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      Metabolic Changes in Urine during and after Pregnancy in a Large, Multiethnic Population-Based Cohort Study of Gestational Diabetes

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          Abstract

          This study aims to identify novel markers for gestational diabetes (GDM) in the biochemical profile of maternal urine using NMR metabolomics. It also catalogs the general effects of pregnancy and delivery on the urine profile. Urine samples were collected at three time points (visit V1: gestational week 8–20; V2: week 28±2; V3∶10–16 weeks post partum) from participants in the STORK Groruddalen program, a prospective, multiethnic cohort study of 823 healthy, pregnant women in Oslo, Norway, and analyzed using 1H-NMR spectroscopy. Metabolites were identified and quantified where possible. PCA, PLS-DA and univariate statistics were applied and found substantial differences between the time points, dominated by a steady increase of urinary lactose concentrations, and an increase during pregnancy and subsequent dramatic reduction of several unidentified NMR signals between 0.5 and 1.1 ppm. Multivariate methods could not reliably identify GDM cases based on the WHO or graded criteria based on IADPSG definitions, indicating that the pattern of urinary metabolites above micromolar concentrations is not influenced strongly and consistently enough by the disease. However, univariate analysis suggests elevated mean citrate concentrations with increasing hyperglycemia. Multivariate classification with respect to ethnic background produced weak but statistically significant models. These results suggest that although NMR-based metabolomics can monitor changes in the urinary excretion profile of pregnant women, it may not be a prudent choice for the study of GDM.

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          Most cited references14

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          Gestational diabetes and the incidence of type 2 diabetes: a systematic review.

          To examine factors associated with variation in the risk for type 2 diabetes in women with prior gestational diabetes mellitus (GDM). We conducted a systematic literature review of articles published between January 1965 and August 2001, in which subjects underwent testing for GDM and then testing for type 2 diabetes after delivery. We abstracted diagnostic criteria for GDM and type 2 diabetes, cumulative incidence of type 2 diabetes, and factors that predicted incidence of type 2 diabetes. A total of 28 studies were examined. After the index pregnancy, the cumulative incidence of diabetes ranged from 2.6% to over 70% in studies that examined women 6 weeks postpartum to 28 years postpartum. Differences in rates of progression between ethnic groups was reduced by adjustment for various lengths of follow-up and testing rates, so that women appeared to progress to type 2 diabetes at similar rates after a diagnosis of GDM. Cumulative incidence of type 2 diabetes increased markedly in the first 5 years after delivery and appeared to plateau after 10 years. An elevated fasting glucose level during pregnancy was the risk factor most commonly associated with future risk of type 2 diabetes. Conversion of GDM to type 2 diabetes varies with the length of follow-up and cohort retention. Adjustment for these differences reveals rapid increases in the cumulative incidence occurring in the first 5 years after delivery for different racial groups. Targeting women with elevated fasting glucose levels during pregnancy may prove to have the greatest effect for the effort required.
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            750 MHz 1H and 1H-13C NMR spectroscopy of human blood plasma.

            High-resolution 750 MHz 1H NMR spectra of control human blood plasma have been measured and assigned by the concerted use of a range of spin-echo, two-dimensional J-resolved, and homonuclear and heteronuclear (1H-13C) correlation methods. The increased spectral dispersion and sensitivity at 750 MHz enable the assignment of numerous 1H and 13C resonances from many molecular species that cannot be detected at lower frequencies. This work presents the most comprehensive assignment of the 1H NMR spectra of blood plasma yet achieved and includes the assignment of signals from 43 low M(r) metabolites, including many with complex or strongly coupled spin systems. New assignments are also provided from the 1H and 13C NMR signals from several important macromolecular species in whole blood plasma, i.e., very-low-density, low-density, and high-density lipoproteins, albumin, and alpha 1-acid glycoprotein. The temperature dependence of the one-dimensional and spin-echo 750 MHz 1H NMR spectra of plasma was investigated over the range 292-310 K. The 1H NMR signals from the fatty acyl side chains of the lipoproteins increased substantially with temperature (hence also molecular mobility), with a disproportionate increase from lipids in low-density lipoprotein. Two-dimensional 1H-13C heteronuclear multiple quantum coherence spectroscopy at 292 and 310 K allowed both the direct detection of cholesterol and choline species bound in high-density lipoprotein and the assignment of their signals and confirmed the assignment of most of the lipoprotein resonances.
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              Computational strategies for metabolite identification in metabolomics.

              Most metabolomic data are characterized by complex spectra or chromatograms containing hundreds of peaks or features. While there are many methods for aligning or comparing these spectral features, there are few approaches for actually identifying which peaks match to which compounds. Indeed, one of the biggest unmet needs in the field of metabolomics lies in the problem of compound identification. This review describes some of the newly emerging computational strategies in metabolomics that are being used to aid in the identification of metabolites from biofluid mixtures analyzed by NMR and MS. The most successful compound-identification strategies typically involve matching spectral features of the unknown compound(s) to curated spectral databases of reference compounds. This approach is known as the identification of 'known unknowns'. However, the identification of truly novel compounds (the 'unknown unknowns') is particularly challenging and requires the use of computer-aided structure elucidation methods being applied to the purified compound. The strengths and limitations of these approaches as applied to different analytical technologies (GC-MS, LC-MS and NMR) will be discussed, as will prospects for potential improvements to existing strategies.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                21 December 2012
                : 7
                : 12
                : e52399
                Affiliations
                [1 ]Department of Medical Biochemistry, University of Oslo, Oslo, Norway
                [2 ]Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway
                [3 ]Department of Chemistry, University of Oslo, Oslo, Norway
                [4 ]Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, Norway
                [5 ]Department of Child and Adolescents Medicine, Akershus University Hospital, Lørenskog, Norway
                [6 ]Department of Endocrinology, Morbid Obesity and Preventive Medicine, University of Oslo, Oslo, Norway
                [7 ]Department of General Practice, University of Oslo, Oslo, Norway
                [8 ]Oslo and Akershus University College of Applied Sciences, Oslo, Norway
                [9 ]Fürst Medical Laboratory, Oslo, Norway
                The Ohio State University, United States of America
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: DS AKJ KIB APP JPB. Performed the experiments: DS LS KM FR. Analyzed the data: DS LS KM FR. Contributed reagents/materials/analysis tools: AKJ FR. Wrote the paper: DS LS KM AKJ KIB FR APP JPB.

                Article
                PONE-D-12-18149
                10.1371/journal.pone.0052399
                3528643
                23285025
                414a3b99-9823-4120-8092-0dacdba28ef1
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 June 2012
                : 13 November 2012
                Page count
                Pages: 12
                Funding
                The study was supported by grants from the University of Oslo and the Oslo Diabetes Research Centre. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Chemistry
                Applied Chemistry
                Chemical Properties
                Nuclear Magnetic Resonance
                Medicine
                Diagnostic Medicine
                Pathology
                General Pathology
                Biomarkers
                Endocrinology
                Diabetic Endocrinology
                Gestational Diabetes
                Obstetrics and Gynecology
                Pregnancy
                Pregnancy Complications

                Uncategorized
                Uncategorized

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