Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Pinto, J (Author) Other; Almeida, LM (Author) Other; Martins, AS (Author) Other; Duarte, D (Author) Other; Barros, AS (Author) Other; Gahano, E (Author) Other; Pita, C (Author) Other; Almeida, MD (Author) Other; Carreira, IM (Author) Other; Gil, AM (Author) Other

Journal

Title: Journal of Proteome Research

Vol. 14

Pages: 2696-2706

ISSN: 1535-3893

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 69 Citations

Scopus - 76 Citations

Other information

Authenticus ID: P-00G-B1Q

DOI: 10.1021/acs.jproteome.5b00260

Abstract (EN): Metabolic biomarkers of pre- and postdiagnosis gestational diabetes mellitus (GDM) were sought, using nuclear magnetic resonance (NMR) metabolomics of maternal plasma and corresponding lipid extracts. Metabolite differences between controls and disease were identified through multivariate analysis of variable selected H-1 NMR spectra. For postdiagnosis GDM, partial least squares regression identified metabolites with higher dependence on normal gestational age evolution. Variable selection of NMR spectra produced good classification Models for both pre- and postdiagnostic GDM. Prediagnosis GDM was accompanied by cholesterol increase and minor increases in lipoproteins (plasma), fatty acids, and triglycerides. (extracts). Small metabolite changes comprised variations in glucose (up regulated), amino acids, betaine, urea, creatine, and metabolites related to gut microflora. Most changes were enhanced upon GDM diagnosis, in addition to newly observed changes in low-M-w compounds. GDM prediction seems possible exploiting multivariate profile changes rather than a,set of univariate changes. Postdiagnosis GDM is successfully classified using a 26-resonance plasma biomarker. Plasma and extracts display comparable classification performance, the former enabling direct and more rapid analysis. Results and putative biochemical hypotheses require further,confirmation in larger cohorts of distinct ethnicities.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11