Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity

  1. on behalf of the MAGIC investigators
  1. 1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.
  2. 2Alexander Fleming, Biomedical Sciences Research Center, 34 Fleming Street, Vari, 16672 Athens, Greece.
  3. 3Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, UK, OX3 7LJ.
  4. 4MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, UK.
  5. 5Department of Medicine and Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
  6. 6Estonian Genome Center, University of Tartu, Tartu, 51010, Estonia.
  7. 7Department of Medicine, Université de Sherbrooke, Sherbrooke (Quebec), Canada.
  8. 8General Medicine Division, Massachusetts General Hospital, Boston,Massachusetts, USA.
  9. 9Department of Biology and Evolution, University of Ferrara, Ferrara, Italy.
  10. 10Boston University Data Coordinating Center, Boston, Massachusetts, MA 02118, USA.
  11. 11Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA.
  12. 12Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
  13. 13Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
  14. 14Charité-Universitätsmedizin Berlin, Department of Endocrinology and Metabolism.
  15. 15Steno Diabetes Center, Gentofte, Denmark.
  16. 16The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
  17. 17Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, NY 10029-6574, USA.
  18. 18Department of Integrative Biology and Physiology, University of California, 610 Charles E. Young Dr. East, Los Angeles, CA 90095, USA.
  19. 19CNRS UMR8199-Institute of Biology, Pasteur Institute, Lille 2-Droit et Santé University, Lille, France.
  20. 20Wellcome Trust Sanger Institute, Hinxton, CB10 1SA, UK.
  21. 21University of Cambridge Metabolic Research Laboratories and NIHR Cambridge Biomedical Research Centre, Level 4, Institute of Metabolic Science, Box 289, Addenbrooke’s Hospital, Cambridge CB2 OQQ, UK.
  22. 22Genome Technology Branch, National Human Genome Research Institute, Bethesda, MD.
  23. 23Leipzig University Medical Center, IFB AdiposityDiseases, Liebigstr. 21, 04103 Leipzig, Germany.
  24. 24Department of Medicine III, Division of Prevention and Care of Diabetes, University of Dresden, 01307 Dresden, Germany.
  25. 25Interdisciplinary Center for Clinical Research (IZKF) Leipzig, Liebigstr. 21, 04103 Leipzig, Germany.
  26. 26Department of Medicine, University of Eastern Finland and Kuopio University Hospital, 70210 Kuopio, Finland.
  27. 27Department of Medicine, University of Leipzig, Liebigstr. 18, 04103 Leipzig, Germany.
  28. 28Department of Genomics of common diseases, Imperial College London, London, UK.
  29. 29Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, Canada.
  30. 30Department of Internal Medicine, Division of Endocrinology, Diabetology, Vascular Medicine, Nephrology and Clinical Chemistry, University of Tübingen, Tübingen, Germany.
  31. 31Lundberg Laboratory for Diabetes Research, Center of Excellence for Metabolic and Cardiovascular Research, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden.
  32. 32Department of Physiology & Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA.
  33. 33Department of Genetics, University of North Carolina Chapel Hill, North Carolina 27599, USA.
  34. 34Diabetes Prevention Unit, National Institute for Health and Welfare, 00271 Helsinki, Finland.
  35. 35Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria.
  36. 36King Abdulaziz University, Jeddah 21589, Saudi Arabia.
  37. 37Department of Medical Sciences, Uppsala University, Akademiska sjukhuset, Uppsala, Sweden.
  38. 38Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
  39. 39Hagedorn Research Institute, Copenhagen, Denmark.
  40. 40Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.
  41. 41Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark.
  42. 42Institute of Cellular Medicine, Newcastle University, UK.
  43. 43German Institute of Human Nutrition, Department of Clinical Nutrition.
  44. 44Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA.
  45. 45Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, MA 02118, USA.
  46. 46The National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts, USA.
  47. 47Departments of Preventive Medicine and Physiology & Biophysics, Keck School of Medicine of USC, Los Angeles, CA 90033, USA.
  48. 48Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
  49. 49Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA.
  50. 50Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK.
  1. Corresponding author: Inga Prokopenko, E-mail: i.prokopenko{at} Mark I. McCarthy Email: mark.mccarthy{at} Erik Ingelsson, E-mail: erik.ingelsson{at} Jose C. Florez, Richard M. Watanabe


Patients with established type 2 diabetes display both beta-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci and indices of proinsulin processing, insulin secretion and insulin sensitivity. We included data from up to 58,614 non-diabetic subjects with basal measures, and 17,327 with dynamic measures. We employed additive genetic models with adjustment for sex, age and BMI, followed by fixed-effects inverse variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (including TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without detectable change in fasting glucose. The final group contained twenty risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.


  • * These authors contributed equally to this work.

  • Received June 18, 2013.
  • Accepted November 23, 2013.

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