diabetes.diabetesjournals.org · 27/06/2013 · supplementary data ©2013 american diabetes...

SUPPLEMENTARY DATA

©2013 American Diabetes Association. Published online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db13-0128/-/DC1

Mendelian Randomisation Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes Short running title: Adiponectin in Insulin Resistance and Type 2 Diabetes

Author list Hanieh Yaghootkar1, Claudia Lamina2, Robert A Scott3, Zari Dastani4, Marie-France Hivert5,6, Liling L Warren7, Alena Stancáková8, Sarah G Buxbaum9, Leo-Pekka Lyytikäinen10,11, Peter Henneman12, Ying Wu13, Chloe YY Cheung14, James S Pankow15, Anne U Jackson16, Stefan Gustafsson32, Jing Hua Zhao3, Christie M Ballantyne17, Weijia Xie1, Richard N Bergman18, Michael Boehnke16, Fatiha el Bouazzaoui12, Francis S Collins19, Sandra H Dunn20, Josee Dupuis21, Nita G Forouhi3 , Christopher Gillson3, Andrew T Hattersley1,22, Jaeyoung Hong21, Mika Kähönen23, Johanna Kuusisto8, Lyudmyla Kedenko24, Florian Kronenberg2, Alessandro Doria25, Themistocles L Assimes26, Ele Ferrannini27, Torben Hansen28,29, Ke Hao30, Hans Häring31, Joshua W Knowles26, Cecilia M Lindgren33, John J Nolan34, Jussi Paananen8, Oluf Pedersen28,35-37, Thomas Quertermous26, Ulf Smith38, the GENESIS consortium, the RISC consortium, Terho Lehtimäki10,11, Ching-Ti Liu21, Ruth JF Loos3,39, Mark I McCarthy33,40,41, Andrew Morris42, Ramachandran S. Vasan43,44, Tim D Spector45, Tanya M Teslovich16, Jaakko Tuomilehto46-49, Ko Willems van Dijk12, Jorma S Viikari50,51, Na Zhu15, Claudia Langenberg3, Erik Ingelsson32,33, Robert K Semple52,53, Alan R Sinaiko54, Colin Palmer42, Mark Walker55, Karen SL Lam14,56, Bernhard Paulweber24, Karen L Mohlke13, Cornelia van Duijn57, Olli T Raitakari58,59, Aurelian Bidulescu60, Nick J Wareham3, Markku Laakso8, Dawn M Waterworth61, Debbie A Lawlor62, James B Meigs6, J Brent Richards45,63, Timothy M Frayling1

Author affiliations

1. Genetics of Complex Traits, University of Exeter Medical School, Exeter, EX1 2LU, UK. 2. Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical

Pharmacology, Innsbruck Medical University, Innsbruck, Austria. 3. MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, UK. 4. Department of Epidemiology, Biostatistics and Occupational Health. Lady Davis Institute,

Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada. 5. Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada. 6. General Medicine Division, Massachusetts General Hospital, Boston, MA, USA. 7. Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, NC, USA. 8. University of Eastern Finland, 70210 Kuopio, Finland. 9. School of Health Sciences, Jackson State University, USA. 10. Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33520, Finland. 11. Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere 33014,

Finland. 12. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands. 13. Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA. 14. Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong

Kong. 15. Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN,

USA.

SUPPLEMENTARY DATA


16. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA.

17. Baylor College of Medicine and Methodist DeBakey Heart and Vascular Center, Houston, TX, USA.

18. Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.

19. Genome Technology Branch, National Human Genome Research Institute, NIH, Bethesda, MD 20892, USA.

20. School of Nursing, University of Pittsburgh, Pittsburgh, PA, USA. 21. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA. 22. Genetics of Diabetes, University of Exeter Medical School, Exeter, EX1 2LU, UK. 23. Department of Clinical Physiology, Tampere University Hospital and University of Tampere

School of Medicine, Tampere 33521, Finland. 24. First Department of Internal Medicine, St. Johann Spital, Paracelsus Private Medical University

Salzburg, 5020 Salzburg, Austria. 25. Section on Genetics and Epidemiology, Joslin Diabetes Center, Boston, Massachusetts 02215,

USA. 26. Department of Medicine and Cardiovascular Institute, Stanford University School of Medicine,

Stanford, California, USA. 27. Department of Internal Medicine, University of Pisa, Pisa, Italy. 28. Novo Nordisk Foundation Center for Basic Metabolic Research Faculty of Health Science,

University of Copenhagen, Copenhagen, Denmark. 29. Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark. 30. Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York,

USA. 31. Department of Internal Medicine, Division of Endocrinology, Diabetology, Nephrology,

Vascular Medicine and Clinical Chemistry, University of Tübingen, Tübingen, Germany. 32. Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory,

Uppsala University, Uppsala, Sweden 33. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK. 34. Steno Diabetes Center, Gentofte, Denmark. 35. Hagedorn Research Institute, Copenhagen, Denmark. 36. Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen,

Copenhagen, Denmark. 37. Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark. 38. The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical

Medicine, Sahlgrenska Academy, Gothenburg, Sweden. 39. Mount Sinai School of Medicine, Depatment of Preventive Medicine, The Charles Bronfman

Institute for Personalized Medicine, Institute of Child Health and Development, New York, NY 10069, USA.

40. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK. 41. Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital,

Oxford, UK. 42. Biomedical Research Institute, University of Dundee, Ninewells Hospital and Medical School,

Dundee, UK. 43. Boston University School of Medicine, Boston, MA, USA. 44. Framingham Heart Study, Framingham MA, USA. 45. Twin Research and Genetic Epidemiology, King’s College London, London, UK.

SUPPLEMENTARY DATA


46. Diabetes Prevention Unit, National Institute for Health and Welfare, 00271 Helsinki, Finland. 47. King Abdulaziz University, Jeddah, Saudi Arabia 48. Red RECAVA Grupo RD06/0014/0015, Hospital Universitario La Paz, 28046 Madrid, Spain. 49. Centre for Vascular Prevention, Danube-University Krems, 3500 Krems, Austria. 50. Department of Medicine, Turku University Hospital, Turku 20521, Finland. 51. Department of Medicine, University of Turku, Turku 20521, Finland. 52. The National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge,

UK. 53. The University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science,

Cambridge, UK. 54. Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota, USA. 55. Institute of Cellular Medicine, The Medical School, Newcastle University, Framlington Place,

Newcastle, UK. 56. Research Centre of Heart, Brain, Hormone and Healthy Aging, Li Ka Shing Faculty of Medicine,

The University of Hong Kong, Hong Kong. 57. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. 58. Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku

20521, Finland. 59. Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku,

Turku 20521, Finland. 60. Morehouse School of Medicine, Cardiovascular Research Institute and Department of

Community Health and PreventiveMedicine, Atlanta, GA, USA. 61. Quantitative Sciences, GlaxoSmithKline, Upper Merion, PA, USA. 62. Department of Social Medicine, University of Bristol, Bristol, UK. 63. Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University,

Montreal, Canada.

Corresponding author: Timothy M. Frayling, Genetics of Complex Traits, University of Exeter Medical School, University of Exeter, Magdalen Rd., Exeter, EX12LU, U.K. Telephone: +44 1392 262935, Fax: +44 1392 262926, E-mail: [email protected].

mailto:[email protected]

SUPPLEMENTARY DATA


Descriptions of studies Atherosclerosis Risk in Communities (ARIC): The ARIC Study is a population-based, longitudinal study. A total of 15,792 participants aged 45-64 were recruited in 1987-89 using probability-based sampling from four US communities: Forsyth County, NC; Jackson, MS; northwest suburbs of Minneapolis, MN; and Washington County, MD (1). Genotype and adiponectin measurements were available for 303 white individuals selected for the subcohort of a case-cohort study of T2D conducted within ARIC. British Women's Heart and Health Study (BWHHS): Between 1999 and 2001 4286 women aged 60–79 years, who were randomly selected from 23 British towns were interviewed, examined, completed medical questionnaires and had detailed reviews of their medical records. Of these 4286 women who participated in the BWHHS, 3938 (92%) had complete data on all anthropometric measurements and of these 3438 (87%) provided consent for genetic testing. The full details of the selection of study participants and their measurements have been reported earlier (2). Cardiovascular Risk in Young Finns Study (YFS): This population-based follow-up study started in 1980 (http://med.utu.fi/cardio/youngfinnsstudy/) (3). A subsequent 27-year-follow-up study was conducted in 2007 (ages 30-45 years). 1,844 individuals with phenotype measurements and genotype data were integrated in this analysis. Caucasian Cohort Lausannoise Study (CoLaus): The CoLaus is a population based study of the population registry of the city of Lausanne, Switzerland. Only individuals with four grandparents of European origin were included in this study. This study aimed to examine the epidemiological and genetic determinants of cardiovascular risk factors and metabolic syndrome. This study has been described in detail previously (4). A total of 5,261 individuals with adiponectin measurements were included in the current analysis. Cebu Longitudinal Health and Nutrition Survey (CLHNS): The CLHNS is an on-going community-based study that began in 1983. The baseline survey randomly recruited 3,327 mother-child pairs from 17 urban and 16 rural areas from the Metropolitan Cebu area, the Philippines. Overnight fasting blood samples for biomarkers and DNA were obtained at the 2005 survey. Plasma samples were analyzed for adiponectin with a commercially available enzyme-linked immunosorbent assay (R&D Systems #DY1065). Data from 1,785 participants with available adiponectin measurements and genotype data were included in this analysis. Erasmus Rucphen Family (ERF): The ERF is a family-based and genetically isolated study cohort from the Rucphen region located in the southwest of the Netherlands and has been described in detail previously (5). The selection of subjects in the ERF cohort was based on pedigree structure and not on health status. All ERF participants are descendants from 22 couples with at least 6 children born in the year 1850. The ERF population includes 3,000 individuals for whom in this meta-analysis for approximately 2000 individuals phenotypic and genotypic information was available. European network on Functional Genomics of Type 2 Diabetes (EUGENE2): The participants included in this study were healthy, non-diabetic offspring of patients with type 2 diabetes. For inclusion, one of the parents had to have type 2 diabetes and the other parent normal glucose tolerance evaluated by an OGTT or a lack of history of type 2 diabetes. The probands were randomly selected from regions of four centres in Europe: Copenhagen, Denmark (n=278), Gothenburg, Sweden (n=100), Kuopio, Finland (n=217) and Tubingen, Germany (n=149). All study participants underwent a standard medical history, routine laboratory testing, assessment of social issues (alcohol consumption, activity, smoking status), and an OGTT. The protocol was approved by the relevant local Ethics Committees and informed written consent was obtained from all participants. Further details about the EUGENE2 consortium are provided in (6). Fenland Study: The Fenland Study is an ongoing, population-based cohort study (started in 2005) designed to investigate the association between genetic and lifestyle environmental factors and the risk

SUPPLEMENTARY DATA


of obesity, insulin sensitivity, hyperglycemia and related metabolic traits in men and women born in the years 1950 to 1975. Potential volunteers were recruited from General Practice sampling frames in the Fenland, Ely and Cambridge areas of the Cambridgeshire Primary Care Trust in the UK. Exclusion criteria for the study were: prevalent diabetes, pregnant and lactating women, inability to participate due to terminal illness, psychotic illness, or inability to walk unaided. All participants had measurements done at the MRC Epidemiology Unit Clinical Research Facilities in Ely, Wisbech and Cambridge. Participants attended after an overnight fast for a detailed clinical examination, and blood samples were collected. The Local Research Ethics Committee granted ethical approval for the study and all participants gave written informed consent (7). Data from 4,338 participants were included in the current analyses. Framingham Heart Study (FHS): The FHS was initiated in 1948 and is comprised of 5,209 participants from Framingham, MA (US), who have undergone examinations every other year to evaluate cardiovascular disease and related risk factors. The Offspring cohort was recruited in 1971 and includes 5,124 children of the Original cohort and the children’s spouses (8). Participants from the Offspring cohort have attended exams roughly every four years. In 2002, the third generation cohort consisting of 4,095 participants who were grandchildren of the original cohort were recruited to form the Gen 3 cohort. Adiponectin levels were measured using specimens collected at the 7th exam from the Offspring cohort and first examination of the Gen 3 cohort. The current analysis includes 4,878 participants (2,071 from Offspring cohort and 2,807 from Gen 3 cohort) with available phenotypic (all had adiponectin measured) and genotypic information. FUSION Study: The FUSION study has been described in detail previously (9). The FUSION GWA study is a T2D case-control study that includes 1,160 Finnish T2D cases and 1,173 normal glucose tolerant (NGT) controls. Cases and controls were approximately frequency matched as previously described, taking into account five-year age category, sex, and birth province within Finland. Genetics of Diabetes Audit and Research Tayside (GoDARTS) study: The GoDARTS study is a sub-study of the Diabetes Audit and Research Tayside (DARTS) study (10, 11), which aims to identify all known diabetes patients in the Tayside region of Scotland using electronic database retrieval. The samples used in this study are a sub-sample of the type 2 diabetes patients identified. The study includes individuals of white European descent, living in the Tayside region when recruited. The diagnosis of diabetes in cases was based on either current treatment with diabetes-specific medication, or laboratory evidence of hyperglycaemia if treated with diet alone. InterAct Study: The InterAct Study is collaboration amongst eight of the 10 countries in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts to investigate gene–lifestyle interaction on 12,403 incident type 2 diabetes cases occurring between 1991 and 2007 and a random subcohort of 16,835 individuals. A genomewide association study was carried out on 9431 individuals using llumina660WQuad GeneChip (12). Jackson Heart Study (JHS): The JHS is a large, population-based observational study evaluating the aetiology of cardiovascular, renal and respiratory diseases (13). Data and biologic materials have been collected from a probability community based sample of African American adults composed of 5301 participants, including 1499 members of 291 families. Participants were enrolled from the three counties that make up the Jackson, Mississippi metropolitan area. Relatives of selected participants were recruited to develop a large, nested family cohort. The JHS participants, aged 21 – 94 years, were recruited and examined at baseline (2000-2004) by certified technicians according to standardized protocols. Clinic visits and interviews occurred approximately every three years. Participants provided extensive medical and social history, had an array of physical and biochemical measurements and diagnostic procedures, and provided genomic DNA. Participants have a high prevalence of diabetes, hypertension, obesity, and related disorders. DNA samples were obtained from 4726 JHS participants.

SUPPLEMENTARY DATA


Annual follow-up interviews and cohort surveillance are ongoing. Data from 2,053 participants with available adiponectin measurements and genotype data were included in this analysis. The METSIM Study (Metabolic Syndrome in Men): is a population-based cross-sectional study comprising a total of 10,197 men (14). Participants, aged from 45 to 70 years, were randomly selected from the population register of Kuopio town, Eastern Finland (population of 95,000) and examined within years 2005-2010. Every participant had one-day outpatient visit to the Clinical Research Unit at the University of Kuopio (presently named as University of Eastern Finland), including an interview on the history of previous diseases and current drug treatment, and an evaluation of glucose tolerance and cardiovascular risk factors. Fasting blood samples were drawn after 12 hours of fasting. The study was approved by the Ethics Committee of the University of Kuopio and Kuopio University Hospital, and it was in accordance with the Helsinki Declaration. Adiponectin measurements and genotype data were available from 7,000 non-diabetic participants included in the current analysis. MINNESOTA Study: The Minnesota study was started in 1985 with a goal of determining the influence of obesity and insulin resistance during childhood on the development of adult cardiovascular disease and type 2 diabetes (15, 16). The initial cohort of 401 was selected randomly after screening of 12,043 5th-8th grade children, and members of this group underwent protocol studies at mean age 13, 15, 19, and 24. After enrolment of the initial cohort, 304 siblings were recruited and underwent similar protocol studies twice. Throughout the course of the study 390 parents of the initial cohort were recruited and underwent protocol studies one time. The adiponectin data used in the present study were collected during adolescence. The Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS): CRISPS is an on-going prospective population-based cohort study of cardiovascular risk factors in Hong Kong Chinese. Details of the CRISPS cohort were previously described (17-20). Briefly, in 1995-1996 (CRISPS1), 2895 unrelated Southern Chinese subjects were recruited from the general population via random telephone numbers in Hong Kong. Subsequent follow-up assessments were conducted at median ~6.4 (CRISPS2) and ~11.8 (CRISPS3) years from baseline, respectively. At each follow-up visit, subjects are invited to undergo a comprehensive assessment of cardiovascular risks, including a 75-g oral glucose tolerance test (OGTT). MRC Ely: The MRC Ely study is a population-based prospective study of European-origin adults (21, 22). Participants were randomly selected from a sampling frame of all adults aged 40-69 years and without diabetes registered at a single general practice in Ely. Baseline measurements were performed between 1990 and 1992. Follow-up examination occurred at phase 2 (1994-1996) and phase 3 (2000-2003) among individuals who were non-diabetic at previous visits. Phase 3 also included new recruitment of individuals from the original sampling frame who had not been randomly selected for the original phase 1 baseline examination. The current analyses included individuals aged 35-79 years, from phase 3. Ethical permission was granted by the Cambridgeshire Research Ethics Committee, and study participants provided written informed consent. Data from 727 individuals were included in the current analyses. Relationship between Insulin Sensitivity and Cardiovascular disease (RISC): The RISC cohort includes individuals aged 30–60 years with no clinical signs of disease, recruited from 19 centres in 14 countries (Austria, Denmark, Finland, France, Germany, Greece, The Netherlands, Ireland, Italy, Sweden, Spain, Switzerland, United Kingdom and Serbia and Montenegro) according to the following initial exclusion criteria: non-European ancestry, treatment for obesity, hypertension, lipid disorders or diabetes, pregnancy, cardiovascular or chronic lung disease, weight change of 5 kg or more in last 6 months, cancer (in last 5 yr), and renal failure. RISC study had undergone appropriate review by the European Commission research program and its ethics committee. Written consent was given by the patients for their information to be stored in the hospital database and used for research purposes. The RISC methodologies have been described in detail previously (23).

SUPPLEMENTARY DATA


Salzburg Atherosclerosis Prevention Program in subjects at High Individual Risk (SAPHIR): The SAPHIR study is an observational study conducted in the years 1999-2002 involving 1,770 healthy, unrelated subjects. Study participants were recruited by health screening programs in large companies in and around the city of Salzburg, Austria. The aim of this study was to investigate the genetic determinants of metabolic phenotypes. This study has been described in detail previously (24). De novo genotyping has been done in the mentioned individuals. Stanford Insulin Suppression Test (IST): Stanford (IST) Cohort (n=381) includes a subset of all subjects participating in various clinical research studies at Stanford University Medical that called for at least one insulin suppression test (IST) between 2002 and 2007. Volunteers for these studies came from the surrounding Stanford communities and were generally free of major chronic medical conditions at the time of their IST. Subjects were not eligible to participate in any protocol if they reported being on medications known to influence insulin sensitivity including corticosteroids, metformin, sulfonylureas or thiazolidinediones. Steady state plasma glucose (SSPG) derived from an IST was used as a direct measure of insulin sensitivity. In this study, we include only those genotyped individuals reporting white-non Hispanic ancestry (n=270). TwinsUK Study: TUK is a population-based sample of British twins, which is representative of the general United Kingdom population, and is has been extensively phenotyped for aging-related traits (25, 26). They were genotyped at two stages as called TUK1 and TUK23 in our study. 968 individuals from TUK1 and 1,229 samples from TUK23 are included in this meta-analysis. ULSAM (Uppsala Longitudinal Study of Adult Men): The ULSAM cohort (http://www.pubcare.uu.se/ULSAM ) is a prospective, community-based, observational cohort in Uppsala, Sweden, that invited all 50-year-old men living in Uppsala, Sweden, to participate in 1970-1974 During a 20 year follow-up visit in 1990-1995), 1,071 men underwent a euglycaemic-hyperinsulinaemic clamp and provided DNA. We examined a subsample of 793 individuals after initial exclusion of individuals with type 2 diabetes (n = 198), patients on lipid medication (n=77), with lipid concentrations outside ±3SD, or low genotyping call rate (< 90%, n = 1). The study was approved by the Ethics committees of Uppsala University, Faculty of Medicine. All participants gave written informed consent. Welcome Trust Case Control Cohort (WTCCC): This study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data and study description is available at www.wtccc.org.uk and has been described previously (11). Acknowledgements by study Atherosclerosis Risk in Communities (ARIC): The Atherosclerosis Risk in Communities Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC-55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, N01- HC-55022, R01HL087641, R01HL59367, R01HL086694 and RC2 HL102419; National Human Genome Research Institute contract U01HG004402; National Institutes of Health contract HHSN268200625226C; and National Institute of Diabetes and Digestive and Kidney Diseases R01DK056918. Infrastructure was partly supported by Grant Number UL1RR025005, a component of the National Institutes of Health and NIH Roadmap for Medical Research. British Women’s Heart and Health Study (BWHHS): The British Women's Heart and Health Study is funded by the Department of Health (of England and Wales) Policy Research Programme and the British Heart Foundation. DAL works in a centre that receives funding from the UK Medical Research Council (G0600705) and the University of Bristol. The views expressed in the publication are those of the authors and not necessarily those of any funding bodies. Cardiovascular Risk in Young Finns Study (YFS): The Young Finns Study has been financially supported by the Academy of Finland: grants 134309 (Eye), 126925, 121584, 124282, 129378 (Salve),

SUPPLEMENTARY DATA


117787 (Gendi), and 41071 (Skidi), the Social Insurance Institution of Finland, Kuopio, Tampere and Turku University Hospital Medical Funds (grant 9M048 and 9N035 for TeLeht), Juho Vainio Foundation, Paavo Nurmi Foundation, Finnish Foundation of Cardiovascular Research and Finnish Cultural Foundation, Tampere Tuberculosis Foundation and Emil Aaltonen Foundation (T.L). The expert technical assistance in the statistical analyses by Irina Lisinen and Ville Aalto are gratefully acknowledged. Cebu Longitudinal Health and Nutrition Survey (CLHNS): We thank the Office of Population Studies Foundation research and data collection teams and the study participants who generously provided their time for this study. This work was supported by National Institutes of Health grants DK078150, TW05596, HL085144, RR20649, ES10126, and DK56350. Caucasian Cohort Lausannoise Study (CoLaus): We thank the co-primary investigators of the CoLaus study, Gerard Waeber and Peter Vollenweider, and the PI of the PsyColaus Study Martin Preisig. We gratefully acknowledge Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey and Sylvie Mermoud for their role in the CoLaus data collection. The CoLaus study was supported by research grants from GlaxoSmithKline and from the Faculty of Biology and Medicine of Lausanne, Switzerland. Erasmus Rucphen Family (ERF): The ERF study was supported by grants from The Netherlands Organisation for Scientific Research, Erasmus MC and the Centre for Medical Systems Biology (CMSB) and the European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium. We thank the participants from the Genetic Research in Isolated Populations, Erasmus Rucphen Family as well the general practitioner and other clinicians who made this work possible. Fenland Study: The Fenland Study is funded by the Medical Research Council (MC_U106179471) and Wellcome Trust. We are grateful to all the volunteers for their time and help, and to the General Practitioners and practice staff for assistance with recruitment. We thank the Fenland Study Investigators, Fenland Study Co-ordination team and the Epidemiology Field, Data and Laboratory teams. Biochemical assays were performed by the National Institute for Health Research, Cambridge Biomedical Research Centre, Core Biochemistry Assay Laboratory, and the Cambridge University Hospitals NHS Foundation Trust, Department of Clinical Biochemistry. Framingham Heart Study (FHS): This research was conducted in part using data and resources from the Framingham Heart Study of the National Heart Lung and Blood Institute of the National Institutes of Health and Boston University School of Medicine. The analyses reflect intellectual input and resource development from the Framingham Heart Study investigators participating in the SNP Health Association Resource (SHARe) project. This work was partially supported by the National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195) and its contract with Affymetrix, Inc for genotyping services (Contract No. N02-HL-6-4278). The variant rs17366653 was genotyped at the Joslin Advanced Genomics and Genetics Core (supported by NIH grant DK36836-25). A portion of this research utilized the Linux Cluster for Genetic Analysis (LinGA-II) funded by the Robert Dawson Evans Endowment of the Department of Medicine at Boston University School of Medicine and Boston Medical Center. Also supported by National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) R01 DK078616 and NIDDK K24 DK080140 and an American Diabetes Association Career Development Award. FUSION Study: We thank the Finnish citizens who generously participated in the FUSION and FINRISK 2002 studies. Support for FUSION was provided by NIH grants DK062370 (M.B.) and DK072193 (K.L.M.), and intramural project number 1Z01 HG000024 (F.S.C.). Genome-wide genotyping was performed by the Johns Hopkins University Genetic Resources Core Facility (GRCF) SNP Center at the Center for Inherited Disease Research (CIDR) with support from CIDR NIH Contract Number N01-HG-65403.

SUPPLEMENTARY DATA


InterAct study: The InterAct study received funding from the European Union (Integrated Project LSHM-CT-2006-037197 in the Framework Programme 6 of the European Community). The European Commission "Europe Against Cancer" Programme substantially supported EPIC financially. Additionally, each centre receives local financial support: We thank all EPIC participants and staff for their contribution to the study. Jackson Heart Study (JHS): The Jackson Heart Study is supported by the National Heart, Lung, and Blood Institute, through contracts with Jackson State University (N01-HC-95170), the University of Mississippi Medical Center (N01-HC-95171), and Tougaloo College (N01-HC-95172). Adiponectin measurements used in the current study were funded by PHS Award UL1 RR025008 from the Clinical and Translational Science Award program, National Institutes of Health, National Center for Research Resources (NCRR). Genotyping was funded by NIH grants T32 NR009759 and UH1 HL073461. MINNESOTA Study: The Minnesota studies were supported by grants HL52851 and MO1-RR-00400 from the National Institutes of Health. The Hong Kong Cardiovascular Risk Factor Prevalence Study (CRISPS): We would like to acknowledge all the subjects who had participated in CRISPS. This work was supported by the Hong Kong Research Grant Council (RGC) Theme-Based Research Scheme (TBRS) Grant T12-705/11 to KSL Lam. MRC Ely: The Ely Study was funded by the Medical Research Council (MC_U106179471) and Diabetes UK. We are grateful to all the volunteers, and to the staff of St. Mary’s Street Surgery, Ely and the study team. Salzburg Atherosclerosis Prevention Program in subjects at High Individual Risk (SAPHIR): Part of this work was funded by the “Genomics of Lipid-associated Disorders” (GOLD) of the “Austrian Genome Research Programme” (GEN-AU) to Florian Kronenberg, and grants from the “Medizinische Forschungsgesellschaft Salzburg”, the “Kamillo Eisner Stiftung” (Switzerland), the Österreichische Nationalbank (Anniversary Fund, Project Nr.13339) and Paracelsus Medical University Forschungsförderungsfonds (FFF-PMU, Project Nr. E-09/09/055-PAU) to Bernhard Paulweber. TwinsUK Study: This work was supported by grants from the Canadian Foundation for Innovation, the Canadian Institutes of Health Research (CIHR), Fonds de la recherche en sante du Québec, Ministère du Développement Economique, Innovation et Exportation du Québec, the Lady Davis Institute and the Jewish General Hospital. Drs. Richards and Dastani are supported by the CIHR. This study was funded by the Wellcome Trust, European Commission Framework (FP7/2007-2013), ENGAGE project HEALTH-F4-2007-201413, and the FP5 GenomEUtwin Project (QLG2-CT-2002-01254). It also receives support from the Arthritis Re- search Campaign, Chronic Disease Research Foundation, the National Institute for Health Research (NIHR) comprehensive Biomedical Re- search Centre award to Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London, and a Biotechnology and Bio- logical Sciences Research Council project grant (G20234).

SUPPLEMENTARY DATA


Supplementary Table 1. Basic characteristics, exclusions, genotyping, quality control and imputation in European studies (NA = "not available").

STUDY BWHHS COLAUS ELY ERF FENLA

ND FHS

GoDARTS

METSIM

MINNESOT

A RISC SAPHIR TUK YFS

Cohort Informa

tion

Ethnicity British White UK Caucasian European European UK Finnish white European Caucasion European Finnish

Country UK Switzerlan

d England Netherlands England USA Scotland Finland USA

Austria, Denmark, Finland, France,

Germany, Greece,

Netherlands, Ireland,

Italy, Sweden, Spain,

Switzerland, uk, Serbia,

Montenegro

Austria UK Finland

Collection type

population-based

population-based

Population-based

General population / family based

Population-based

Population-based

Type 2 diabetes

case-control

cross-sectional population study

cohort study

Population-based

Population-based

twins

age cohort follow-

up study

Samples

Age Mean (SD), years

68.83 (5.5) 53 (11.0) 53.27 (7.7)

49.76(15.0) 46.15 (7.2)

49.26 (9.3)

62.57 (12.1)

57.50 (7.0)

21.13 (2.7)

43.96 (8.4) 51.39 (6.0) 48.10 ( 11.3)

39.00 (5.0)

Age [Mean (SD)

males / Mean (sd)

females], years

- / 68.83 (5.5)

53 (11.0) / 54 (11.0)

53.61 (7.8) / 53.02 (7.6)

50.19 (14.7) / 49.43 (15.2)

46.05 (7.3) / 46.24 (7.1)

49.2 (9.3) / 49.31 (9.3)

63.84 (11.3) / 61.31 (12.9)

57.50 (7.0) / -

21.12 (2.6) / 21.14 (2.8)

43.31 (8.6) / 44.46 (8.2)

48.84 (5.4 / 55.66 (4.3)

- / 48.10 ( 11.3)

37.60 (5.0) / 37.71 (5.0)

SUPPLEMENTARY DATA


N (N males /

N females)

3904 (0 / 3904)

6152 (2922 / 3230)

1570 (731 / 839)

2812 (1248 / 1564)

4338 (2010 / 2328)

4488 (2064 / 2424)

3696 (1842 / 1854)

8156 (8156 /

0)

221(116 / 105)

1031(453/578)

1770 (1107 / 663)

1399 (0 / 1399)

1844 (825 / 1019)

Details of

exclusions (if

different from the general exclusio

n chriteria

)

T1DM NA NA NA NA

call rate < 97%;

heterozygosity rate > 5SD from mean

NA NA NA NA NA NA

T1DM, insulin treatme

nt, outliers ±4 SD

Additional

covariates

Coronary Heart

Disease Status

NA NA kinship NA NA NA NA NA NA NA NA

Center, princip

al components

Adiponectin

Measurements

Assay ELISA ELISA

Timeresolved

immunofluorometric assay, reagentssupplied

by R&D Systems

RIA

DELFIA, antibodie

s and stanadrds supplied by R&D Systems

ELISA NA ELISA

Quantikine

ELISA kit

(R&D System

s)

A novel in-house time-

resolved immunofluor

ometric assay (TR-

IFMA) based on two

antibodies and

recombinant human

adiponectin (R & D

Systems, Abingdon,

UK)

ELISA kit from

BioCat (Heidelber

g, Germany)

ELISA

Radioimmunoassay

(Human

Adiponectin and

Leptin RIA kits,

Linco Research Inc.)

N (N males /

N

1100 (0 / 1100)

6053 (2885 / 3168)

694 (292 / 402)

2117(913 / 1204)

4272 (1973 / 2299)

4488 (2064 / 2424)

3696 (1842 / 1854)

7040 (7040 /

0)

221(116 / 105)

1006 (444 / 562)

1691 (1054 / 637)

1399 (0 / 1399)

1844 (825 / 1019)

SUPPLEMENTARY DATA


females)

Mean (SD)

(µg/ml) (SD)

16.04 (7.6) 9.9 (8.1) 7.62 (3.8)

10.52(5.6) 5.8 (2.2) 9.38 (5.9)

4.96 (3.9)*

6.90 (4.1)

8.62(3.5)

8.34 (3.7) 7.6 (5.27)* 7.99 (3.7)

9.04 (6.8)*

Mean (SD)

males / Mean (SD)

females, (µg/ml)

(SD)

- / 16.04 (7.6)

7.3 (5.4) / 12.3 (9.3)

5.78 (2.6) / 8.95 (3.9)

8.05(4.2) / 12.40(5.8)

4.40 (1.3) / 7.50 (2.3)

6.64 (4.0) / 11.71 (6.1)

4.10 (3.0) / 6.07 (4.3)

6.90 (4.1) / -

7.72(2.8) /

9.62(3.8)

6.47 (2.6) / 9.82 (3.7)

6.52 (3.9) / 10.29 (6.6)*

- / 7.99 (3.7)

7.09 (4.2) / 11.46 (7.1)

Fasting plasma insulin

measurements

Assay ELISA

Solid-phase, two-site chemiluminescent immunometric assay

Medgenix IRMA

RIA

AutoDELFIA

automatic

immunoassay

system using a

two-step time

resolved fluorometric assay (Perkin Elmer Life

Sciences)

DPC Coat-A-Count RIA (total immunoreactive insulin)

ELISA assay

(Mercodia)

Immunoassay,

luminometric

measurement

RIA

fluoroimmunoassay

(AutoDELFIA Insulin

kit, Wallac Oy, Turku,

Finland)

Immunoassay

(Abbott, IMX-

system)

immunoassay

(Abbott Laboratories Ltd., Maidenhead, UK)

Microparticle

enzyme immunoassay

kit (Abbott Laboratories,

Diagnostic

Division,

Dainabot)

N (N males /

N females)

3862 (0 / 3862)

5417 (2623 / 2794)

727 (309 / 418)

2103 (901 / 1202)

4316 (2000 / 2316)

4160 (1934 / 2226)

647 (293 / 354)

7038 (7038 /

0)

220(115 / 105)

1000 (437 / 563)

1698 (1060 / 638)

1399 (0 / 1399)

1844 (825 / 1019)

Mean (SD) (

pmol/l) (SD)

9.46 (24.7) 52.8 (37.2) 44.92 (31.9)

13.25(7.5) 35.9

(14.8) 24.33 (18.4)

36 (27.6) 38.40 (34.2)

49 (20.5)

34.40 (18.7) 36 (27.5)* 12.60 (16.1)

47.99 (42.8)*

SUPPLEMENTARY DATA


Mean (SD)

males / Mean (SD)

females, (pmol/l)

(SD)

- / 9.46 (24.7)

57.6 (40.8) / 48.0 (33)

45.84 (37.7) / 44.24 (26.9)

13.76(8.6)/12.87(6.5)

40.4 (16.7) /

32.3 (13)

26.52 (19.2) / 22.43 (17.5)

6 (4.7) / 5 (4.6)

38.40 (34.2) / -

49(24) /

49(20.5)

36.80 (20) / 32.54 (17.4)

35.4 (28.8) / 36

(26.4)*

- /12.60 (16.1)

49.03 (40.8) / 47.02 (44.2)

Fasting LDL-

cholesterol

measurements

Assay

Not assayed - calculated from total cholesterol

, HDLc and trigs

using Friedwald equation

Calculated with the

Friedewald formula

LDL is calculated from

the triglyceride, HDL

and cholester

ol concentr

ations using the Friedwal

d formula

Enzymatically

LDL is calculated from

the triglyceride, HDL

and cholester

ol concentr

ations using the Friedwal

d formula

Homogeneous

enzymatic

colorimetric test

NA

Homogeneous

enzymatic

colorimetric test

Calculated by Friedewald

equation

Homogeneous enzymatic colourimetri

c test

Enzymatic colorimetri

c assay (Roche

Diagnostics)

NA

Calculated by Friedewald

equation

N (N males /

N females)

3756 (0 / 3756)

5839 (2738 / 3101)

711 (302 / 409)

(1193 / 1483)

4303 (1982 / 2321)

4430 (2015 / 2415)

1411 (608 / 803)

7039 (7039 /

0)

221(116 / 105)

1004 (441 / 563)

1707 (1064 / 643)

NA 1844 (825 / 1019)

Mean (SD)

(mmol/l) (SD)

4.14 (1.1) 3.5 (1.0) 4.41 (1.2)

3.71 (1.0) 3.35 (1.0)

3.04 (0.82)

3.18 (1.0)

3.40 (0.9)

2.49 (0.7)

2.91 (0.8) 3.74 (1.0) NA 2.99

(0.8)*

Mean (SD)

males / Mean (SD)

females, (mmol/l)

(SD)

- / 4.14 (1.1)

3.6 (1.0) / 3.4 (1.0)

4.41 (1.0) / 4.42 (1.3)

3.72(1.0) / 3.70(1.0)

3.47 (0.9) / 3.25 (0.9)

3.18 (0.78) /

2.92 (0.83)

3.22 (0.8) / 3.14 (0.9)

3.40 (0.9) / -

2.48(0.7) /

2.50(0.7)

3.10 (0.8) / 2.76 (0.8)

3.73 (1.0) / 3.76 (1.0)

NA

3.27 (0.8) / 2.92 (0.7)

SUPPLEMENTARY DATA


Fasting HDL-

cholesterol

measurements

Assay

Enzymatically

(Hitachi 747

analyser) CHOD-PAP + PEG +

cyclodextrin

RA 1000, (Bayer

Diagnostics)

Enzymatically

Homogeneous

enzymatic

colorimetric assay (Siemens Healthca

re)

Homogeneous

enzymatic

colorimetric test

NA

Homogeneous

enzymatic

colorimetric test

Determined after

precipitation of

non-HDL

by mg/dex

tran precipit

ation

Homogeneous enzymatic colourimetri

c test


c assay (Roche

Diagnostics)

NA

Rnzymatic

cholesterol

esterase (Choles

terol reagent

, Olymp

us)

N (N males /

N females)

3840 (0 / 3840)

5929 (2819 / 3110)

712 (302 / 410)

2111 (911 / 1200)

4333 (2007 / 2326)

4486 (2063 / 2423)

1739 (787 / 952)

7040 (7040 /

0)

221(116 / 105)

1013 (447 / 566)

1707 (1064 / 643)

NA 1844 (825 / 1019)

Mean (SD)

(mmol/l) (SD)

1.66 (0.5) 1.6 (0.4) 1.47 (0.4)

1.29(0.4) 1.495 (0.4)

1.42 (0.43)

1.57 (0.4)

1.41 (0.5)

1.16 (0.3)

1.43 (0.4) 1.50 (0.5)* NA 1.31 (0.3)

Mean (SD)

males / Mean (SD)

females, (mmol/l)

(SD)

- / 1.66 (0.5)

1.4 (0.4) / 1.8 (0.4)

1.29 (0.3) / 1.61 (0.4)

1.14(0.3) / 1.40(0.4)

1.32 (0.3) / 1.65 (0.4)

1.21 (0.33) /

1.6 (0.42)

1.40 (0.4) / 1.71 (0.2)

1.41 (0.5) / -

1.11(0.3) /

1.22(0.3)

1.24 (0.3) / 1.58 (0.4)

1.40 (0.4) / 1.74 (0.6)*

NA

1.22 (0.3) / 1.44 (0.3)

Fasting total

cholesterol

measurements

Assay

Enzymatically

(Hitachi 747

analyser)

CHOD-PAP

RA 1000, (Bayer

Diagnostics)

Enzymatically

Enzymatic-

cholestrol

oxidase-based

method (Siemens Healthca

re)

Enzymatic

colorimetric test

NA

Enzymatic

colorimetric test

Enzymatic-

cholestrol

oxidase-based method

Enzymatic colourimetri

c test


c assay (Roche

Diagnostics)

NA

Enzymatic

cholesterol

esterase (Choles

terol reagent

, Olymp

us) N (N

males / N

3846 (0 / 3846)

5929 (2819 / 3110)

722 (307 / 415)

(1205 / 1487)

4333 (2007 / 2326)

4488 (2064 / 2424)

1780 (810 / 970)

7042 (7042 /

0)

221(116 / 105)

1012 (447 / 566)

1707 (1064 / 643)

NA 1844 (825 / 1019)

SUPPLEMENTARY DATA


females)

Mean (SD)

(mmol/l) (SD)

6.64 (1.2) 5.6 (1.0) 6.52 (1.3)

5.56 (1.1) 5.34 (1.0)

5.08 (0.90)

5.58 (1.0)

5.37 (1.0)

4.16 (0.8)

4.83 (0.9) 5.93 (1.0) NA 4.90 (0.9)

Mean (SD)

males / Mean (SD)

females, (mmol/l)

(SD)

- / 6.64 (1.2)

5.6 (1.0) / 5.6 (1.0)

6.41 (1.1) / 6.60 (1.4)

5.49(1.1) / 5.62(1.1)

5.39 (1.0) /

5.30(1.0)

5.09 (0.88) /

5.08 (0.92)

5.50 (0.9) / 5.63

(1.02)

5.37 (1.0) / -

4.14 (0.8) / 4.18 (0.8)

1.24 (0.3) / 1.58 (0.4)

5.86 (1.0) / 6.06 (1.0)

NA

5.15 (0.9) / 4.87 (0.8)

Fasting plasma glucose

measurements

Assay Elisa Glucose

dehydrogenase

Hexokinase assay

Enzymatically

Hexokinase

enzymatic assay

(Siemens Healthca

re)

Hexokinase reagent kit (a-gent glucose test, Abbott, South Pasadena, California)

NA

Enzymatic

photometric test, Glucose hexokina

se

Beckman

Glucose

Analyzer II

Glucose Oxidase

Technique (Cobas Integra, Roche).

Glucose hexokinase

method (Hitachi

911, Roche)

analyser using an enzymati

c colorimetric slide

assay (Johnson

and Johnson Clinical Diagnost

ic Systems, Amersham, UK)

Enzymatically with a clinical chemist

ry analyze

r (Olymp

us, AU400

)

N (N males /

N females)

3824 (0 / 3824)

5929 (2819 / 3110)

723 (307 / 416)

2100 (907 / 1193)

4322 (2316 / 2006)

4454 (2043 / 2411)

657 (296 / 361)

7042 (7042 /

0)

221(116 / 105)

1031 (453 / 578)

1707 (1064 / 643)

1392 (0 / 1392)

1844 (825 / 1019)

Mean (SD)

(mmol/l) (SD)

6.1 (1.6) 5.6 (1.1) 5.67 (0.5)

4.57(1.0) 4.80 (0.5)

5.29 (0.50)

4.91 (0.5)

5.70 (0.5)

4.76 (0.4)

5.05 (0.6) 5.05 (0.7)* 4.78 (0.9)

5.25 (0.5)

SUPPLEMENTARY DATA


Mean (SD)

males / Mean (SD)

females, (mmol/l)

(SD)

- / 6.05 (1.6)

5.8 (1.2) / 5.3 (1.0)

5.79 (0.5) / 5.59 (0.5)

4.76(1.0) / 4.44(0.9)

4.94 (0.5) / 4.67 (0.5)

5.46 (0.47) /

5.14 (0.48)

5.02 (0.5) / 4.81 (0.5)

5.70 (0.5) / -

4.84(0.4) /

4.66(0.4)

5.20 (0.5) / 4.92 (0.6)

5.05 (0.7) / 4.94 (0.7)*

- / 4.78(0.9)

5.41 (0.5) / 5.12 (0.5)

Fasting triglycer

ide measure

ments

Assay

Enzymatically

(Hitachi 747

analyser)

GPO-PAP

RA 1000, (Bayer

Diagnostics)

Enzymatically

Enzymatic assay

(Siemens Healthca

re)

Enzymatic

colorimetric test

NA

Enzymatic

colorimetric test

Enyzmatic

method

Enzymatic colourimetri

c test


c assay (Roche

Diagnostics)

NA

Enzymatic

glycerol kinase (Triglyceride

reagent,

Olympus)

N (N males /

N females)

3846 (0 / 3846)

5929 (2819 / 3110)

721 (306/415

)

2110 (910 / 1200)

4333 (2326 / 2007)

4488 (2064 / 2424)

1439 (627 / 812)

7002 (7002 /

0)

221 (116 / 105)

1013 (447 / 566)

1707 (1064 / 643)

NA 1844 (825 / 1019)

Mean (SD)

(mmol/l) (SD)

1.87 (1.2) 1.4 (1.2) 1.37 (0.9)

1.36 (0.8) 1.0 (0.4) 1.38

(0.96) 1.37

(1.0) * 1.18 (0.8)

0.99 (0.4)

1.09 (0.7) 1.13 (0.8)* NA 1.05

(0.7)*

Mean (SD)

males / Mean (SD)

females, (mmol/l)

(SD)

- / 1.87 (1.2)

1.7 (1.5) / 1.2 (0.7)

1.54 (1.0) / 1.25 (0.7)

1.51(0.9) / 1.24(0.6)

1.2 (0.5) / 0.8 (0.3)

1.58 (1.17) /

1.21 (0.70)

1.57 (1.1) / 1.24 (0.8)

1.18 (0.8) / -

1.07 (0.4) / 0.88 (0.3)

1.28 (0.9)/ 0.94 (0.5)

1.19 (1.1) / 1.05 (0.6)*

NA

1.26 (0.9) / 0.95 (0.6)

BMI

N (N males /

N females)

NA 6150 (2921

/ 3229) 727 (309

/ 418) 2114 (911 /

1203)

4338 (2010 / 2328)

4483 (2063 / 2420)

1928 (883 / 1045)

6999 (6999 /

0)

221(116 / 105)

1031 (453 / 578)

1706 (1063 / 643)

1399 1844 (825 / 1019)

Mean (SD) NA 25.8 (4.6)

25.65 (3.8)

26.84 (4.6) 26.83 (4.9)

27.32 (5.2)

27.50 (4.6)

26.80 (3.8)

24.17(3.7)

25.47 (4.0) 25.99 (5.1)*

25.43(4.9)

25.13 (5.5)*

SUPPLEMENTARY DATA


Mean (SD)

males / Mean (SD)

females

NA 26.6 (4.1) / 25.1 (4.9)

25.87 (3.0) / 25.48 (4.3)

27.26(4.1) / 26.52(5.0)

27.20 (4.1) / 26.52 (5.5)

28.19 (4.4) / 26.57 (5.8)

27.60 (4.0) / 27.42 (5.0)

26.80 (3.8) / -

24.58(3.6) /

24.01(4.3)

26.36 (3.5) / 24.78(4.2)

26.28 (4.6) / 25.39 (6.1)*

- / 25.43(4.9

)

25.93 (4.9) / 24.24 (5.6)

M Value

N (N males /

N females)

NA NA NA NA NA NA NA NA 201

(103 / 98)

1009 (446 / 563)

NA NA NA

Mean (SD)

(micromol/kgbodywt/min)

(SD)

NA NA NA NA NA NA NA NA 8.24 (3.0)

39.70 (16.2) NA NA NA

Mean (SD)

males / Mean (SD)

females, (micromol/kgbodywt/min)

(SD)

NA NA NA NA NA NA NA NA

8.32(3.1) /

8.16(3.0)

37.70 (16.5) / 41.30 (15.8)

NA NA NA

Type 2 diabetes

N (N case / N control)

3857(404 / 3453)

NA NA NA NA 4878 (390 / 4488)

3154 (1928 / 1226)

5506 (1114/43

92) NA NA

1745 (57 / 1688)

NA NA

Age [Mean (SD)

cases / Mean (sd)

controls], years

69.38 (5.6) / 68.77 (5.5)

NA NA NA NA

59.87 (10.2) / 49.26 (9.3)

59.21 (11.1) / 63.97 (9.5)

60.30 (6.6) / 55.32 (6.8)

NA NA 54.33 (4.6)

/ 51.28 (6.1)

NA NA

rs17366653

Genotyping

platform and SNP

panel

Specific candidate

SNPs KASPar Taqman Taqman Taqman

ABI Taqman

NA TaqMan ABI

Taqman

NA

ViiA7 (Applied

Biosystems)

Taq man assay

Illumina 670k custom

SUPPLEMENTARY DATA


Genotyping

centre

Kbioscience

KBIO

MRC Epidemi

ology Unit

Leiden, the Netherlands

MRC Epidemi

ology Unit

Joslin Advance

d Genomic

s and Genetics

Core

Kbiosience

University of

Eastern Finland, Kuopio

University of

Minnesota

Biomedical

Genomics

Center

Kbiosience

University Clinic fir Internal

Medicine I,

Salzburg, Austria

KBiosciences

Welcome

Trust Sanger Institute, UK

Genotyping

calling algorith

m

FAST assay

FAST assay

Manually calling using

Applied Biosystem SDS

2.3

Genotyper (Lifetechnol

gies)



2.3



2.3

NA

Applied Biosystem, SDS

1.4 + manual check

NA NA NA

KBiosciences

KlusterCaller

software

Illuminus

IMPUTED?

0=No, 1=Yes

0 0 0 0 0 0 0 0 0 0 0 0 1

Imputation

software NA NA NA NA NA N/A NA NA NA NA NA NA

IMPUTE v. 2.0.4

Imputation

quality NA NA NA NA NA N/A NA NA NA NA NA NA 0.772

MAF 0.01 0.02 0.02 0.02 0.02 0.01 0.01 0.21 0.02 0.02 0.02 0.01 0.01 HWE 0.02 P>0.05 0.51 0.72 0.70 0.3222 0.60 0.98 0.76 0.60 0.64 0.63 NA

Call rate NA 0.99 0.97 0.79 0.97 0.99 0.96 0.97 0.93 0.96 0.99 0.98 NA

N 3652 6005 1578 2203 4351 4336

(4699 for T2D)

3646 8000 221 983 1757 1399 1844

rs17300539

Genotyping

platform and SNP

panel

Specific candidate

SNPs KASPar Taqman

Illumina (317K/318K

) Taqman

Affymetrix 500K

and MIPS 50K

NA Sequenom iPLEX

Sequenom

MassARRAY (custo

m panel)

NA

MALDI_TOF mass

spectroscopy

Taq man assay

Illumina 670k custom

SUPPLEMENTARY DATA


Genotyping

centre

Kbioscience

KBIO

MRC Epidemi

ology Unit

Leiden / Rotterdam,

the Netherlands

MRC Epidemi

ology Unit

Affymetrix

Kbiosience

University of


University of

Minnesota

Biomedical

Genomics

Center

Kbiosience

Helmholtz Center, Munich, Germany

KBiosciences

Welcome


Genotyping

calling algorith

m

FAST assay

FAST assay



2.3

Bead studio



2.3

BRLMM NA

Sequenom, Typer

4.0 + manual check

NA NA NA

KBiosciences

KlusterCaller

software

Illuminus

IMPUTED?

0=No, 1=Yes

0 0 0 1 0 0 / 1 0 0 0 0 0 0 0

Imputation

software NA NA NA MACH NA MACH NA NA NA NA NA NA NA

Imputation

quality NA NA NA 0.8821 NA 0.642961 NA NA NA NA NA NA NA

MAF 0.08 0.09 0.09 0.05 0.08 0.07 0.08 0.03 0.08 0.07 0.10 0.08 0.03 HWE 0.25 P>0.05 0.41 0.37 0.29 NA 0.20 0.98 0.51 0.70 0.79 0.07 0.41

Call rate NA 0.99 0.97 >98% 0.99 NA 0.96 0.99 NA 0.96 0.98 0.98 0.9995

905

N 3653 6005 1573 2697 4399 3101

(3562 for T2D)

3594 8106 221 793 1734 1399 1844

rs3774261

Genotyping

platform and SNP

panel

Specific candidate

SNPs Affy 500K Taqman

Illumina (317K/318K

) Taqman

Affymetrix 500K

and MIPS 50K

NA Sequenom iPLEX

Sequenom

MassARRAY (custo

m panel)

NA

MALDI_TOF mass

spectroscopy

NA Illumina 670k custom

SUPPLEMENTARY DATA


Genotyping

centre

Kbioscience

Affymetrix

MRC Epidemi

ology Unit

Leiden / Rotterdam,

the Netherlands

MRC Epidemi

ology Unit

Affymetrix

Kbiosience

University of


University of

Minnesota

Biomedical

Genomics

Center

Kbiosience

Helmholtz Center, Munich, Germany

NA

Welcome


Genotyping

calling algorith

m

FAST assay

BRLMM



2.3

Bead studio



2.3

BRLMM NA

Sequenom, Typer

4.0 + manual check

NA NA NA NA Illumin

us

IMPUTED?

0=No, 1=Yes

0 0 0 1 0 1 0 0 0 1 0 1 1

Imputation

software NA NA NA MACH NA MACH NA NA NA

MACH and minimac

NA IMPUT2 IMPUT

E v. 2.0.4

Imputation

quality NA NA NA 0.7966 NA 0.984215 NA NA NA 1 NA 0.899 0.983

MAF 0.39 0.32 0.38 0.33 0.39 0.49 0.36 0.35 0.37 0.38 0.42 0.38 0.37 HWE 0.15 P>0.05 0.84 NA 0.85 NA 0.85 0.14 0.30 NA 0.03 NA NA

Call rate NA >95% 0.95 NA 0.97 NA 0.96 0.99 NA NA 0.94 NA NA

N 3683 5569 1541 2697 4306 3101

(3562 for T2D)

3607 8089 221 1032 1657 1399 1844

rs3821799

Genotyping

platform and SNP

panel

Specific candidate

SNPs Affy 500K Taqman

Illumina (317K/318K

) Taqman

Affymetrix 500K

and MIPS 50K

NA Sequenom iPLEX

Sequenom

MassARRAY (custo

m panel)

NA

ViiA7 (Applied

Biosystems)

NA Illumina 670k custom

SUPPLEMENTARY DATA


Genotyping

centre

Kbioscience

Expression Analysis

MRC Epidemi

ology Unit

Leiden / Rotterdam,

the Netherlands

MRC Epidemi

ology Unit

Affymetrix

Kbiosience

University of


University of

Minnesota

Biomedical

Genomics

Center

Kbiosience

University Clinic fir Internal

Medicine I,

Salzburg, Austria

NA

Welcome


Genotyping

calling algorith

m

FAST assay

BRLMM



2.3

Bead studio



2.3

BRLMM NA

Sequenom, Typer

4.0 + manual check

NA NA NA NA Illumin

us

IMPUTED?

0=No, 1=Yes

0 0 0 1 0 1 0 0 0 1 0 1 0

Imputation

software NA NA NA MACH NA MACH NA NA NA

MACH and minimac

NA IMPUT2 NA

Imputation

quality NA NA NA 0.7207 NA 0.987991 NA NA NA 1 NA 1 NA

MAF 0.45 0.38 0.44 0.39 0.45 0.43 0.42 0.43 0.44 0.45 0.45 0.43 0.44 HWE 0.03 P>0.05 0.71 NA 0.92 NA 0.70 0.86 0.51 NA 0.21 NA 0.41

Call rate NA >95% 0.98 NA 0.98 NA 0.96 0.98 NA NA 0.99 NA 0.9995

905

N 3660 5569 1541 2697 4371 3101

(3562 for T2D)

3573 7969 221 1032 1760 1399 1844

SUPPLEMENTARY DATA


Supplementary Table 2. Basic characteristics, exclusions, genotyping, quality control and imputation in non-European studies (NA = "not available"). STUDY CLHNS CRISPS JHS Cohort Information Ethnicity Filipino (Asian) Southern Chinese African American

Country Philippines Hong Kong (China) United States of America

Collection type population-based Population-based cohort population study

Samples Age Mean (SD), years

48.4 (6.1) 51.47(9.83) 55.2 (12.5)

Age [Mean (SD) males / Mean (sd) females], years

- / 48.4 (6.1) 52.24(10.04) / 50.84(9.63) 55.0 (12.5) / 55.2 (12.5)

N (N males / N females)

1798 (0 / 1798) 1672 (754 / 918) 2500 (863 / 1637)

Details of exclusions (if different from the general exclusion chriteria)

NA NA NA

Additional covariates

NA NA ancestry estimate from AIMs

Adiponectin measurements

Assay enzyme-linked immunosorbent assay (R&D Systems #DY1065)

in-house sandwich ELISA ELISA system (R&D Systems; Minneapolis, MN)


1785 (0 / 1785) 1206 (556 / 650) 2053 (716 / 1337)

Mean (SD) (µg/ml) (SD)

2.45 (0.7) * 6.98 (3.1) 5.24 (3.7)

Mean (SD) males / Mean (SD) females, (µg/ml) (SD)

- / 2.45 (0.7) 5.83(2.5) / 8.16(3.1) 5.20 (3.9) / 5.27 (3.6)

Fasting plasma insulin measurements

Assay enzyme immunoassay protocol (DY1065; R&D Systems, Minneapolis, Minnesota)

Immunoassay radioimmunoassay method using the Linco Human Insulin Specific RIA Kit (Linco Research, Inc., St. Charles, MO 63304)

SUPPLEMENTARY DATA



1620 (0 / 1620) 1396 (610 / 786) 3063 (1056 / 2007)

Mean (SD) ( pmol/l) (SD)

45.3 (21) * 42.60 (14.7) NA

Mean (SD) males / Mean (SD) females, (pmol/l) (SD)

- / 45.3 (21) 44.1(16.2) / 41.70(13.6) NA

Fasting LDL-cholesterol measurements

Assay homogenous assays Direct LDL Cholesterol (Equal Diagnostics, Exton, PA)

Enzymatic Assay LDL-cholesterol is calculated in plasma specimens having a triglyceride value <400 mg/dL using the formula of Friedewald et al. (Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499-502)


1624 (0 / 1624) 1377 (598 / 779) 3265 (1134 / 2131)

Mean (SD) (mmol/l) (SD)

3.08 (0.9) 3.27 (0.8) 3.24 (0.91)

Mean (SD) males / Mean (SD) females, (mmol/l) (SD)

- / 3.08 (0.86) 3.37(0.7) / 3.19(0.8) 3.22 (0.93) / 3.25 (0.91)

Fasting HDL-cholesterol measurements

Assay homogenous assays Direct HDL Cholesterol (Equal Diagnostics, Exton, PA)

Enzymatic Assay cholesterol oxidase cholesterol method (Roche Diagnostics) after precipitation of non-HDL- cholesterol with magnesium/dextran


1624 (0 / 1624) 1399 (614/785) 3284 (1141 / 2143)

SUPPLEMENTARY DATA



1.06 (0.3) 1.38 (0.2) 1.34 (0.38)


- / 1.06 (0.3) 1.21(0.2) / 1.52(0.2) 1.32 (0.37) / 1.34 (0.38)

Fasting total cholesterol measurements

Assay enzymatic methods with reagents from Beckman Diagnostics on the Beckman Diagnostics CX5 chemistry analyzer (Fullerton, CA)

Enzymatic Assay cholesterol oxidase method (Roche Diagnostics, Indianapolis, IN 46250) on a Roche 911 analyzer (Roche Diagnostics Corporation)


1624 (0 / 1624) 1397 (612 / 785) 3283 (1141 / 2142)


4.79 (1.0) 5.20 (0.6) 5.13 (0.99)


- / 4.79 (1.0) 5.30(0.6) / 5.20(0.6) 5.08 (0.99) / 5.13 (0.99)

Fasting plasma glucose measurements

Assay with a glucose dehydrogenase method, using the OneTouch Ultra Blood Glucose Monitoring System (Lifescan; Johnson and Johnson, Milpitas, California)

Enzymatic Assay Roche hexokinase method (Roche Diagnostics, Indianapolis, IN 46250) on a Roche 911 analyzer (Roche Diagnostics Corporation)


1625 (0 / 1625) 1405 (616 / 789) 3062 (1055 / 2007)


5.04 (0.6) 5.06 (0.5) 5.40 (1.20)


- / 5.04 (0.6) 5.17(0.5) / 4.97(0.5) 5.42 (1.28) / 5.38 (1.16)

Fasting triglyceride measurements

Assay measured with a glycerol blank as a 2-step reaction (Beckman Coulter Diagnostics, Fullerton, CA)

Enzymatic Assay using Triglyceride GB reagent (Roche Diagnostics, Indianapolis, IN 46250) on a Roche 911 analyzer. (Roche Diagnostics Corporation)

SUPPLEMENTARY DATA



1624 (0 / 1624) 1401 (614 / 787) 3283 (1141 / 2142)


1.22 (0.4) * 1.10 (0.4) 1.19 (0.68)


- / 1.22 (0.4) 1.30(0.5) / 1.00(0.3) 1.19 (0.68) / 1.19 (0.68)

BMI N (N males / N females)

1614 (0 / 1614) 1400 (615 / 785) 3280 (1139 / 2141

Mean (SD) 24.15 (4.4) 23.92 (3.3) 31.57 (6.72)

Mean (SD) males / Mean (SD) females

- / 24.15 ( 4.4) 24.29 (3.1) / 23.62 (3.4) 31.41 (6.61) / 31.66 ( 6.78)

Type 2 diabetes N (N case / N control)

1783 (158 / 1625) 1672 (267 / 1405) 2985 (422 / 2563)

Age [Mean (SD) cases / Mean (sd) controls], years

49.5 (6.1) / 48.3 ( 6.1) 57.95 (10.6) / 50.24 (9.2) 62.0 (8.9) / 55.00 (12.5)

rs17366653 Genotyping platform and SNP panel

NA NA Taqman allele discrimination platform

Genotyping centre NA NA University of Pittsburgh School of Nursing Genomics Laboratory

Genotyping calling algorithm

NA NA genotype data was called by two blinded, independent individuals and discrepancies reconciled by review of raw data or re-genotyping

MAF NA NA 0.0024

HWE NA NA 0.889

Call rate NA NA 0.998

N NA NA 3282

rs17300539 Genotyping platform and SNP

NA NA Taqman allele discrimination platform

SUPPLEMENTARY DATA


panel

Genotyping centre NA NA University of Pittsburgh School of Nursing Genomics Laboratory


NA NA genotype data was called by two blinded, independent individuals and discrepancies reconciled by review of raw data or re-genotyping

MAF NA NA 0.008

HWE NA NA 0.639

Call rate NA NA 0.985

N NA NA 3242


Genome-Wide Human SNP Array 5.0

NA Taqman allele discrimination platform

Genotyping centre Vanderbilt Microarray Shared Resource at Vanderbilt University Medical Center

NA University of Pittsburgh School of Nursing Genomics Laboratory


Birdseed (version 2) NA genotype data was called by two blinded, independent individuals and discrepancies reconciled by review of raw data or re-genotyping

MAF 0.372 NA 0.435

HWE 0.71 NA 0.418

Call rate 99.99 NA 0.986

N 1797 NA 3243



NA Taqman allele discrimination platform


NA University of Pittsburgh School of Nursing Genomics Laboratory

SUPPLEMENTARY DATA



Birdseed (version 2) NA genotype data was called by two blinded, independent individuals and discrepancies reconciled by review of raw data or re-genotyping

MAF 0.348 NA 0.42

HWE 0.52 NA 0.780

Call rate 100 NA 0.972

N 1798 NA 3199



Seqeunom NA


Centre for Genomic Sciences, University of Hong Kong

NA


Birdseed (version 2) NA NA

MAF 0.372 0.437 NA

HWE 0.71 0.606 NA

Call rate 100 100.00% NA

N 1798 1672 NA

SUPPLEMENTARY DATA


Supplementary Table 3. Basic characteristics, genotyping and quality control in European studies in case-control analysis (NA = "not available").

STUDY ARIC FUSION WTCCC InterAct

COHORT INFORMATI

ON

Ethnicity Caucasian European European EU Country

USA Finland UK France, Spain, Italy, UK, Netherlands,

Germany, Sweden Collection type Cross-sectional

data case-control case-control Population-based case-cohort design

SAMPLES

Age Mean (SD), years 54.3 (5.7) 63.25 (7.5) NA 63.0 (8.9) Age [Mean (SD) males / Mean (sd) females],

years 54.68 (5.7) / 53.96

(5.7) 62.71 (7.5) / 63.84 (7.6) NA 63(8.8), 63 (9.3)

N (N males / N females) 9287 (4380 / 4907)

2333 (1226 / 1107)

4337 (2286 / 2051) 8499 (3505/4994)

Details of exclusions (if different from the general exclusion chriteria) NA NA NA

diabetes_status==1 & age<35; HbA1c>6.4% in controls

Additional covariates Center study region Centres within each country

Clinical Characteristic

s

N (N males / N females) in case 768 (416 / 352) 1160 (653 / 507) 1723 (1001 /

722) 4285 (2263 / 2022)

N (N males / N females) in control 8519 (3964 / 4555)

1173 (573 / 600) 2614 (1285 /

1329) 4214 (2731 / 1483)

Age diagnosis (mean years (SD)) in case 52.98 (7.9) 53.67 (9.1) 50.30 (9.2) 62.0(8.3) Age at study (mean years (SD)) in case 56.29 (5.6) 62.92 (7.6) 58.60 (10.1) 55.0 (7.9)

Age at study (mean years (SD)) in control 54.12 (5.7) 63.57 (7.4) NA 51.0 (9.3) BMI (mean kgm (SD)) in case 30.49 (5.5) 30.20 (4.7) 30.70 (5.9) 30.0 (4.8)

BMI (mean kgm (SD)) in control 26.66 (4.6) 27.10 (3.9) NA 26.0 (4.2) % insulin treated 9.64% 29.31% NA NA

rs17300539

Genotyping platform and SNP pane NA NA Affymetrix

500K llumina660WQuad Genotyping centre NA NA Affymetrix WTSI

Genotyping calling algorithm NA NA NA Illuminus MAF NA 0.029 0.07 0.0943 HWE NA NA NA 0.2558

Call rate NA NA > 95% 99.98924 IMPUTED? 0=No, 1=Yes 1 1 1 0

Imputation software MACH2QTL MACH Impute/SNPTES

T IMPUTE

SUPPLEMENTARY DATA


Imputation quality 0.6826 0.8442 >0.8 >0.95 N 9281 2333 4358 9177

rs3774261

Genotyping platform and SNP pane Affymettix 6.0 NA Affymetrix

500K llumina660WQuad Genotyping centre Broad Institute NA Affymetrix WTSI

Genotyping calling algorithm Birdseed NA NA Illuminus MAF 0.3863 0.345 0.38 0.4229 HWE 0.78 NA NA 0.582

Call rate 0.998 NA > 95% 100 IMPUTED? 0=No, 1=Yes 0 1 1 1

Imputation software NA MACH Impute/SNPTES

T IMPUTE Imputation quality NA 0.9946 >0.8 >0.95

N 9281 2333 4345 9177

rs3821799

Genotyping platform and SNP pane Affymettix 6.0

Illumina Human Hap 300 Bead

Array Affymetrix

500K llumina660WQuad

Genotyping centre Broad Institute

Center for Inherited Disease

Research Affymetrix WTSI Genotyping calling algorithm Birdseed BeadStudio NA Illuminus

MAF 0.4469 0.433 0.45 0.4756 HWE 0.25 1 NA 0.6535

Call rate 0.9996 0.997 > 95% 100

IMPUTED? 0=No, 1=Yes 0

1 (missing values imputed) 1 0

Imputation software NA MACH Impute/SNPTES

T IMPUTE Imputation quality NA 0.9984 >0.8 >0.95

N 9281 2333 4346 9177

SUPPLEMENTARY DATA


Supplementary Table 4. Summary details of relevant characteristics of RISC, ULSAM, EUGENE2 and Stanford. Traits (mean (min, max))

Unit RISC (n=1004) ULSAM (n=796) EUGENE2 (n=493) Stanford (n=270)

Female (%) - 56% 0% 57% 54%

Age years 43.91 (30, 61) 70.97 (70, 74) 39.85 (23, 66) 52.01 (22, 71)

Fasting insulin pmol/l 34.30 (3.00, 116.00) 76.48 (3.47, 294.47) 50.69 (4.01, 218.67) NA

Fasting glucose mmol/l 5.05 (2.90, 6.80) 5.32 (3.60, 6.90) 5.09 (2.40, 6.70) 5.38 (3.61, 6.94)

Insulin sensitivity* M-value: micromol/kgbodywt/min SSPG: mg/dl

39.84 (4.92, 114.25) 30.91 (4.73, 64.02) 40.12 (11.25, 116.12) 149.54 (41, 323)

BMI kg/m2 25.42 (16.90, 43.90) 25.91 (16.69, 39.07) 26.71 (17.41, 47.26) 29.94 (18.81, 53.78)

* In RISC, ULSAM and EUGENE2 studies, the insulin sensitivity was measured by euglycaemic-hyperinsulinaemic clamp (M-value; micromol/kgbodywt/min). The M-value has a positive correlation with insulin sensitivity (i.e. an individual with a high M-value has high insulin sensitivity). In the Stanford study, insulin sensitivity was measured by steady-state plasma glucose (SSPG) method (mg/dl). The SSPG value is highly inversely correlated to M-value (r = -0.93, P<0.001). Supplementary Table 5. Associations between adiponectin levels and other traits using instrumental variable analysis and linear regression (results from fixed effects meta-analysis).

TRAIT Observational Regression analysis Instrumental variable analysis Effect StdErr P-value N Effect StdErr P-value N

BMI -0.26 0.01 1.64e-477 31277 -0.01 0.03 0.67 30588

Cholesterol 0.02 0.01 1.804E-05 30706 -0.04 0.03 0.23 29951

Fasting glucose -0.15 0.01 2.16E-149 30931 -0.02 0.03 0.58 30234

Fasting insulin -0.31 0.01 8.35e-680 30458 -0.03 0.03 0.37 29771

HDL 0.40 0.01 8.08e-1281 30651 0.06 0.03 0.05 29899

LDL -0.05 0.01 5.372E-19 30211 -0.03 0.03 0.31 29498

T2D 0.61 0.02 2.17E-89 16075 1.06 0.12 0.61 15788

Triglyceride -0.28 0.01 1.27e-554 30362 -0.03 0.03 0.32 29646

SUPPLEMENTARY DATA


Supplementary Table 6. Associations between adiponectin levels and other traits using instrumental variable analysis stratified in men and women.

TRAIT Instrumental variable analysis in men Instrumental variable analysis in women p-value of t-

test on gender-

difference* Effect 95%

CI_low 95%

CI_up StdErr P-value I2 Effect 95% CI_low

95% CI_up StdErr P-

value I2

BMI -0.02 -0.09 0.05 0.04 0.57 0.00 -0.01 -0.13 0.11 0.06 0.82 38.24 0.94

Cholesterol -0.07 -0.15 0.00 0.04 0.05 0.00 0.00 -0.11 0.11 0.06 0.98 23.80 0.33

Fasting glucose 0.00 -0.10 0.10 0.05 0.96 26.64 -0.03 -0.12 0.05 0.04 0.47 0.00 0.70

Fasting insulin -0.04 -0.14 0.06 0.05 0.44 29.32 0.00 -0.10 0.09 0.05 0.92 16.19 0.67

HDL 0.06 -0.04 0.16 0.05 0.26 37.77 0.09 0.00 0.18 0.05 0.05 0.00 0.68

LDL -0.07 -0.18 0.03 0.05 0.16 36.45 -0.01 -0.12 0.09 0.05 0.81 18.87 0.48

T2D 0.13 -0.15 0.40 0.14 0.37 0.00 -0.11 -0.55 0.33 0.22 0.62 14.53 0.43

Triglyceride -0.05 -0.13 0.02 0.04 0.17 0.00 -0.03 -0.12 0.06 0.05 0.50 0.00 0.76

Supplementary Table 7. Associations between three adiponectin SNPs and hyperinsulinaemic-euglycaemic clamp and insulin suppression test.

rsnumber Chr position Alleles (Effect/Other) Effect StdErr p value N

rs17300539 3 188042162 A/G 0.11 0.05 0.03 2969

rs3821799 3 188054188 C/T -0.01 0.03 0.72 2969

rs3774261 3 188054261 A/G 0.00 0.03 0.99 2969

SUPPLEMENTARY DATA


Supplementary Figure 1. Flow chart of the study design and analyses.

SUPPLEMENTARY DATA


Supplementary Figure 2. Forest plots of the associations between adiponectin and rs17366653 in Europeans in the analysis of pooled samples, women and men. No difference in the association was observed between males and females. The dashed line indicates the effect size from the overall meta-analysis.

SUPPLEMENTARY DATA



SUPPLEMENTARY DATA


Supplementary Figure 6. Forest plots of the associations between adiponectin and fasting insulin in Europeans from instrumental variable analysis in the analysis of pooled samples, women and men. No difference in the association was observed between males and females. The dashed line indicates the effect size from the overall meta-analysis. The effects are for 1 SD increase in adiponectin levels.

SUPPLEMENTARY DATA


Supplementary Figure 7. Forest plots of the associations between adiponectin and fasting insulin in Europeans in instrumental variable analysis. Exclusion of the rare SNP (rs17366653) did not change these estimates. The dashed line indicates the effect size from the overall meta-analysis. The effects are for 1 SD increase in adiponectin levels.

SUPPLEMENTARY DATA


Supplementary Figure 8. Overall effect of fasting insulin SNPs on circulating adiponectin levels.

SUPPLEMENTARY DATA


Supplementary Figure 9. Forest plots of the associations between adiponectin and type 2 diabetes risk in Europeans from instrumental variable analysis in the analysis of pooled samples, women and men. No difference in the association was observed between males and females. The dashed line indicates the effect size from the overall meta-analysis. The effects are for 1 SD increase in adiponectin levels.

SUPPLEMENTARY DATA


Supplementary Figure 10. Forest plots of the associations between adiponectin and type 2 diabetes risk in Europeans in instrumental variable analysis. Exclusion of the rare SNP (rs17366653) did not change these estimates. The dashed line indicates the effect size from the overall meta-analysis. The effects are for 1 SD increase in adiponectin levels.

SUPPLEMENTARY DATA


References

1. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am J Epidemiol. 1989 Apr;129(4):687-702.

2. Lawlor DA, Bedford C, Taylor M, Ebrahim S. Geographical variation in cardiovascular disease, risk factors, and their control in older women: British Women's Heart and Health Study. J Epidemiol Community Health. 2003 Feb;57(2):134-40.

3. Raitakari OT, Juonala M, Ronnemaa T, Keltikangas-Jarvinen L, Rasanen L, Pietikainen M, et al. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol. 2008 Dec;37(6):1220-6.

4. Firmann M, Mayor V, Vidal PM, Bochud M, Pecoud A, Hayoz D, et al. The CoLaus study: a population-based study to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. BMC Cardiovasc Disord. 2008;8:6.

5. Aulchenko YS, Heutink P, Mackay I, Bertoli-Avella AM, Pullen J, Vaessen N, et al. Linkage disequilibrium in young genetically isolated Dutch population. Eur J Hum Genet. 2004 Jul;12(7):527-34.

6. Laakso M, Zilinskaite J, Hansen T, Boesgaard TW, Vanttinen M, Stancakova A, et al. Insulin sensitivity, insulin release and glucagon-like peptide-1 levels in persons with impaired fasting glucose and/or impaired glucose tolerance in the EUGENE2 study. Diabetologia. 2008 Mar;51(3):502-11.

7. Speliotes EK, Willer CJ, Berndt SI, Monda KL, Thorleifsson G, Jackson AU, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010 Nov;42(11):937-48.

8. Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham Study. Ann N Y Acad Sci. 1963 May 22;107:539-56.

9. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007 Jun 1;316(5829):1341-5.

10. Morris AD, Boyle DI, MacAlpine R, Emslie-Smith A, Jung RT, Newton RW, et al. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. DARTS/MEMO Collaboration. BMJ. 1997 Aug 30;315(7107):524-8.

11. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007 Jun 1;316(5829):1336-41.

12. Langenberg C, Sharp S, Forouhi NG, Franks PW, Schulze MB, Kerrison N, et al. Design and cohort description of the InterAct Project: an examination of the interaction of genetic and lifestyle factors on the incidence of type 2 diabetes in the EPIC Study. Diabetologia. 2011 Sep;54(9):2272-82.

13. Taylor HA, Jr. The Jackson Heart Study: an overview. Ethn Dis. 2005 Autumn;15(4 Suppl 6):S6-1-3.

14. Stancakova A, Javorsky M, Kuulasmaa T, Haffner SM, Kuusisto J, Laakso M. Changes in insulin sensitivity and insulin release in relation to glycemia and glucose tolerance in 6,414 Finnish men. Diabetes. 2009 May;58(5):1212-21.

15. Rasmussen-Torvik LJ, Pankow JS, Jacobs DR, Jr., Steinberger J, Moran AM, Sinaiko AR. Influence of waist on adiponectin and insulin sensitivity in adolescence. Obesity (Silver Spring). 2009 Jan;17(1):156-61.

SUPPLEMENTARY DATA


16. Sinaiko AR, Jacobs DR, Jr., Steinberger J, Moran A, Luepker R, Rocchini AP, et al. Insulin resistance syndrome in childhood: associations of the euglycemic insulin clamp and fasting insulin with fatness and other risk factors. J Pediatr. 2001 Nov;139(5):700-7.

17. Janus ED. Epidemiology of cardiovascular risk factors in Hong Kong. Clin Exp Pharmacol Physiol. 1997 Dec;24(12):987-8.

18. Tan KC, Wat NM, Tam SC, Janus ED, Lam TH, Lam KS. C-reactive protein predicts the deterioration of glycemia in chinese subjects with impaired glucose tolerance. Diabetes Care. 2003 Aug;26(8):2323-8.

19. Cheung BM, Wat NM, Man YB, Tam S, Thomas GN, Leung GM, et al. Development of diabetes in Chinese with the metabolic syndrome: a 6-year prospective study. Diabetes Care. 2007 Jun;30(6):1430-6.

20. Cheung CY, Tso AW, Cheung BM, Xu A, Ong KL, Law LS, et al. Genetic variants associated with persistent central obesity and the metabolic syndrome in a 12-year longitudinal study. Eur J Endocrinol. 2011 Mar;164(3):381-8.

21. Forouhi NG, Luan J, Hennings S, Wareham NJ. Incidence of Type 2 diabetes in England and its association with baseline impaired fasting glucose: the Ely study 1990-2000. Diabet Med. 2007 Feb;24(2):200-7.

22. Williams DR, Wareham NJ, Brown DC, Byrne CD, Clark PM, Cox BD, et al. Undiagnosed glucose intolerance in the community: the Isle of Ely Diabetes Project. Diabet Med. 1995 Jan;12(1):30-5.

23. Hills SA, Balkau B, Coppack SW, Dekker JM, Mari A, Natali A, et al. The EGIR-RISC STUDY (The European group for the study of insulin resistance: relationship between insulin sensitivity and cardiovascular disease risk): I. Methodology and objectives. Diabetologia. 2004 Mar;47(3):566-70.

24. Heid IM, Wagner SA, Gohlke H, Iglseder B, Mueller JC, Cip P, et al. Genetic architecture of the APM1 gene and its influence on adiponectin plasma levels and parameters of the metabolic syndrome in 1,727 healthy Caucasians. Diabetes. 2006 Feb;55(2):375-84.

25. Andrew T, Hart DJ, Snieder H, de Lange M, Spector TD, MacGregor AJ. Are twins and singletons comparable? A study of disease-related and lifestyle characteristics in adult women. Twin Res. 2001 Dec;4(6):464-77.

26. Richards JB, Rivadeneira F, Inouye M, Pastinen TM, Soranzo N, Wilson SG, et al. Bone mineral density, osteoporosis, and osteoporotic fractures: a genome-wide association study. Lancet. 2008 May 3;371(9623):1505-12.

diabetes.diabetesjournals.org · 27/06/2013 · supplementary data ©2013 american diabetes...

Documents