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      The Emerging Role of Biomarkers in the Diagnosis of Gestational Diabetes Mellitus

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          Abstract

          Gestational diabetes mellitus (GDM) is a common complication of pregnancy; its rising incidence is a result of increased maternal obesity and older maternal age together with altered diagnostic criteria identifying a greater proportion of pregnant women with GDM. Its consequences are far-reaching, associated with poorer maternal and neonatal outcomes compared to non-GDM pregnancies, and GDM has implications for metabolic health in both mother and offspring. Objective markers to identify women at high risk for the development of GDM are useful to target therapy and potentially prevent its development. Established clinical risk factors for GDM include overweight/obesity, age, ethnicity, and family history of diabetes, though they lack specificity for its development. The addition of biomarkers to predictive models of GDM may improve the ability to identify women at risk of GDM prior to its development. These biomarkers reflect the pathophysiologic mechanisms of GDM involving insulin resistance, chronic inflammation, and altered placental function. In addition, the role of epigenetic changes in GDM pathogenesis highlights the complex interplay between genetic and environmental factors, potentially offering further refinement of the prediction of GDM risk. In this review, we will discuss the clinical challenges associated with the diagnosis of GDM and its current pathophysiologic basis, giving rise to potential biomarkers that may aid in its identification. While not yet validated for clinical use, we explore the possible clinical role of biomarkers in the future. We also explore novel diagnostic tools, including high throughput methodologies, that may have potential future application in the identification of women with GDM.

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          Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

          By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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            Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis

            OBJECTIVE To conduct a systematic review of cross-sectional and prospective human studies evaluating metabolite markers identified using high-throughput metabolomics techniques on prediabetes and type 2 diabetes. RESEARCH DESIGN AND METHODS We searched MEDLINE and EMBASE databases through August 2015. We conducted a qualitative review of cross-sectional and prospective studies. Additionally, meta-analyses of metabolite markers, with data estimates from at least three prospective studies, and type 2 diabetes risk were conducted, and multivariable-adjusted relative risks of type 2 diabetes were calculated per study-specific SD difference in a given metabolite. RESULTS We identified 27 cross-sectional and 19 prospective publications reporting associations of metabolites and prediabetes and/or type 2 diabetes. Carbohydrate (glucose and fructose), lipid (phospholipids, sphingomyelins, and triglycerides), and amino acid (branched-chain amino acids, aromatic amino acids, glycine, and glutamine) metabolites were higher in individuals with type 2 diabetes compared with control subjects. Prospective studies provided evidence that blood concentrations of several metabolites, including hexoses, branched-chain amino acids, aromatic amino acids, phospholipids, and triglycerides, were associated with the incidence of prediabetes and type 2 diabetes. We meta-analyzed results from eight prospective studies that reported risk estimates for metabolites and type 2 diabetes, including 8,000 individuals of whom 1,940 had type 2 diabetes. We found 36% higher risk of type 2 diabetes per study-specific SD difference for isoleucine (pooled relative risk 1.36 [1.24–1.48]; I 2 = 9.5%), 36% for leucine (1.36 [1.17–1.58]; I 2 = 37.4%), 35% for valine (1.35 [1.19–1.53]; I 2 = 45.8%), 36% for tyrosine (1.36 [1.19–1.55]; I 2 = 51.6%), and 26% for phenylalanine (1.26 [1.10–1.44]; I 2 = 56%). Glycine and glutamine were inversely associated with type 2 diabetes risk (0.89 [0.81–0.96] and 0.85 [0.82–0.89], respectively; both I 2 = 0.0%). CONCLUSIONS In studies using high-throughput metabolomics, several blood amino acids appear to be consistently associated with the risk of developing type 2 diabetes.
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              High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia.

              The role of intrauterine hyperglycemia and future risk of type 2 diabetes in human offspring is debated. We studied glucose tolerance in adult offspring of women with either gestational diabetes mellitus (GDM) or type 1 diabetes, taking the impact of both intrauterine hyperglycemia and genetic predisposition to type 2 diabetes into account. The glucose tolerance status following a 2-h 75-g oral glucose tolerance test (OGTT) was evaluated in 597 subjects, primarily Caucasians, aged 18-27 years. They were subdivided into four groups according to maternal glucose metabolism during pregnancy and genetic predisposition to type 2 diabetes: 1) offspring of women with diet-treated GDM (O-GDM), 2) offspring of genetically predisposed women with a normal OGTT (O-NoGDM), 3) offspring of women with type 1 diabetes (O-type 1), and 4) offspring of women from the background population (O-BP). The prevalence of type 2 diabetes and pre-diabetes (impaired glucose tolerance or impaired fasting glucose) in the four groups was 21, 12, 11, and 4%, respectively. In multiple logistic regression analysis, the adjusted odds ratios (ORs) for type 2 diabetes/pre-diabetes were 7.76 (95% CI 2.58-23.39) in O-GDM and 4.02 (1.31-12.33) in O-type 1 compared with O-BP. In O-type 1, the risk of type 2 diabetes/pre-diabetes was significantly associated with elevated maternal blood glucose in late pregnancy: OR 1.41 (1.04-1.91) per mmol/l. A hyperglycemic intrauterine environment appears to be involved in the pathogenesis of type 2 diabetes/pre-diabetes in adult offspring of primarily Caucasian women with either diet-treated GDM or type 1 diabetes during pregnancy.
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                Author and article information

                Journal
                J Clin Med
                J Clin Med
                jcm
                Journal of Clinical Medicine
                MDPI
                2077-0383
                23 May 2018
                June 2018
                : 7
                : 6
                : 120
                Affiliations
                [1 ]Department of Diabetes, Endocrinology & Metabolism, Royal North Shore Hospital, St Leonards, Sydney 2065, Australia; natassia.rodrigo@ 123456sydney.edu.au
                [2 ]The Kolling Institute of Medical Research, St Leonards, Sydney 2065, Australia
                [3 ]Faculty of Medicine, The University of Sydney, Sydney 2006, Australia
                Author notes
                [* ]Correspondence: sarah.glastras@ 123456sydney.edu.au ; Tel.: +61-2-9463-1680
                Author information
                https://orcid.org/0000-0002-9317-1348
                Article
                jcm-07-00120
                10.3390/jcm7060120
                6024961
                29882903
                250aa73e-8241-412b-9edf-a7657ae32bf5
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 April 2018
                : 14 May 2018
                Categories
                Review

                gestational diabetes mellitus,biomarkers,pathophysiology,predictive diagnosis

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