twenty-one children affected by nephrouropathies and 19 healthy controls. Urine samples were analyzed with a 400 MHz Varian spectrometer and multivariate statistical techniques were applied for data interpretation. Linear discriminant analysis-based classification of the spectral data demonstrated high accuracy (95 per cent) in the separation of the two groups of samples. The urine metabolite profiles were shown to correlate with nephrourological disorders in our model. In conclusion, 1H NMR-based metabonomic analysis of urine appears to be a promising, non-invasive approach to investigate and monitor pediatric nephrourological diseases.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/110429
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