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Cited by 117Genetic Associations and EpidemiologyPancreatic function and diabetesDiabetes and associated disorders

A Genome-Wide Association Study of IVGTT-Based Measures of First-Phase Insulin Secretion Refines the Underlying Physiology of Type 2 Diabetes Variants

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Wood, Andrew R., Jonsson, Anna, Jackson, Anne U., Wang, Nan, van Leewen, Nienke, Palmer, Nicholette D., Kobes, Sayuko, Deelen, Joris +44 more·2017·Diabetes
DOI 10.2337/db16-1452PMC PMC5521867
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Abstract

Understanding the physiological mechanisms by which common variants predispose to type 2 diabetes requires large studies with detailed measures of insulin secretion and sensitivity. Here we performed the largest genome-wide association study of first-phase insulin secretion, as measured by intravenous glucose tolerance tests, using up to 5,567 individuals without diabetes from 10 studies. We aimed to refine the mechanisms of 178 known associations between common variants and glycemic traits and identify new loci. Thirty type 2 diabetes or fasting glucose–raising alleles were associated with a measure of first-phase insulin secretion at P < 0.05 and provided new evidence, or the strongest evidence yet, that insulin secretion, intrinsic to the islet cells, is a key mechanism underlying the associations at the HNF1A, IGF2BP2, KCNQ1, HNF1B, VPS13C/C2CD4A, FAF1, PTPRD, AP3S2, KCNK16, MAEA, LPP, WFS1, and TMPRSS6 loci. The fasting glucose–raising allele near PDX1, a known key insulin transcription factor, was strongly associated with lower first-phase insulin secretion but has no evidence for an effect on type 2 diabetes risk. The diabetes risk allele at TCF7L2 was associated with a stronger effect on peak insulin response than on C-peptide–based insulin secretion rate, suggesting a possible additional role in hepatic insulin clearance or insulin processing. In summary, our study provides further insight into the mechanisms by which common genetic variation influences type 2 diabetes risk and glycemic traits.

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  1. [1]Perry JR, Frayling TM New gene variants alter type 2 diabetes risk predominantly through reduced beta-cell function. Curr Opin Clin Nutr Metab Care 2008; 11: 371– 377 18541994
  2. [2]Dimas AS, Lagou V, Barker A,.; MAGIC Investigators Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes 2014; 63: 2158– 2171 24296717 PMC4030103
  3. [3]Prokopenko I, Poon W, Mägi R, A central role for GRB10 in regulation of islet function in man. PLoS Genet 2014; 10: e1004235 24699409 PMC3974640
  4. [4]Gjesing AP, Hornbak M, Allin KH, High heritability and genetic correlation of intravenous glucose- and tolbutamide-induced insulin secretion among non-diabetic family members of type 2 diabetic patients. Diabetologia 2014; 57: 1173– 1181 24604100
  5. [5]Hu C, Zhang R, Wang C, Variants from GIPR, TCF7L2, DGKB, MADD, CRY2, GLIS3, PROX1, SLC30A8 and IGF1 are associated with glucose metabolism in the Chinese. PLoS One 2010; 5: e15542 21103350 PMC2984505
  6. [6]Rich SS, Bowden DW, Haffner SM,.; Insulin Resistance Atherosclerosis Study Family Study Identification of quantitative trait loci for glucose homeostasis: the Insulin Resistance Atherosclerosis Study (IRAS) Family Study. Diabetes 2004; 53: 1866– 1875 15220212
  7. [7]Sakul H, Pratley R, Cardon L, Ravussin E, Mott D, Bogardus C Familiality of physical and metabolic characteristics that predict the development of non-insulin-dependent diabetes mellitus in Pima Indians. Am J Hum Genet 1997; 60: 651– 656 9042926 PMC1712495
  8. [8]Simonis-Bik AM, Nijpels G, van Haeften TW, Gene variants in the novel type 2 diabetes loci CDC123/CAMK1D, THADA, ADAMTS9, BCL11A, and MTNR1B affect different aspects of pancreatic beta-cell function. Diabetes 2010; 59: 293– 301 19833888 PMC2797936
  9. [9]Staiger H, Machicao F, Schäfer SA, Polymorphisms within the novel type 2 diabetes risk locus MTNR1B determine beta-cell function. PLoS One 2008; 3: e3962 19088850 PMC2597741
  10. [10]Rich SS, Goodarzi MO, Palmer ND, A genome-wide association scan for acute insulin response to glucose in Hispanic-Americans: the Insulin Resistance Atherosclerosis Family Study (IRAS FS). Diabetologia 2009; 52: 1326– 1333 19430760 PMC2793118
  11. [11]Palmer ND, Langefeld CD, Ziegler JT, Candidate loci for insulin sensitivity and disposition index from a genome-wide association analysis of Hispanic participants in the Insulin Resistance Atherosclerosis (IRAS) Family Study. Diabetologia 2010; 53: 281– 289 19902172 PMC2809812
  12. [12]Groenewoud MJ, Dekker JM, Fritsche A, Variants of CDKAL1 and IGF2BP2 affect first-phase insulin secretion during hyperglycaemic clamps. Diabetologia 2008; 51: 1659– 1663 18618095
  13. [13]‘t Hart LM, Simonis-Bik AM, Nijpels G, et al. Combined risk allele score of eight type 2 diabetes genes is associated with reduced first-phase glucose-stimulated insulin secretion during hyperglycemic clamps. Diabetes 2010;59:287–292 PMC2797935 19808892
  14. [14]van Vliet-Ostaptchouk JV, van Haeften TW, Landman GW, Common variants in the type 2 diabetes KCNQ1 gene are associated with impairments in insulin secretion during hyperglycaemic glucose clamp. PLoS One 2012; 7: e32148 22403629 PMC3293880
  15. [15]Palmer ND, Goodarzi MO, Langefeld CD, Genetic variants associated with quantitative glucose homeostasis traits translate to type 2 diabetes in Mexican Americans: The GUARDIAN (Genetics Underlying Diabetes in Hispanics) Consortium. Diabetes 2015; 64: 1853– 1866 25524916 PMC4407862
  16. [16]Henkin L, Bergman RN, Bowden DW, Genetic epidemiology of insulin resistance and visceral adiposity. The IRAS Family Study design and methods. Ann Epidemiol 2003; 13: 211– 217 12684185
  17. [17]Azen SP, Peters RK, Berkowitz K, Kjos S, Xiang A, Buchanan TA TRIPOD (TRoglitazone In the Prevention Of Diabetes): a randomized, placebo-controlled trial of troglitazone in women with prior gestational diabetes mellitus. Control Clin Trials 1998; 19: 217– 231 9551285
  18. [18]Watanabe RM, Allayee H, Xiang AH, Transcription factor 7-like 2 (TCF7L2) is associated with gestational diabetes mellitus and interacts with adiposity to alter insulin secretion in Mexican Americans. Diabetes 2007; 56: 1481– 1485 17317761 PMC2925638
  19. [19]Staiger H, Stancáková A, Zilinskaite J, A candidate type 2 diabetes polymorphism near the HHEX locus affects acute glucose-stimulated insulin release in European populations: results from the EUGENE2 study. Diabetes 2008; 57: 514– 517 18039816
  20. [20]Hills SA, Balkau B, Coppack SW,.; EGIR-RISC Study Group The EGIR-RISC STUDY (The European group for the study of insulin resistance: relationship between insulin sensitivity and cardiovascular disease risk): I. Methodology and objectives. Diabetologia 2004; 47: 566– 570 14968294
  21. [21]Hansen T, Ambye L, Grarup N, Genetic variability of the SUR1 promoter in relation to beta-cell function and type II diabetes mellitus. Diabetologia 2001; 44: 1330– 1334 11692183
  22. [22]Valle T, Tuomilehto J, Bergman RN, Mapping genes for NIDDM. Design of the Finland-United States Investigation of NIDDM Genetics (FUSION) Study. Diabetes Care 1998; 21: 949– 958 9614613
  23. [23]Hanson RL, Muller YL, Kobes S, A genome-wide association study in American Indians implicates DNER as a susceptibility locus for type 2 diabetes. Diabetes 2014; 63: 369– 376 24101674 PMC3868048
  24. [24]Voight BF, Kang HM, Ding J, The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 2012; 8: e1002793 22876189 PMC3410907
  25. [25]Mari A, Tura A, Natali A,.; RISC Investigators Influence of hyperinsulinemia and insulin resistance on in vivo β-cell function: their role in human β-cell dysfunction. Diabetes 2011; 60: 3141– 3147 22028180 PMC3219936
  26. [26]Matthews JN, Altman DG, Campbell MJ, Royston P Analysis of serial measurements in medical research. BMJ 1990; 300: 230– 235 2106931 PMC1662068
  27. [27]Hovorka R, Soons PA, Young MA ISEC: a program to calculate insulin secretion. Comput Methods Programs Biomed 1996; 50: 253– 264 8894385
  28. [29]Hovorka R, Koukkou E, Southerden D, Powrie JK, Young MA Measuring pre-hepatic insulin secretion using a population model of C-peptide kinetics: accuracy and required sampling schedule. Diabetologia 1998; 41: 548– 554 9628272
  29. [30]Van Cauter E, Mestrez F, Sturis J, Polonsky KS Estimation of insulin secretion rates from C-peptide levels. Comparison of individual and standard kinetic parameters for C-peptide clearance. Diabetes 1992; 41: 368– 377 1551497
  30. [31]Pacini G, Bergman RN MINMOD: a computer program to calculate insulin sensitivity and pancreatic responsivity from the frequently sampled intravenous glucose tolerance test. Comput Methods Programs Biomed 1986; 23: 113– 122 3640682
  31. [32]Li Y, Willer CJ, Ding J, Scheet P, Abecasis GR MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet Epidemiol 2010; 34: 816– 834 21058334 PMC3175618
  32. [33]Fuchsberger C, Abecasis GR, Hinds DA minimac2: faster genotype imputation. Bioinformatics 2015; 31: 782– 784 25338720 PMC4341061
  33. [35]Auton A, Brooks LD, Durbin RM,.; 1000 Genomes Project Consortium A global reference for human genetic variation. Nature 2015; 526: 68– 74 26432245 PMC4750478
  34. [38]Uh HW, Beekman M, Meulenbelt I, Houwing-Duistermaat JJ Genotype-based score test for association testing in families. Stat Biosci 2015; 7: 394– 416 26473021 PMC4596911
  35. [39]Morris AP, Voight BF, Teslovich TM,.; Wellcome Trust Case Control Consortium; Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators; Genetic Investigation of ANthropometric Traits (GIANT) Consortium; Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium; South Asian Type 2 Diabetes (SAT2D) Consortium; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet 2012; 44: 981– 990 22885922 PMC3442244
  36. [41]Manning AK, Hivert MF, Scott RA,.; DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium; Multiple Tissue Human Expression Resource (MUTHER) Consortium A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 2012; 44: 659– 669 22581228 PMC3613127
  37. [42]Prasad RB, Lessmark A, Almgren P, Excess maternal transmission of variants in the THADA gene to offspring with type 2 diabetes. Diabetologia 2016; 59: 1702– 1713 27155871
  38. [43]Scott RA, Lagou V, Welch RP,.; DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 2012; 44: 991– 1005 22885924 PMC3433394
  39. [45]Gjesing AP, Ribel-Madsen R, Harder MN, Genetic and phenotypic correlations between surrogate measures of insulin release obtained from OGTT data. Diabetologia 2015; 58: 1006– 1012 25660259
  40. [46]Stoffers DA, Ferrer J, Clarke WL, Habener JF Early-onset type-II diabetes mellitus (MODY4) linked to IPF1. Nat Genet 1997; 17: 138– 139 9326926
  41. [47]Heni M, Ketterer C, Hart LM, The impact of genetic variation in the G6PC2 gene on insulin secretion depends on glycemia. J Clin Endocrinol Metab 2010; 95: E479– E484 20826583
  42. [48]Sparsø T, Andersen G, Albrechtsen A, Impact of polymorphisms in WFS1 on prediabetic phenotypes in a population-based sample of middle-aged people with normal and abnormal glucose regulation. Diabetologia 2008; 51: 1646– 1652 18568334
  43. [49]da Silva Xavier G, Mondragon A, Sun G, Abnormal glucose tolerance and insulin secretion in pancreas-specific Tcf7l2-null mice. Diabetologia 2012; 55: 2667– 2676 22717537 PMC6101215
  44. [50]McCarthy MI, Rorsman P, Gloyn AL TCF7L2 and diabetes: a tale of two tissues, and of two species. Cell Metab 2013; 17: 157– 159 23395164
  45. [51]Nobrega MA TCF7L2 and glucose metabolism: time to look beyond the pancreas. Diabetes 2013; 62: 706– 708 23431017 PMC3581232
  46. [52]Mitchell RK, Mondragon A, Chen L, Selective disruption of Tcf7l2 in the pancreatic β cell impairs secretory function and lowers β cell mass. Hum Mol Genet 2015; 24: 1390– 1399 25355422 PMC4321446
  47. [53]Boj SF, van Es JH, Huch M, Diabetes risk gene and Wnt effector Tcf7l2/TCF4 controls hepatic response to perinatal and adult metabolic demand. Cell 2012; 151: 1595– 1607 23260145
  48. [54]Lyssenko V, Lupi R, Marchetti P, Mechanisms by which common variants in the TCF7L2 gene increase risk of type 2 diabetes. J Clin Invest 2007; 117: 2155– 2163 17671651 PMC1934596
  49. [56]Kirchhoff K, Machicao F, Haupt A, Polymorphisms in the TCF7L2, CDKAL1 and SLC30A8 genes are associated with impaired proinsulin conversion. Diabetologia 2008; 51: 597– 601 18264689
  50. [57]Loos RJ, Franks PW, Francis RW, TCF7L2 polymorphisms modulate proinsulin levels and beta-cell function in a British Europid population. Diabetes 2007; 56: 1943– 1947 17416797 PMC2668957
  51. [58]Jonsson A, Ladenvall C, Ahluwalia TS, Effects of common genetic variants associated with type 2 diabetes and glycemic traits on α- and β-cell function and insulin action in humans. Diabetes 2013; 62: 2978– 2983 23557703 PMC3717852
  52. [59]Cho YS, Chen CH, Hu C,.; DIAGRAM Consortium; MuTHER Consortium Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 2011; 44: 67– 72 22158537 PMC3582398
  53. [60]Tsai FJ, Yang CF, Chen CC, A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet 2010; 6: e1000847 20174558 PMC2824763

Cited by

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Authors
Wood, A. R., Jonsson, A., Jackson, A. U., Wang, N., van Leewen, N., Palmer, N. D., Kobes, S., Deelen, J., Boquete-Vilarino, L., Paananen, J., Stančáková, A., Boomsma, D. I., de Geus, E. J. C., Eekhoff, E. M. W., Fritsche, A., Kramer, M., Nijpels, G., Simonis-Bik, A., van Haeften, T. W., Mahajan, A., Boehnke, M., Bergman, R. N., Tuomilehto, J., Collins, F. S., Mohlke, K. L., Banasik, K., Groves, C. J., McCarthy, M. I., (DIRECT), D. R. O. P. S., Pearson, E. R., Natali, A., Mari, A., Buchanan, T. A., Taylor, K. D., Xiang, A. H., Gjesing, A. P., Grarup, N., Eiberg, H., Pedersen, O., Chen, Y.-D., Laakso, M., Norris, J. M., Smith, U., Wagenknecht, L. E., Baier, L., Bowden, D. W., Hansen, T., Walker, M., Watanabe, R. M., ‘t Hart, L. M., Hanson, R. L., & Frayling, T. M.
Affiliations
  • University of Exeter (GB)
  • University of Copenhagen (DK)
  • University of Michigan (US)
  • University of Southern California (US)
  • Department of Molecular Cell Biology, Leiden University Medical Center, Leiden, the Netherlands
  • Wake Forest University Health Sciences (US)
  • National Institutes of Health (US)
  • Max Planck Institute for Biology of Ageing (DE)
  • Genetics of Complex Traits, Institute of Biomedical and Clinical Science, University of Exeter Medical School, Royal Devon and Exeter Hospital, Exeter, U.K.
  • University of Eastern Finland (FI)
  • Vrije Universiteit Amsterdam (NL)
  • Department of Biological Psychology, VU University, Amsterdam, the Netherlands
  • Amsterdam UMC Location Vrije Universiteit Amsterdam (NL)
  • German Center for Diabetes Research (DE)
  • Amsterdam Public Health (NL)
  • Department of Internal Medicine, Utrecht University Medical Center, Utrecht, the Netherlands
  • Centre for Human Genetics (GB)
  • Cedars-Sinai Medical Center (US)
  • Universität für Weiterbildung Krems (AT)
  • University of North Carolina at Chapel Hill (US)
  • University of Oxford (GB)
  • University of Dundee (GB)
  • University of Pisa (IT)
  • National Research Council (IT)
  • University of California, Los Angeles (US)
  • Kaiser Permanente (US)
  • The Lundquist Institute (US)
  • Colorado School of Public Health (US)
  • University of Gothenburg (SE)
  • Newcastle University (GB)
Corresponding author
  • Bowden, Donald W.
  • Hansen, Torben
  • Walker, Mark
  • Watanabe, Richard M.
  • Hanson, Robert L.
  • Frayling, Timothy M.
ORCID
Venue
Diabetes
Publisher
American Diabetes Association
Published
2017-05-10
Volume
66
Issue
8
Pages
2296-2309
ISSN
  • 0012-1797 (ISSN-L)
  • 1939-327X
DOI
10.2337/db16-1452
PMC
PMC5521867
OpenAlex
W2613232859
Cited by
117
References
7
Citations by year
2026: 5 · 2025: 3 · 2024: 12 · 2023: 15 · 2022: 21 · 2021: 15 · 2020: 15 · 2019: 13 · 2018: 16 · 2017: 2
FWCI
1.62
Type
journal article
Language
en
Open access
Licence
www.diabetesjournals.org/content/license (vor, 72 d)
Locations
  • Diabetes (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • Discovery Research Portal (University of Dundee) (publishedVersion, other-oa)
  • Pure Amsterdam UMC (publishedVersion, other-oa)
  • VU Research Portal (publishedVersion, other-oa)
  • Open Research Exeter (University of Exeter) (acceptedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion)
  • Leiden Repository (Leiden University) (submittedVersion)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
  • Research at the University of Copenhagen (University of Copenhagen) (submittedVersion)
  • Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion)
  • PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
  • Newcastle University ePrints (Newcastle Univesity)
Funders
  • European Research Council (SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC, 230374)
  • National Institute of Diabetes and Digestive and Kidney Diseases (DK085175, DK063491)
  • European Union's Seventh Framework Programme (FP7/2007-2013)
  • Netherlands Organisation for Health Research and Development (Priority Medicines Elderly Program 113102006)
  • Biobanking and BioMolecular resources Research Infrastructure–The Netherlands (NWO 184.021.007)
  • Netherlands Organisation for Scientific Research (NWO 480-04-004, NWO/SPI 56-464-14192)
  • European Union (QLG1-CT-2001-01252)
  • National Institutes of Health (HL060944, HL061019, HL060919, HG007112, UL1-TR-000124, R01-DK062370, R01-DK072193, N01-HG-65403)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Health Sciences › Medicine › Surgery › Pancreatic function and diabetes
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Keywords
type 2 diabetes, first-phase insulin secretion, intravenous glucose tolerance test, genetic variants, islet function, fasting blood glucose, HNF1A, IGF2BP2, KCNQ1, HNF1B, PDX1, TCF7L2, C-peptide, hepatic insulin clearance, insulin processing, glycemic traits
MeSH terms
Insulin Secretion, Alleles, Blood Glucose, C-Peptide, Diabetes Mellitus, Type 2, Genotype, Glucose Tolerance Test, Humans, Insulin, Liver, Genetic Variation, Linear Models, Insulin-Secreting Cells, Genome-Wide Association Study, Transcription Factor 7-Like 2 Protein, Genotyping Techniques

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