To date, we have little knowledge on the overall metabolic status of neonates with intrauterine growth retardation (IUGR). In the last few years, the analysis of metabolomics has assumed an important clinical role in identifying "disorders" in the metabolic profile of patients. The aim of this work has been to analyze the urine metabolic profiles of neonates with IUGR and compare them with controls to define the metabolic patterns associated with this pathology. To our knowledge, this is the first study of metabolomics performed on neonates with IUGR. Recruited for the study were 26 neonates with IUGR diagnosed in the neonatal period and with weight at birth below the 10th percentile and 30 neonates of proper gestational weight at birth (controls). In the first 24 hours (prior to feeding) (T1) and about 4 days after birth (T2), a urine sample was taken non-invasively from each neonate. The samples were then frozen at -80°C up to the time of the analysis by proton nuclear magnetic resonance spectroscopy (1H-NMR). The data contained in the NMR spectra obtained from the single samples were statistically analyzed using the Principal Components Analysis and the Partial Least Squares-Discriminate Analysis. By means of a multivariate analysis of the NMR spectra obtained, it was possible to highlight the differences between the two groups (IUGRs and controls) owing to the presence of different metabolic patterns. The discriminants in the urine metabolic profiles derived essentially from significant differences in certain metabolites such as: myo-inositol, sarcosine, creatine and creatinine. The metabolomic analysis showed different urine metabolic profiles between neonates with IUGR and controls and made it possible to identify the molecules responsible for such differences.

The preliminary results of this study suggest that metabolomics may provide a promising tool to study aspects related to the nutrition and health of preterm infant.

The purpose of this article is to study one of the most significant causes of neonatal morbidity and mortality: neonatal sepsis. This pathology is due to a bacterial or fungal infection acquired during the perinatal period. Neonatal sepsis has been categorized into two groups: early onset if it occurs within 3-6 days and late onset after 4-7 days. Due to the not-specific clinical signs, along with the inaccuracy of available biomarkers, the diagnosis is still a major challenge. In this regard, the use of a combined approach based on both nuclear magnetic resonance ( 1 H-NMR) and gas-chromatography-mass spectrometry (GC-MS) techniques, coupled with a multivariate statistical analysis, may help to uncover features of the disease that are still hidden. The objective of our study was to evaluate the capability of the metabolomics approach to identify a potential metabolic profile related to the neonatal septic condition. The study population included 25 neonates (15 males and 10 females): 9 (6 males and 3 females) patients had a diagnosis of sepsis and 16 were healthy controls (9 males and 7 females). This study showed a unique metabolic profile of the patients affected by sepsis compared to non-affected ones with a statistically significant difference between the two groups (p = 0.05).

Autism spectrum disorders (ASD) make a dishomogeneous group of psychiatric diseases having either genetic and environmental components, including changes of the microbiota. The rate of diagnosis, based on a series of psychological tests and observed behavior, dramatically increased in the past few decades. Currently, no biological markers are available and the pathogenesis is not defined. The purpose of this study was to evaluate the potential use of H-NMR metabolomics to analyze the global biochemical signature of ASD patients (n = 21) and controls (n = 21), these being siblings of autistic patients. A multivariate model has been used to extrapolate the variables of importance. The discriminating urinary metabolites were identified; in particular, significantly increased levels of hippurate, glycine, creatine, tryptophan, and d-threitol and decreased concentrations of glutamate, creatinine, lactate, valine, betaine, and taurine were observed in ASD patients. Based on the identified discriminant metabolites, the attention was focused on two possible mechanisms that could be involved in ASD: oxidative stress conditions and gut microflora modifications. In conclusion, nuclear magnetic resonance-based metabolomics analysis of the urine seems to have the potential for the identification of a metabolic fingerprint of ASD phenotypes and appears to be suitable for further investigation of the disease mechanisms. Autism Res 2017. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. Autism Res 2017, 10: 1058-1066. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

NMR-based metabolomics was used to compare the metabolic urinary profiles of exclusively breast-fed term infants (n = 11) with those of a double-blinded controlled trial with 49 formula-fed term newborns randomized to receive either an infant formula enriched by functional ingredients (n = 24) or a standard formula (n = 25). Anthropometric measurements and urine samples were taken at enrollment (within the first month of life), at around 60 days of life, and at the end of study period (average age of 130 days). The metabolic profiles were examined in relation to time and diet strategy. A common age-dependent modification of the urine metabolome was observed for the three types of nutrition, mainly characterized by similar temporal trends of choline, betaine, myoinositol, taurine, and citrate. Contrariwise, differences in the metabolic profiles were identified according to the type of diet (human versus formula milk), while no significant difference was observed between the two formulas. These modifications are discussed mainly in terms of the different milk compositions. Despite the low number of enrolled infants (n = 60), these findings pointed out the potential of the metabolomics approach for neonatal nutritional science, in particular to provide important contributions to the optimization of formula milk.