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Perinatal asphyxia is an event affecting around four million newborns worldwide. The 0.5 to 2 per 1000 of full term asphyxiated newborns suffer from hypoxic-ischemic encephalopathy (HIE), which is a frequent cause of death or severe disability and, as consequence, the most common birth injury claim for obstetrics, gynaecologists, and paediatricians. Perinatal asphyxia results from a compromised gas exchange that leads to hypoxemia, hypercapnia, and metabolic acidosis. In this work, we applied a metabolomics approach to investigate the metabolic profiles of urine samples collected from full term asphyxiated newborns with HIE undergoing therapeutic hypothermia (TH), with the aim of identifying a pattern of metabolites associated with HIE and to follow their modifications over time. Urine samples were collected from 10 HIE newborns at birth, during hypothermia (48 hours), at the end of the therapeutic treatment (72 hours), at 1 month of life, and compared with a matched control population of 16 healthy full term newborns. The metabolic profiles were investigated by 1H NMR spectroscopy coupled with multivariate statistical methods such as principal component analysis and orthogonal partial least square discriminant analysis. Multivariate analysis indicated significant differences between the urine samples of HIE and healthy newborns at birth. The altered metabolic patterns, mainly originated from the depletion of cellular energy and homeostasis, seem to constitute a characteristic of perinatal asphyxia. The HIE urine metabolome changes over time reflected either the effects of TH and the physiological growth of the newborns. Of interest, the urine metabolic profiles of the HIE non-surviving babies, characterized by the increased excretion of lactate, resulted significantly different from the rest of HIE population.

The diagnosis of pain nature is a troublesome task and a wrong attribution often leads to an increase of costs and to avoidable pharmaceutical adverse reactions. An objective and specific approach to achieve this diagnosis is highly desirable. The aim of this work was to investigate urine samples collected from patients suffering from pain of different nature by a metabolomics approach based on (1)H NMR spectroscopy and multivariate statistical analysis. We performed a prospective study on 74 subjects: 37 suffering from pain (12 with nociceptive and 25 with neuropathic pain), and 37 controls not suffering from any kind of chronic pain. The application of discriminant analysis on the urine spectral profiles allowed us to classify these two types of pain with high sensibility and specificity. Although the classification relies on the global urine metabolic profile, the individual contribution in discriminating neuropathic pain patients of metabolites such as choline and phosphocholine, taurine and alanine, suggests potential lesions to the nervous system. To the best of our knowledge, this is the first time that a urine metabolomics profile is used to classify these two kinds of pain. This methodology, although based on a limited sample, may constitute the basis for a new helpful tool in the clinical diagnosis.

The purpose of this study was to evaluate the feasibility of a (1)H-NMR-based metabolomic approach to explore the metabolomic signature of different topographical areas of vitreous humor (VH) in an animal model. Five ocular globes were enucleated from five goats and immediately frozen at -80 °C. Once frozen, three of them were sectioned, and four samples corresponding to four different VH areas were collected: the cortical, core, and basal, which was further divided into a superior and an inferior fraction. An additional two samples were collected that were representative of the whole vitreous body. (1)H-NMR spectra were acquired for twenty-three goat vitreous samples with the aim of characterizing the metabolomic signature of this biofluid and identifying whether any site-specific patterns were present. Multivariate statistical analysis (MVA) of the spectral data were carried out, including Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Partial Least Squares Discriminant Analysis (PLS-DA). A unique metabolomic signature belonging to each area was observed. The cortical area was characterized by lactate, glutamine, choline, and its derivatives, N-acetyl groups, creatine, and glycerol; the core area was characterized by glucose, acetate, and scyllo-inositol; and the basal area was characterized by branched-chain amino acids (BCAA), betaine, alanine, ascorbate, lysine, and myo-inositol. We propose a speculative approach on the topographic role of these molecules that are mainly responsible for metabolic differences among the as-identified areas. (1)H-NMR-based metabolomic analysis has shown to be an important tool for investigating the VH. In particular, this approach was able to assess in the samples here analyzed the presence of different functional areas on the basis of a different metabolite distribution.