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Metabolomics is a technique used to non-invasively determine metabolic status of an organism. Aim of our study was to analyze urinary metabolic profiles in term and preterm infants in order to identify gestational age-related metabolic differences and to predict metabolic maturity at birth. Twenty-six healthy term infants and 41 preterm infants were prospectively enrolled. A urine sample was collected non-invasively within the first hours of life. Samples were analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy and NMR urine spectra were analyzed by multivariate statistical analysis. Distinct metabolic patterns were found between term infants and preterm infants, as well as between preterm infants of 23-32 weeks' gestation and those of 33-36 weeks' gestation. Individual metabolites discriminating between these groups were hippurate, tryptophan, phenylalanine, malate, tyrosine, hydroxybutyrate, N-acetyl-glutamate, and proline. Metabolomic analysis revealed distinct urinary metabolic profiles in neonates of different gestational ages, and identified the discriminating metabolites. This holistic approach appears to be a promising tool for investigating newborn metabolic maturation over time, and might lead to a tailored management of neonatal disorders.

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.