Metabolic profiling of maternal urine can aid clinical management of gestational diabetes mellitus

• Type: Article in International Scientific Journal
• Date: 2016

Authors

Pinto, J (Author) Other; Diaz, SO (Author) Other; Aguiar, E (Author) Other; Duarte, D (Author) Other; Barros, AS (Author) Other; Galhano, E (Author) Other; Pita, C (Author) Other; Almeida, MD (Author) Other; Carreira, IM (Author) Other; Spraul, M (Author) Other; Gil, AM (Author) Other

Journal

Title: Metabolomics

Vol. 12

ISSN: 1573-3882

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00K-GKY

DOI: 10.1007/s11306-016-1046-1

Abstract (EN): Introduction The clinical management of Gestational diabetes mellitus (GDM) would benefit from enhanced metabolic knowledge both at the time of diagnosis and during therapy. Objectives This work aimed at unveiling metabolic markers of GDM and of the subjects' response to therapy. Methods Urine NMR metabolomics was used with a variable selection methodology to reduce uninformative variability. The NMR data was analysed by multivariate and univariate analysis methodologies. Results The results showed that urine NMR metabolomics enables a metabolic signature of GDM to be identified at the time of diagnosis. This signature comprises relevant changes in 12 NMR metabolites/resonances and qualitative variations in a number of additional metabolites. The metabolite changes characterizing GDM suggest adaptations in a number of different pathways and highlight the relevance of gut microflora disturbances in relation to the disease. The impact of diet and insulin treatments on the excreted metabolome of pregnant GDM women was measured and enabled responsive and resistant metabolic pathways to be identified, as well as side-effects of treatment i.e. metabolic changes induced by treatment and previously unrelated to the disease (including changes in the gut microflora). Furthermore, treatment duration was found to be associated to urine metabolic profile, thus emphasizing the possible future use of urine metabolomics in treatment follow-up and efficacy evaluation. Finally, a possible association of a priori urinary metabolome with future treatment requirements is reported, albeit requiring demonstration in larger cohorts. This result supports the hypothesis of different metabotypes characterizing different subjects and relating to individual response to treatment. Conclusion A 12-resonance metabolic signature of GDN at the time of diagnosis was identified and the evaluation of the impact of insulin and/or diet therapies enabled responsive/resistant metabolic pathways and treatment side-effects to be identified.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12