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      Transcriptomic alterations in the heart of non-obese type 2 diabetic Goto-Kakizaki rats

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          Abstract

          Background

          There is a spectacular rise in the global prevalence of type 2 diabetes mellitus (T2DM) due to the worldwide obesity epidemic. However, a significant proportion of T2DM patients are non-obese and they also have an increased risk of cardiovascular diseases. As the Goto-Kakizaki (GK) rat is a well-known model of non-obese T2DM, the goal of this study was to investigate the effect of non-obese T2DM on cardiac alterations of the transcriptome in GK rats.

          Methods

          Fasting blood glucose, serum insulin and cholesterol levels were measured at 7, 11, and 15 weeks of age in male GK and control rats. Oral glucose tolerance test and pancreatic insulin level measurements were performed at 11 weeks of age. At week 15, total RNA was isolated from the myocardium and assayed by rat oligonucleotide microarray for 41,012 genes, and then expression of selected genes was confirmed by qRT-PCR. Gene ontology and protein–protein network analyses were performed to demonstrate potentially characteristic gene alterations and key genes in non-obese T2DM.

          Results

          Fasting blood glucose, serum insulin and cholesterol levels were significantly increased, glucose tolerance and insulin sensitivity were significantly impaired in GK rats as compared to controls. In hearts of GK rats, 204 genes showed significant up-regulation and 303 genes showed down-regulation as compared to controls according to microarray analysis. Genes with significantly altered expression in the heart due to non-obese T2DM includes functional clusters of metabolism (e.g. Cyp2e1, Akr1b10), signal transduction (e.g. Dpp4, Stat3), receptors and ion channels (e.g. Sln, Chrng), membrane and structural proteins (e.g. Tnni1, Mylk2, Col8a1, Adam33), cell growth and differentiation (e.g . Gpc3, Jund), immune response (e.g. C3, C4a), and others (e.g. Lrp8, Msln, Klkc1, Epn3). Gene ontology analysis revealed several significantly enriched functional inter-relationships between genes influenced by non-obese T2DM. Protein–protein interaction analysis demonstrated that Stat is a potential key gene influenced by non-obese T2DM.

          Conclusions

          Non-obese T2DM alters cardiac gene expression profile. The altered genes may be involved in the development of cardiac pathologies and could be potential therapeutic targets in non-obese T2DM.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s12933-016-0424-3) contains supplementary material, which is available to authorized users.

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          Most cited references77

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          Global prevalence of diabetes: estimates for the year 2000 and projections for 2030.

          The goal of this study was to estimate the prevalence of diabetes and the number of people of all ages with diabetes for years 2000 and 2030. Data on diabetes prevalence by age and sex from a limited number of countries were extrapolated to all 191 World Health Organization member states and applied to United Nations' population estimates for 2000 and 2030. Urban and rural populations were considered separately for developing countries. The prevalence of diabetes for all age-groups worldwide was estimated to be 2.8% in 2000 and 4.4% in 2030. The total number of people with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030. The prevalence of diabetes is higher in men than women, but there are more women with diabetes than men. The urban population in developing countries is projected to double between 2000 and 2030. The most important demographic change to diabetes prevalence across the world appears to be the increase in the proportion of people >65 years of age. These findings indicate that the "diabetes epidemic" will continue even if levels of obesity remain constant. Given the increasing prevalence of obesity, it is likely that these figures provide an underestimate of future diabetes prevalence.
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            Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.

            Insulin resistance contributes to the pathophysiology of diabetes and is a hallmark of obesity, metabolic syndrome, and many cardiovascular diseases. Therefore, quantifying insulin sensitivity/resistance in humans and animal models is of great importance for epidemiological studies, clinical and basic science investigations, and eventual use in clinical practice. Direct and indirect methods of varying complexity are currently employed for these purposes. Some methods rely on steady-state analysis of glucose and insulin, whereas others rely on dynamic testing. Each of these methods has distinct advantages and limitations. Thus, optimal choice and employment of a specific method depends on the nature of the studies being performed. Established direct methods for measuring insulin sensitivity in vivo are relatively complex. The hyperinsulinemic euglycemic glucose clamp and the insulin suppression test directly assess insulin-mediated glucose utilization under steady-state conditions that are both labor and time intensive. A slightly less complex indirect method relies on minimal model analysis of a frequently sampled intravenous glucose tolerance test. Finally, simple surrogate indexes for insulin sensitivity/resistance are available (e.g., QUICKI, HOMA, 1/insulin, Matusda index) that are derived from blood insulin and glucose concentrations under fasting conditions (steady state) or after an oral glucose load (dynamic). In particular, the quantitative insulin sensitivity check index (QUICKI) has been validated extensively against the reference standard glucose clamp method. QUICKI is a simple, robust, accurate, reproducible method that appropriately predicts changes in insulin sensitivity after therapeutic interventions as well as the onset of diabetes. In this Frontiers article, we highlight merits, limitations, and appropriate use of current in vivo measures of insulin sensitivity/resistance.
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              Guidelines for healthy weight.

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                Author and article information

                Contributors
                sarkozy.marta@med.u-szeged.hu
                szucs.gergo@med.u-szeged.hu
                veronikafeketedr@gmail.com
                pipicz.marton@med.u-szeged.hu
                ederkati@gmail.com
                gaspar.renata@med.u-szeged.hu
                soja.andrea@med.u-szeged.hu
                judit.pipis@pharmahungary.com
                peter.ferdinandy@pharmahungary.com
                csonka.csaba@med.u-szeged.hu
                +36 62 545096 , csont.tamas@med.u-szeged.hu
                Journal
                Cardiovasc Diabetol
                Cardiovasc Diabetol
                Cardiovascular Diabetology
                BioMed Central (London )
                1475-2840
                5 August 2016
                5 August 2016
                2016
                : 15
                : 110
                Affiliations
                [1 ]Department of Biochemistry, Faculty of Medicine, University of Szeged, Dóm tér 9, Szeged, 6720 Hungary
                [2 ]Department of Physiology, Anatomy and Neuroscience, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
                [3 ]Department of Genetics, Cell- and Immunobiology, Semmelweis University, Budapest, Hungary
                [4 ]Pharmahungary Group, Szeged, Hungary
                [5 ]Department of Pharmacology and Pharmacotherapy, Semmelweis University, Budapest, Hungary
                Article
                424
                10.1186/s12933-016-0424-3
                4975916
                27496100
                d56460da-304f-4fab-a532-0bb4e65798ee
                © The Author(s) 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 11 April 2016
                : 14 July 2016
                Funding
                Funded by: Anyos Jedlik Program
                Award ID: MED_FOOD TECH_08-A1-2008-0275
                Award Recipient :
                Funded by: Gabor Baross Program
                Award ID: DA_TECH_07-METABBET
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100003549, Országos Tudományos Kutatási Alapprogramok;
                Award ID: OTKA K115990
                Award Recipient :
                Categories
                Original Investigation
                Custom metadata
                © The Author(s) 2016

                Endocrinology & Diabetes
                spontaneous diabetes mellitus,inherited diabetes mellitus,non-obese type 2 diabetes mellitus,myocardium,dna microarray,go,string,insulin resistance

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