251
|
Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, ODonnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE. Metabolite profiles and the risk of developing diabetes. ACTA ACUST UNITED AC 2011. [DOI: 10.14341/2071-8713-4841] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
252
|
Kanoni S, Nettleton JA, Hivert MF, Ye Z, van Rooij FJ, Shungin D, Sonestedt E, Ngwa JS, Wojczynski MK, Lemaitre RN, Gustafsson S, Anderson JS, Tanaka T, Hindy G, Saylor G, Renstrom F, Bennett AJ, van Duijn CM, Florez JC, Fox CS, Hofman A, Hoogeveen RC, Houston DK, Hu FB, Jacques PF, Johansson I, Lind L, Liu Y, McKeown N, Ordovas J, Pankow JS, Sijbrands EJ, Syvänen AC, Uitterlinden AG, Yannakoulia M, Zillikens MC, Wareham NJ, Prokopenko I, Bandinelli S, Forouhi NG, Cupples LA, Loos RJ, Hallmans G, Dupuis J, Langenberg C, Ferrucci L, Kritchevsky SB, McCarthy MI, Ingelsson E, Borecki IB, Witteman JC, Orho-Melander M, Siscovick DS, Meigs JB, Franks PW, Dedoussis GV. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes 2011; 60:2407-16. [PMID: 21810599 PMCID: PMC3161318 DOI: 10.2337/db11-0176] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2011] [Accepted: 06/01/2011] [Indexed: 01/23/2023]
Abstract
OBJECTIVE Many genetic variants have been associated with glucose homeostasis and type 2 diabetes in genome-wide association studies. Zinc is an essential micronutrient that is important for β-cell function and glucose homeostasis. We tested the hypothesis that zinc intake could influence the glucose-raising effect of specific variants. RESEARCH DESIGN AND METHODS We conducted a 14-cohort meta-analysis to assess the interaction of 20 genetic variants known to be related to glycemic traits and zinc metabolism with dietary zinc intake (food sources) and a 5-cohort meta-analysis to assess the interaction with total zinc intake (food sources and supplements) on fasting glucose levels among individuals of European ancestry without diabetes. RESULTS We observed a significant association of total zinc intake with lower fasting glucose levels (β-coefficient ± SE per 1 mg/day of zinc intake: -0.0012 ± 0.0003 mmol/L, summary P value = 0.0003), while the association of dietary zinc intake was not significant. We identified a nominally significant interaction between total zinc intake and the SLC30A8 rs11558471 variant on fasting glucose levels (β-coefficient ± SE per A allele for 1 mg/day of greater total zinc intake: -0.0017 ± 0.0006 mmol/L, summary interaction P value = 0.005); this result suggests a stronger inverse association between total zinc intake and fasting glucose in individuals carrying the glucose-raising A allele compared with individuals who do not carry it. None of the other interaction tests were statistically significant. CONCLUSIONS Our results suggest that higher total zinc intake may attenuate the glucose-raising effect of the rs11558471 SLC30A8 (zinc transporter) variant. Our findings also support evidence for the association of higher total zinc intake with lower fasting glucose levels.
Collapse
Affiliation(s)
- Stavroula Kanoni
- Department of Nutrition-Dietetics, Harokopio University, Athens, Greece
- Wellcome Trust Sanger Institute, Hinxton, U.K
| | - Jennifer A. Nettleton
- Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Science Center, Houston, Texas
| | - Marie-France Hivert
- Department of Medicine, Division of Endocrinology, Université de Sherbrooke, Sherbrooke, Canada
| | - Zheng Ye
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Frank J.A. van Rooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Dmitry Shungin
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Odontology, Umeå University, Umeå, Sweden
| | - Emily Sonestedt
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Julius S. Ngwa
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Mary K. Wojczynski
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Rozenn N. Lemaitre
- Cardiovascular Health Research Unit, Department of Medicine and Epidemiology, University of Washington, Seattle, Washington
| | - Stefan Gustafsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | | | - Toshiko Tanaka
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - George Hindy
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Georgia Saylor
- Baptist Medical Center, Wake Forest University, Winston-Salem, North Carolina
| | - Frida Renstrom
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Amanda J. Bennett
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
| | - Cornelia M. van Duijn
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Jose C. Florez
- Diabetes Unit, Center for Human Genetic Research and Diabetes Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Caroline S. Fox
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | - Ron C. Hoogeveen
- Section of Atherosclerosis and Vascular Medicine, Department of Medicine, Baylor College of Medicine, Houston, Texas
- Center for Cardiovascular Disease Prevention, Methodist DeBakey Heart Center, Houston, Texas
| | - Denise K. Houston
- Sticht Center on Aging, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Paul F. Jacques
- Nutrition Epidemiology Program, U.S. Department of Agriculture Human Nutrition Research Center on Aging (USDA HNRCA) at Tufts University, Boston, Massachusetts
| | | | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University Health Sciences, Winston-Salem, North Carolina
| | - Nicola McKeown
- Nutrition Epidemiology Program, U.S. Department of Agriculture Human Nutrition Research Center on Aging (USDA HNRCA) at Tufts University, Boston, Massachusetts
- Friedman School of Nutrition Science and Policy, Tufts University, Boston, Massachusetts
| | - Jose Ordovas
- Nutrition and Genomics Laboratory, Jean Mayer USDA HNRCA at Tufts University, Boston, Massachusetts
| | - James S. Pankow
- Department of Epidemiology, University of Minnesota, Minneapolis, Minnesota
| | - Eric J.G. Sijbrands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - André G. Uitterlinden
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Mary Yannakoulia
- Department of Nutrition-Dietetics, Harokopio University, Athens, Greece
| | - M. Carola Zillikens
- Department of Internal Medicine, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - Nick J. Wareham
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Inga Prokopenko
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
| | | | - Nita G. Forouhi
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Ruth J. Loos
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Goran Hallmans
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Claudia Langenberg
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K
| | - Luigi Ferrucci
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland
| | - Stephen B. Kritchevsky
- Sticht Center on Aging, Department of Internal Medicine, Section on Gerontology and Geriatric Medicine, Wake Forest University School of Medicine, Winston-Salem, North Carolina
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, University of Oxford, Churchill Hospital, Oxford, U.K
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K
- Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, U.K
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ingrid B. Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri
| | - Jacqueline C.M. Witteman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
- The Netherlands Genomics Initiative–Sponsored Netherlands Consortium for Healthy Aging (NGI-NCHA), Leiden, the Netherlands
| | | | - David S. Siscovick
- Cardiovascular Health Research Unit, Department of Medicine and Epidemiology, University of Washington, Seattle, Washington
| | - James B. Meigs
- General Medicine Division, Clinical Epidemiology Unit and Diabetes Research Unit, Massachusetts General Hospital, Boston, Massachusetts
| | - Paul W. Franks
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section of Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | | |
Collapse
|
253
|
McCaffery JM, Jablonski KA, Franks PW, Dagogo-Jack S, Wing RR, Knowler WC, Delahanty L, Dabelea D, Hamman R, Shuldiner AR, Florez JC. TCF7L2 polymorphism, weight loss and proinsulin:insulin ratio in the diabetes prevention program. PLoS One 2011; 6:e21518. [PMID: 21814547 PMCID: PMC3144193 DOI: 10.1371/journal.pone.0021518] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2011] [Accepted: 06/02/2011] [Indexed: 01/17/2023] Open
Abstract
Aims TCF7L2 variants have been associated with type 2 diabetes, body mass index (BMI), and deficits in proinsulin processing and insulin secretion. Here we sought to test whether these effects were apparent in high-risk individuals and modify treatment responses. Methods We examined the potential role of the TCF7L2 rs7903146 variant in predicting resistance to weight loss or a lack of improvement of proinsulin processing during 2.5-years of follow-up participants (N = 2,994) from the Diabetes Prevention Program (DPP), a randomized controlled trial designed to prevent or delay diabetes in high-risk adults. Results We observed no difference in the degree of weight loss by rs7903146 genotypes. However, the T allele (conferring higher risk of diabetes) at rs7903146 was associated with higher fasting proinsulin at baseline (P<0.001), higher baseline proinsulin∶insulin ratio (p<0.0001) and increased proinsulin∶insulin ratio over a median of 2.5 years of follow-up (P = 0.003). Effects were comparable across treatment arms. Conclusions The combination of a lack of impact of the TCF7L2 genotypes on the ability to lose weight, but the presence of a consistent effect on the proinsulin∶insulin ratio over the course of DPP, suggests that high-risk genotype carriers at this locus can successfully lose weight to counter diabetes risk despite persistent deficits in insulin production.
Collapse
Affiliation(s)
- Jeanne M McCaffery
- The Biostatistics Center, The George Washington University, Rockville, Maryland, United States of America.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
254
|
Pollin TI, Jablonski KA, McAteer JB, Saxena R, Kathiresan S, Kahn SE, Goldberg RB, Altshuler D, Florez JC. Triglyceride response to an intensive lifestyle intervention is enhanced in carriers of the GCKR Pro446Leu polymorphism. J Clin Endocrinol Metab 2011; 96:E1142-7. [PMID: 21525158 PMCID: PMC3205512 DOI: 10.1210/jc.2010-2324] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
CONTEXT Glucokinase regulatory protein (GCKR) regulates the trafficking and enzymatic activity of hepatic glucokinase, the rate-limiting enzyme in glycogen synthesis and glycolysis. The intronic single-nucleotide polymorphism (SNP) rs780094 (intron 16) and the missense SNP rs1260326 (P446L) in the GCKR gene are strongly associated with increased circulating triglyceride and C-reactive protein levels and, paradoxically, reductions in diabetes incidence, fasting glucose levels, and insulin resistance. OBJECTIVE, SETTING, AND PATIENTS: We sought to replicate these associations and evaluate interactions with lifestyle and metformin interventions in the multiethnic Diabetes Prevention Program (DPP). INTERVENTIONS AND MAIN OUTCOME MEASURES We genotyped the two GCKR SNP in 3346 DPP participants and evaluated association with progression to diabetes and both baseline levels and changes in triglycerides, homeostasis model assessment of insulin resistance (HOMA-IR), oral disposition index, and inflammatory markers along with their interactions with DPP interventions. RESULTS GCKR variation did not predict development of type 2 diabetes. At baseline, the 446L allele was associated with higher triglyceride and C-reactive protein levels (both P < 0.0001) and lower fasting glucose (P = 0.001) and HOMA-IR (P = 0.06). The lifestyle intervention was associated with a decrease in magnitude of the effect of the 446L allele on triglyceride levels (interaction P = 0.04). Metformin was more effective in reducing HOMA-IR in carriers of the P446 allele (interaction P = 0.05). CONCLUSIONS Intensive lifestyle intervention appears to partially mitigate the effect of the 446L allele on higher triglycerides, whereas the P446 allele appears to enhance responsiveness to the HOMA-IR-lowering effect of metformin.
Collapse
Affiliation(s)
- Toni I Pollin
- Division of Endocrinology, Diabetes, and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland21201, USA.
| | | | | | | | | | | | | | | | | | | |
Collapse
|
255
|
Böger CA, Chen MH, Tin A, Olden M, Köttgen A, de Boer IH, Fuchsberger C, O'Seaghdha CM, Pattaro C, Teumer A, Liu CT, Glazer NL, Li M, O'Connell JR, Tanaka T, Peralta CA, Kutalik Z, Luan J, Zhao JH, Hwang SJ, Akylbekova E, Kramer H, van der Harst P, Smith AV, Lohman K, de Andrade M, Hayward C, Kollerits B, Tönjes A, Aspelund T, Ingelsson E, Eiriksdottir G, Launer LJ, Harris TB, Shuldiner AR, Mitchell BD, Arking DE, Franceschini N, Boerwinkle E, Egan J, Hernandez D, Reilly M, Townsend RR, Lumley T, Siscovick DS, Psaty BM, Kestenbaum B, Haritunians T, Bergmann S, Vollenweider P, Waeber G, Mooser V, Waterworth D, Johnson AD, Florez JC, Meigs JB, Lu X, Turner ST, Atkinson EJ, Leak TS, Aasarød K, Skorpen F, Syvänen AC, Illig T, Baumert J, Koenig W, Krämer BK, Devuyst O, Mychaleckyj JC, Minelli C, Bakker SJL, Kedenko L, Paulweber B, Coassin S, Endlich K, Kroemer HK, Biffar R, Stracke S, Völzke H, Stumvoll M, Mägi R, Campbell H, Vitart V, Hastie ND, Gudnason V, Kardia SLR, Liu Y, Polasek O, Curhan G, Kronenberg F, Prokopenko I, Rudan I, Arnlöv J, Hallan S, Navis G, Parsa A, Ferrucci L, Coresh J, Shlipak MG, Bull SB, Paterson NJ, Wichmann HE, Wareham NJ, Loos RJF, Rotter JI, Pramstaller PP, Cupples LA, Beckmann JS, Yang Q, Heid IM, Rettig R, Dreisbach AW, Bochud M, Fox CS, Kao WHL. CUBN is a gene locus for albuminuria. J Am Soc Nephrol 2011; 22:555-70. [PMID: 21355061 DOI: 10.1681/asn.2010060598] [Citation(s) in RCA: 185] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Identification of genetic risk factors for albuminuria may alter strategies for early prevention of CKD progression, particularly among patients with diabetes. Little is known about the influence of common genetic variants on albuminuria in both general and diabetic populations. We performed a meta-analysis of data from 63,153 individuals of European ancestry with genotype information from genome-wide association studies (CKDGen Consortium) and from a large candidate gene study (CARe Consortium) to identify susceptibility loci for the quantitative trait urinary albumin-to-creatinine ratio (UACR) and the clinical diagnosis microalbuminuria. We identified an association between a missense variant (I2984V) in the CUBN gene, which encodes cubilin, and both UACR (P = 1.1 × 10(-11)) and microalbuminuria (P = 0.001). We observed similar associations among 6981 African Americans in the CARe Consortium. The associations between this variant and both UACR and microalbuminuria were significant in individuals of European ancestry regardless of diabetes status. Finally, this variant associated with a 41% increased risk for the development of persistent microalbuminuria during 20 years of follow-up among 1304 participants with type 1 diabetes in the prospective DCCT/EDIC Study. In summary, we identified a missense CUBN variant that associates with levels of albuminuria in both the general population and in individuals with diabetes.
Collapse
Affiliation(s)
- Carsten A Böger
- Department of Internal Medicine II, University Medical Center Regensburg, Regensburg, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
256
|
Hivert MF, Jablonski KA, Perreault L, Saxena R, McAteer JB, Franks PW, Hamman RF, Kahn SE, Haffner S, Meigs JB, Altshuler D, Knowler WC, Florez JC. Updated genetic score based on 34 confirmed type 2 diabetes Loci is associated with diabetes incidence and regression to normoglycemia in the diabetes prevention program. Diabetes 2011; 60:1340-8. [PMID: 21378175 PMCID: PMC3064108 DOI: 10.2337/db10-1119] [Citation(s) in RCA: 136] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
OBJECTIVE Over 30 loci have been associated with risk of type 2 diabetes at genome-wide statistical significance. Genetic risk scores (GRSs) developed from these loci predict diabetes in the general population. We tested if a GRS based on an updated list of 34 type 2 diabetes-associated loci predicted progression to diabetes or regression toward normal glucose regulation (NGR) in the Diabetes Prevention Program (DPP). RESEARCH DESIGN AND METHODS We genotyped 34 type 2 diabetes-associated variants in 2,843 DPP participants at high risk of type 2 diabetes from five ethnic groups representative of the U.S. population, who had been randomized to placebo, metformin, or lifestyle intervention. We built a GRS by weighting each risk allele by its reported effect size on type 2 diabetes risk and summing these values. We tested its ability to predict diabetes incidence or regression to NGR in models adjusted for age, sex, ethnicity, waist circumference, and treatment assignment. RESULTS In multivariate-adjusted models, the GRS was significantly associated with increased risk of progression to diabetes (hazard ratio [HR] = 1.02 per risk allele [95% CI 1.00-1.05]; P = 0.03) and a lower probability of regression to NGR (HR = 0.95 per risk allele [95% CI 0.93-0.98]; P < 0.0001). At baseline, a higher GRS was associated with a lower insulinogenic index (P < 0.001), confirming an impairment in β-cell function. We detected no significant interaction between GRS and treatment, but the lifestyle intervention was effective in the highest quartile of GRS (P < 0.0001). CONCLUSIONS A high GRS is associated with increased risk of developing diabetes and lower probability of returning to NGR in high-risk individuals, but a lifestyle intervention attenuates this risk.
Collapse
Affiliation(s)
- Marie-France Hivert
- Division of Endocrinology, Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | | | - Leigh Perreault
- Department of Medicine, Division of Endocrinology, Metabolism and Diabetes, University of Colorado at Denver School of Medicine, Aurora, Colorado
| | - Richa Saxena
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Jarred B. McAteer
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| | - Paul W. Franks
- Department of Public Health and Clinical Medicine, Division of Medicine, Genetic Epidemiology and Clinical Research Group, Umeå University Hospital, Umeå, Sweden
- Department of Clinical Sciences, Lund University Diabetes Center, Lund University, Malmö, Sweden
| | - Richard F. Hamman
- Department of Epidemiology, Colorado School of Public Health, University of Colorado at Denver, Aurora, Colorado
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Veterans’ Affairs Puget Sound Health Care System and the University of Washington, Seattle, Washington
| | | | | | - James B. Meigs
- General Medicine Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - David Altshuler
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Jose C. Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Corresponding author: Jose C. Florez, and
| | | |
Collapse
|
257
|
Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Boström KB, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney ASF, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PRV, Jørgensen T, Kao WHL, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JRB, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CNA, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI. Erratum: Corrigendum: Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2011. [DOI: 10.1038/ng0411-388b] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
258
|
Rhee EP, Cheng S, Larson MG, Walford GA, Lewis GD, McCabe E, Yang E, Farrell L, Fox CS, O'Donnell CJ, Carr SA, Vasan RS, Florez JC, Clish CB, Wang TJ, Gerszten RE. Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans. J Clin Invest 2011; 121:1402-11. [PMID: 21403394 DOI: 10.1172/jci44442] [Citation(s) in RCA: 477] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2010] [Accepted: 01/19/2011] [Indexed: 11/17/2022] Open
Abstract
Dyslipidemia is an independent risk factor for type 2 diabetes, although exactly which of the many plasma lipids contribute to this remains unclear. We therefore investigated whether lipid profiling can inform diabetes prediction by performing liquid chromatography/mass spectrometry-based lipid profiling in 189 individuals who developed type 2 diabetes and 189 matched disease-free individuals, with over 12 years of follow up in the Framingham Heart Study. We found that lipids of lower carbon number and double bond content were associated with an increased risk of diabetes, whereas lipids of higher carbon number and double bond content were associated with decreased risk. This pattern was strongest for triacylglycerols (TAGs) and persisted after multivariable adjustment for age, sex, BMI, fasting glucose, fasting insulin, total triglycerides, and HDL cholesterol. A combination of 2 TAGs further improved diabetes prediction. To explore potential mechanisms that modulate the distribution of plasma lipids, we performed lipid profiling during oral glucose tolerance testing, pharmacologic interventions, and acute exercise testing. Levels of TAGs associated with increased risk for diabetes decreased in response to insulin action and were elevated in the setting of insulin resistance. Conversely, levels of TAGs associated with decreased diabetes risk rose in response to insulin and were poorly correlated with insulin resistance. These studies identify a relationship between lipid acyl chain content and diabetes risk and demonstrate how lipid profiling could aid in clinical risk assessment.
Collapse
Affiliation(s)
- Eugene P Rhee
- Nephrology Division, Massachusetts General Hospital, Boston, Massachusetts 02129, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
259
|
Dabelea D, Dolan LM, D'Agostino R, Hernandez AM, McAteer JB, Hamman RF, Mayer-Davis EJ, Marcovina S, Lawrence JM, Pihoker C, Florez JC. Association testing of TCF7L2 polymorphisms with type 2 diabetes in multi-ethnic youth. Diabetologia 2011; 54:535-9. [PMID: 21109996 PMCID: PMC3766323 DOI: 10.1007/s00125-010-1982-7] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2010] [Accepted: 10/25/2010] [Indexed: 10/18/2022]
Abstract
AIM/HYPOTHESIS Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been associated with type 2 diabetes in adults. However, it is not known whether TCF7L2 variation increases the risk of early onset type 2 diabetes. Using a case-control design, we examined whether the reported variants [rs12255372 (T/G) and rs7903146 (T/C)] are associated with type 2 diabetes in SEARCH for Diabetes in Youth study participants. METHODS Variants were genotyped in 694 non-Hispanic white (NHW) youth (86 cases, mean age 15.5 years, mean BMI 34.8; and 608 controls, mean age 14.4 years, mean BMI 22.3) and 545 African-American (AA) youth (154 cases, mean age 15.9, mean BMI 37; and 391 controls, mean age 14.8, mean BMI 23.8). Logistic regression adjusted for age, sex, BMI and West African ancestry. RESULTS The association of the risk T allele with case/control status was different in AA and NHW youth (p = 0.025). Among AA youth, each copy of the T allele (rs7903146) was associated with a 1.97-fold (1.37, 2.82) increased odds for type 2 diabetes (p < 0.0001), after adjustment for age, sex, BMI and African ancestry. No significant association was detected in NHW youth (adjusted OR, 1.14; 0.73, 1.79). CONCLUSION/INTERPRETATION TCF7L2 variation is associated with an increased risk of early-onset type 2 diabetes among AA youth, and the association appears to be stronger in AA than NHW youth. This suggests potential different contributions of genetic and environmental factors to early-onset type 2 diabetes by race.
Collapse
Affiliation(s)
- D Dabelea
- Department of Epidemiology, Colorado School of Public Health, University of Colorado Denver, 13001 17th Place, Aurora, CO 80045, USA.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
260
|
Manning AK, LaValley M, Liu CT, Rice K, An P, Liu Y, Miljkovic I, Rasmussen-Torvik L, Harris TB, Province MA, Borecki IB, Florez JC, Meigs JB, Cupples LA, Dupuis J. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet Epidemiol 2011; 35:11-8. [PMID: 21181894 PMCID: PMC3312394 DOI: 10.1002/gepi.20546] [Citation(s) in RCA: 133] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
INTRODUCTION Genetic discoveries are validated through the meta-analysis of genome-wide association scans in large international consortia. Because environmental variables may interact with genetic factors, investigation of differing genetic effects for distinct levels of an environmental exposure in these large consortia may yield additional susceptibility loci undetected by main effects analysis. We describe a method of joint meta-analysis (JMA) of SNP and SNP by Environment (SNP × E) regression coefficients for use in gene-environment interaction studies. METHODS In testing SNP × E interactions, one approach uses a two degree of freedom test to identify genetic variants that influence the trait of interest. This approach detects both main and interaction effects between the trait and the SNP. We propose a method to jointly meta-analyze the SNP and SNP × E coefficients using multivariate generalized least squares. This approach provides confidence intervals of the two estimates, a joint significance test for SNP and SNP × E terms, and a test of homogeneity across samples. RESULTS We present a simulation study comparing this method to four other methods of meta-analysis and demonstrate that the JMA performs better than the others when both main and interaction effects are present. Additionally, we implemented our methods in a meta-analysis of the association between SNPs from the type 2 diabetes-associated gene PPARG and log-transformed fasting insulin levels and interaction by body mass index in a combined sample of 19,466 individuals from five cohorts.
Collapse
Affiliation(s)
- Alisa K Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
261
|
Renström F, Shungin D, Johansson I, Florez JC, Hallmans G, Hu FB, Franks PW. Genetic predisposition to long-term nondiabetic deteriorations in glucose homeostasis: Ten-year follow-up of the GLACIER study. Diabetes 2011; 60:345-54. [PMID: 20870969 PMCID: PMC3012192 DOI: 10.2337/db10-0933] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
OBJECTIVE To assess whether recently discovered genetic loci associated with hyperglycemia also predict long-term changes in glycemic traits. RESEARCH DESIGN AND METHODS Sixteen fasting glucose-raising loci were genotyped in middle-aged adults from the Gene x Lifestyle interactions And Complex traits Involved in Elevated disease Risk (GLACIER) Study, a population-based prospective cohort study from northern Sweden. Genotypes were tested for association with baseline fasting and 2-h postchallenge glycemia (N = 16,330), and for changes in these glycemic traits during a 10-year follow-up period (N = 4,059). RESULTS Cross-sectional directionally consistent replication with fasting glucose concentrations was achieved for 12 of 16 variants; 10 variants were also associated with impaired fasting glucose (IFG) and 7 were independently associated with 2-h postchallenge glucose concentrations. In prospective analyses, the effect alleles at four loci (GCK rs4607517, ADRA2A rs10885122, DGKB-TMEM195 rs2191349, and G6PC2 rs560887) were nominally associated with worsening fasting glucose concentrations during 10-years of follow-up. MTNR1B rs10830963, which was predictive of elevated fasting glucose concentrations in cross-sectional analyses, was associated with a protective effect on postchallenge glucose concentrations during follow-up; however, this was only when baseline fasting and 2-h glucoses were adjusted for. An additive effect of multiple risk alleles on glycemic traits was observed: a weighted genetic risk score (80th vs. 20th centiles) was associated with a 0.16 mmol/l (P = 2.4 × 10⁻⁶) greater elevation in fasting glucose and a 64% (95% CI: 33-201%) higher risk of developing IFG during 10 years of follow-up. CONCLUSIONS Our findings imply that genetic profiling might facilitate the early detection of persons who are genetically susceptible to deteriorating glucose control; studies of incident type 2 diabetes and discrete cardiovascular end points will help establish whether the magnitude of these changes is clinically relevant.
Collapse
Affiliation(s)
- Frida Renström
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Dmitry Shungin
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Odontology, Umeå University Hospital, Umeå, Sweden
| | | | - the MAGIC Investigators
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, U.K
| | - Jose C. Florez
- Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Göran Hallmans
- Department of Public Health and Clinical Medicine, Section for Nutritional Research, Umeå University Hospital, Umeå, Sweden
| | - Frank B. Hu
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
| | - Paul W. Franks
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, Massachusetts
- Department of Clinical Sciences, Skåne University Hospital, Lund University, Malmö, Sweden
- Corresponding author: Paul W. Franks,
| |
Collapse
|
262
|
de Miguel-Yanes JM, Shrader P, Pencina MJ, Fox CS, Manning AK, Grant RW, Dupuis J, Florez JC, D'Agostino RB, Cupples LA, Meigs JB. Genetic risk reclassification for type 2 diabetes by age below or above 50 years using 40 type 2 diabetes risk single nucleotide polymorphisms. Diabetes Care 2011; 34:121-5. [PMID: 20889853 PMCID: PMC3005447 DOI: 10.2337/dc10-1265] [Citation(s) in RCA: 140] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score. RESULTS In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02). CONCLUSIONS Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.
Collapse
|
263
|
Nettleton JA, McKeown NM, Kanoni S, Lemaitre RN, Hivert MF, Ngwa J, van Rooij FJA, Sonestedt E, Wojczynski MK, Ye Z, Tanaka T, Garcia M, Anderson JS, Follis JL, Djousse L, Mukamal K, Papoutsakis C, Mozaffarian D, Zillikens MC, Bandinelli S, Bennett AJ, Borecki IB, Feitosa MF, Ferrucci L, Forouhi NG, Groves CJ, Hallmans G, Harris T, Hofman A, Houston DK, Hu FB, Johansson I, Kritchevsky SB, Langenberg C, Launer L, Liu Y, Loos RJ, Nalls M, Orho-Melander M, Renstrom F, Rice K, Riserus U, Rolandsson O, Rotter JI, Saylor G, Sijbrands EJG, Sjogren P, Smith A, Steingrímsdóttir L, Uitterlinden AG, Wareham NJ, Prokopenko I, Pankow JS, van Duijn CM, Florez JC, Witteman JCM, Dupuis J, Dedoussis GV, Ordovas JM, Ingelsson E, Cupples LA, Siscovick DS, Franks PW, Meigs JB. Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies. Diabetes Care 2010; 33:2684-91. [PMID: 20693352 PMCID: PMC2992213 DOI: 10.2337/dc10-1150] [Citation(s) in RCA: 112] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Whole-grain foods are touted for multiple health benefits, including enhancing insulin sensitivity and reducing type 2 diabetes risk. Recent genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) associated with fasting glucose and insulin concentrations in individuals free of diabetes. We tested the hypothesis that whole-grain food intake and genetic variation interact to influence concentrations of fasting glucose and insulin. RESEARCH DESIGN AND METHODS Via meta-analysis of data from 14 cohorts comprising ∼ 48,000 participants of European descent, we studied interactions of whole-grain intake with loci previously associated in GWAS with fasting glucose (16 loci) and/or insulin (2 loci) concentrations. For tests of interaction, we considered a P value <0.0028 (0.05 of 18 tests) as statistically significant. RESULTS Greater whole-grain food intake was associated with lower fasting glucose and insulin concentrations independent of demographics, other dietary and lifestyle factors, and BMI (β [95% CI] per 1-serving-greater whole-grain intake: -0.009 mmol/l glucose [-0.013 to -0.005], P < 0.0001 and -0.011 pmol/l [ln] insulin [-0.015 to -0.007], P = 0.0003). No interactions met our multiple testing-adjusted statistical significance threshold. The strongest SNP interaction with whole-grain intake was rs780094 (GCKR) for fasting insulin (P = 0.006), where greater whole-grain intake was associated with a smaller reduction in fasting insulin concentrations in those with the insulin-raising allele. CONCLUSIONS Our results support the favorable association of whole-grain intake with fasting glucose and insulin and suggest a potential interaction between variation in GCKR and whole-grain intake in influencing fasting insulin concentrations.
Collapse
Affiliation(s)
- Jennifer A Nettleton
- Division of Epidemiology, Human Genetics, and Environmental Sciences, University of Texas Health Sciences Center, Houston, Houston, Texas, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
264
|
Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AFR, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Böhm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Böttcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJC, Dei M, Deloukas P, Döring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Köttgen A, Kovacs P, Krohn K, Kühnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJF, Luan J, Lyssenko V, Mägi R, Magnusson PKE, Mälarstig A, Mangino M, Martínez-Larrad MT, März W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O'Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Ríos M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tönjes A, Usala G, Vitart V, Völzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, Zabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WHL, Florez JC, Uda M, Wareham NJ, Barroso I, Meigs JB. Common variants at 10 genomic loci influence hemoglobin A₁(C) levels via glycemic and nonglycemic pathways. Diabetes 2010; 59:3229-39. [PMID: 20858683 PMCID: PMC2992787 DOI: 10.2337/db10-0502] [Citation(s) in RCA: 311] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
OBJECTIVE Glycated hemoglobin (HbA₁(c)), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA₁(c). We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA₁(c) levels. RESEARCH DESIGN AND METHODS We studied associations with HbA₁(c) in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA₁(c) loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA(1c), including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10⁻²⁶), HFE (rs1800562/P = 2.6 × 10⁻²⁰), TMPRSS6 (rs855791/P = 2.7 × 10⁻¹⁴), ANK1 (rs4737009/P = 6.1 × 10⁻¹²), SPTA1 (rs2779116/P = 2.8 × 10⁻⁹) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10⁻⁹), and four known HbA₁(c) loci: HK1 (rs16926246/P = 3.1 × 10⁻⁵⁴), MTNR1B (rs1387153/P = 4.0 × 10⁻¹¹), GCK (rs1799884/P = 1.5 × 10⁻²⁰) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10⁻¹⁸). We show that associations with HbA₁(c) are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA₁(c)) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA₁(c). CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA₁(c). Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA₁(c) levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA₁(c).
Collapse
Affiliation(s)
- Nicole Soranzo
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton, U.K
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
265
|
Abstract
Type 2 diabetes mellitus has been at the forefront of human diseases and phenotypes studied by new genetic analyses. Thanks to genome-wide association studies, we have made substantial progress in elucidating the genetic basis of type 2 diabetes. This review summarizes the concept, history, and recent discoveries produced by genome-wide association studies for type 2 diabetes and glycemic traits, with a focus on the key notions we have gleaned from these efforts. Genome-wide association findings have illustrated novel pathways, pointed toward fundamental biology, confirmed prior epidemiological observations, drawn attention to the role of β-cell dysfunction in type 2 diabetes, explained ~10% of disease heritability, tempered our expectations with regard to their use in clinical prediction, and provided possible targets for pharmacotherapy and pharmacogenetic clinical trials. We can apply these lessons to future investigation so as to improve our understanding of the genetic basis of type 2 diabetes.
Collapse
Affiliation(s)
- Liana K. Billings
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jose C. Florez
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Diabetes Research Center (Diabetes Unit), Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts
| |
Collapse
|
266
|
Yang Q, Liu T, Shrader P, Yesupriya A, Chang MH, Dowling NF, Ned RM, Dupuis J, Florez JC, Khoury MJ, Meigs JB. Racial/ethnic differences in association of fasting glucose-associated genomic loci with fasting glucose, HOMA-B, and impaired fasting glucose in the U.S. adult population. Diabetes Care 2010; 33:2370-7. [PMID: 20805255 PMCID: PMC2963497 DOI: 10.2337/dc10-0898] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To estimate allele frequencies and the marginal and combined effects of novel fasting glucose (FG)-associated single nucleotide polymorphisms (SNPs) on FG levels and on risk of impaired FG (IFG) among non-Hispanic white, non-Hispanic black, and Mexican Americans. RESEARCH DESIGN AND METHODS DNA samples from 3,024 adult fasting participants in the National Health and Nutrition Examination Survey (NHANES) III (1991-1994) were genotyped for 16 novel FG-associated SNPs in multiple genes. We determined the allele frequencies and influence of these SNPs alone and in a weighted genetic risk score on FG, homeostasis model assessment of β-cell function (HOMA-B), and IFG by race/ethnicity, while adjusting for age and sex. RESULTS All allele frequencies varied significantly by race/ethnicity. A weighted genetic risk score, based on 16 SNPs, was associated with a 0.022 mmol/l (95% CI 0.009-0.035), 0.036 mmol/l (0.019-0.052), and 0.033 mmol/l (0.020-0.046) increase in FG levels per risk allele among non-Hispanic whites, non-Hispanic blacks, and Mexican Americans, respectively. Adjusted odds ratios for IFG were 1.78 for non-Hispanic whites (95% CI 1.00-3.17), 2.40 for non-Hispanic blacks (1.07-5.37), and 2.39 for Mexican Americans (1.37-4.14) when we compared the highest with the lowest quintiles of genetic risk score (P=0.365 for testing heterogeneity of effect across race/ethnicity). CONCLUSIONS We conclude that allele frequencies of 16 novel FG-associated SNPs vary significantly by race/ethnicity, but the influence of these SNPs on FG levels, HOMA-B, and IFG were generally consistent across all racial/ethnic groups.
Collapse
Affiliation(s)
- Quanhe Yang
- Office of Public Health Genomics, Centers for Disease Control and Prevention, Atlanta, Georgia, USA.
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
267
|
Jablonski KA, McAteer JB, de Bakker PIW, Franks PW, Pollin TI, Hanson RL, Saxena R, Fowler S, Shuldiner AR, Knowler WC, Altshuler D, Florez JC. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes 2010; 59:2672-81. [PMID: 20682687 PMCID: PMC3279522 DOI: 10.2337/db10-0543] [Citation(s) in RCA: 208] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions. RESEARCH DESIGN AND METHODS We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates. RESULTS We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09-1.40, P = 7 × 10(-4)). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates. CONCLUSIONS We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.
Collapse
Affiliation(s)
- Kathleen A Jablonski
- The Biostatistics Center, George Washington University, Rockville, Maryland, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
268
|
Voight BF, Scott LJ, Steinthorsdottir V, Morris AP, Dina C, Welch RP, Zeggini E, Huth C, Aulchenko YS, Thorleifsson G, McCulloch LJ, Ferreira T, Grallert H, Amin N, Wu G, Willer CJ, Raychaudhuri S, McCarroll SA, Langenberg C, Hofmann OM, Dupuis J, Qi L, Segrè AV, van Hoek M, Navarro P, Ardlie K, Balkau B, Benediktsson R, Bennett AJ, Blagieva R, Boerwinkle E, Bonnycastle LL, Bengtsson Boström K, Bravenboer B, Bumpstead S, Burtt NP, Charpentier G, Chines PS, Cornelis M, Couper DJ, Crawford G, Doney ASF, Elliott KS, Elliott AL, Erdos MR, Fox CS, Franklin CS, Ganser M, Gieger C, Grarup N, Green T, Griffin S, Groves CJ, Guiducci C, Hadjadj S, Hassanali N, Herder C, Isomaa B, Jackson AU, Johnson PRV, Jørgensen T, Kao WHL, Klopp N, Kong A, Kraft P, Kuusisto J, Lauritzen T, Li M, Lieverse A, Lindgren CM, Lyssenko V, Marre M, Meitinger T, Midthjell K, Morken MA, Narisu N, Nilsson P, Owen KR, Payne F, Perry JRB, Petersen AK, Platou C, Proença C, Prokopenko I, Rathmann W, Rayner NW, Robertson NR, Rocheleau G, Roden M, Sampson MJ, Saxena R, Shields BM, Shrader P, Sigurdsson G, Sparsø T, Strassburger K, Stringham HM, Sun Q, Swift AJ, Thorand B, Tichet J, Tuomi T, van Dam RM, van Haeften TW, van Herpt T, van Vliet-Ostaptchouk JV, Walters GB, Weedon MN, Wijmenga C, Witteman J, Bergman RN, Cauchi S, Collins FS, Gloyn AL, Gyllensten U, Hansen T, Hide WA, Hitman GA, Hofman A, Hunter DJ, Hveem K, Laakso M, Mohlke KL, Morris AD, Palmer CNA, Pramstaller PP, Rudan I, Sijbrands E, Stein LD, Tuomilehto J, Uitterlinden A, Walker M, Wareham NJ, Watanabe RM, Abecasis GR, Boehm BO, Campbell H, Daly MJ, Hattersley AT, Hu FB, Meigs JB, Pankow JS, Pedersen O, Wichmann HE, Barroso I, Florez JC, Frayling TM, Groop L, Sladek R, Thorsteinsdottir U, Wilson JF, Illig T, Froguel P, van Duijn CM, Stefansson K, Altshuler D, Boehnke M, McCarthy MI. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2010; 42:579-89. [PMID: 20581827 DOI: 10.1038/ng.609] [Citation(s) in RCA: 1338] [Impact Index Per Article: 95.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2010] [Accepted: 05/26/2010] [Indexed: 12/11/2022]
Abstract
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.
Collapse
Affiliation(s)
- Benjamin F Voight
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
269
|
Wang TJ, Zhang F, Richards JB, Kestenbaum B, van Meurs JB, Berry D, Kiel D, Streeten EA, Ohlsson C, Koller DL, Palotie L, Cooper JD, O'Reilly PF, Houston DK, Glazer NL, Vandenput L, Peacock M, Shi J, Rivadeneira F, McCarthy MI, Anneli P, de Boer IH, Mangino M, Kato B, Smyth DJ, Booth SL, Jacques PF, Burke GL, Goodarzi M, Cheung CL, Wolf M, Rice K, Goltzman D, Hidiroglou N, Ladouceur M, Hui SL, Wareham NJ, Hocking LJ, Hart D, Arden NK, Cooper C, Malik S, Fraser WD, Hartikainen AL, Zhai G, Macdonald H, Forouhi NG, Loos RJ, Reid DM, Hakim A, Dennison E, Liu Y, Power C, Stevens HE, Jaana L, Vasan RS, Soranzo N, Bojunga J, Psaty BM, Lorentzon M, Foroud T, Harris TB, Hofman A, Jansson JO, Cauley JA, Uitterlinden AG, Gibson Q, Järvelin MR, Karasik D, Siscovick DS, Econs MJ, Kritchevsky SB, Florez JC, Todd JA, Dupuis J, Hypponen E, Spector TD. Common genetic determinants of vitamin D insufficiency: a genome-wide association study. Lancet 2010; 376:180-8. [PMID: 20541252 PMCID: PMC3086761 DOI: 10.1016/s0140-6736(10)60588-0] [Citation(s) in RCA: 1163] [Impact Index Per Article: 83.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Vitamin D is crucial for maintenance of musculoskeletal health, and might also have a role in extraskeletal tissues. Determinants of circulating 25-hydroxyvitamin D concentrations include sun exposure and diet, but high heritability suggests that genetic factors could also play a part. We aimed to identify common genetic variants affecting vitamin D concentrations and risk of insufficiency. METHODS We undertook a genome-wide association study of 25-hydroxyvitamin D concentrations in 33 996 individuals of European descent from 15 cohorts. Five epidemiological cohorts were designated as discovery cohorts (n=16 125), five as in-silico replication cohorts (n=9367), and five as de-novo replication cohorts (n=8504). 25-hydroxyvitamin D concentrations were measured by radioimmunoassay, chemiluminescent assay, ELISA, or mass spectrometry. Vitamin D insufficiency was defined as concentrations lower than 75 nmol/L or 50 nmol/L. We combined results of genome-wide analyses across cohorts using Z-score-weighted meta-analysis. Genotype scores were constructed for confirmed variants. FINDINGS Variants at three loci reached genome-wide significance in discovery cohorts for association with 25-hydroxyvitamin D concentrations, and were confirmed in replication cohorts: 4p12 (overall p=1.9x10(-109) for rs2282679, in GC); 11q12 (p=2.1x10(-27) for rs12785878, near DHCR7); and 11p15 (p=3.3x10(-20) for rs10741657, near CYP2R1). Variants at an additional locus (20q13, CYP24A1) were genome-wide significant in the pooled sample (p=6.0x10(-10) for rs6013897). Participants with a genotype score (combining the three confirmed variants) in the highest quartile were at increased risk of having 25-hydroxyvitamin D concentrations lower than 75 nmol/L (OR 2.47, 95% CI 2.20-2.78, p=2.3x10(-48)) or lower than 50 nmol/L (1.92, 1.70-2.16, p=1.0x10(-26)) compared with those in the lowest quartile. INTERPRETATION Variants near genes involved in cholesterol synthesis, hydroxylation, and vitamin D transport affect vitamin D status. Genetic variation at these loci identifies individuals who have substantially raised risk of vitamin D insufficiency. FUNDING Full funding sources listed at end of paper (see Acknowledgments).
Collapse
Affiliation(s)
- Thomas J. Wang
- Massachusetts General Hospital, Division of Cardiology, Department of Medicine, Boston MA
- Harvard Medical School, Boston MA
- Framingham Heart Study, Framingham MA
| | - Feng Zhang
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| | - J. Brent Richards
- McGill University, Jewish General Hospital, Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Montreal Canada
| | - Bryan Kestenbaum
- University of Washington, Kidney Research Institute, Department of Medicine, Division of Nephrology, Harborview Medical Center, Seattle, WA
| | - Joyce B. van Meurs
- Erasmus Medical Center, Department of Internal Medicine, Rotterdam Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam Netherlands
| | - Diane Berry
- UCL Institute of Child Health, MRC Centre of Epidemiology for Child Health and Centre for Paediatric Epidemiology and Biostatistics, London England
| | - Douglas Kiel
- Harvard Medical School, Boston MA
- Framingham Heart Study, Framingham MA
- Hebrew SeniorLife, Institute for Aging Research, Genetic Epidemiology Program, Harvard Medical School, Boston MA
| | | | - Claes Ohlsson
- University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, Department of Internal Medicine, Gothenburg Sweden
| | | | - Leena Palotie
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1HH, United Kingdom
- University of Helsinki and National Institute for Health and Welfare, Partnership for Molecular Medicine, Institute for Molecular Medicine Finland FIMM, Helsinki Finland
- National Institute for Health and Welfare, Helsinki Finland
| | - Jason D. Cooper
- University of Cambridge, JDRF/WT Diabetes and Inflammation Laboratory, Cambridge United Kingdom
| | - Paul F. O'Reilly
- Imperial College, Faculty of Medicine, Department of Epidemiology and Public Health, London England
| | - Denise K. Houston
- Wake Forest University School of Medicine, Sticht Center on Aging, Winston Salem NC
| | - Nicole L. Glazer
- University of Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle WA
| | - Liesbeth Vandenput
- University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, Department of Internal Medicine, Gothenburg Sweden
| | - Munro Peacock
- Indiana University, School of Medicine, Indianapolis Indiana
| | - Julia Shi
- University of Maryland School of Medicine, Division of Endocrinology, Baltimore MD
| | - Fernando Rivadeneira
- Erasmus Medical Center, Department of Internal Medicine, Rotterdam Netherlands
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam Netherlands
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), Oxford United Kingdom
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford, UK
| | - Pouta Anneli
- National Institute of Health and Welfare, Oulu Finland
| | - Ian H. de Boer
- University of Washington, Kidney Research Institute, Department of Medicine, Division of Nephrology, Harborview Medical Center, Seattle, WA
| | - Massimo Mangino
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| | - Bernet Kato
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| | - Deborah J. Smyth
- University of Cambridge, JDRF/WT Diabetes and Inflammation Laboratory, Cambridge United Kingdom
| | - Sarah L. Booth
- Tufts University, Jean Mayer USDA Human Nutrition Research Center on Aging, Boston MA
| | - Paul F. Jacques
- Tufts University, Jean Mayer USDA Human Nutrition Research Center on Aging, Boston MA
| | - Greg L. Burke
- Wake Forest University Health Sciences, Division of Public Health Sciences, Winston-Salem, NC
| | - Mark Goodarzi
- Cedars-Sinai Medical Center, Department of Medicine, Los Angeles CA
| | - Ching-Lung Cheung
- Harvard Medical School, Boston MA
- Hebrew SeniorLife, Institute for Aging Research, Genetic Epidemiology Program, Harvard Medical School, Boston MA
- Genome Institute of Singapore, Computational and Mathematical Biology, ASTAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Myles Wolf
- University of Miami Miller School of Medicine, Division of Nephrology and Hypertension, Miami FL
| | - Kenneth Rice
- University of Washington, Cardiovascular Health Research Unit and Department of Medicine, Seattle WA
| | - David Goltzman
- McGill University, Department of Medicine, Montreal Canada
- McGill University Health Centre, Montreal, Canada
| | | | - Martin Ladouceur
- McGill University, Jewish General Hospital, Departments of Medicine, Human Genetics, Epidemiology and Biostatistics, Montreal Canada
| | - Siu L. Hui
- Indiana University, School of Medicine, Indianapolis Indiana
| | - Nicholas J. Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Lynne J. Hocking
- University of Aberdeen, Division of Applied Medicine, Bone and Musculoskeletal Research Programme, Aberdeen United Kingdom
| | - Deborah Hart
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| | - Nigel K. Arden
- University of Southampton, MRC Epidemiology Resource Centre, Southampton England
- University of Oxford, NIHR Musculoskeletal Biomedical Research Unit, Oxford England
| | - Cyrus Cooper
- University of Southampton, MRC Epidemiology Resource Centre, Southampton England
- University of Oxford, NIHR Musculoskeletal Biomedical Research Unit, Oxford England
| | - Suneil Malik
- Office of Biotechnology, Genomics and Population Health, Public Health Agency of Canada, Toronto, Canada
| | - William D. Fraser
- Unit of Clinical Chemistry, School of Clinical Sciences, University of Liverpool, Liverpool
| | | | - Guangju Zhai
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| | - Helen Macdonald
- University of Aberdeen, Division of Applied Medicine, Bone and Musculoskeletal Research Programme, Aberdeen United Kingdom
| | - Nita G. Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Ruth J.F. Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - David M. Reid
- University of Aberdeen, Division of Applied Medicine, Bone and Musculoskeletal Research Programme, Aberdeen United Kingdom
| | - Alan Hakim
- Whipps Cross Rheumatology Department, London England
| | - Elaine Dennison
- University of Southampton, MRC Epidemiology Resource Centre, Southampton England
| | - Yongmei Liu
- Wake Forest University School of Medicine, Sticht Center on Aging, Winston Salem NC
| | - Chris Power
- UCL Institute of Child Health, MRC Centre of Epidemiology for Child Health and Centre for Paediatric Epidemiology and Biostatistics, London England
| | - Helen E. Stevens
- University of Cambridge, JDRF/WT Diabetes and Inflammation Laboratory, Cambridge United Kingdom
| | - Laitinen Jaana
- Finnish Institute of Occupational Health, Oulu Finland
- University of Oulu, Institute of Health Sciences, Oulu Finland
| | - Ramachandran S. Vasan
- Framingham Heart Study, Framingham MA
- Boston University School of Medicine, Division of Preventive Medicine, Boston MA
| | - Nicole Soranzo
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
- Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1HH, United Kingdom
| | - Jörg Bojunga
- Klinikum der Johann Wolfgang Goethe University, Frankfurt Germany
| | - Bruce M. Psaty
- University of Washington, Departments of Medicine, Epidemiology and Health Services, Seattle WA
| | - Mattias Lorentzon
- University of Gothenburg, Sahlgrenska Academy, Institute of Medicine, Department of Internal Medicine, Gothenburg Sweden
| | - Tatiana Foroud
- Indiana University, School of Medicine, Indianapolis Indiana
| | - Tamara B. Harris
- National Institutes of Health, National Institute on Aging, Bethesda MD
| | - Albert Hofman
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam Netherlands
- Erasmus Medical Center, Department of Epidemiology, Rotterdam Netherlands
| | - John-Olov Jansson
- University of Gothenburg, Sahlgrenska Academy, Institute of Neuroscience and Physiology, Department of Physiology, Gothenburg Sweden
| | - Jane A. Cauley
- University of Pittsburgh, Department of Epidemiology, Pittsburgh PA
| | - Andre G. Uitterlinden
- Netherlands Genomics Initiative (NGI)-sponsored Netherlands Consortium for Healthy Aging (NCHA), Rotterdam Netherlands
- Erasmus Medical Center, Departments of Internal, Epidemiology and Klinical Genetics, Rotterdam Netherlands
| | - Quince Gibson
- Erasmus Medical Center, Department of Internal Medicine, Rotterdam Netherlands
| | - Marjo-Riitta Järvelin
- Imperial College, Faculty of Medicine, Department of Epidemiology and Public Health, London England
- National Institute of Health and Welfare, Oulu Finland
- University of Oulu, Institute of Health Sciences, Oulu Finland
- University of Oulu, Biocenter Oulu, Oulu Finland
| | - David Karasik
- Harvard Medical School, Boston MA
- Hebrew SeniorLife, Institute for Aging Research, Genetic Epidemiology Program, Harvard Medical School, Boston MA
| | - David S. Siscovick
- University of Washington, Cardiovascular Health Research Unit and Departments of Medicine and Epidemiology, Seattle WA
| | | | | | - Jose C. Florez
- Harvard Medical School, Boston MA
- Massachusetts General Hospital, Diabetes Research Center (Diabetes Unit) and Center for Human Genetic Research, Boston MA
- Broad Institute, Program in Medical and Population Genetics, Cambridge MA
| | - John A. Todd
- University of Cambridge, JDRF/WT Diabetes and Inflammation Laboratory, Cambridge United Kingdom
| | - Josee Dupuis
- Framingham Heart Study, Framingham MA
- Boston University School of Public Health, Department of Biostatistics, Boston MA
| | - Elina Hypponen
- UCL Institute of Child Health, MRC Centre of Epidemiology for Child Health and Centre for Paediatric Epidemiology and Biostatistics, London England
| | - Timothy D. Spector
- King's College London, Department of Twin Research and Genetic Epidemiology, London England
| |
Collapse
|
270
|
Ingelsson E, Langenberg C, Hivert MF, Prokopenko I, Lyssenko V, Dupuis J, Mägi R, Sharp S, Jackson AU, Assimes TL, Shrader P, Knowles JW, Zethelius B, Abbasi FA, Bergman RN, Bergmann A, Berne C, Boehnke M, Bonnycastle LL, Bornstein SR, Buchanan TA, Bumpstead SJ, Böttcher Y, Chines P, Collins FS, Cooper CC, Dennison EM, Erdos MR, Ferrannini E, Fox CS, Graessler J, Hao K, Isomaa B, Jameson KA, Kovacs P, Kuusisto J, Laakso M, Ladenvall C, Mohlke KL, Morken MA, Narisu N, Nathan DM, Pascoe L, Payne F, Petrie JR, Sayer AA, Schwarz PEH, Scott LJ, Stringham HM, Stumvoll M, Swift AJ, Syvänen AC, Tuomi T, Tuomilehto J, Tönjes A, Valle TT, Williams GH, Lind L, Barroso I, Quertermous T, Walker M, Wareham NJ, Meigs JB, McCarthy MI, Groop L, Watanabe RM, Florez JC. Detailed physiologic characterization reveals diverse mechanisms for novel genetic Loci regulating glucose and insulin metabolism in humans. Diabetes 2010; 59:1266-75. [PMID: 20185807 PMCID: PMC2857908 DOI: 10.2337/db09-1568] [Citation(s) in RCA: 194] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action. RESEARCH DESIGN AND METHODS We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084). RESULTS The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 x 10(-71)). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction. CONCLUSIONS Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.
Collapse
Affiliation(s)
- Erik Ingelsson
- Corresponding authors: Erik Ingelsson, ; Leif Groop, ; Richard M. Watanabe, ; Jose C. Florez,
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | - Leif Groop
- Corresponding authors: Erik Ingelsson, ; Leif Groop, ; Richard M. Watanabe, ; Jose C. Florez,
| | - Richard M. Watanabe
- Corresponding authors: Erik Ingelsson, ; Leif Groop, ; Richard M. Watanabe, ; Jose C. Florez,
| | - Jose C. Florez
- Corresponding authors: Erik Ingelsson, ; Leif Groop, ; Richard M. Watanabe, ; Jose C. Florez,
| | | |
Collapse
|
271
|
Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Mägi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JRB, Egan JM, Lajunen T, Grarup N, Sparsø T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proença C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O'Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YDI, Chines P, Clarke R, Coin LJM, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day INM, de Geus EJC, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PRV, Jørgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orrù M, Pakyz R, Palmer CNA, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AFH, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, SigurÐsson G, Sijbrands EJG, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Thorand B, Tichet J, Tönjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JCM, Yarnell JWG, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Consortium DIAGRAM, Consortium GIANT, Consortium GBP, Borecki IB, Loos RJF, Meneton P, Magnusson PKE, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WHL, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Hamsten A, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BWJH, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I. Erratum: New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010. [DOI: 10.1038/ng0510-464a] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
|
272
|
Qi L, Cornelis MC, Kraft P, Stanya KJ, Linda Kao WH, Pankow JS, Dupuis J, Florez JC, Fox CS, Paré G, Sun Q, Girman CJ, Laurie CC, Mirel DB, Manolio TA, Chasman DI, Boerwinkle E, Ridker PM, Hunter DJ, Meigs JB, Lee CH, Hu FB, van Dam RM. Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum Mol Genet 2010; 19:2706-15. [PMID: 20418489 DOI: 10.1093/hmg/ddq156] [Citation(s) in RCA: 159] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
To identify type 2 diabetes (T2D) susceptibility loci, we conducted genome-wide association (GWA) scans in nested case-control samples from two prospective cohort studies, including 2591 patients and 3052 controls of European ancestry. Validation was performed in 11 independent GWA studies of 10,870 cases and 73,735 controls. We identified significantly associated variants near RBMS1 and ITGB6 genes at 2q24, best-represented by SNP rs7593730 (combined OR=0.90, 95% CI=0.86-0.93; P=3.7x10(-8)). The frequency of the risk-lowering allele T is 0.23. Variants in this region were nominally related to lower fasting glucose and HOMA-IR in the MAGIC consortium (P<0.05). These data suggest that the 2q24 locus may influence the T2D risk by affecting glucose metabolism and insulin resistance.
Collapse
Affiliation(s)
- Lu Qi
- Department of Nutrition, Harvard School of Public Health, and Brigham and Women's Hospital, Boston, MA, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
273
|
Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Mägi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JRB, Egan JM, Lajunen T, Grarup N, Sparsø T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proença C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YDI, Chines P, Clarke R, Coin LJM, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day INM, de Geus E, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PRV, Jørgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martínez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orrù M, Pakyz R, Palmer CNA, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AFH, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurðsson G, Sijbrands EJG, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvänen AC, Tanaka T, Thorand B, Tichet J, Tönjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JCM, Yarnell JWG, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Borecki IB, Loos RJF, Meneton P, Magnusson PKE, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Ríos M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WHL, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Hamsten A, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BWJH, Boomsma D, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010; 42:105-16. [PMID: 20081858 PMCID: PMC3018764 DOI: 10.1038/ng.520] [Citation(s) in RCA: 1655] [Impact Index Per Article: 118.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2009] [Accepted: 10/14/2009] [Indexed: 02/08/2023]
Abstract
Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.
Collapse
Affiliation(s)
- Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
- National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts 01702, USA
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Inga Prokopenko
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Richa Saxena
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Nicole Soranzo
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Twin Research & Genetic Epidemiology Department, King’s College London, St Thomas' Hospital Campus, Lambeth Palace Rd, London SE1 7EH, UK
| | - Anne U Jackson
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Eleanor Wheeler
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Nicole L Glazer
- Cardiovascular Health Research Unit and Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Nabila Bouatia-Naji
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Anna L Gloyn
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | - Cecilia M Lindgren
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Reedik Mägi
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Andrew P Morris
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Joshua Randall
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Toby Johnson
- Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
- University Institute of Social and Preventative Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, 1005 Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Switzerland
| | - Paul Elliott
- Department of Epidemiology and Public Health, Imperial College of London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK
| | - Denis Rybin
- Boston University Data Coordinating Center, Boston, Massachusetts 02118, USA
| | | | | | - Peter Henneman
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Harald Grallert
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Abbas Dehghan
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Jouke Jan Hottenga
- Department of Biological Psychology, VU, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | | | - Pau Navarro
- MRC Human Genetics Unit, IGMM, Edinburgh EH4 2XU, UK
| | - Kijoung Song
- Division of Genetics, R&D, Glaxo SmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Anuj Goel
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Department of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - John R B Perry
- Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter EX1 2LU, UK
| | - Josephine M Egan
- Laboratory of Clinical Investigation, National Institute of Aging, Baltimore, Maryland 21250, USA
| | - Taina Lajunen
- Unit for Child and Adolescent Health and Welfare, National Institute for Health and Welfare, Biocenter Oulu, University of Oulu, 90014 Oulu, Finland
| | - Niels Grarup
- Hagedorn Research Institute, 2820 Gentofte, Denmark
| | | | - Alex Doney
- Department of Medicine & Therapeutics, Level 7, Ninewells Hospital & Medical School, Dundee DD1 9SY, UK
| | - Benjamin F Voight
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Heather M Stringham
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Man Li
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21287, USA
| | - Stavroula Kanoni
- Department of Nutrition - Dietetics, Harokopio University, 17671 Athens, Greece
| | - Peter Shrader
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | - Meena Kumari
- Department of Epidemiology and Public Health, University College London, UK
| | - Lu Qi
- Depts. of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Nicholas J Timpson
- MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol BS8 2PR, UK
| | - Christian Gieger
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Carina Zabena
- Fundación para la Investigación Biomédica del Hospital Clínico San Carlos, Madrid, Spain
| | - Ghislain Rocheleau
- Departments of Medicine and Human Genetics, McGill University, Montreal, Canada
- Genome Quebec Innovation Centre, Montreal H3A 1A4, Canada
| | - Erik Ingelsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
| | - Ping An
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Jeffrey O’Connell
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Jian'an Luan
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Amanda Elliott
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Steven A McCarroll
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
| | - Felicity Payne
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Rosa Maria Roccasecca
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - François Pattou
- INSERM U859, Universite de Lille-Nord de France, F-59000 Lille, France
| | - Praveen Sethupathy
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Kristin Ardlie
- The Broad Institute, Cambridge, Massachusetts 02141, USA
| | - Yavuz Ariyurek
- Leiden Genome Technology Center, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
| | - Beverley Balkau
- INSERM U780-IFR69, Paris Sud University, F-94807 Villejuif, France
| | - Philip Barter
- The Heart Research Institute, Sydney, New South Wales, Australia
| | - John P Beilby
- PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, NEDLANDS WA 6009, Australia
- School of Surgery and Pathology, University of Western Australia, Nedlands WA 6009, Australia
| | - Yoav Ben-Shlomo
- Department of Social Medicine, University of Bristol, Bristol BS8 2PR, UK
| | - Rafn Benediktsson
- Landspitali University Hospital, 101 Reykjavik, Iceland
- Icelandic Heart Association, 201 Kopavogur, Iceland
| | - Amanda J Bennett
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | - Sven Bergmann
- Department of Medical Genetics, University of Lausanne, 1005 Lausanne, Switzerland
- University Institute of Social and Preventative Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, 1005 Lausanne, Switzerland
| | - Murielle Bochud
- University Institute of Social and Preventative Medicine, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne, 1005 Lausanne, Switzerland
| | - Eric Boerwinkle
- The Human Genetics Center and Institute of Molecular Medicine, University of Texas Health Science Center, Houston, Texas 77030, USA
| | - Amélie Bonnefond
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Lori L Bonnycastle
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Knut Borch-Johnsen
- Steno Diabetes Center, DK-2820 Gentofte, Copenhagen, Denmark
- Faculty of Health Science, University of Aarhus, Aarhus DK-8000, Denmark
| | - Yvonne Böttcher
- Department of Medicine, University of Leipzig, Liebigstr. 18, 04103 Leipzig, Germany
| | - Eric Brunner
- Department of Epidemiology and Public Health, University College London, UK
| | | | | | - Yii-Der Ida Chen
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Peter Chines
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford OX3 7LF, UK
| | - Lachlan J M Coin
- Department of Epidemiology and Public Health, Imperial College of London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK
| | - Matthew N Cooper
- Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia
| | - Marilyn Cornelis
- Depts. of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
| | - Gabe Crawford
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
| | - Laura Crisponi
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Ian N M Day
- MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol BS8 2PR, UK
| | - Eco de Geus
- Department of Biological Psychology, VU, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | - Jerome Delplanque
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Christian Dina
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Michael R Erdos
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Annette C Fedson
- Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
| | - Antje Fischer-Rosinsky
- Department of Endocrinology, Diabetes and Nutrition, Charite-Universitaetsmedizin Berlin, Berlin, Germany
- Department of Clinical Nutrition, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Nita G Forouhi
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Caroline S Fox
- National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Massachusetts 01702, USA
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Rune Frants
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Maria Grazia Franzosi
- Department of Cardiovascular Research, Istituto di Ricerche Farmacologiche 'Mario Negri', Milan, Italy
| | - Pilar Galan
- U557 Institut National de la Santé et de la Recherche Médicale, U1125 Institut National de la Recherche Agronomique, Université Paris 13, 74 rue Marcel Cachin, 93017 Bobigny Cedex, France
| | - Mark O Goodarzi
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Jürgen Graessler
- Department of Medicine III, Division Prevention and Care of Diabetes, University of Dresden, 01307 Dresden
| | - Christopher J Groves
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | - Scott Grundy
- Center for Human Nutrition, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Rhian Gwilliam
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Ulf Gyllensten
- Department of Genetics and Pathology, Rudbeck Laboratory, Uppsala University, S-751 85 Uppsala, Sweden
| | - Samy Hadjadj
- CHU de Poitiers, Endocrinologie Diabetologie, CIC INSERM 0802, INSERM U927, Université de Poitiers, UFR, Médecine Pharmacie, Poitiers, France
| | - Göran Hallmans
- Department of Public Health & Clinical Medicine, Section for Nutritional Research, Umeå University, Umeå, Sweden
| | - Naomi Hammond
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Xijing Han
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Anna-Liisa Hartikainen
- Department of Clinical Sciences, Obstetrics and Gynecology, University of Oulu, Box 5000, Fin-90014 University of Oulu, Finland
| | - Neelam Hassanali
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | | | - Simon C Heath
- Centre National de Génotypage/IG/CEA, 2 rue Gaston Crémieux CP 5721, 91057 Evry Cedex, France
| | - Serge Hercberg
- U872 Institut National de la Santé et de la Recherche Médicale, Faculté de Médecine Paris Descartes, 15 rue de l’Ecole de Médecine, 75270 Paris Cedex, France
| | - Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Andrew A Hicks
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy, Affiliated Institute of the University Lübeck, Germany
| | - David R Hillman
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
- Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Perth, Australia
| | - Aroon D Hingorani
- Department of Epidemiology and Public Health, University College London, UK
| | - Albert Hofman
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Jennie Hui
- PathWest Laboratory of Western Australia, Department of Molecular Genetics, J Block, QEII Medical Centre, NEDLANDS WA 6009, Australia
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Perth, Australia
| | - Joe Hung
- Heart Institute of Western Australia, Sir Charles Gairdner Hospital, Nedlands WA 6009, Australia
- School of Medicine and Pharmacology, University of Western Australia, Nedlands, WA 6009, Australia
| | - Bo Isomaa
- Folkhalsan Research Centre, Helsinki, Finland
- Malmska Municipal Health Care Center and Hospital, Jakobstad, Finland
| | - Paul R V Johnson
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Nuffield Department of Surgery, University of Oxford, Oxford OX3 9DU, UK
| | - Torben Jørgensen
- Research Centre for Prevention and Health, Glostrup University Hospital, Glostrup, Denmark
- Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
| | - Antti Jula
- National Institute for Health and Welfare, Unit of Population Studies, Turku, Finland
| | - Marika Kaakinen
- Institute of Health Sciences and Biocenter Oulu, Box 5000, Fin-90014 University of Oulu, Finland
| | - Jaakko Kaprio
- Department of Public Health, Faculty of Medicine, P.O. Box 41 (Mannerheimintie 172), University of Helsinki, 00014 Helsinki, Finland
- National Institute for Health and Welfare, Unit for Child and Adolescent Mental Health, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | | | - Mika Kivimaki
- Department of Epidemiology and Public Health, University College London, UK
| | - Beatrice Knight
- Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 5DW, UK
| | - Seppo Koskinen
- National Institute for Health and Welfare, Unit of Living Conditions, Health and Wellbeing, Helsinki, Finland
| | - Peter Kovacs
- Interdisciplinary Centre for Clinical Research, University of Leipzig, Inselstr. 22, 04103 Leipzig, Germany
| | - Kirsten Ohm Kyvik
- The Danish Twin Registry, Epidemiology, Institute of Public Health, University of Southern Denmark, J.B. Winsløws Vej 9B, 5000 Odense, Denmark
| | - G Mark Lathrop
- Centre National de Génotypage/IG/CEA, 2 rue Gaston Crémieux CP 5721, 91057 Evry Cedex, France
| | - Debbie A Lawlor
- MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol BS8 2PR, UK
| | - Olivier Le Bacquer
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Cécile Lecoeur
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Yun Li
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Valeriya Lyssenko
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, University Hospital Malmo, Malmo, Sweden
| | - Robert Mahley
- Gladstone Institute of Cardiovascular Disease, University of California, San Francisco, California, USA
| | - Massimo Mangino
- Twin Research & Genetic Epidemiology Department, King’s College London, St Thomas' Hospital Campus, Lambeth Palace Rd, London SE1 7EH, UK
| | - Alisa K Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts 02118, USA
| | | | - Jarred B McAteer
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Laura J McCulloch
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
| | - Ruth McPherson
- Division of Cardiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada
| | - Christa Meisinger
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - David Melzer
- Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter EX1 2LU, UK
| | - David Meyre
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Mario A Morken
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Sutapa Mukherjee
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
- Department of Pulmonary Physiology, Sir Charles Gairdner Hospital, Perth, Australia
| | - Silvia Naitza
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Narisu Narisu
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Matthew J Neville
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Ben A Oostra
- Department of Clinical Genetics, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Marco Orrù
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Ruth Pakyz
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Colin N A Palmer
- Biomedical Research Institute, University of Dundee, Ninewells Hospital & Medical School, Dundee DD1 9SY, UK
| | - Giuseppe Paolisso
- Department of Geriatric Medicine and Metabolic Disease, Second University of Naples, Naples, Italy
| | - Cristian Pattaro
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy, Affiliated Institute of the University Lübeck, Germany
| | - Daniel Pearson
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - John F Peden
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Department of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Nancy L. Pedersen
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Markus Perola
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Unit of Public Health Genomics, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Andreas F H Pfeiffer
- Department of Endocrinology, Diabetes and Nutrition, Charite-Universitaetsmedizin Berlin, Berlin, Germany
- Department of Clinical Nutrition, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Irene Pichler
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy, Affiliated Institute of the University Lübeck, Germany
| | - Ozren Polasek
- Department of Medical Statistics, Epidemiology and Medical Informatics, Andrija Stampar School of Public Health, Medical School, University of Zagreb, Rockefellerova 4, 10000 Zagreb, Croatia
| | - Danielle Posthuma
- Department of Biological Psychology, VU, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
- Department of Clinical Genetics, VUMC, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands
| | - Simon C Potter
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Anneli Pouta
- Department of Obstetrics and Gynaecology, Oulu University Hospital, Oulu, Finland
| | - Michael A Province
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Bruce M Psaty
- Departments of Medicine, Epidemiology, and Health Services, University of Washington, Seattle, Washington, USA
- Group Health Center for Health Studies, Seattle, Washington, USA
| | - Wolfgang Rathmann
- Institute of Biometrics and Epidemiology, German Diabetes Centre, Leibniz Centre at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Nigel W Rayner
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Kenneth Rice
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Unit of Public Health Genomics, Helsinki, Finland
| | - Fernando Rivadeneira
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
- Department of Internal Medicine, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
- Department of Medicine/Metabolic Diseases, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
| | - Olov Rolandsson
- Department of Public Health & Clinical Medicine, Section for Family Medicine, Umeå University Hospital, Umeå, Sweden
| | - Annelli Sandbaek
- School of Public Health, Department of General Practice, University of Aarhus, Aarhus DK-8000, Denmark
| | - Manjinder Sandhu
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
- Department of Public Health and Primary Care, Strangeways Research Laboratory, University of Cambridge, Cambridge, UK
| | - Serena Sanna
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Avan Aihie Sayer
- MRC Epidemiology Resource Centre, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK
| | - Paul Scheet
- Department of Epidemiology, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, 77030, USA
| | - Laura J Scott
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Udo Seedorf
- Leibniz-Institut für Arterioskleroseforschung an der Universität Münster,Münster, Germany
| | - Stephen J Sharp
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Beverley Shields
- Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 5DW, UK
| | - Gunnar Sigurðsson
- Landspitali University Hospital, 101 Reykjavik, Iceland
- Icelandic Heart Association, 201 Kopavogur, Iceland
| | - Erik J G Sijbrands
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
- Department of Internal Medicine, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Angela Silveira
- Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Laila Simpson
- Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
| | - Andrew Singleton
- Laboratory of Neurogenetics, National Institute on Aging, Bethesda, Maryland 20892, USA
| | - Nicholas L Smith
- Department of Epidemiology, University of Washington, Seattle, Washington 98195, USA
- Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, Washington, USA
| | - Ulla Sovio
- Department of Epidemiology and Public Health, Imperial College of London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK
| | - Amy Swift
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Holly Syddall
- MRC Epidemiology Resource Centre, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK
| | | | - Toshiko Tanaka
- Medstar Research Institute, Baltimore, Maryland 21250, USA
- Clinical Research Branch, National Institute on Aging, Baltimore, Maryland 21250, USA
| | - Barbara Thorand
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Jean Tichet
- Institut interrégional pour la santé (IRSA), F-37521 La Riche, France
| | - Anke Tönjes
- Department of Medicine, University of Leipzig, Liebigstr. 18, 04103 Leipzig, Germany
- Coordination Centre for Clinical Trials, University of Leipzig, Härtelstr. 16-18, 04103 Leipzig, Germany
| | - Tiinamaija Tuomi
- Folkhalsan Research Centre, Helsinki, Finland
- Department of Medicine, Helsinki University Hospital, University of Helsinki, Helsinki, Finland
| | - André G Uitterlinden
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
- Department of Internal Medicine, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Ko Willems van Dijk
- Department of Human Genetics, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
- Department of Internal Medicine, Leiden University Medical Centre, 2300 RC Leiden, The Netherlands
| | - Mandy van Hoek
- Department of Internal Medicine, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Dhiraj Varma
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Sophie Visvikis-Siest
- Research Unit, Cardiovascular Genetics, Nancy University Henri Poincaré, Nancy, France
| | | | - Nicole Vogelzangs
- EMGO Institute/Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Gérard Waeber
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Peter J Wagner
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Unit of Public Health Genomics, Helsinki, Finland
| | - Andrew Walley
- Genomic Medicine, Imperial College London, Hammersmith Hospital, W12 0NN, London, UK
| | | | - Kim L Ward
- Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
| | - Hugh Watkins
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Department of Cardiovascular Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - Michael N Weedon
- Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter EX1 2LU, UK
| | - Sarah H Wild
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Gonneke Willemsen
- Department of Biological Psychology, VU, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | | | - John W G Yarnell
- Epidemiology & Public Health, Queen's University Belfast, Belfast BT12 6BJ, UK
| | - Eleftheria Zeggini
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Diana Zelenika
- Centre National de Génotypage/IG/CEA, 2 rue Gaston Crémieux CP 5721, 91057 Evry Cedex, France
| | - Björn Zethelius
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden
- Medical Products Agency, Uppsala, Sweden
| | - Guangju Zhai
- Twin Research & Genetic Epidemiology Department, King’s College London, St Thomas' Hospital Campus, Lambeth Palace Rd, London SE1 7EH, UK
| | - Jing Hua Zhao
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - M Carola Zillikens
- Department of Internal Medicine, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | | | | | | | - Ingrid B Borecki
- Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ruth J F Loos
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Pierre Meneton
- U872 Institut National de la Santé et de la Recherche Médicale, Faculté de Médecine Paris Descartes, 15 rue de l’Ecole de Médecine, 75270 Paris Cedex, France
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - David M Nathan
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Gordon H Williams
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Andrew T Hattersley
- Diabetes Genetics, Institute of Biomedical and Clinical Science, Peninsula College of Medicine and Dentistry, University of Exeter, Exeter EX2 5DW, UK
| | - Kaisa Silander
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Unit of Public Health Genomics, Helsinki, Finland
| | - Veikko Salomaa
- National Institute for Health and Welfare, Unit of Chronic Disease Epidemiology and Prevention, Helsinki, Finland
| | - George Davey Smith
- MRC Centre for Causal Analyses in Translational Epidemiology, University of Bristol, Bristol BS8 2PR, UK
| | - Stefan R Bornstein
- Department of Medicine III, Division Prevention and Care of Diabetes, University of Dresden, 01307 Dresden
| | - Peter Schwarz
- Department of Medicine III, Division Prevention and Care of Diabetes, University of Dresden, 01307 Dresden
| | - Joachim Spranger
- Department of Endocrinology, Diabetes and Nutrition, Charite-Universitaetsmedizin Berlin, Berlin, Germany
- Department of Clinical Nutrition, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany
| | - Fredrik Karpe
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Alan R Shuldiner
- Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland 21201, USA
| | - Cyrus Cooper
- MRC Epidemiology Resource Centre, University of Southampton, Southampton General Hospital, Southampton SO16 6YD, UK
| | - George V Dedoussis
- Department of Nutrition - Dietetics, Harokopio University, 17671 Athens, Greece
| | - Manuel Serrano-Ríos
- Fundación para la Investigación Biomédica del Hospital Clínico San Carlos, Madrid, Spain
| | - Andrew D Morris
- Biomedical Research Institute, University of Dundee, Ninewells Hospital & Medical School, Dundee DD1 9SY, UK
| | - Lars Lind
- Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Lyle J Palmer
- Centre for Genetic Epidemiology and Biostatistics, University of Western Australia, Perth, Australia
- Western Australian Sleep Disorders Research Institute, Queen Elizabeth Medical Centre II, Perth, Australia
- Busselton Population Medical Research Foundation, Sir Charles Gairdner Hospital, Perth, Australia
| | - Frank B. Hu
- Departments of Nutrition and Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA
- Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Paul W Franks
- Genetic Epidemiology & Clinical Research Group, Department of Public Health & Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden
| | - Shah Ebrahim
- London School of Hygiene and Tropical Medicine, London WC1E 7HT, UK
| | - Michael Marmot
- Department of Epidemiology and Public Health, University College London, UK
| | - W H Linda Kao
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21287, USA
- Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21287, USA
- The Welch Center for Prevention, Epidemiology, and Clinical Research, School of Medicine and Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21287, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota 55454, USA
| | - Michael J Sampson
- Department of Endocrinology and Diabetes, Norfolk and Norwich University Hospital NHS Trust, Norwich, NR1 7UY, UK
| | - Johanna Kuusisto
- Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio 70210, Finland
| | - Markku Laakso
- Department of Medicine, University of Kuopio and Kuopio University Hospital, Kuopio 70210, Finland
| | - Torben Hansen
- Hagedorn Research Institute, 2820 Gentofte, Denmark
- Faculty of Health Science, University of Southern Denmark, Odense, Denmark
| | - Oluf Pedersen
- Hagedorn Research Institute, 2820 Gentofte, Denmark
- Faculty of Health Science, University of Aarhus, Aarhus DK-8000, Denmark
- Institute of Biomedical Science, Faculty of Health Science, University of Copenhagen, Denmark
| | - Peter Paul Pramstaller
- Institute of Genetic Medicine, European Academy Bozen/Bolzano (EURAC), Viale Druso 1, 39100 Bolzano, Italy, Affiliated Institute of the University Lübeck, Germany
- Department of Neurology, General Central Hospital, 39100 Bolzano, Italy
- Department of Neurology, University of Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - H Erich Wichmann
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Institute of Medical Informatics, Biometry and Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
- Klinikum Grosshadern, Munich, Germany
| | - Thomas Illig
- Institute of Epidemiology, Helmholtz Zentrum Muenchen, German Research Center for Environmental Health, 85764 Neuherberg, Germany
| | - Igor Rudan
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh EH8 9AG, UK
- School of Medicine, University of Split, Soltanska 2, 21000 Split, Croatia
- Gen-Info Ltd, Ruzmarinka 17, 10000 Zagreb, Croatia
| | - Alan F Wright
- MRC Human Genetics Unit, IGMM, Edinburgh EH4 2XU, UK
| | - Michael Stumvoll
- Department of Medicine, University of Leipzig, Liebigstr. 18, 04103 Leipzig, Germany
| | - Harry Campbell
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - James F Wilson
- Centre for Population Health Sciences, University of Edinburgh, Edinburgh EH8 9AG, UK
| | - Anders Hamsten
- Atherosclerosis Research Unit, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Richard N Bergman
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
| | - Thomas A Buchanan
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
- Department of Medicine, Division of Endocrinology, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
| | - Francis S Collins
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Karen L Mohlke
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599, USA
| | - Jaakko Tuomilehto
- Department of Public Health, Faculty of Medicine, P.O. Box 41 (Mannerheimintie 172), University of Helsinki, 00014 Helsinki, Finland
- National Institute for Health and Welfare, Unit of Diabetes Prevention, Helsinki, Finland
| | - Timo T Valle
- National Institute for Health and Welfare, Unit of Diabetes Prevention, Helsinki, Finland
| | - David Altshuler
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Jerome I Rotter
- Medical Genetics Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - David S Siscovick
- Departments of Medicine and Epidemiology, University of Washington, Seattle, Washington, USA
| | - Brenda W J H Penninx
- EMGO Institute/Department of Psychiatry, VU University Medical Center, Amsterdam, The Netherlands
| | - Dorret Boomsma
- Department of Biological Psychology, VU, Van der Boechorststraat 1, 1081 BT Amsterdam, The Netherlands
| | - Panos Deloukas
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Timothy D Spector
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Twin Research & Genetic Epidemiology Department, King’s College London, St Thomas' Hospital Campus, Lambeth Palace Rd, London SE1 7EH, UK
| | - Timothy M Frayling
- Genetics of Complex Traits, Institute of Biomedical and Clinical Sciences, Peninsula College of Medicine and Dentistry, University of Exeter EX1 2LU, UK
| | - Luigi Ferrucci
- Longitudinal Studies Section, Clinical Research Branch, National Institute on Aging, NIH, Baltimore, Maryland, USA
| | | | - Unnur Thorsteinsdottir
- deCODE Genetics, 101 Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland
| | - Kari Stefansson
- deCODE Genetics, 101 Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101 Reykjavík, Iceland
| | | | - Yurii S Aulchenko
- Department of Epidemiology, Erasmus MC Rotterdam, 3000 CA, The Netherlands
| | - Antonio Cao
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Angelo Scuteri
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
- Lab of Cardiovascular Sciences, National Institute on Aging, NIH, Baltimore, Maryland, USA
| | - David Schlessinger
- Genome Technology Branch, National Human Genome Research Institute, Bethesda, Maryland 20892, USA
| | - Manuela Uda
- Istituto di Neurogenetica e Neurofarmacologia (INN), Consiglio Nazionale delle Ricerche, c/o Cittadella Universitaria di Monserrato, Monserrato, Cagliari 09042, Italy
| | - Aimo Ruokonen
- Department of Clinical Sciences/Clinical Chemistry, University of Oulu, Box 5000, Fin-90014 University of Oulu, Finland
| | - Marjo-Riitta Jarvelin
- Department of Epidemiology and Public Health, Imperial College of London, Faculty of Medicine, Norfolk Place, London W2 1PG, UK
- Institute of Health Sciences and Biocenter Oulu, Box 5000, Fin-90014 University of Oulu, Finland
- National Institute of Health and Welfare, Aapistie 1, P.O. Box 310, Fin-90101 Oulu, Finland
| | - Dawn M Waterworth
- Division of Genetics, R&D, Glaxo SmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Peter Vollenweider
- Department of Internal Medicine, Centre Hospitalier Universitaire Vaudois, 1011 Lausanne, Switzerland
| | - Leena Peltonen
- Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- The Broad Institute, Cambridge, Massachusetts 02141, USA
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
- National Institute for Health and Welfare, Unit of Public Health Genomics, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Vincent Mooser
- Division of Genetics, R&D, Glaxo SmithKline, King of Prussia, Pennsylvania 19406, USA
| | - Goncalo R Abecasis
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Nicholas J Wareham
- MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Robert Sladek
- Departments of Medicine and Human Genetics, McGill University, Montreal, Canada
- Genome Quebec Innovation Centre, Montreal H3A 1A4, Canada
| | - Philippe Froguel
- CNRS-UMR8090, Pasteur Institute, Lille 2-Droit et Santé University, F-59000 Lille, France
- Genomic Medicine, Imperial College London, Hammersmith Hospital, W12 0NN, London, UK
| | - Richard M Watanabe
- Department of Physiology and Biophysics, Keck School of Medicine, University of Southern California, Los Angeles, California 90033, USA
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, 90033, USA
| | - James B Meigs
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Leif Groop
- Department of Clinical Sciences, Diabetes and Endocrinology, Lund University, University Hospital Malmo, Malmo, Sweden
| | - Michael Boehnke
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, Michigan 48109, USA
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK
- Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
- Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK
| | - Jose C Florez
- Program in Medical and Population Genetics, Broad Institute, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Inês Barroso
- Metabolic Disease Group, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | | |
Collapse
|
274
|
Affiliation(s)
- Jose C Florez
- Massachusetts General Hospital/Harvard Medical School, Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Boston, Massachusetts, USA.
| |
Collapse
|
275
|
Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, Lyssenko V, Bouatia-Naji N, Dupuis J, Jackson AU, Kao WHL, Li M, Glazer NL, Manning AK, Luan J, Stringham HM, Prokopenko I, Johnson T, Grarup N, Boesgaard TW, Lecoeur C, Shrader P, O'Connell J, Ingelsson E, Couper DJ, Rice K, Song K, Andreasen CH, Dina C, Köttgen A, Le Bacquer O, Pattou F, Taneera J, Steinthorsdottir V, Rybin D, Ardlie K, Sampson M, Qi L, van Hoek M, Weedon MN, Aulchenko YS, Voight BF, Grallert H, Balkau B, Bergman RN, Bielinski SJ, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Böttcher Y, Brunner E, Buchanan TA, Bumpstead SJ, Cavalcanti-Proença C, Charpentier G, Chen YDI, Chines PS, Collins FS, Cornelis M, J Crawford G, Delplanque J, Doney A, Egan JM, Erdos MR, Firmann M, Forouhi NG, Fox CS, Goodarzi MO, Graessler J, Hingorani A, Isomaa B, Jørgensen T, Kivimaki M, Kovacs P, Krohn K, Kumari M, Lauritzen T, Lévy-Marchal C, Mayor V, McAteer JB, Meyre D, Mitchell BD, Mohlke KL, Morken MA, Narisu N, Palmer CNA, Pakyz R, Pascoe L, Payne F, Pearson D, Rathmann W, Sandbaek A, Sayer AA, Scott LJ, Sharp SJ, Sijbrands E, Singleton A, Siscovick DS, Smith NL, Sparsø T, Swift AJ, Syddall H, Thorleifsson G, Tönjes A, Tuomi T, Tuomilehto J, Valle TT, Waeber G, Walley A, Waterworth DM, Zeggini E, Zhao JH, Illig T, Wichmann HE, Wilson JF, van Duijn C, Hu FB, Morris AD, Frayling TM, Hattersley AT, Thorsteinsdottir U, Stefansson K, Nilsson P, Syvänen AC, Shuldiner AR, Walker M, Bornstein SR, Schwarz P, Williams GH, Nathan DM, Kuusisto J, Laakso M, Cooper C, Marmot M, Ferrucci L, Mooser V, Stumvoll M, Loos RJF, Altshuler D, Psaty BM, Rotter JI, Boerwinkle E, Hansen T, Pedersen O, Florez JC, McCarthy MI, Boehnke M, Barroso I, Sladek R, Froguel P, Meigs JB, Groop L, Wareham NJ, Watanabe RM. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet 2010; 42:142-8. [PMID: 20081857 PMCID: PMC2922003 DOI: 10.1038/ng.521] [Citation(s) in RCA: 481] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2009] [Accepted: 12/10/2009] [Indexed: 12/18/2022]
Abstract
Glucose levels 2 h after an oral glucose challenge are a clinical measure of glucose tolerance used in the diagnosis of type 2 diabetes. We report a meta-analysis of nine genome-wide association studies (n = 15,234 nondiabetic individuals) and a follow-up of 29 independent loci (n = 6,958-30,620). We identify variants at the GIPR locus associated with 2-h glucose level (rs10423928, beta (s.e.m.) = 0.09 (0.01) mmol/l per A allele, P = 2.0 x 10(-15)). The GIPR A-allele carriers also showed decreased insulin secretion (n = 22,492; insulinogenic index, P = 1.0 x 10(-17); ratio of insulin to glucose area under the curve, P = 1.3 x 10(-16)) and diminished incretin effect (n = 804; P = 4.3 x 10(-4)). We also identified variants at ADCY5 (rs2877716, P = 4.2 x 10(-16)), VPS13C (rs17271305, P = 4.1 x 10(-8)), GCKR (rs1260326, P = 7.1 x 10(-11)) and TCF7L2 (rs7903146, P = 4.2 x 10(-10)) associated with 2-h glucose. Of the three newly implicated loci (GIPR, ADCY5 and VPS13C), only ADCY5 was found to be associated with type 2 diabetes in collaborating studies (n = 35,869 cases, 89,798 controls, OR = 1.12, 95% CI 1.09-1.15, P = 4.8 x 10(-18)).
Collapse
Affiliation(s)
- Richa Saxena
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
276
|
Heid IM, Henneman P, Hicks A, Coassin S, Winkler T, Aulchenko YS, Fuchsberger C, Song K, Hivert MF, Waterworth DM, Timpson NJ, Richards JB, Perry JRB, Tanaka T, Amin N, Kollerits B, Pichler I, Oostra BA, Thorand B, Frants RR, Illig T, Dupuis J, Glaser B, Spector T, Guralnik J, Egan JM, Florez JC, Evans DM, Soranzo N, Bandinelli S, Carlson OD, Frayling TM, Burling K, Smith GD, Mooser V, Ferrucci L, Meigs JB, Vollenweider P, Dijk KWV, Pramstaller P, Kronenberg F, van Duijn CM. Clear detection of ADIPOQ locus as the major gene for plasma adiponectin: results of genome-wide association analyses including 4659 European individuals. Atherosclerosis 2009; 208:412-20. [PMID: 20018283 DOI: 10.1016/j.atherosclerosis.2009.11.035] [Citation(s) in RCA: 135] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/27/2009] [Revised: 11/20/2009] [Accepted: 11/20/2009] [Indexed: 12/22/2022]
Abstract
OBJECTIVE Plasma adiponectin is strongly associated with various components of metabolic syndrome, type 2 diabetes and cardiovascular outcomes. Concentrations are highly heritable and differ between men and women. We therefore aimed to investigate the genetics of plasma adiponectin in men and women. METHODS We combined genome-wide association scans of three population-based studies including 4659 persons. For the replication stage in 13795 subjects, we selected the 20 top signals of the combined analysis, as well as the 10 top signals with p-values less than 1.0 x 10(-4) for each the men- and the women-specific analyses. We further selected 73 SNPs that were consistently associated with metabolic syndrome parameters in previous genome-wide association studies to check for their association with plasma adiponectin. RESULTS The ADIPOQ locus showed genome-wide significant p-values in the combined (p=4.3 x 10(-24)) as well as in both women- and men-specific analyses (p=8.7 x 10(-17) and p=2.5 x 10(-11), respectively). None of the other 39 top signal SNPs showed evidence for association in the replication analysis. None of 73 SNPs from metabolic syndrome loci exhibited association with plasma adiponectin (p>0.01). CONCLUSIONS We demonstrated the ADIPOQ gene as the only major gene for plasma adiponectin, which explains 6.7% of the phenotypic variance. We further found that neither this gene nor any of the metabolic syndrome loci explained the sex differences observed for plasma adiponectin. Larger studies are needed to identify more moderate genetic determinants of plasma adiponectin.
Collapse
Affiliation(s)
- Iris M Heid
- Department of Epidemiology and Preventive Medicine, Regensburg University Medical Center, Regensburg, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
277
|
Florez JC. Novel genetic findings applied to the clinic in type 2 diabetes. Endocrinol Nutr 2009; 56 Suppl 4:21-25. [PMID: 20629226 DOI: 10.1016/s1575-0922(09)73512-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Affiliation(s)
- Jose C Florez
- Center for Human Genetic Research and Diabetes Center (Diabetes Unit), Massachusetts General Hospital, Boston 02114, USA.
| |
Collapse
|
278
|
Richards JB, Waterworth D, O'Rahilly S, Hivert MF, Loos RJF, Perry JRB, Tanaka T, Timpson NJ, Semple RK, Soranzo N, Song K, Rocha N, Grundberg E, Dupuis J, Florez JC, Langenberg C, Prokopenko I, Saxena R, Sladek R, Aulchenko Y, Evans D, Waeber G, Erdmann J, Burnett MS, Sattar N, Devaney J, Willenborg C, Hingorani A, Witteman JCM, Vollenweider P, Glaser B, Hengstenberg C, Ferrucci L, Melzer D, Stark K, Deanfield J, Winogradow J, Grassl M, Hall AS, Egan JM, Thompson JR, Ricketts SL, König IR, Reinhard W, Grundy S, Wichmann HE, Barter P, Mahley R, Kesaniemi YA, Rader DJ, Reilly MP, Epstein SE, Stewart AFR, Van Duijn CM, Schunkert H, Burling K, Deloukas P, Pastinen T, Samani NJ, McPherson R, Davey Smith G, Frayling TM, Wareham NJ, Meigs JB, Mooser V, Spector TD. A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet 2009; 5:e1000768. [PMID: 20011104 PMCID: PMC2781107 DOI: 10.1371/journal.pgen.1000768] [Citation(s) in RCA: 136] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2009] [Accepted: 11/12/2009] [Indexed: 12/22/2022] Open
Abstract
The adipocyte-derived protein adiponectin is highly heritable and inversely associated with risk of type 2 diabetes mellitus (T2D) and coronary heart disease (CHD). We meta-analyzed 3 genome-wide association studies for circulating adiponectin levels (n = 8,531) and sought validation of the lead single nucleotide polymorphisms (SNPs) in 5 additional cohorts (n = 6,202). Five SNPs were genome-wide significant in their relationship with adiponectin (P< or =5x10(-8)). We then tested whether these 5 SNPs were associated with risk of T2D and CHD using a Bonferroni-corrected threshold of P< or =0.011 to declare statistical significance for these disease associations. SNPs at the adiponectin-encoding ADIPOQ locus demonstrated the strongest associations with adiponectin levels (P-combined = 9.2x10(-19) for lead SNP, rs266717, n = 14,733). A novel variant in the ARL15 (ADP-ribosylation factor-like 15) gene was associated with lower circulating levels of adiponectin (rs4311394-G, P-combined = 2.9x10(-8), n = 14,733). This same risk allele at ARL15 was also associated with a higher risk of CHD (odds ratio [OR] = 1.12, P = 8.5x10(-6), n = 22,421) more nominally, an increased risk of T2D (OR = 1.11, P = 3.2x10(-3), n = 10,128), and several metabolic traits. Expression studies in humans indicated that ARL15 is well-expressed in skeletal muscle. These findings identify a novel protein, ARL15, which influences circulating adiponectin levels and may impact upon CHD risk.
Collapse
Affiliation(s)
- J Brent Richards
- Departments of Medicine, Human Genetics, and Epidemiology and Biostatistics, Jewish General Hospital, McGill University, Montréal, Québec, Canada.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
279
|
Grant RW, Hivert M, Pandiscio JC, Florez JC, Nathan DM, Meigs JB. The clinical application of genetic testing in type 2 diabetes: a patient and physician survey. Diabetologia 2009; 52:2299-2305. [PMID: 19727660 PMCID: PMC3829642 DOI: 10.1007/s00125-009-1512-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2009] [Accepted: 07/30/2009] [Indexed: 11/25/2022]
Abstract
AIMS/HYPOTHESIS Advances in type 2 diabetes genetics have raised hopes that genetic testing will improve disease prediction, prevention and treatment. Little is known about current physician and patient views regarding type 2 diabetes genetic testing. We hypothesised that physician and patient views would differ regarding the impact of genetic testing on motivation and adherence. METHODS We surveyed a nationally representative sample of US primary care physicians and endocrinologists (n = 304), a random sample of non-diabetic primary care patients (n = 152) and patients enrolled in a diabetes pharmacogenetics study (n = 89). RESULTS Physicians and patients favoured genetic testing for diabetes risk prediction (79% of physicians vs 80% of non-diabetic patients would be somewhat/very likely to order/request testing, p = 0.7). More patients than physicians (71% vs 23%, p < 0.01) indicated that a 'high risk' result would be very likely to improve motivation to adopt preventive lifestyle changes. Patients favoured genetic testing to guide therapy (78% of patients vs 48% of physicians very likely to request/recommend testing, p < 0.01) and reported that genetic testing would make them 'much more motivated' to adhere to medications (72% vs 18% of physicians, p < 0.01). Many physicians (39%) would be somewhat/very likely to order genetic testing before published evidence of clinical efficacy. CONCLUSIONS/INTERPRETATION Despite the paucity of current data, physicians and patients reported high expectations that genetic testing would improve patient motivation to adopt key behaviours for the prevention or control of type 2 diabetes. This suggests the testable hypothesis that 'genetic' risk information might have greater value to motivate behaviour change compared with standard risk information.
Collapse
Affiliation(s)
- R W Grant
- Division of General Medicine, Massachusetts General Hospital, 50 Staniford St, 9th floor, Boston, MA, 02114, USA.
- Harvard Medical School, Boston, MA, USA.
| | - M Hivert
- Division of General Medicine, Massachusetts General Hospital, 50 Staniford St, 9th floor, Boston, MA, 02114, USA
| | - J C Pandiscio
- Division of General Medicine, Massachusetts General Hospital, 50 Staniford St, 9th floor, Boston, MA, 02114, USA
| | - J C Florez
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA, USA
| | - D M Nathan
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - J B Meigs
- Division of General Medicine, Massachusetts General Hospital, 50 Staniford St, 9th floor, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, USA
| |
Collapse
|
280
|
Abstract
Our understanding of the genetics of type 2 diabetes mellitus (T2DM) has changed, in part owing to implementation of genome-wide association studies as a method for unraveling the genetic architecture of complex traits. These studies enable a global search throughout the nuclear genome for variants that are associated with specific phenotypes. Currently, single nucleotide polymorphisms in about 24 different genetic loci have been associated with T2DM. Most of these genetic loci are associated with the insulin secretion pathway rather than insulin resistance. Study design, heritability differences and the intrinsic properties of in vivo insulin resistance measures might partially explain why only a few loci associated with insulin resistance have been detected through genome-wide association approaches. Despite the success of these approaches at detecting loci associated with T2DM, currently known associations explain only a small amount of the genetic variance involved in the disease. Compared with previous studies, larger cohorts might be needed to identify variants of smaller effect sizes and lower allele frequencies. Finally, the current list of genetic loci that are related to T2DM does not seem to offer greater predictive value in determining diabetes risk than do commonly used phenotypic risk factors and family history.
Collapse
Affiliation(s)
- Elliot S Stolerman
- Diabetes Unit-Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | | |
Collapse
|
281
|
Abstract
Since 2000, we have witnessed an explosion of known genetic determinants of type 2 diabetes risk. These findings have seeded the expectation that our ability to make personalized, predictive, therapeutic clinical decisions is imminent. However, the loci discovered to date explain only a small fraction of overall inheritable risk for this disease. In many cases, the reported associations merely signal regions of the genome that are overrepresented in disease versus health but do not identify the causal variants. Well-powered cohort studies have shown that the set of markers detected thus far does not significantly improve individual risk prediction or stratification over common clinical variables, with the possible exception of younger subjects. On the other hand, risk genotypes may help target subgroups for more intensive surveillance or prevention efforts, although whether such a strategy improves patient outcomes and/or is cost-effective should be examined. Similarly, whether genetic information will help guide therapeutic decisions must be tested in adequately designed and rigorously conducted clinical trials.
Collapse
Affiliation(s)
- Jose C Florez
- Center for Human Genetic Research and Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
| |
Collapse
|
282
|
Affiliation(s)
- Richard W Grant
- Division of General Medicine, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.
| | | | | |
Collapse
|
283
|
Stolerman ES, Manning AK, McAteer JB, Fox CS, Dupuis J, Meigs JB, Florez JC. TCF7L2 variants are associated with increased proinsulin/insulin ratios but not obesity traits in the Framingham Heart Study. Diabetologia 2009; 52:614-20. [PMID: 19183934 PMCID: PMC3430962 DOI: 10.1007/s00125-009-1266-2] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2008] [Accepted: 01/08/2009] [Indexed: 01/15/2023]
Abstract
AIMS/HYPOTHESIS Common variants in the TCF7L2 gene are associated with type 2 diabetes via impaired insulin secretion. One hypothesis is that variation in TCF7L2 impairs insulin processing in the beta cell. In contrast, the association of related TCF7L2 polymorphisms with obesity is controversial in that it has only been shown in cohorts susceptible to ascertainment bias. We reproduced the association of diabetes-associated variants with proinsulin/insulin ratios, and also examined the association of a TCF7L2 haplotype with obesity in the Framingham Heart Study (FHS). METHODS We genotyped the TCF7L2 single nucleotide polymorphisms rs7903146 and rs12255372 (previously associated with type 2 diabetes) and rs10885406 and rs7924080 (which tag haplotype A [HapA], a haplotype reported to be associated with obesity) in 2,512 FHS participants. We used age- and sex-adjusted linear mixed-effects models to test for association with glycaemic traits, proinsulin/insulin ratios and obesity measures. RESULTS As expected, the T risk allele of rs7903146 was associated with higher fasting plasma glucose (p = 0.01). T/T homozygotes had a 23.5% increase in the proinsulin/insulin ratio (p = 1 x 10(-7)) compared with C/C homozygotes. There was no association of HapA with BMI (p = 0.98), waist circumference (p = 0.89), subcutaneous adipose tissue (p = 0.32) or visceral adipose tissue (p = 0.92). CONCLUSIONS/INTERPRETATION We confirmed that the risk allele of rs7903146 is associated with hyperglycaemia and a higher proinsulin/insulin ratio. We did not detect any association of the TCF7L2 HapA with adiposity measures, suggesting that this may have been a spurious association from ascertainment bias, possibly induced by the evaluation of obesity in separate groups of glycaemic cases and controls.
Collapse
Affiliation(s)
- E S Stolerman
- Simches Research Building-CPZN 5.250, Diabetes Unit/Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, 02114, USA
| | | | | | | | | | | | | |
Collapse
|
284
|
Lieb W, Manning AK, Florez JC, Dupuis J, Cupples LA, McAteer JB, Vasan RS, Hoffmann U, O'Donnell CJ, Meigs JB, Fox CS. Variants in the CNR1 and the FAAH genes and adiposity traits in the community. Obesity (Silver Spring) 2009; 17:755-60. [PMID: 19165169 PMCID: PMC3039277 DOI: 10.1038/oby.2008.608] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Pharmacologic blockade of the endocannabinoid receptor 1 leads to weight loss and an improved metabolic risk profile in overweight and obese individuals. We hypothesize that common genetic variants in the CNR1 (encoding endocannabinoid receptor 1) and FAAH genes (encoding fatty acid amide hydrolase, a key enzyme hydrolyzing endocannabinoids) are associated with adiposity traits. We genotyped 18 single-nucleotide polymorphisms (SNPs) in the CNR1 gene and 9 SNPs in the FAAH gene in 2,415 Framingham Offspring Study participants (mean age 61 +/- 10 years; 52.6% women; mean BMI 28.2 +/- 5.4 kg/m(2); 30.3% obese) and studied them for association with cross-sectional and longitudinal measures of adiposity (BMI, waist circumference, change over time in BMI and waist circumference, visceral and subcutaneous adipose tissue) using linear mixed-effect models. The selected SNPs captured 85% (r(2) = 0.8) of the common variation (minor allele frequency >5%) at the CNR1 locus and 96% (r(2) = 0.8) of the common variation at the FAAH locus (defined as the genomic segment containing the gene +20 kb upstream and +10 kb downstream). After correction for multiple testing, none of the SNPs in the CNR1 gene or in the FAAH gene displayed statistical evidence for association with BMI, waist circumference, and visceral adipose tissue or subcutaneous adipose tissue (all P > 0.18). Despite comprehensive SNP mapping across the genes and their regulatory regions in a large unselected sample, we failed to find evidence for an association of common variants in the CNR1 and FAAH genes with measures of adiposity in our community-based sample.
Collapse
Affiliation(s)
- Wolfgang Lieb
- National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA
| | - Alisa K. Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jose C. Florez
- Diabetes Unit (Department of Medicine) and Center for Human Genetic Research, Massachusetts General Hospital; Department of Medicine, Harvard Medical School, Boston, MA, and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jarred B. McAteer
- Diabetes Unit (Department of Medicine) and Center for Human Genetic Research, Massachusetts General Hospital; Department of Medicine, Harvard Medical School, Boston, MA, and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | | | - Udo Hoffmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | | | - James B. Meigs
- General Medicine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Caroline S. Fox
- National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA
- Endocrinology Division, Brigham and Women's Hospital, Harvard Medical School, Boston, MA
| |
Collapse
|
285
|
Abstract
PURPOSE OF REVIEW To highlight recent type 2 diabetes (T2D)-associated genetic discoveries and their potential for clinical application. RECENT FINDINGS The advent of genome-wide association screening has uncovered many loci newly associated with T2D. This review describes the techniques applied to discover novel T2D genes and compares their relative strengths, biases, and findings to date. The results of large-scale genome-wide association studies carried out since 2007 are summarized, and limitations of interpreting this preliminary data are offered. Recent studies exploring the clinical potential of these discoveries are reviewed, focusing on insights into T2D pathogenesis, risk prediction of future diabetes, and utility in guiding pharmacotherapy. The new T2D-associated loci have been implicated in beta-cell development and function, highlighting insulin secretion in the disease process. Preliminary risk prediction studies show that more loci are needed to improve T2D risk indices. Studies have also revealed that genes may play a role in the pharmacologic response to antidiabetic medications. SUMMARY Since 2007, genome-wide association studies have rapidly increased the number of T2D-associated loci. This review summarizes the history of genetic association studies, the results from the new genome-wide association studies, and the clinical application of these findings.
Collapse
Affiliation(s)
- Amit R Majithia
- Diabetes Research Center (Diabetes Unit), Massachusetts General Hospital, Boston, Boston, MA 02114, USA
| | | |
Collapse
|
286
|
Florez JC. The dawn of prospective pharmacogenetic testing in type 2 diabetes. Curr Diab Rep 2009; 9:95-7. [PMID: 19323951 DOI: 10.1007/s11892-009-0016-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Jose C Florez
- Center for Human Genetic Research/Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA. jcfl
| |
Collapse
|
287
|
Hivert MF, Manning AK, McAteer JB, Dupuis J, Fox CS, Cupples LA, Meigs JB, Florez JC. Association of variants in RETN with plasma resistin levels and diabetes-related traits in the Framingham Offspring Study. Diabetes 2009; 58:750-6. [PMID: 19074981 PMCID: PMC2646076 DOI: 10.2337/db08-1339] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
OBJECTIVE The RETN gene encodes the adipokine resistin. Associations of RETN with plasma resistin levels, type 2 diabetes, and related metabolic traits have been inconsistent. Using comprehensive linkage disequilibrium mapping, we genotyped tag single nucleotide polymorphisms (SNPs) in RETN and tested associations with plasma resistin levels, risk of diabetes, and glycemic traits. RESEARCH DESIGN AND METHODS We examined 2,531 Framingham Offspring Study participants for resistin levels, glycemic phenotypes, and incident diabetes over 28 years of follow-up. We genotyped 21 tag SNPs that capture common (minor allele frequency >0.05) or previously reported SNPs at r2 > 0.8 across RETN and its flanking regions. We used sex- and age-adjusted linear mixed-effects models (with/without BMI adjustment) to test additive associations of SNPs with traits, adjusted Cox proportional hazards models accounting for relatedness for incident diabetes, and generated empirical P values (Pe) to control for type 1 error. RESULTS Four tag SNPs (rs1477341, rs4804765, rs1423096, and rs10401670) on the 3' side of RETN were strongly associated with resistin levels (all minor alleles associated with higher levels, Pe<0.05 after multiple testing correction). rs10401670 was also associated with fasting plasma glucose (Pe = 0.02, BMI adjusted) and mean glucose over follow-up (Pe = 0.01; BMI adjusted). No significant association was observed for adiposity traits. On meta-analysis, the previously reported association of SNP -420C/G (rs1862513) with resistin levels remained significant (P = 0.0009) but with high heterogeneity across studies (P < 0.0001). CONCLUSIONS SNPs in the 3' region of RETN are associated with resistin levels, and one of them is also associated with glucose levels, although replication is needed.
Collapse
Affiliation(s)
- Marie-France Hivert
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts, USA
| | | | | | | | | | | | | | | |
Collapse
|
288
|
Moore AF, Jablonski KA, Mason CC, McAteer JB, Arakaki RF, Goldstein BJ, Kahn SE, Kitabchi AE, Hanson RL, Knowler WC, Florez JC. The association of ENPP1 K121Q with diabetes incidence is abolished by lifestyle modification in the diabetes prevention program. J Clin Endocrinol Metab 2009; 94:449-55. [PMID: 19017751 PMCID: PMC2646511 DOI: 10.1210/jc.2008-1583] [Citation(s) in RCA: 42] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
CONTEXT Insulin resistance is an important feature of type 2 diabetes. Ectoenzyme nucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) inhibits insulin signaling, and a recent meta-analysis reported a nominal association between the Q allele in the K121Q (rs1044498) single nucleotide polymorphism in its gene ENPP1 and type 2 diabetes. OBJECTIVE AND INTERVENTION: We examined the impact of this polymorphism on diabetes incidence as well as insulin secretion and sensitivity at baseline and after treatment with a lifestyle intervention or metformin vs. placebo in the Diabetes Prevention Program (DPP). DESIGN, SETTING, PARTICIPANTS, AND OUTCOME: We genotyped ENPP1 K121Q in 3548 DPP participants and performed Cox regression analyses using genotype, intervention, and interactions as predictors of diabetes incidence. RESULTS Fasting glucose and glycated hemoglobin were higher in QQ homozygotes at baseline (P < 0.001 for both). There was a significant interaction between genotype at rs1044498 and intervention under the dominant model (P = 0.03). In analyses stratified by treatment arm, a positive association with diabetes incidence was found in Q allele carriers compared to KK homozygotes [hazard ratio (HR), 1.38; 95% confidence interval (CI), 1.08-1.76; P = 0.009] in the placebo arm (n = 996). Lifestyle modification eliminated this increased risk. These findings persisted after adjustment for body mass index and race/ethnicity. Association of ENPP1 K121Q genotype with diabetes incidence under the additive and recessive genetic models showed consistent trends [HR, 1.10 (95% CI, 0.99-1.23), P = 0.08; and HR, 1.16 (95% CI, 0.92-1.45), P = 0.20, respectively] but did not reach statistical significance. CONCLUSIONS ENPP1 K121Q is associated with increased diabetes incidence; the DPP lifestyle intervention eliminates this increased risk.
Collapse
Affiliation(s)
- Allan F Moore
- Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114-2622, USA
| | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
289
|
Marucci A, Miscio G, Padovano L, Boonyasrisawat W, Florez JC, Doria A, Trischitta V, Paola RD. The role of HSP70 on ENPP1 expression and insulin-receptor activation. J Mol Med (Berl) 2008; 87:139-144. [PMID: 19083193 DOI: 10.1007/s00109-008-0429-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2008] [Revised: 11/10/2008] [Accepted: 11/26/2008] [Indexed: 10/21/2022]
Abstract
Ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) inhibits insulin-receptor (IR) signaling and, when over-expressed, induces insulin resistance in vitro and in vivo. Understanding the regulation of ENPP1 expression may, thus, unravel new molecular mechanisms of insulin resistance. Recent data point to a pivotal role of the ENPP1 3'UTR, in modulating ENPP1 mRNA stability and expression. We sought to identify trans-acting proteins binding the ENPP1-3'UTR and to investigate their role on ENPP1 expression and on IR signaling. By RNA electrophoresis mobility shift analysis and tandem mass spectrometry, we demonstrated the binding of heat shock protein 70 (HSP70) to ENPP1-3'UTR. Through this binding, HSP70 stabilizes ENPP1 mRNA and increases ENPP1 transcript and protein levels. This positive modulation of ENPP1 expression is paralleled by a reduced insulin-induced IR and IRS-1 phosphorylation. Taken together these data suggest that HSP70, by affecting ENPP1 expression, may be a novel mediator of altered insulin signaling.
Collapse
Affiliation(s)
- Antonella Marucci
- Research Unit of Diabetes and Endocrine Diseases, Scientific Institute, Poliambulatorio Giovanni Paolo II, Viale Padre Pio, 71013 San Giovanni Rotondo, Italy
| | - Giuseppe Miscio
- Research Unit of Diabetes and Endocrine Diseases, Scientific Institute, Poliambulatorio Giovanni Paolo II, Viale Padre Pio, 71013 San Giovanni Rotondo, Italy
| | - Libera Padovano
- Research Unit of Diabetes and Endocrine Diseases, Scientific Institute, Poliambulatorio Giovanni Paolo II, Viale Padre Pio, 71013 San Giovanni Rotondo, Italy
| | | | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | | | - Vincenzo Trischitta
- Research Unit of Diabetes and Endocrine Diseases, Scientific Institute, Poliambulatorio Giovanni Paolo II, Viale Padre Pio, 71013 San Giovanni Rotondo, Italy
| | - Rosa Di Paola
- Research Unit of Diabetes and Endocrine Diseases, Scientific Institute, Poliambulatorio Giovanni Paolo II, Viale Padre Pio, 71013 San Giovanni Rotondo, Italy
| |
Collapse
|
290
|
Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G, Loos RJF, Manning AK, Jackson AU, Aulchenko Y, Potter SC, Erdos MR, Sanna S, Hottenga JJ, Wheeler E, Kaakinen M, Lyssenko V, Chen WM, Ahmadi K, Beckmann JS, Bergman RN, Bochud M, Bonnycastle LL, Buchanan TA, Cao A, Cervino A, Coin L, Collins FS, Crisponi L, de Geus EJC, Dehghan A, Deloukas P, Doney ASF, Elliott P, Freimer N, Gateva V, Herder C, Hofman A, Hughes TE, Hunt S, Illig T, Inouye M, Isomaa B, Johnson T, Kong A, Krestyaninova M, Kuusisto J, Laakso M, Lim N, Lindblad U, Lindgren CM, McCann OT, Mohlke KL, Morris AD, Naitza S, Orrù M, Palmer CNA, Pouta A, Randall J, Rathmann W, Saramies J, Scheet P, Scott LJ, Scuteri A, Sharp S, Sijbrands E, Smit JH, Song K, Steinthorsdottir V, Stringham HM, Tuomi T, Tuomilehto J, Uitterlinden AG, Voight BF, Waterworth D, Wichmann HE, Willemsen G, Witteman JCM, Yuan X, Zhao JH, Zeggini E, Schlessinger D, Sandhu M, Boomsma DI, Uda M, Spector TD, Penninx BW, Altshuler D, Vollenweider P, Jarvelin MR, Lakatta E, Waeber G, Fox CS, Peltonen L, Groop LC, Mooser V, Cupples LA, Thorsteinsdottir U, Boehnke M, Barroso I, Van Duijn C, Dupuis J, Watanabe RM, Stefansson K, McCarthy MI, Wareham NJ, Meigs JB, Abecasis GR. Variants in MTNR1B influence fasting glucose levels. Nat Genet 2008; 41:77-81. [PMID: 19060907 DOI: 10.1038/ng.290] [Citation(s) in RCA: 549] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2008] [Accepted: 10/14/2008] [Indexed: 12/11/2022]
Abstract
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 x 10(-50)) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 x 10(-15)). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 x 10(-7)) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 x 10(-57)) and GCK (rs4607517, P = 1.0 x 10(-25)) loci.
Collapse
Affiliation(s)
- Inga Prokopenko
- [1] Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford OX3 7LJ, UK. [2] Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. [3] These authors contributed equally to this work
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
291
|
Hivert MF, Manning AK, McAteer JB, Florez JC, Dupuis J, Fox CS, O'Donnell CJ, Cupples LA, Meigs JB. Common variants in the adiponectin gene (ADIPOQ) associated with plasma adiponectin levels, type 2 diabetes, and diabetes-related quantitative traits: the Framingham Offspring Study. Diabetes 2008; 57:3353-9. [PMID: 18776141 PMCID: PMC2584143 DOI: 10.2337/db08-0700] [Citation(s) in RCA: 133] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2008] [Accepted: 08/22/2008] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Variants in ADIPOQ have been inconsistently associated with adiponectin levels or diabetes. Using comprehensive linkage disequilibrium mapping, we genotyped single nucleotide polymorphisms (SNPs) in ADIPOQ to evaluate the association of common variants with adiponectin levels and risk of diabetes. RESEARCH DESIGN AND METHODS Participants in the Framingham Offspring Study (n = 2,543, 53% women) were measured for glycemic phenotypes and incident diabetes over 28 years of follow-up; adiponectin levels were quantified at exam 7. We genotyped 22 tag SNPs that captured common (minor allele frequency >0.05) variation at r(2) > 0.8 across ADIPOQ plus 20 kb 5' and 10 kb 3' of the gene. We used linear mixed effects models to test additive associations of each SNP with adiponectin levels and glycemic phenotypes. Hazard ratios (HRs) for incident diabetes were estimated using an adjusted Cox proportional hazards model. RESULTS Two promoter SNPs in strong linkage disequilibrium with each other (r(2) = 0.80) were associated with adiponectin levels (rs17300539; P(nominal) [P(n)] = 2.6 x 10(-8); P(empiric) [P(e)] = 0.0005 and rs822387; P(n) = 3.8 x 10(-5); P(e) = 0.001). A 3'-untranslated region (3'UTR) SNP (rs6773957) was associated with adiponectin levels (P(n) = 4.4 x 10(-4); P(e) = 0.005). A nonsynonymous coding SNP (rs17366743, Y111H) was confirmed to be associated with diabetes incidence (HR 1.94 [95% CI 1.16-3.25] for the minor C allele; P(n) = 0.01) and with higher mean fasting glucose over 28 years of follow-up (P(n) = 0.0004; P(e) = 0.004). No other significant associations were found with other adiposity and metabolic phenotypes. CONCLUSIONS Adiponectin levels are associated with SNPs in two different regulatory regions (5' promoter and 3'UTR), whereas diabetes incidence and time-averaged fasting glucose are associated with a missense SNP of ADIPOQ.
Collapse
Affiliation(s)
- Marie-France Hivert
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Alisa K. Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Jarred B. McAteer
- Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Jose C. Florez
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Caroline S. Fox
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - Christopher J. O'Donnell
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Framingham Heart Study, National Heart, Lung, and Blood Institute, Framingham, Massachusetts
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - James B. Meigs
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
292
|
Abstract
CONTEXT Over the last few months, genome-wide association studies have contributed significantly to our understanding of the genetic architecture of type 2 diabetes. If and how this information will impact clinical practice is not yet clear. EVIDENCE ACQUISITION Primary papers reporting genome-wide association studies in type 2 diabetes or establishing a reproducible association for specific candidate genes were compiled. Further information was obtained from background articles, authoritative reviews, and relevant meeting conferences and abstracts. EVIDENCE SYNTHESIS As many as 17 genetic loci have been convincingly associated with type 2 diabetes; 14 of these were not previously known, and most of them were unsuspected. The associated polymorphisms are common in populations of European descent but have modest effects on risk. These loci highlight new areas for biological exploration and allow the initiation of experiments designed to develop prediction models and test possible pharmacogenetic and other applications. CONCLUSIONS Although substantial progress in our knowledge of the genetic basis of type 2 diabetes is taking place, these new discoveries represent but a small proportion of the genetic variation underlying the susceptibility to this disorder. Major work is still required to identify the causal variants, test their role in disease prediction and ascertain their therapeutic implications.
Collapse
Affiliation(s)
- Jose C Florez
- Simches Research Building-CPZN 5.250, 185 Cambridge Street, Diabetes Unit/Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts 02114, USA.
| |
Collapse
|
293
|
Franks PW, Jablonski KA, Delahanty LM, McAteer JB, Kahn SE, Knowler WC, Florez JC. Assessing gene-treatment interactions at the FTO and INSIG2 loci on obesity-related traits in the Diabetes Prevention Program. Diabetologia 2008; 51:2214-23. [PMID: 18839134 PMCID: PMC2947367 DOI: 10.1007/s00125-008-1158-x] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2008] [Accepted: 08/22/2008] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS The single nucleotide polymorphism (SNP) rs9939609 in the fat mass and obesity associated gene (FTO) and the rs7566605 SNP located 10 kb upstream of the insulin-induced gene 2 gene (INSIG2) have been proposed as risk factors for common obesity. METHODS We tested for genotype-treatment interactions on changes in obesity-related traits in the Diabetes Prevention Program (DPP). The DPP is a randomised controlled trial of 3,548 high-risk individuals from 27 participating centres throughout the USA who were originally randomised to receive metformin, troglitazone, intensive lifestyle modification or placebo to prevent the development of type 2 diabetes. Measures of adiposity from computed tomography were available in a subsample (n = 908). This report focuses on the baseline and 1 year results. RESULTS The minor A allele at FTO rs9939609 was positively associated with baseline BMI (p = 0.003), but not with baseline adiposity or the change at 1 year in any anthropometric trait. For the INSIG2 rs7566605 genotype, the minor C allele was associated with more subcutaneous adiposity (second and third lumbar vertebrae [L2/3]) at baseline (p = 0.04). During follow-up, CC homozygotes lost more weight than G allele carriers (p = 0.009). In an additive model, we observed nominally significant gene-lifestyle interactions on weight change (p = 0.02) and subcutaneous (L2/3 [p = 0.01] and L4/5 [p = 0.03]) and visceral (L2/3 [p = 0.02]) adipose areas. No statistical evidence of association with physical activity energy expenditure or energy intake was observed for either genotype. CONCLUSIONS/INTERPRETATION Within the DPP study population, common variants in FTO and INSIG2 are nominally associated with quantitative measures of obesity, directly and possibly by interacting with metformin or lifestyle intervention.
Collapse
Affiliation(s)
- P W Franks
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Section for Medicine, Umeå University Hospital, Umeå, Sweden.
| | | | | | | | | | | | | | | |
Collapse
|
294
|
Meigs JB, Shrader P, Sullivan LM, McAteer JB, Fox CS, Dupuis J, Manning AK, Florez JC, Wilson PWF, D'Agostino RB, Cupples LA. Genotype score in addition to common risk factors for prediction of type 2 diabetes. N Engl J Med 2008; 359:2208-19. [PMID: 19020323 PMCID: PMC2746946 DOI: 10.1056/nejmoa0804742] [Citation(s) in RCA: 547] [Impact Index Per Article: 34.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
BACKGROUND Multiple genetic loci have been convincingly associated with the risk of type 2 diabetes mellitus. We tested the hypothesis that knowledge of these loci allows better prediction of risk than knowledge of common phenotypic risk factors alone. METHODS We genotyped single-nucleotide polymorphisms (SNPs) at 18 loci associated with diabetes in 2377 participants of the Framingham Offspring Study. We created a genotype score from the number of risk alleles and used logistic regression to generate C statistics indicating the extent to which the genotype score can discriminate the risk of diabetes when used alone and in addition to clinical risk factors. RESULTS There were 255 new cases of diabetes during 28 years of follow-up. The mean (+/-SD) genotype score was 17.7+/-2.7 among subjects in whom diabetes developed and 17.1+/-2.6 among those in whom diabetes did not develop (P<0.001). The sex-adjusted odds ratio for diabetes was 1.12 per risk allele (95% confidence interval, 1.07 to 1.17). The C statistic was 0.534 without the genotype score and 0.581 with the score (P=0.01). In a model adjusted for sex and self-reported family history of diabetes, the C statistic was 0.595 without the genotype score and 0.615 with the score (P=0.11). In a model adjusted for age, sex, family history, body-mass index, fasting glucose level, systolic blood pressure, high-density lipoprotein cholesterol level, and triglyceride level, the C statistic was 0.900 without the genotype score and 0.901 with the score (P=0.49). The genotype score resulted in the appropriate risk reclassification of, at most, 4% of the subjects. CONCLUSIONS A genotype score based on 18 risk alleles predicted new cases of diabetes in the community but provided only a slightly better prediction of risk than knowledge of common risk factors alone.
Collapse
Affiliation(s)
- James B Meigs
- General Medicine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA 02114, USA.
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
295
|
Meigs JB, Manning AK, Dupuis J, Liu C, Florez JC, Cupples LA. Ordered stratification to reduce heterogeneity in linkage to diabetes-related quantitative traits. Obesity (Silver Spring) 2008; 16:2314-22. [PMID: 18719643 PMCID: PMC3747653 DOI: 10.1038/oby.2008.354] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Phenotypic heterogeneity complicates detection of genomic loci predisposing to type 2 diabetes, potentially obscuring or unmasking specific loci. We conducted ordered-subsets linkage analyses (OSAs) for diabetes-related quantitative traits (fasting insulin and glucose, hemoglobin A1c (HbA1c), and 28-year-time-averaged fasting plasma glucose (FPG)) from 330 families of the Framingham Offspring Study. We calculated mean BMI, waist circumference (WC), and a diabetes "age-of-onset score" for each family. We constructed subsets by adding one family at a time in increasing (lean family to obese) or decreasing (obese to lean) adiposity order, or increasing or decreasing propensity to develop diabetes at a younger age, with the OSA LOD reported as the maximum LOD observed in any subset. Permutation P values tested the hypothesis that phenotypic ordering showed stronger linkage than random ordering. On chromosome 1, ordering by increasing family mean WC increased linkage to time-averaged FPG at 256 cM from LOD = 2.4 to 3.5 (permuted P = 0.02) and to HbA1c at 180 cM from LOD = 2.0 to 3.3 (P = 0.01). On chromosome 19, ordering by decreasing WC increased linkage to fasting insulin at 68 cM from LOD = 2.7 to 4.6 (P = 0.002), and ordering by decreasing propensity to develop diabetes at a young age increased linkage to fasting insulin at 73 cM from LOD = 2.7 to 4.0 (P = 0.046). We conclude that chromosomes 1 and 19 could harbor adiposity-interacting diabetes susceptibility genes. Such interactions might also influence trait-locus associations and may be useful to consider in diabetes genome-wide association studies.
Collapse
Affiliation(s)
- James B. Meigs
- General Medicine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Alisa K. Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Chunyu Liu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Jose C. Florez
- Diabetes Unit, Department of Medicine and Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| |
Collapse
|
296
|
Abstract
Epidemiological studies suggest that men with type 2 diabetes are less likely than non-diabetic men to develop prostate cancer. The cause of this association is not known. Recent genetic studies have highlighted a potential genetic link between the two diseases. Two studies have identified a version (allele) of a variant in the HNF1B (also known as TCF2) gene that predisposes to type 2 diabetes, and one of them showed that the same allele protects men from prostate cancer. Other, separate, studies have identified different variants in the JAZF1 gene, one associated with type 2 diabetes, another associated with prostate cancer. These findings are unlikely to completely explain the epidemiological association between the two diseases but they provide new insight into a possible direct causal link, rather than one that is confounded or biased in some way.
Collapse
Affiliation(s)
- T M Frayling
- Genetics of Complex Traits, Peninsula College of Medicine and Dentistry, University of Exeter, Magdalen Road, Exeter, UK.
| | | | | |
Collapse
|
297
|
Moore AF, Jablonski KA, McAteer JB, Saxena R, Pollin TI, Franks PW, Hanson RL, Shuldiner AR, Knowler WC, Altshuler D, Florez JC. Extension of type 2 diabetes genome-wide association scan results in the diabetes prevention program. Diabetes 2008; 57:2503-10. [PMID: 18544707 PMCID: PMC2518503 DOI: 10.2337/db08-0284] [Citation(s) in RCA: 87] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2008] [Accepted: 06/01/2008] [Indexed: 11/25/2022]
Abstract
OBJECTIVE Genome-wide association scans (GWASs) have identified novel diabetes-associated genes. We evaluated how these variants impact diabetes incidence, quantitative glycemic traits, and response to preventive interventions in 3,548 subjects at high risk of type 2 diabetes enrolled in the Diabetes Prevention Program (DPP), which examined the effects of lifestyle intervention, metformin, and troglitazone versus placebo. RESEARCH DESIGN AND METHODS We genotyped selected single nucleotide polymorphisms (SNPs) in or near diabetes-associated loci, including EXT2, CDKAL1, CDKN2A/B, IGF2BP2, HHEX, LOC387761, and SLC30A8 in DPP participants and performed Cox regression analyses using genotype, intervention, and their interactions as predictors of diabetes incidence. We evaluated their effect on insulin resistance and secretion at 1 year. RESULTS None of the selected SNPs were associated with increased diabetes incidence in this population. After adjustments for ethnicity, baseline insulin secretion was lower in subjects with the risk genotype at HHEX rs1111875 (P = 0.01); there were no significant differences in baseline insulin sensitivity. Both at baseline and at 1 year, subjects with the risk genotype at LOC387761 had paradoxically increased insulin secretion; adjustment for self-reported ethnicity abolished these differences. In ethnicity-adjusted analyses, we noted a nominal differential improvement in beta-cell function for carriers of the protective genotype at CDKN2A/B after 1 year of troglitazone treatment (P = 0.01) and possibly lifestyle modification (P = 0.05). CONCLUSIONS We were unable to replicate the GWAS findings regarding diabetes risk in the DPP. We did observe genotype associations with differences in baseline insulin secretion at the HHEX locus and a possible pharmacogenetic interaction at CDKNA2/B.
Collapse
Affiliation(s)
- Allan F. Moore
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Diabetes Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | | - Jarred B. McAteer
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Richa Saxena
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Toni I. Pollin
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland
| | - Paul W. Franks
- Genetic Epidemiology and Clinical Research Group, Department of Public Health and Clinical Medicine, Division of Medicine, Umeå University Hospital, Umeå, Sweden
| | - Robert L. Hanson
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Alan R. Shuldiner
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, Maryland
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - David Altshuler
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Diabetes Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Genetics, Harvard Medical School, Boston, Massachusetts
| | - Jose C. Florez
- Center for Human Genetic Research, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Diabetes Center, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | |
Collapse
|
298
|
Köttgen A, Hwang SJ, Rampersaud E, Coresh J, North KE, Pankow JS, Meigs JB, Florez JC, Parsa A, Levy D, Boerwinkle E, Shuldiner AR, Fox CS, Kao WHL. TCF7L2 variants associate with CKD progression and renal function in population-based cohorts. J Am Soc Nephrol 2008; 19:1989-99. [PMID: 18650481 DOI: 10.1681/asn.2007121291] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022] Open
Abstract
Genetic variants may increase susceptibility to both diabetes and kidney disease. Whether known diabetes-associated variants in the transcription factor 7-like 2 (TCF7L2) gene are associated with chronic kidney disease (CKD) progression and markers of kidney function is unknown. Participants of the Atherosclerosis Risk in Communities Study (ARIC; n = 11,061 self-identified white and n = 4014 black), Framingham Heart Offspring Cohort (FHS; n = 2468), and Heredity and Phenotype Intervention Heart Study (HAPI; n = 861) were genotyped at five (ARIC) and two (FHS) common TCF7L2 variants. The diabetes-conferring risk alleles at rs7903146 and rs7901695 were significantly associated with CKD progression among ARIC participants overall and among those without baseline diabetes. The overall adjusted hazard ratios per rs7903146 T allele were 1.17 (95% confidence interval [CI] 1.04 to 1.32) for white individuals and 1.20 (95% CI 1.03 to 1.41) for black individuals. Similarly, the overall hazard ratios per rs7901695 C allele were 1.19 (95% CI 1.06 to 1.34) for white individuals and 1.27 (95% CI 1.09 to 1.48) for black individuals. The FHS cohort supported these results: The rs7903146 T allele was significantly associated with lower estimated GFR (P = 0.01) and higher cystatin C (P = 0.004) in adjusted analyses overall and among those without diabetes. In the HAPI cohort, the rs7901695 C allele was significantly associated with lower estimated GFR in adjusted analyses (P = 0.049), as were several variants upstream and downstream of TCF7L2 (P < 0.003). No identified variant in the ARIC or FHS cohorts was associated with albuminuria. In conclusion, several population-based samples suggest that variants in the TCF7L2 gene are associated with reduced kidney function or CKD progression, overall and specifically among participants without diabetes.
Collapse
Affiliation(s)
- Anna Köttgen
- Department of Epidemiology and Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, Maryland, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
299
|
Stolerman ES, Manning AK, McAteer JB, Dupuis J, Fox CS, Cupples LA, Meigs JB, Florez JC. Haplotype structure of the ENPP1 Gene and Nominal Association of the K121Q missense single nucleotide polymorphism with glycemic traits in the Framingham Heart Study. Diabetes 2008; 57:1971-7. [PMID: 18426862 PMCID: PMC2453609 DOI: 10.2337/db08-0266] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2008] [Accepted: 04/16/2008] [Indexed: 12/03/2022]
Abstract
OBJECTIVE A recent meta-analysis demonstrated a nominal association of the ectonucleotide pyrophosphatase phosphodiesterase 1 (ENPP1) K-->Q missense single nucleotide polymorphism (SNP) at position 121 with type 2 diabetes. We set out to confirm the association of ENPP1 K121Q with hyperglycemia, expand this association to insulin resistance traits, and determine whether the association stems from K121Q or another variant in linkage disequilibrium with it. RESEARCH DESIGN AND METHODS We characterized the haplotype structure of ENPP1 and selected 39 tag SNPs that captured 96% of common variation in the region (minor allele frequency > or =5%) with an r(2) value > or =0.80. We genotyped the SNPs in 2,511 Framingham Heart Study participants and used age- and sex-adjusted linear mixed effects (LME) models to test for association with quantitative metabolic traits. We also examined whether interaction between K121Q and BMI affected glycemic trait levels. RESULTS The Q allele of K121Q (rs1044498) was associated with increased fasting plasma glucose (FPG), A1C, fasting insulin, and insulin resistance by homeostasis model assessment (HOMA-IR; all P = 0.01-0.006). Two noncoding SNPs (rs7775386 and rs7773477) demonstrated similar associations, but LME models indicated that their effects were not independent from K121Q. We found no association of K121Q with obesity, but interaction models suggested that the effect of the Q allele on FPG and HOMA-IR was stronger in those with a higher BMI (P = 0.008 and 0.01 for interaction, respectively). CONCLUSIONS The Q allele of ENPP1 K121Q is associated with hyperglycemia and insulin resistance in whites. We found an adiposity-SNP interaction, with a stronger association of K121Q with diabetes-related quantitative traits in people with a higher BMI.
Collapse
Affiliation(s)
- Elliot S. Stolerman
- Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Alisa K. Manning
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Jarred B. McAteer
- Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - Caroline S. Fox
- Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, Massachusetts
| | - L. Adrienne Cupples
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
| | - James B. Meigs
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts
| | - Jose C. Florez
- Center for Human Genetic Research and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Program in Medical and Population Genetics, Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
300
|
Florez JC. Newly identified loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 2008; 51:1100-10. [PMID: 18504548 DOI: 10.1007/s00125-008-1025-9] [Citation(s) in RCA: 232] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2008] [Accepted: 04/04/2008] [Indexed: 12/13/2022]
Abstract
Although type 2 diabetes has been traditionally understood as a metabolic disorder initiated by insulin resistance, it has recently become apparent that an impairment in insulin secretion contributes to its manifestation and may play a prominent role in its early pathophysiology. The genetic dissection of Mendelian and, more recently, polygenic types of diabetes confirms the notion that primary defects in insulin synthesis, processing and/or secretion often give rise to the common form of this disorder. This concept, first advanced with the discovery and physiological characterisation of various genetic subtypes of MODY, has been extended to other forms of monogenic diabetes (e.g. neonatal diabetes). It has also led to the identification of common risk variants via candidate gene approaches (e.g. the E23K polymorphism in KCNJ11 or common variants in the MODY genes), and it has been validated by the description of the robust physiological effects conferred by polymorphisms in the TCF7L2 gene. More recently, the completion and integration of genome-wide association scans for this disease has uncovered a number of heretofore unsuspected variants, several of which also affect insulin secretion. This review provides an up-to-date account of genetic loci that influence risk of common type 2 diabetes via impairment of beta cell function, outlines their presumed mechanisms of action, and places them in the context of gene-gene and/or gene-environment interactions. Finally, a strategy for the analogous discovery of insulin resistance genes is proposed.
Collapse
Affiliation(s)
- J C Florez
- Diabetes Unit and Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA 02114, USA.
| |
Collapse
|