Exploring the Role of Different Neonatal Nutrition Regimens during the First Week of Life by Urinary GC-MS Metabolomics.

In this study, a gas-chromatography mass spectrometry (GC-MS) metabolomics study was applied to examine urine metabolite profiles of different classes of neonates under different nutrition regimens. The study population included 35 neonates, exclusively either breastfed or formula milk fed, in a seven-day timeframe. Urine samples were collected from intrauterine growth restriction (IUGR), large for gestational age (LGA), and appropriate gestational age (AGA) neonates. At birth, IUGR and LGA neonates showed similarities in their urine metabolite profiles that differed from AGA. When neonates started milk feeding, their metabolite excretion profile was strongly characterized by the different diet regimens. After three days of formula milk nutrition, urine had higher levels of glucose, galactose, glycine and myo-inositol, while up-regulated aconitic acid, aminomalonic acid and adipic acid were found in breast milk fed neonates. At seven days, neonates fed with formula milk shared higher levels of pseudouridine with IUGR and LGA at birth. Breastfed neonates shared up-regulated pyroglutamic acid, citric acid, and homoserine, with AGA at birth. The role of most important metabolites is herein discussed.

The biomarkers of fetal growth in intrauterine growth retardation and large for gestational age cases: from adipocytokines to a metabolomic all-in-one tool.

Adipose tissue is no longer considered as inert; the literature describes the role it plays in the production of many substances, such as adiponectin, visfatin, ghrelin, S100B, apelin, TNF, IL-6 and leptin. These molecules have specific roles in humans and their potential as biomarkers useful for identifying alterations related to intrauterine growth retardation and large for gestational age neonates is emerging. Infants born in such conditions have undergone metabolic changes, such as fetal hypo- or hyperinsulinemia, which may lead to development of dysmetabolic syndrome and other chronic diseases in adulthood. In this review, these biomarkers are analyzed specifically and it is discussed how metabolomics may be an advantageous tool for detection, discrimination and prediction of metabolic alterations and diseases. Thus, a holistic approach, such as metabolomics, could help the prevention and early diagnosis of metabolic syndrome.