Background: "Metabonomics" is the comprehensive and simultaneous systematic determination of metabolite levels in the whole organism. 1H NMR spectroscopy of biofluids and Pattern Recognition Techniques have been successfully used to classify the status of an organism. Aim: To investigate whether Pattern Recognition Techniques applied to 1H NMR-based urine analysis were able to identify the premature status of the kidney, and renal maturation towards normal development. Methods: The first urine passed by two neonatal groups 1) premature (n=40) and 2) full term infants (n=28) was analyzed by 1H NMR (Varian 400 MHz INOVA). In addition, urine samples were collected from the premature group at different time points during hospital stay. Results: A preliminary PLS-DA model classifying full term infants vs. premature infants was applied to identify pathological conditions associated to the following metabolic profile: pyruvic aldehyde, 1-methil-nicotinamide, purine, tyrosine, phosphoglycolic acid, urea, glutamine, lactate acid, beta-OH-butyric acid, valine. Conclusions: Using non-invasive techniques, we were able to classify newborn infants according to their gestational age and their evolution towards normal maturity. It was possible to identify the compounds responsible for the different metabolic profiles. The technique used to evaluate the variability in metabonomic data might also have implications in evaluating drug response or predicting renal disorders in later life.
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|Titolo:||A 1H NMR-based metabonomic study of urine from premature infants|
|Data di pubblicazione:||2007|
|Appare nelle tipologie:||1.5 Abstract in rivista|