Objective:Currently there are few studies that may clearlyestablish scientific criteria for diagnosis of labour. We conducted aprospective cohort study to evaluate if metabolomic analysis ofurine samples collected from term pregnant women couldrepresent a new tool for diagnosis of labour.Methods:Urine samples were collected from 45 women withphysiological singleton pregnancy at 37–42 weeks of gestationreferred to our delivery settings for term pregnancy monitoring oradmitted for labour. Written informed consent was obtained fromeach woman. After aliquoting samples were stored at)80°C.Urines were chemically analysed by means of GC/MS and 1H-NMR techniques as described before.1Data matrix generated bythese procedures were analysed by means of PLS-DA models(Partial least squares discriminant analysis;SIMCA-P+ version12.0, Umetrics, Sweden) to test the hypothesis of thediscriminating power of urinary metabolites concentrations aslabour starts. The analysis on the variables of primary importancein this separation was applied to evaluate the set of discriminatingmetabolites.Results:PLS-DA models from both 1H-NMR and GC/MSanalysis were able to discriminate between the pregnant women inlabour state (n= 20) from the ones far from the delivery (n= 25)[PLS-DA_NMR (R2X = 0.46;R2Y = 0.753Q2= 0.573;P-value = 0.002); PLS-DA_GC_MS (R2X = 0.3;R2Y = 0.8;Q2= 0.4;P-value = 0.005)]. A metabolic fingerprint based mainly on:succinate, hyppurate, creatinine, alanine, X267, hydroxybutanoicacid, X175, hetanedioic acid, ribonic acid, glicine, galactose,xilitol, was identified as metabolic pattern responsible of thisseparation.Conclusions:Wrong diagnosis of labour can lead to inadequatemanagement of labour with possible adverse maternal outcomeand fetal distress. Correct diagnosis of labour can reducecaesarean sections and instrumental delivery rate and improveperinatal outcomes. Our experimental data confirm the hypothesisthat urine is a biological fluid adequate to identify pregnantwomen in labour. Collecting urine is a simple and non invasivetechnique, and it is extremely relevant from a clinical point ofview because it makes possible for the first time to diagnoselabour prospectively. Moreover by the identification of thediscriminating metabolites we may better understand themolecular pathways of labour.

Metabolomic approach to diagnosis of labour

Ragusa A.
2012-01-01

Abstract

Objective:Currently there are few studies that may clearlyestablish scientific criteria for diagnosis of labour. We conducted aprospective cohort study to evaluate if metabolomic analysis ofurine samples collected from term pregnant women couldrepresent a new tool for diagnosis of labour.Methods:Urine samples were collected from 45 women withphysiological singleton pregnancy at 37–42 weeks of gestationreferred to our delivery settings for term pregnancy monitoring oradmitted for labour. Written informed consent was obtained fromeach woman. After aliquoting samples were stored at)80°C.Urines were chemically analysed by means of GC/MS and 1H-NMR techniques as described before.1Data matrix generated bythese procedures were analysed by means of PLS-DA models(Partial least squares discriminant analysis;SIMCA-P+ version12.0, Umetrics, Sweden) to test the hypothesis of thediscriminating power of urinary metabolites concentrations aslabour starts. The analysis on the variables of primary importancein this separation was applied to evaluate the set of discriminatingmetabolites.Results:PLS-DA models from both 1H-NMR and GC/MSanalysis were able to discriminate between the pregnant women inlabour state (n= 20) from the ones far from the delivery (n= 25)[PLS-DA_NMR (R2X = 0.46;R2Y = 0.753Q2= 0.573;P-value = 0.002); PLS-DA_GC_MS (R2X = 0.3;R2Y = 0.8;Q2= 0.4;P-value = 0.005)]. A metabolic fingerprint based mainly on:succinate, hyppurate, creatinine, alanine, X267, hydroxybutanoicacid, X175, hetanedioic acid, ribonic acid, glicine, galactose,xilitol, was identified as metabolic pattern responsible of thisseparation.Conclusions:Wrong diagnosis of labour can lead to inadequatemanagement of labour with possible adverse maternal outcomeand fetal distress. Correct diagnosis of labour can reducecaesarean sections and instrumental delivery rate and improveperinatal outcomes. Our experimental data confirm the hypothesisthat urine is a biological fluid adequate to identify pregnantwomen in labour. Collecting urine is a simple and non invasivetechnique, and it is extremely relevant from a clinical point ofview because it makes possible for the first time to diagnoselabour prospectively. Moreover by the identification of thediscriminating metabolites we may better understand themolecular pathways of labour.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/69097
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