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Background: Physiological changes leading to parturition are not completely understood while clinical diagnosis of labour is still retrospective. Gas chromatography mass spectrometry (GC/MS) and nuclear magnetic resonance spectroscopy (NMR) represent two of the main analytical platforms used in clinical metabolomics. Metabolomics might help us to improve our knowledge about the biochemical mechanisms underlying labour. Methods: Urine samples ( n = 59), collected from pregnant women at term of gestation before and/or after the onset of labour, were analysed by GC/MS and NMR techniques in order to identify the metabolic profile. Both GC/MS and NMR data matrices containing the identified metabolites were analysed by multivariate statistical techniques in order to characterise the discriminant variables between labour (L) and not labour (NL) status. Results: 18 potential metabolites (11 with 1H-NMR, eight with GC-MS: glycine was relevant in both) were found discriminant in urine of women during labour. Taken together, the identified metabolites produced a composite biomarker pattern, a sort of barcode, capable of differentiating between labour and not labour conditions. Major discriminant metabolites for NMR and GC/MS analysis were: alanine, glycine, acetone, 3-hydroxybutiyric acid, 2,3,4-trihydroxybutyric acid and succinic acid, giving a urine metabolite signature on the late phase of labour. Conclusions: The metabolomics analysis evidenced clusters of metabolites involved in labour condition able to discriminate between urine samples collected before the onset and during labour, potentially offering the promise of a robust screening test. [ABSTRACT FROM AUTHOR]

In this feasibility study, we propose, for the first time, 1H NMR spectroscopy coupled with mathematical strategies as a valid tool for body fluid (BF) trace identification in forensic science. In order to assess the ability of this approach to identify traces composed either by a single or by two different BFs, samples of blood, urine, saliva, and semen were collected from different donors, and binary mixtures were prepared. 1H NMR analyses were carried out for all samples. Spectral data of the whole set were firstly submitted to unsupervised principal component analysis (PCA); it showed that samples of the same BF cluster well on the basis of their characterizing molecular components and that mixtures exhibit intermediate characteristics among BF typologies. Furthermore, samples were divided into a training set and a test set. An average NMR spectral profile for each typology of BF was obtained from the training set and validated as representative of each BF class. Finally, a fitting procedure, based on a system of linear equations with the four obtained average spectral profiles, was applied to the test set and the mixture samples; it showed that BFs can be unambiguously identified, even as components of a mixture. The successful use of this mathematical procedure has the advantage, in forensics, of overcoming bias due to the analyst's personal judgment. We therefore propose this combined approach as a valid, fast, and non-destructive tool for addressing the challenges in the identification of composite traces in forensics. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Objective: The aim of the present study was to investigate the metabolic profile of preterm human breast milk (HBM) by using a metabolomic approach. Methods: NMR spectroscopy and GC/MS were used to analyze the water-soluble and lipid fractions extracted from milk samples obtained from mothers giving birth at 26-36 weeks of gestation. For the sake of comparison, preterm formula milk was also studied. Results: The multivariate statistical analysis of the data evidenced biochemical variability both between preterm HBM and commercial milk and within the group of HBM samples. Conclusions: 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. [ABSTRACT FROM AUTHOR]

Bicuspid aortic valve (BAV) is the most common congenital heart defect responsible for valvular and aortic complications in affected patients. Causes and mechanisms of this pathology are still elusive and thus the lack of early detection biomarkers leads to challenges in its diagnosis and prevention of associated cardiovascular anomalies. The aim of this study was to explore the potential use of urine Nuclear Magnetic Resonance (NMR) metabolomics to evaluate a molecular fingerprint of BAV. Both multivariate and univariate statistical analyses were performed to compare the urinary metabolome of 20 patients with BAV with that of 24 matched controls. Orthogonal partial least squared discriminant analysis (OPLS-DA) showed statistically significant discrimination between cases and controls, suggesting seven metabolites (3-hydroxybutyrate, alanine, betaine, creatine, glycine, hippurate, and taurine) as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed medium sensitivity and specificity by receiver operating characteristic (ROC) analysis. Pathway analysis indicated two metabolic pathways likely perturbed in BAV subjects. Possible contributions of gut microbiota activity and energy imbalance are also discussed. These results constitute encouraging preliminary findings in favor of the use of urine-based metabolomics for early diagnosis of BAV. [ABSTRACT FROM AUTHOR]