45
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Association between DNA Methylation in Whole Blood and Measures of Glucose Metabolism: KORA F4 Study

      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

          Epigenetic regulation has been postulated to affect glucose metabolism, insulin sensitivity and the risk of type 2 diabetes. Therefore, we performed an epigenome-wide association study for measures of glucose metabolism in whole blood samples of the population-based Cooperative Health Research in the Region of Augsburg F4 study using the Illumina HumanMethylation 450 BeadChip. We identified a total of 31 CpG sites where methylation level was associated with measures of glucose metabolism after adjustment for age, sex, smoking, and estimated white blood cell proportions and correction for multiple testing using the Benjamini-Hochberg (B-H) method (four for fasting glucose, seven for fasting insulin, 25 for homeostasis model assessment-insulin resistance [HOMA-IR]; B-H-adjusted p-values between 9.2x10 -5 and 0.047). In addition, DNA methylation at cg06500161 (annotated to ABCG1) was associated with all the aforementioned phenotypes and 2-hour glucose (B-H-adjusted p-values between 9.2x10 -5 and 3.0x10 -3). Methylation status of additional three CpG sites showed an association with fasting insulin only after additional adjustment for body mass index (BMI) (B-H-adjusted p-values = 0.047). Overall, effect strengths were reduced by around 30% after additional adjustment for BMI, suggesting that this variable has an influence on the investigated phenotypes. Furthermore, we found significant associations between methylation status of 21 of the aforementioned CpG sites and 2-hour insulin in a subset of samples with seven significant associations persisting after additional adjustment for BMI. In a subset of 533 participants, methylation of the CpG site cg06500161 ( ABCG1) was inversely associated with ABCG1 gene expression (B-H-adjusted p-value = 1.5x10 -9). Additionally, we observed an enrichment of the top 1,000 CpG sites for diabetes-related canonical pathways using Ingenuity Pathway Analysis. In conclusion, our study indicates that DNA methylation and diabetes-related traits are associated and that these associations are partially BMI-dependent. Furthermore, the interaction of ABCG1 with glucose metabolism is modulated by epigenetic processes.

          Related collections

          Most cited references58

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

          High density DNA methylation array with single CpG site resolution.

          We have developed a new generation of genome-wide DNA methylation BeadChip which allows high-throughput methylation profiling of the human genome. The new high density BeadChip can assay over 480K CpG sites and analyze twelve samples in parallel. The innovative content includes coverage of 99% of RefSeq genes with multiple probes per gene, 96% of CpG islands from the UCSC database, CpG island shores and additional content selected from whole-genome bisulfite sequencing data and input from DNA methylation experts. The well-characterized Infinium® Assay is used for analysis of CpG methylation using bisulfite-converted genomic DNA. We applied this technology to analyze DNA methylation in normal and tumor DNA samples and compared results with whole-genome bisulfite sequencing (WGBS) data obtained for the same samples. Highly comparable DNA methylation profiles were generated by the array and sequencing methods (average R2 of 0.95). The ability to determine genome-wide methylation patterns will rapidly advance methylation research. Copyright © 2011 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Diabetes in Europe: an update.

              Diabetes is among the leading causes of death in the IDF Europe Region (EUR), continues to increase in prevalence with diabetic macro- and microvascular complications resulting in increased disability and enormous healthcare costs. In 2013, the number of people with diabetes is estimated to be 56 million in EUR with an overall estimated prevalence of 8.5%. However, estimates of diabetes prevalence in 2013 vary widely in the 56 diverse countries in EUR from 2.4% in Moldova to 14.9% in Turkey. Trends in diabetes prevalence also vary between countries with stable prevalence since 2002 for many countries but a doubling of diabetes prevalence in Turkey. For 2035, a further increase of nearly 10 million people with diabetes is projected for the EUR. Prevalence of type 1 has also increased over the past 20 years in EUR and there was estimated to be 129,350 cases in children aged 0-14 years in 2013. Registries provide valid information on incidence of type 1 diabetes with more complete data available for children than for adults. There are large differences in distribution of risk factors for diabetes at the population level in EUR. Modifiable risk factors such as obesity, physical inactivity, smoking behaviour (including secondhand smoking), environmental pollutants, psychosocial factors and socioeconomic deprivation could be tackled to reduce the incidence of type 2 diabetes in Europe. In addition, diabetes management is a major challenge to health services in the European countries. Improved networking practices of health professionals and other stakeholders in combination with empowerment of people with diabetes and continuous quality monitoring need to be further developed in Europe. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 March 2016
                2016
                : 11
                : 3
                : e0152314
                Affiliations
                [1 ]Research Unit of Molecular Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
                [2 ]Institute of Epidemiology II, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
                [3 ]German Center for Diabetes Research (DZD), Muenchen-Neuherberg, Germany
                [4 ]Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Duesseldorf, Duesseldorf, Germany
                [5 ]Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Duesseldorf, Duesseldorf, Germany
                [6 ]Institute of Human Genetics, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, Neuherberg, Germany
                [7 ]Institute of Human Genetics, Technische Universitaet Muenchen, Munich, Germany
                [8 ]Hannover Unified Biobank, Hannover Medical School, Hanover, Germany
                [9 ]Institute of Human Genetics, Hannover Medical School, Hanover, Germany
                [10 ]Department of Endocrinology and Diabetology, University Hospital Duesseldorf, Duesseldorf, Germany
                CEA - Institut de Genomique, FRANCE
                Author notes

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

                Conceived and designed the experiments: JK CH WR CG AP TI HP MR HG. Performed the experiments: JK SK. Analyzed the data: JK SW SM NV KS MCK. Contributed reagents/materials/analysis tools: CH WR MW CG AP HP HG. Wrote the paper: JK CH WR SW SK SM NV KS MCK MW CG AP TI HP MR HG.

                ‡ These authors also contributed equally to this work.

                Article
                PONE-D-15-44480
                10.1371/journal.pone.0152314
                4809492
                27019061
                f301d0c9-d641-488a-aad2-7a35786042bf
                © 2016 Kriebel et al

                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
                : 9 October 2015
                : 11 March 2016
                Page count
                Figures: 2, Tables: 7, Pages: 25
                Funding
                The KORA study was initiated and financed by the Helmholtz Zentrum München – German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. Furthermore, KORA research was supported within the Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universität, as part of LMUinnovativ. This work was supported by the Ministry of Science and Research of the State of North Rhine-Westphalia (MIWF NRW) and the German Federal Ministry of Health (BMG). The diabetes part of the KORA F4 study was funded by a grant from the German Research Foundation (DFG; RA 459/3-1). This study was supported by the German Center for Diabetes Research (DZD e.V.).
                Categories
                Research Article
                Biology and life sciences
                Cell biology
                Chromosome biology
                Chromatin
                Chromatin modification
                DNA methylation
                Biology and life sciences
                Genetics
                Epigenetics
                Chromatin
                Chromatin modification
                DNA methylation
                Biology and life sciences
                Genetics
                Gene expression
                Chromatin
                Chromatin modification
                DNA methylation
                Biology and life sciences
                Genetics
                DNA
                DNA modification
                DNA methylation
                Biology and life sciences
                Biochemistry
                Nucleic acids
                DNA
                DNA modification
                DNA methylation
                Biology and life sciences
                Genetics
                Epigenetics
                DNA modification
                DNA methylation
                Biology and life sciences
                Genetics
                Gene expression
                DNA modification
                DNA methylation
                Medicine and Health Sciences
                Endocrinology
                Diabetic Endocrinology
                Insulin
                Biology and Life Sciences
                Biochemistry
                Hormones
                Insulin
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Metabolic Disorders
                Diabetes Mellitus
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Carbohydrate Metabolism
                Glucose Metabolism
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Cell Biology
                Signal Transduction
                Cell Signaling
                Glucose Signaling
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Medicine and Health Sciences
                Endocrinology
                Endocrine Physiology
                Insulin Signaling
                Biology and Life Sciences
                Physiology
                Endocrine Physiology
                Insulin Signaling
                Medicine and Health Sciences
                Physiology
                Endocrine Physiology
                Insulin Signaling
                Custom metadata
                Data are subject to national data protection laws and only available upon formal request. The KORA data are easily accessible via the online portal KORA.passt. ( https://epi.helmholtz-muenchen.de/).

                Uncategorized
                Uncategorized

                Comments

                Comment on this article