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.
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.
Authors: Alessandra Meloni; Francesco Palmas; Luigi Barberini; Rossella Mereu; Sara Francesca Deiana; Maria Francesca Fais; Antonio Noto; Claudia Fattuoni; Michele Mussap; Antonio Ragusa; Angelica Dessì; Roberta Pintus; Vassilios Fanos; Gian Benedetto Melis Journal: Dis Markers Date: 2018-02-04 Impact factor: 3.434
Authors: Samuel K Handelman; Roberto Romero; Adi L Tarca; Percy Pacora; Brian Ingram; Eli Maymon; Tinnakorn Chaiworapongsa; Sonia S Hassan; Offer Erez Journal: PLoS One Date: 2019-11-14 Impact factor: 3.240
Authors: Giovanni Monni; Luigi Atzori; Valentina Corda; Francesca Dessolis; Ambra Iuculano; K Joseph Hurt; Federica Murgia Journal: Front Med (Lausanne) Date: 2021-06-25
Authors: Kiana Ashley West; Chidimma Kanu; Tanya Maric; Julie Anne Kathryn McDonald; Jeremy K Nicholson; Jia V Li; Mark R Johnson; Elaine Holmes; Makrina D Savvidou Journal: Gut Date: 2020-01-21 Impact factor: 23.059