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 H-1-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.

Urinary metabolomics of pregnant women at term: A combined GC/MS and NMR approach

Ragusa A.;
2014-01-01

Abstract

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 H-1-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.
2014
GC/MS
NMR
labour
metabolomics
urine
Biomarkers
Female
Gas Chromatography-Mass Spectrometry
Humans
Italy
Magnetic Resonance Spectroscopy
Metabolomics
Pregnancy
Term Birth
File in questo prodotto:
File Dimensione Formato  
Urinary metabolomics of pregnant women at term A combined GC MS and NMR approach.pdf

solo utenti autorizzati

Licenza: Copyright dell'editore
Dimensione 893.33 kB
Formato Adobe PDF
893.33 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/69087
Citazioni
  • ???jsp.display-item.citation.pmc??? 10
  • Scopus 12
  • ???jsp.display-item.citation.isi??? 12
social impact