RESUMO
Since pathologies and complications occurring during pregnancy and/or during labour may cause adverse outcomes for both newborns and mothers, there is a growing interest in metabolomic applications on pregnancy investigation. In fact, metabolomics has proved to be an efficient strategy for the description of several perinatal conditions. In particular, this study focuses on premature rupture of membranes (PROM) in pregnancy at term. For this project, urine samples were collected at three different clinical conditions: out of labour before PROM occurrence (Ph1), out of labour with PROM (Ph2), and during labour with PROM (Ph3). GC-MS analysis, followed by univariate and multivariate statistical analysis, was able to discriminate among the different classes, highlighting the metabolites most involved in the discrimination.
Assuntos
Ruptura Prematura de Membranas Fetais/diagnóstico , Ruptura Prematura de Membranas Fetais/urina , Metaboloma , Adulto , Análise de Variância , Análise Discriminante , Feminino , Ruptura Prematura de Membranas Fetais/patologia , Cromatografia Gasosa-Espectrometria de Massas , Humanos , Recém-Nascido , Metabolômica/métodos , Projetos Piloto , Gravidez , Nascimento a Termo/urinaRESUMO
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 (1)H-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.