7
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Insulin Sensitivity Is Associated with Lipoprotein Lipase ( LPL) and Catenin Delta 2 ( CTNND2) DNA Methylation in Peripheral White Blood Cells in Non-Diabetic Young Women

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Hyperglycaemia and type 2 diabetes (T2D) are associated with impaired insulin secretion and/or insulin action. Since few studies have addressed the relation between DNA methylation patterns with elaborated surrogates of insulin secretion/sensitivity based on the intravenous glucose tolerance test (IVGTT), the aim of this study was to evaluate the association between DNA methylation and an insulin sensitivity index based on IVGTT (calculated insulin sensitivity index (CSi)) in peripheral white blood cells from 57 non-diabetic female volunteers. The CSi and acute insulin response (AIR) indexes, as well as the disposition index (DI = CSi × AIR), were estimated from abbreviated IVGTT in 49 apparently healthy Chilean women. Methylation levels were assessed using the Illumina Infinium Human Methylation 450k BeadChip. After a statistical probe filtering, the two top CpGs whose methylation was associated with CSi were cg04615668 and cg07263235, located in the catenin delta 2 ( CTNND2) and lipoprotein lipase ( LPL) genes, respectively. Both CpGs conjointly predicted insulin sensitivity status with an area under the curve of 0.90. Additionally, cg04615668 correlated with homeostasis model assessment insulin-sensitivity (HOMA-S) and AIR, whereas cg07263235 was associated with plasma creatinine and DI. These results add further insights into the epigenetic regulation of insulin sensitivity and associated complications, pointing the CTNND2 and LPL genes as potential underlying epigenetic biomarkers for future risk of insulin-related diseases.

          Related collections

          Most cited references50

          • Record: found
          • Abstract: found
          • Article: not found

          The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of Type 2 diabetes.

          S E Kahn (2003)
          The relative contributions of insulin resistance and beta-cell dysfunction to the pathophysiology of Type 2 diabetes have been debated extensively. The concept that a feedback loop governs the interaction of the insulin-sensitive tissues and the beta cell as well as the elucidation of the hyperbolic relationship between insulin sensitivity and insulin secretion explains why insulin-resistant subjects exhibit markedly increased insulin responses while those who are insulin-sensitive have low responses. Consideration of this hyperbolic relationship has helped identify the critical role of beta-cell dysfunction in the development of Type 2 diabetes and the demonstration of reduced beta-cell function in high risk subjects. Furthermore, assessments in a number of ethnic groups emphasise that beta-cell function is a major determinant of oral glucose tolerance in subjects with normal and reduced glucose tolerance and that in all populations the progression from normal to impaired glucose tolerance and subsequently to Type 2 diabetes is associated with declining insulin sensitivity and beta-cell function. The genetic and molecular basis for these reductions in insulin sensitivity and beta-cell function are not fully understood but it does seem that body-fat distribution and especially intra-abdominal fat are major determinants of insulin resistance while reductions in beta-cell mass contribute to beta-cell dysfunction. Based on our greater understanding of the relative roles of insulin resistance and beta-cell dysfunction in Type 2 diabetes, we can anticipate advances in the identification of genes contributing to the development of the disease as well as approaches to the treatment and prevention of Type 2 diabetes.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Glucose clamp technique: a method for quantifying insulin secretion and resistance.

            Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique) are described. Hyperglycemic clamp technique. The plasma glucose concentration is acutely raised to 125 mg/dl above basal levels by a priming infusion of glucose. The desired hyperglycemic plateau is subsequently maintained by adjustment of a variable glucose infusion, based on the negative feedback principle. Because the plasma glucose concentration is held constant, the glucose infusion rate is an index of glucose metabolism. Under these conditions of constant hyperglycemia, the plasma insulin response is biphasic with an early burst of insulin release during the first 6 min followed by a gradually progressive increase in plasma insulin concentration. Euglycemic insulin clamp technique. The plasma insulin concentration is acutely raised and maintained at approximately 100 muU/ml by a prime-continuous infusion of insulin. The plasma glucose concentration is held constant at basal levels by a variable glucose infusion using the negative feedback principle. Under these steady-state conditions of euglycemia, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue sensitivity to exogenous insulin.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Complete pipeline for Infinium(®) Human Methylation 450K BeadChip data processing using subset quantile normalization for accurate DNA methylation estimation.

              Huge progress has been made in the development of array- or sequencing-based technologies for DNA methylation analysis. The Illumina Infinium(®) Human Methylation 450K BeadChip (Illumina Inc., CA, USA) allows the simultaneous quantitative monitoring of more than 480,000 CpG positions, enabling large-scale epigenotyping studies. However, the assay combines two different assay chemistries, which may cause a bias in the analysis if all signals are merged as a unique source of methylation measurement. We confirm in three 450K data sets that Infinium I signals are more stable and cover a wider dynamic range of methylation values than Infinium II signals. We evaluated the methylation profile of Infinium I and II probes obtained with different normalization protocols and compared these results with the methylation values of a subset of CpGs analyzed by pyrosequencing. We developed a subset quantile normalization approach for the processing of 450K BeadChips. The Infinium I signals were used as 'anchors' to normalize Infinium II signals at the level of probe coverage categories. Our normalization approach outperformed alternative normalization or correction approaches in terms of bias correction and methylation signal estimation. We further implemented a complete preprocessing protocol that solves most of the issues currently raised by 450K array users. We developed a complete preprocessing pipeline for 450K BeadChip data using an original subset quantile normalization approach that performs both sample normalization and efficient Infinium I/II shift correction. The scripts, being freely available from the authors, will allow researchers to concentrate on the biological analysis of data, such as the identification of DNA methylation signatures.
                Bookmark

                Author and article information

                Journal
                Int J Mol Sci
                Int J Mol Sci
                ijms
                International Journal of Molecular Sciences
                MDPI
                1422-0067
                15 June 2019
                June 2019
                : 20
                : 12
                : 2928
                Affiliations
                [1 ]Department of Nutrition, Food Sciences and Physiology and Centre for Nutrition Research, University of Navarra, 31008 Pamplona, Spain; aarpon.1@ 123456alumni.unav.es (A.A.); fmilagro@ 123456unav.es (F.I.M.); jiriezu@ 123456unav.es (J.-I.R.-B.); jalfmtz@ 123456unav.es (J.A.M.)
                [2 ]Department of Nutrition, Diabetes and Metabolism, School of Medicine, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile; rodrigo.cataldo_buscunan@ 123456med.lu.se (L.R.C.); carobravo.vasquez@ 123456gmail.com (C.B.)
                [3 ]Navarra Institute for Health Research (IdiSNa), 31008 Pamplona, Spain
                [4 ]Centro de Investigación Biomédica en Red Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, 28029 Madrid, Spain
                [5 ]Precision Nutrition and Cardiometabolic Health Program, Madrid Institute for Advanced Studies (IMDEA), IMDEA Food, 28049 Madrid, Spain
                Author notes
                [* ]Correspondence: jsantosm@ 123456uc.cl ; Tel.: +56-2-3543862-3865
                [†]

                These authors contributed equally to this work.

                [‡]

                These authors share senior authorship.

                Author information
                https://orcid.org/0000-0002-9508-0431
                https://orcid.org/0000-0002-3228-9916
                https://orcid.org/0000-0001-6712-2430
                https://orcid.org/0000-0002-1885-8457
                https://orcid.org/0000-0001-5218-6941
                Article
                ijms-20-02928
                10.3390/ijms20122928
                6627674
                31208038
                b2df81c3-1d7b-4844-ad15-cbb8756e2551
                © 2019 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
                : 10 May 2019
                : 12 June 2019
                Categories
                Article

                Molecular biology
                insulin resistance,diabetes,epigenetics,insulin sensitivity index,ewas
                Molecular biology
                insulin resistance, diabetes, epigenetics, insulin sensitivity index, ewas

                Comments

                Comment on this article