Skip to main content
  • More from ADA
    • Diabetes Care
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care in Diabetes
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care
  • Subscribe
  • Log in
  • My Cart
  • Follow ada on Twitter
  • RSS
  • Visit ada on Facebook
Diabetes

Advanced Search

Main menu

  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • ADA Scientific Sessions Abstracts
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • ADA Scientific Sessions Abstracts
    • Diabetes COVID-19 Article Collection
    • Diabetes Symposium 2020
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Submit Cover Art
    • ADA Journal Policies
    • Instructions for Authors
    • ADA Peer Review
  • More from ADA
    • Diabetes Care
    • Clinical Diabetes
    • Diabetes Spectrum
    • ADA Standards of Medical Care in Diabetes
    • ADA Scientific Sessions Abstracts
    • BMJ Open Diabetes Research & Care

User menu

  • Subscribe
  • Log in
  • My Cart

Search

  • Advanced search
Diabetes
  • Home
  • Current
    • Current Issue
    • Online Ahead of Print
    • ADA Scientific Sessions Abstracts
  • Browse
    • By Topic
    • Issue Archive
    • Saved Searches
    • ADA Scientific Sessions Abstracts
    • Diabetes COVID-19 Article Collection
    • Diabetes Symposium 2020
  • Info
    • About the Journal
    • About the Editors
    • ADA Journal Policies
    • Instructions for Authors
    • Guidance for Reviewers
  • Reprints/Reuse
  • Advertising
  • Subscriptions
    • Individual Subscriptions
    • Institutional Subscriptions and Site Licenses
    • Access Institutional Usage Reports
    • Purchase Single Issues
  • Alerts
    • E­mail Alerts
    • RSS Feeds
  • Podcasts
    • Diabetes Core Update
    • Special Podcast Series: Therapeutic Inertia
    • Special Podcast Series: Influenza Podcasts
    • Special Podcast Series: SGLT2 Inhibitors
    • Special Podcast Series: COVID-19
  • Submit
    • Submit a Manuscript
    • Submit Cover Art
    • ADA Journal Policies
    • Instructions for Authors
    • ADA Peer Review
Original Research

Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes

  1. Hanieh Yaghootkar1,
  2. Claudia Lamina2,
  3. Robert A. Scott3,
  4. Zari Dastani4,
  5. Marie-France Hivert5,6,
  6. Liling L. Warren7,
  7. Alena Stancáková8,
  8. Sarah G. Buxbaum9,
  9. Leo-Pekka Lyytikäinen10,11,
  10. Peter Henneman12,
  11. Ying Wu13,
  12. Chloe Y.Y. Cheung14,
  13. James S. Pankow15,
  14. Anne U. Jackson16,
  15. Stefan Gustafsson17,
  16. Jing Hua Zhao3,
  17. Christie M. Ballantyne18,
  18. Weijia Xie1,
  19. Richard N. Bergman19,
  20. Michael Boehnke16,
  21. Fatiha el Bouazzaoui12,
  22. Francis S. Collins20,
  23. Sandra H. Dunn21,
  24. Josee Dupuis22,
  25. Nita G. Forouhi3,
  26. Christopher Gillson3,
  27. Andrew T. Hattersley1,23,
  28. Jaeyoung Hong22,
  29. Mika Kähönen24,
  30. Johanna Kuusisto8,
  31. Lyudmyla Kedenko25,
  32. Florian Kronenberg2,
  33. Alessandro Doria26,
  34. Themistocles L. Assimes27,28,
  35. Ele Ferrannini29,
  36. Torben Hansen30,31,
  37. Ke Hao32,
  38. Hans Häring33,
  39. Joshua W. Knowles27,28,
  40. Cecilia M. Lindgren34,
  41. John J. Nolan35,
  42. Jussi Paananen8,
  43. Oluf Pedersen30,36,37,38,
  44. Thomas Quertermous27,28,
  45. Ulf Smith39,
  46. the GENESIS Consortium,
  47. the RISC Consortium,
  48. Terho Lehtimäki10,11,
  49. Ching-Ti Liu22,
  50. Ruth J.F. Loos3,40,
  51. Mark I. McCarthy34,41,42,
  52. Andrew D. Morris43,
  53. Ramachandran S. Vasan44,45,
  54. Tim D. Spector46,
  55. Tanya M. Teslovich16,
  56. Jaakko Tuomilehto47,48,49,50,
  57. Ko Willems van Dijk12,
  58. Jorma S. Viikari51,52,
  59. Na Zhu15,
  60. Claudia Langenberg3,
  61. Erik Ingelsson17,34,
  62. Robert K. Semple53,54,
  63. Alan R. Sinaiko55,
  64. Colin N.A. Palmer43,
  65. Mark Walker56,
  66. Karen S.L. Lam14,57,
  67. Bernhard Paulweber25,
  68. Karen L. Mohlke13,
  69. Cornelia van Duijn58,
  70. Olli T. Raitakari59,60,
  71. Aurelian Bidulescu61,62,
  72. Nick J. Wareham3,
  73. Markku Laakso8,
  74. Dawn M. Waterworth63,
  75. Debbie A. Lawlor64,
  76. James B. Meigs6,
  77. J. Brent Richards46,65 and
  78. Timothy M. Frayling1⇑
  1. 1Genetics of Complex Traits, University of Exeter Medical School, Exeter, U.K.
  2. 2Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria
  3. 3MRC Epidemiology Unit, Institute of Metabolic Science, Cambridge, U.K.
  4. 4Department of Epidemiology, Biostatistics and Occupational Health, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada
  5. 5Department of Medicine, Université de Sherbrooke, Sherbrooke, Quebec, Canada
  6. 6General Medicine Division, Massachusetts General Hospital, Boston, Massachusetts
  7. 7Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina
  8. 8University of Eastern Finland, Kuopio, Finland
  9. 9School of Health Sciences, Jackson State University, Jackson, Mississippi
  10. 10Department of Clinical Chemistry, Fimlab Laboratories, Tampere, Finland
  11. 11Department of Clinical Chemistry, University of Tampere School of Medicine, Tampere, Finland
  12. 12Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
  13. 13Department of Genetics, University of North Carolina, Chapel Hill, North Carolina
  14. 14Department of Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
  15. 15Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota
  16. 16Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan
  17. 17Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
  18. 18Baylor College of Medicine and Methodist DeBakey Heart and Vascular Center, Houston, Texas
  19. 19Diabetes and Obesity Research Institute, Cedars-Sinai Medical Center, Los Angeles, California
  20. 20Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland
  21. 21School of Nursing, University of Pittsburgh, Pittsburgh, Pennsylvania
  22. 22Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts
  23. 23Genetics of Diabetes, University of Exeter Medical School, Exeter, U.K.
  24. 24Department of Clinical Physiology, Tampere University Hospital and University of Tampere School of Medicine, Tampere, Finland
  25. 25First Department of Internal Medicine, St. Johann Spital, Paracelsus Private Medical University Salzburg, Salzburg, Austria
  26. 26Section on Genetics and Epidemiology, Joslin Diabetes Center, Boston, Massachusetts
  27. 27Department of Medicine, Stanford University School of Medicine, Stanford, California
  28. 28Cardiovascular Institute, Stanford University School of Medicine, Stanford, California
  29. 29Department of Internal Medicine, University of Pisa, Pisa, Italy
  30. 30Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health Science, University of Copenhagen, Copenhagen, Denmark
  31. 31Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
  32. 32Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York
  33. 33Division of Endocrinology, Diabetology, Nephrology, Vascular Medicine and Clinical Chemistry, Department of Internal Medicine, University of Tübingen, Tübingen, Germany
  34. 34Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K.
  35. 35Steno Diabetes Center, Gentofte, Denmark
  36. 36Hagedorn Research Institute, Copenhagen, Denmark
  37. 37Institute of Biomedical Science, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark
  38. 38Faculty of Health Sciences, University of Aarhus, Aarhus, Denmark
  39. 39Department of Molecular and Clinical Medicine, The Lundberg Laboratory for Diabetes Research, Sahlgrenska Academy, Gothenburg, Sweden
  40. 40Department of Preventive Medicine, Mount Sinai School of Medicine, The Charles Bronfman Institute for Personalized Medicine, Institute of Child Health and Development, New York, New York
  41. 41Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K.
  42. 42Oxford National Institute for Health Research Biomedical Research Centre, Churchill Hospital, Oxford, U.K.
  43. 43Medical Research Institute, University of Dundee, Ninewells Hospital and Medical School, Dundee, U.K.
  44. 44Boston University School of Medicine, Boston, Massachusetts
  45. 45Framingham Heart Study, Framingham, Massachusetts
  46. 46Twin Research and Genetic Epidemiology, King’s College London, London, U.K.
  47. 47Diabetes Prevention Unit, National Institute for Health and Welfare, Helsinki, Finland
  48. 48King Abdulaziz University, Jeddah, Saudi Arabia
  49. 49Red RECAVA Grupo RD06/0014/0015, Hospital Universitario La Paz, Madrid, Spain
  50. 50Centre for Vascular Prevention, Danube-University Krems, Krems, Austria
  51. 51Department of Medicine, Turku University Hospital, Turku, Finland
  52. 52Department of Medicine, University of Turku, Turku, Finland
  53. 53The National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge, U.K.
  54. 54University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, Cambridge, U.K.
  55. 55Department of Pediatrics, University of Minnesota, Minneapolis, Minnesota
  56. 56Institute of Cellular Medicine, The Medical School, Newcastle University, Newcastle, U.K.
  57. 57Research Centre of Heart, Brain, Hormone and Healthy Aging, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong
  58. 58Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
  59. 59Department of Clinical Physiology and Nuclear Medicine, Turku University Hospital, Turku, Finland
  60. 60Research Centre of Applied and Preventive Cardiovascular Medicine, University of Turku, Turku, Finland
  61. 61Cardiovascular Research Institute, Morehouse School of Medicine, Atlanta, Georgia
  62. 62Department of Community Health and Preventive Medicine, Morehouse School of Medicine, Atlanta, Georgia
  63. 63Quantitative Sciences, GlaxoSmithKline, Upper Merion, Pennsylvania
  64. 64Department of Social Medicine, University of Bristol, Bristol, U.K.
  65. 65Department of Medicine, Human Genetics, Epidemiology and Biostatistics, McGill University, Montreal, Canada.
  1. Corresponding author: Timothy M. Frayling, tim.frayling{at}pms.ac.uk.
Diabetes 2013 Oct; 62(10): 3589-3598. https://doi.org/10.2337/db13-0128
PreviousNext
  • Article
  • Figures & Tables
  • Suppl Material
  • Info & Metrics
  • PDF
Loading

Abstract

Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.

Circulating adiponectin levels are strongly inversely correlated with insulin resistance and risk of type 2 diabetes (1,2), but the causal directions of these associations are unclear. The correlation between fasting insulin and circulating adiponectin levels is between ∼0.3 and 0.4, a correlation of about half of that between fasting insulin and BMI. Adiponectin is also inversely correlated with BMI, and its association with insulin resistance might be confounded by BMI. There are some studies that suggest that the association between adiponectin and insulin remains as strong, or even stronger, when correcting for BMI (3–5). The strength of the association has led to suggestions that adiponectin could be used as a putative insulin-sensitizing treatment (6–8). Evidence from genetically or pharmacologically manipulated murine models suggests lowering adiponectin could induce insulin resistance. These studies were usually conducted using models challenged by a metabolic stressor such as high-fat feeding or lipodystrophy (8,50–55). Evidence from human studies is less clear (9,40–42,45–48,56–58) but includes data from a recent genome-wide association study (GWAS) that showed an association between an adiponectin genetic risk score and fasting insulin and type 2 diabetes (12) and a recent Mendelian randomization study using 942 individuals that suggested a causal role for adiponectin in insulin sensitivity (10).

In this study, we used the principle of Mendelian randomization (11) to investigate the causal nature of the association among circulating adiponectin levels, insulin resistance, type 2 diabetes, and related metabolic traits. We used a combination of four genetic variants within the adiponectin-encoding gene ADIPOQ that explain 4% of the variance in circulating adiponectin levels and up to 31,000 individuals with adiponectin, genetic variants, and metabolic trait outcomes measured. In contrast to previous studies that have used genetic variants to examine causation in this relationship (10,12,13), our analyses used an instrumental variables approach, limited genetic variants to those in the ADIPOQ gene (providing a test very unlikely to be influenced by pleiotropy), and performed the analyses using tens of thousands of individuals with both circulating adiponectin and fasting insulin measurements.

RESEARCH DESIGN AND METHODS

Study design.

We used two study designs (Supplementary Fig. 1). In the first design, we used an instrumental variables approach. We used studies in which adiponectin had been measured as well as fasting insulin or type 2 diabetes status (our two primary outcomes) and other related metabolic traits (fasting glucose, BMI, triglycerides, HDL cholesterol [HDL-C], LDL cholesterol [LDL-C], and total cholesterol). We used up to 31,000 individuals of European descent from 13 studies (Table 1 and Supplementary Table 1) and up to 5,100 individuals of non-European descent from 3 studies (Supplementary Table 2). These data included 1,860 individuals from 3 studies with single nucleotide polymorphisms (SNPs), adiponectin, and a measure of insulin sensitivity, including the previously published Uppsala Longitudinal Study of Adult Men (ULSAM) (10), Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC), and Minnesota study.

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 1

Summary details and relevant characteristics of European studies

In the second study design, we used an adiponectin summary statistics genetic risk score, in which measured adiponectin levels were not required. For type 2 diabetes, we used a total of 15,960 diabetic case subjects and 64,731 control subjects (including results for three available adiponectin SNPs from the DIAbetes Genetics Replication And Meta-analysis [DIAGRAM] [8,130 case subjects vs. 38,987 control subjects]) (14) and results from seven studies not in the DIAGRAM (7,830 case subjects vs. 25,744 control subjects; Supplementary Tables 1 and 3). For insulin sensitivity, we used a meta-analysis of M-value and insulin suppression test GWAS results from the GENESIS consortium (RISC, ULSAM, Eugene2, Stanford; Supplementary Table 4) and the Minnesota study (Supplementary Table 1) consisting of 2,969 individuals of European descent.

Selection of SNPs.

We limited our selection of genetic variants to those in or near ADIPOQ, the gene that encodes the adiponectin protein. This approach meant that our genetic instrument was less likely to violate the Mendelian randomization assumption that the instrument should only affect the outcome through the exposure of interest. We selected a set of SNPs (rs17366653, rs17300539, rs3774261, and rs3821799) that explained 4% of the variance in adiponectin levels. Details of genotyping and quality control are given in Supplementary Table 1.

Exposure and outcome variables.

Details of adiponectin measures (exposure of interest) are given in Supplementary Table 1. Our primary outcomes were fasting insulin (as a proxy of insulin resistance) and type 2 diabetes. Our secondary outcomes were insulin sensitivity (M-value or insulin suppression test), fasting glucose, HDL-C, LDL-C, BMI, triglycerides, and total cholesterol (Supplementary Table 1).

For each European study, individuals of non-European descent were removed. For the analyses of continuous metabolic outcomes (fasting insulin, fasting glucose, HDL-C, LDL-C, BMI, glucose, triglycerides, and total cholesterol) we excluded: 1) individuals with type 2 diabetes; 2) individuals with fasting glucose values ≥7.0 mmol/L and/or 2-h oral glucose tolerance test glucose ≥11.1 mmol/L. For the analyses of type 2 diabetes, we excluded: in case subjects, 1) individuals aged at diagnosis <35 or >70 years; 2) individuals who needed insulin treatment within 1 year of diagnosis; and 3) individuals aged <45 years whose age at diagnosis was not known at the time of study; and in control subjects, 1) individuals aged <35 or >70 years at the time of study; and 2) individuals with HbA1c >6.4% and/or fasting glucose >7 mmol/L.

Continuous variables (Supplementary Table 1) that were not normally distributed were log10-transformed. We then took the residuals of the standard linear regression using two covariates, age and sex, and, if applicable to the study, principle components, center, or other measures required to correct for ethnicity. We inverse-normal transformed all variable levels in each individual study to enable meta-analyses.

SNP–trait association.

We performed SNP–trait associations in each study using two different models: 1) a univariable model in which each SNP was analyzed separately; and 2) a multivariable model in which all four SNPs were used together. The multivariable model accounts for correlation between the SNPs due to linkage disequilibrium. We used an additive genetic model.

Instrumental variable analysis.

To estimate the causal effect of adiponectin levels on metabolic outcomes, we performed instrumental variable analyses using the four ADIPOQ SNPs entered separately into the same model (11). We applied the two-stage least-squares estimator method that uses predicted levels of adiponectin per genotype and regresses each outcome against these predicted values.

For continuous metabolic outcomes, we performed all of the instrumental variable analyses either in Stata using the ivreg2 command or in R using the tsls command from library (sem). The Framingham Heart Study (FHS) used a two-stage approach (similar to the approach used for type 2 diabetes, please see the following) to correct for familial correlation. For type 2 diabetes, we performed instrumental variable analysis in two stages. First, we assessed the association between the four SNPs and inverse-normal transformed adiponectin levels. We saved the predicted values and residuals from this regression model. Second, we used the predicted values from stage 1 as the independent variable (reflecting an unconfounded estimate of adiponectin levels) and diabetes status as the dependent variable in a logistic regression analysis. Both stages were performed either in R or Stata. We examined F-statistics from first-stage regressions to evaluate the strength of the instruments; weak instruments can bias results toward the (confounded) multivariable regression association (15,16).

Association between adiponectin and metabolic outcomes.

To compare the result of instrumental variable analysis with a standard association test, we regressed each metabolic outcome against adiponectin levels using linear regression for continuous outcome variables and logistic regression for type 2 diabetes. We adjusted for age and sex in all studies and age, sex, and either BMI or triglyceride levels in a subset of studies (RISC, Genetics of Diabetes Audit and Research Tayside Scotland [GoDARTS], Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk [SAPHIR], FHS, and Cohorte Lausannoise [CoLaus]; n = up to 11,829).

Summary statistics genetic risk score.

We used a summary statistics genetic risk score calculated in each study using three available common SNPs associated with adiponectin levels (rs17300539, rs3774261, and rs3821799). We did not use rs17366653 because it was not well-imputed in these studies. We calculated the genetic risk score using summary statistics of phenotype–genotype association weighted by each SNP’s corresponding effect size with adiponectin (17). We confirmed that this summary statistics genetic risk score was valid by calculating the score using individual level genotype data available in a subset of studies as below:Embedded Imagewhere sj is the score for individual j, gij is the number of risk alleles (0, 1, 2, or dosage of the risk allele) for SNP i carried by individuals j, and wi is the effect size on adiponectin levels for SNP i from the meta-analysis results of 13 studies (up to 33,671 individuals): wrs17300539 (G as effect allele) = −0.330; wrs3774261 (G as effect allele) = −0.354; and wrs3821799 (T as effect allele) = −0.352. We performed a logistic regression with the outcome variable of type 2 diabetes status and exposure variable as genetic risk score and covariates including age, sex, and principle components or center or other measures required to correct for ethnicity.

Summary statistics genetic risk score for fasting insulin-associated variants.

We used recently identified genetic variants associated with fasting insulin levels (18) to perform a reciprocal analysis to test the hypothesis that genetic determinants of insulin resistance (as measured by higher fasting insulin levels) are causally associated with lower circulating adiponectin levels. We used a summary statistics genetic risk score using 17 SNPs identified as associated with fasting insulin and or fasting insulin adjusted for BMI (18).

Sensitivity analysis.

We performed two sets of sensitivity analyses: 1) to assess whether or not associations differed between sexes, we repeated the inverse-variance meta-analyses in men and women separately (sex-difference P values were calculated by t tests); and 2) since rs17366653 is predicted to alter the splicing pattern of adiponectin (13) and may produce different transcripts or proteins, we reran analyses excluding this SNP.

Meta-analysis.

We performed meta-analysis using METAL 2009-10-10 release (19) and package metafor in R (20). Overall associations from observational analyses and instrumental variable analyses were evaluated across the studies with fixed-effects inverse variance–weighted meta-analysis. Heterogeneity statistics were calculated in the meta-analysis by the I2 statistic, which is a measure of the variation in effect size attributable to heterogeneity (21). Random effects and meta-regression were used to allow for and explore associations with evidence of heterogeneity.

Measures of insulin sensitivity.

For measures of insulin sensitivity, we used five studies (RISC, Eugene2, ULSAM, Stanford Insulin Suppression Test [IST], and Minnesota) and meta-analyzed results using the program METAL. In Eugene2, ULSAM, Minnesota, and RISC, insulin sensitivity was measured using the hyperinsulinemic-euglycemic clamp based protocol (22). In the Stanford study, insulin sensitivity was measured by the insulin suppression test with a readout of steady-state plasma glucose. The steady-state plasma glucose value is highly inversely correlated to M-value [r = −0.87 (23) and −0.93 (24)], so meta-analysis was performed among the five studies by reversing the signs of the effect sizes in Stanford.

Power calculation.

To assess the power of our study, we calculated the approximate number of individuals we would need to detect the expected instrumental variable (four ADIPOQ SNPs): fasting insulin or type 2 diabetes associations given the instrumental variable–adiponectin and adiponectin–fasting insulin or type 2 diabetes associations. We used the product of the variance explained by the instrumental variable–adiponectin and adiponectin–fasting insulin or type 2 diabetes associations and a P value of 0.01.

RESULTS

A combination of four ADIPOQ variants explained 4% of the variation in circulating adiponectin levels.

We identified four SNPs (rs17366653, rs17300539, rs3774261, and rs3821799) at the ADIPOQ locus that explained 4% variation in adiponectin levels in a multivariable analysis (n = up to 33,671; Table 2 and Fig. 1). We did not observe any difference in these associations between males and females (Supplementary Figs. 2–5). These variants, used together as an instrument, provided us with >99% statistical power to detect associations that explain 0.1% variance at P = 0.01. The figure of 0.1% variance is the product of the variance explained by the four SNPs (4%) and the variance explained between adiponectin and fasting insulin levels when corrected for BMI (correlation r = 0.16; variance r2 = 2.5%).

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 2

Associations between four SNPs and adiponectin levels in univariable and multivariable models from 13 European studies

FIG. 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIG. 1.

Adiponectin: SNP association in univariable analysis (triangles) and multivariable analysis (circles). chr3, chromosome 3; LD, linkage disequilibrium.

Instrumental variables and summary statistics genetic risk score approaches provide no evidence of a causal association between circulating adiponectin and insulin resistance in up to 29,771 individuals.

Lower circulating adiponectin levels were strongly correlated with increased fasting insulin. A 1-SD decrease in adiponectin levels was associated with a 0.31 SD (95% CI 0.26–0.35) increase in fasting insulin (P = 5E-40; Table 3 and Fig. 2A). In contrast, the instrumental variable analysis did not provide any evidence of a causal association between lower adiponectin and increased fasting insulin; the mean difference in fasting insulin per SD of adiponectin was 0.02 (95% CI −0.07 to 0.11; P = 0.60; n = 29,771) (Fig. 2B). The 95% CIs from the instrumental variable analysis clearly excluded the observational regression estimate (Fig. 3 and Table 3). The 95% CIs from the instrumental variables analysis also clearly excluded the observational regression estimate when adjusting for BMI (0.16 [95% CI 0.15–0.18]; n = 11,829) or triglyceride levels (0.19 [0.17–0.20]; n = 11,346). There was some evidence of heterogeneity (Table 3 and Supplementary Table 5) but meta-regression analysis, including the variables of average age, proportion of males, and average BMI, did not reduce heterogeneity (test of moderators, P = 0.39). Sensitivity analyses did not appreciably change these estimates (Supplementary Table 6 and Supplementary Figs. 6 and 7).

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 3

Associations between lower adiponectin levels and metabolic traits using linear regression and instrumental variable analysis (results from random effects meta-analysis)

FIG. 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIG. 2.

Forest plots of the associations between circulating adiponectin levels and fasting insulin in European studies. A: Meta-analysis of observational linear regression results of mean difference in fasting insulin per 1-SD lower adiponectin levels. B: Meta-analysis of instrumental variables results of mean difference in fasting insulin per 1-SD lower adiponectin levels. Although linear regression suggests a strong relationship between lower circulating adiponectin levels and increased fasting insulin, instrumental variable analysis does not support a causal association. In each plot, the dashed line indicates the effect size from the overall meta-analysis. The effects are for 1-SD decrease in adiponectin levels. RE, random effects.

FIG. 3.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIG. 3.

Comparison of linear relationships between circulating adiponectin levels and fasting insulin adjusted for age and sex (line A); age, sex, and BMI (line B); and when estimated using the four adiponectin SNPs together as an instrument (line C). The x- and y-axes represent circulating adiponectin levels and fasting insulin (both variables inverse-normal transformed), respectively. Light gray points represent a scatter plot of the correlation between circulating adiponectin levels and fasting insulin based on the data from three studies (RISC, GoDARTS, and BWHHS) in which individual level data were available. Gray areas constrained by dashed lines represent 95% CI around each estimate. Observational and instrumental variable slopes and CIs have been formulated based on the meta-analysis results of 13 studies.

Lower circulating adiponectin levels were strongly correlated with insulin sensitivity as measured by hyperinsulinemic-euglycemic clamp in 2,109 individuals from the RISC, ULSAM, and Minnesota studies. A 1-SD decrease in adiponectin levels was associated with a 0.34-SD (95% CI 0.30–0.38; P = 3E-61) decrease in M-value. We observed nominal evidence of a causal association between genetically lower adiponectin levels and insulin sensitivity (−0.20 SD [−0.38 to −0.02]; P = 0.03) in 1,860 individuals from the ULSAM, RISC, and Minnesota studies in which adiponectin levels were measured and we could perform an instrumental variable analysis using three ADIPOQ SNPs. In contrast, a summary statistics genetic risk score (Supplementary Table 7) provided no evidence of a causal association between circulating adiponectin levels and insulin sensitivity in 2,969 individuals (−0.03 SD [−0.07 to −0.01]; P = 0.12).

A summary statistic genetic risk score approach provides evidence of a causal association between insulin resistance as measured by fasting insulin levels and lower circulating adiponectin levels.

We used 17 SNPs recently identified as associated with fasting insulin at the genome-wide significance level [by the Meta-Analyses of Glucose and Insulin Related Traits Consortium (18)] to test the reciprocal hypothesis that genetic determinants of insulin resistance (as measured by fasting insulin) causally influence circulating adiponectin. The fasting insulin summary statistics genetic risk score was strongly associated with adiponectin using >29,000 individuals (12) (per weighted fasting insulin raising allele was associated with a −0.01 SD (P = 2E-20) change in adiponectin levels (Supplementary Fig. 8).

A summary statistics genetic risk score approach provides no evidence of a causal association between circulating adiponectin and type 2 diabetes in 15,960 case subjects vs. 64,731 control subjects.

Lower adiponectin levels were strongly correlated with an increased risk of type 2 diabetes; a decrease of 1 SD in adiponectin levels was associated with an odds ratio of 1.75 (95% CI 1.47–2.13; P = 5E-10) (Table 4 and Fig. 4A). Conversely, the analysis of the weighted adiponectin summary statistics genetic risk score, constructed based on three SNPs (rs17300539, rs3774261, and rs3821799), provided no evidence that individuals with lower genetically influenced adiponectin levels were at increased risk of type 2 diabetes (OR per weighted adiponectin lowering allele: 0.99 [0.95–1.04]; P = 0.77; 15,960 case subjects vs. 64,731 control subjects). This result was consistent with an allele score calculated from a subset of five studies using individual-level genotype data (OR per weighted adiponectin-lowering allele: 1.03 [0.86–1.24]; 8,552 case subjects vs. 24,050 control subjects). We also observed no evidence of a causal association between genetically lower adiponectin levels and increased risk of type 2 diabetes (OR 0.94 [0.75–1.19]; P = 0.61) in the 2,777 case subjects and 13,011 control subjects in whom we had adiponectin levels measured and could perform an instrumental variable analysis (Table 4 and Fig. 4B). The 95% CIs from the instrumental variable analysis clearly excluded the observational regression slope (Table 4). We observed heterogeneity in observational analysis (I2 = 90.4). Sensitivity analyses did not appreciably change these estimates (Supplementary Table 6 and Supplementary Figs. 9 and 10).

View this table:
  • View inline
  • View popup
  • Download powerpoint
TABLE 4

Associations between lower adiponectin levels and type 2 diabetes using logistic regression, instrumental variable analysis, allele score, and summary statistics genetic risk score

FIG. 4.
  • Download figure
  • Open in new tab
  • Download powerpoint
FIG. 4.

Forest plots of the associations between circulating adiponectin levels and type 2 diabetes risk in Europeans. A: Meta-analysis of observational linear regression results of OR of type 2 diabetes per 1-SD lower adiponectin levels. B: Meta-analysis of instrumental variables results of OR of type 2 diabetes per 1-SD lower adiponectin levels. Although linear regression suggests a strong relationship between lower circulating adiponectin levels and higher risk of type 2 diabetes, instrumental variable analysis does not support a causal association. In each plot, the dashed gray line indicates the effect size from the overall meta-analysis. The ORs are for 1-SD decrease in adiponectin levels. RE, random effects.

An instrumental variables approach provides no evidence of a causal association between circulating adiponectin and other metabolic traits in up to 30,588 individuals.

Instrumental variable analyses did not provide any evidence that genetically decreased circulating adiponectin levels have a causal effect on fasting glucose, BMI, triglycerides, HDL-C, and cholesterol (Table 3). In all analyses, the 95% CIs from the instrumental variable analysis had no overlap with the 95% CIs from the observational analysis and clearly excluded the observational regression slope, except the analysis of LDL-C (observational 95% CI 0.03–0.10; instrumental variable 95% CI −0.03 to 0.09; Table 3). Sensitivity analyses did not appreciably change these estimates (Supplementary Table 6). We observed heterogeneity in observational analyses (I2 81.6–90.4), but meta-regression did not detect variables that reduced this heterogeneity.

Non-European studies.

Using data from two Asian studies including the Cebu Longitudinal Health and Nutrition Study (CLHNS) and Cardiovascular Risk Factor Prevalence Study (CRISPS) (total n = 2,991), we did not find any evidence of a causal effect of adiponectin on fasting insulin or risk of type 2 diabetes using one available SNP (rs6773957), which is in complete linkage disequilibrium with rs3774261 and rs3821799 in Asian populations. In the Jackson Heart Study (JHS) of African American individuals (n = 2,053), none of the SNPs were associated with adiponectin levels.

DISCUSSION

Our approach allowed us to plot a genetically determined regression line between adiponectin and secondary metabolic traits. Our study adds to the current literature, as it included a large enough number of individuals to confidently exclude the observational regression estimates for fasting insulin and type 2 diabetes. Limited sample size meant that we could not confidently include or exclude the observational regression estimates for insulin sensitivity as measured by hyperinsulinemic-euglycemic clamp or insulin suppression tests. Previous studies studied fewer individuals, included variants likely to have pleiotropic effects, or did not conduct an instrumental variables analysis. Our results provided no evidence that genetically determined lower adiponectin levels increase insulin resistance, as assessed by fasting insulin, or type 2 diabetes risk. The 95% CIs around our instrumental variables estimate of the adiponectin–fasting insulin association excluded effects approximately one-third and above of the observed (age- and sex-adjusted) association between adiponectin and fasting insulin. Total circulating adiponectin levels are significantly higher in females than males (25,26), but our sex-dichotomized analyses did not show any evidence for differences between sexes in its association with fasting insulin, type 2 diabetes, or other outcomes.

A large number of studies have tested associations between ADIPOQ SNPs and insulin resistance and type 2 diabetes (13,27–38). Most of these studies have been appreciably smaller than our study. The largest study (5,145 case subjects vs. 6,374 control subjects) that tested specifically the association between ADIPOQ SNPs and type 2 diabetes, and overlapped with our data, was negative (13). In a recent GWAS study of adiponectin levels, a multi-SNP allele risk score, calculated based on 196 SNPs from across the genome, was associated with type 2 diabetes risk and a number of related traits (12). Contrary to our results, these findings could be interpreted as providing causal evidence for the association of adiponectin with these outcomes. However, as the authors noted, their results may have been influenced by pleiotropy at loci other than ADIPOQ and therefore do not constitute a Mendelian randomization study. To clarify further the potentially confusing messages between our study and the adiponectin GWAS study, we tested the 10 SNPs associated with adiponectin levels outside of the ADIPOQ region and confirmed that they are associated with fasting insulin in the Meta-Analyses of Glucose and Insulin Related Traits Consortium study (18) The overall effect of non-ADIPOQ adiponectin-decreasing alleles was associated with a 0.24-SD increase in fasting insulin (95% CI 0.18–0.30; P = 3E-14). This association, together with our null association of ADIPOQ SNPs, strongly suggests that the non-ADIPOQ SNPs operate through secondary or pleiotropic mechanisms. Our results add to a recent Mendelian randomization study that showed evidence of a causal association between adiponectin levels and insulin resistance assessed by euglycemic clamp in 942 men from ULSAM (10). Our meta-analysis of 1,860 individuals, including the ULSAM study, indicates that larger numbers will be needed to confidently include or exclude the observational association between adiponectin and insulin sensitivity. Testing insulin sensitivity in very large numbers, however, is not very feasible given the complexity and invasiveness of the physiological tests, and a combination of our summary statistics–based results in 2,969 individuals and the results with fasting insulin in 29,771 individuals suggest the weight of evidence is against a causal role of adiponectin in insulin resistance.

Although the conclusion that genetically determined low levels of adiponectin are not associated with increased risk of insulin resistance is at odds with the widely held view of adiponectin as an insulin-sensitizing hormone, the direct evidence supporting this notion comes largely from rodent models, and the situation in humans is more complex (39). Indeed, in humans with extreme insulin resistance due to loss of insulin receptor function, plasma adiponectin levels are often extremely high (40–44). Moreover in healthy volunteers, insulin infusion lowers plasma adiponectin (45), and in type 1 diabetes, it is elevated (46–48). Allied to other findings, including the observation that in a single family with insulin resistance, due to mutation of the intracellular signal transducer AKT2, adiponectin levels are very low (42), this has raised the possibility that the association between insulin resistance in humans may be explained by high levels of insulin suppressing adiponectin production through intact signaling pathways (39). In other words, it is possible to interpret current human data as providing evidence that it is the hyperinsulinemia caused by prevalent forms of insulin resistance that leads to low plasma adiponectin levels rather than vice versa. The current results, including the association between the fasting insulin raising genetic score and lower adiponectin levels, are consistent with this model.

Our study has limitations. First, the SNPs we used are associated with altered levels of adiponectin protein and not its function; we have tested the role of increased and decreased circulating adiponectin levels rather than its function in other tissues such as the liver. Second, we cannot rule out a causal association between circulating adiponectin and insulin sensitivity as measured by hyperinsulinemic-euglycemic clamp, and we cannot completely rule out a causal association between fasting-based measures of insulin resistance, because our study is consistent with a regression slope of 0.11 (the upper 95% CI of our instrumental variable estimate). Third, we observed appreciable heterogeneity between studies in our observational associations that mean our estimates of the nongenetic correlations are noisy. However, there was little heterogeneity in the genetic associations. Finally, the Mendelian randomization approach has limitations. For example, we cannot account for complex feedback loops or canalization, the body’s adaptation to early physiological changes caused by subtle genetic changes. We cannot also rule out the possibility that the relationship between adiponectin and outcome metabolic traits varies by age or after diabetes diagnosis, potentially adding more noise to the instrumental variables analysis.

In summary, we have performed a Mendelian randomization study to test the causal role of lower adiponectin levels with increased insulin resistance and type 2 diabetes. Our results provide no consistent evidence that genetically influenced decreased circulating adiponectin levels increase the risk of insulin resistance or type 2 diabetes. These results do not provide any evidence that pharmaceutical and lifestyle interventions designed to alter adiponectin levels will improve insulin resistance or prevent type 2 diabetes.

ACKNOWLEDGMENTS

Major funding for the research in this study is listed in the Supplementary Data online.

No potential conflicts of interest relevant to this article were reported.

H.Y. designed the study, wrote the first draft of the manuscript, contributed to the writing and revision of the manuscript, performed the meta-analyses and other key analyses, and performed the statistical analyses for the British Women's Heart and Health Study (BWHHS), RISC, GoDARTS, and Wellcome Trust Case Control Consortium (WTCCC) studies. C.Lam. contributed to the writing and revision of the manuscript, performed the meta-analyses and other key analyses, and performed the statistical analyses for the SAPHIR study. R.A.S. contributed to the writing and revision of the manuscript and performed the statistical analyses of the Fenland and Ely studies. Z.D. contributed to the writing and revision of the manuscript, was involved in the design, and performed the statistical analyses of the TwinsUK (TUK) study. M.-F.H. contributed to the writing and revision of the manuscript and was involved in genotyping and performed the statistical analyses for the Framingham study. L.L.W. (CoLaus), A.S. (Metabolic Syndrome in Men [METSIM]), S.G.B. (JHS), P.H. (Erasmus Rucphen Family [ERF] study), Y.W. (CLHNS), C.Y.Y.C. (CRISPS), J.S.P. and N.Z. (Minnesota), J.S.P. (ARIC), A.U.J. and T.M.T. (Finland-United States Investigation of NIDDM Genetics [FUSION]), J.D., J.H., and C.-T.L. (Framingham), S.G. (ULSAM), and J.H.Z. (InterAct) performed the statistical analyses of the studies specified in parentheses. L.-P.L. performed the statistical analyses and was involved in genotyping of the Cardiovascular Risk in Young Finns (YF) study. P.H. was involved in sample collection, phenotyping, genotyping, and design of the ERF study. J.S.P. and A.R.S. (Minnesota), C.M.B. (ARIC), A.T.H. and M.I.M. (WTCCC), J.K. and M.L. (METSIM), and R.S.V. (Framingham) were involved in sample collection and phenotyping of the studies specified in parentheses. W.X. and E.F. (RISC), T.L.A., J.W.K., and T.Q. (Stanford), T.H., H.H., O.P., U.S., and M.L. (Eugene2), and K.H., C.M.L., J.P., and A.D.M. were involved in the GWAS of the euglycemic clamp. R.N.B., M.B., F.S.C., J.T., and K.L.M. (FUSION), F.e.B. (ERF), J.D. (Framingham), R.J.F.L. (Fenland and Ely), A.D.M. and C.N.A.P. (GoDARTS), A.R.S. (Minnesota), K.L.M. (CLHNS), A.B. (JHS), and D.M.W. (CoLaus) were involved in the design of the studies specified in parentheses. S.H.D. (JHS), C.G. (Fenland and Ely), A.D. (Framingham), and T.L. (YF) were involved in genotyping of the studies specified in parentheses. N.G.F. and N.J.W. were involved in sample collection, phenotyping, and design of the Fenland and Ely studies. M.K., T.L., J.S.V., and O.T.R. were involved in the sample collection, phenotyping, and design of the YF study. L.K. and F.K. were involved in sample collection, phenotyping, and genotyping of the SAPHIR study. J.J.N. and M.W. were involved in sample collection, phenotyping, and design of the RISC study and in GWAS of the euglycemic clamp. T.D.S. was involved in sample collection, phenotyping, and genotyping of the TUK study. K.W.v.D. performed the statistical analyses and was involved in genotyping of the ERF study. C.Lan. was involved in sample collection, phenotyping, and design of the Fenland and Ely studies. E.I. was involved in sample collection, phenotyping, genotyping, and design of the ULSAM study and in GWAS of the euglycemic clamp. R.K.S. contributed to the writing and revision of the manuscript. K.S.L.L. was involved in sample collection, phenotyping, and design of the CRISPS study. B.P. was involved in sample collection, phenotyping, genotyping, and design of the SAPHIR study. C.v.D. performed the statistical analyses and was involved in sample collection, phenotyping, and design of the ERF study. D.A.L. contributed to the writing and revision of the manuscript and was involved in the design of the BWHHS study. J.B.M. contributed to the writing and revision of the manuscript and was involved in sample collection, phenotyping and genotyping of the Framingham study. J.B.R. contributed to the writing and revision of the manuscript, and was involved in sample collection, phenotyping, and design of the TUK study. T.M.F. designed the study, contributed to the writing and revision of the manuscript, and was involved in genotyping of the GoDARTS study. T.M.F. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Parts of this study were presented in abstract/poster form at the Diabetes UK Professional Conference, Manchester, U.K., 13–15 March 2013, and at the International Conference of Quantitative Genetics, Edinburgh, U.K., 17–22 June 2012.

The authors thank David Savage, Metabolic Research Laboratories, Institute of Metabolic Science, University of Cambridge, and Stephen O'Rahilly, University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science, for helpful comments on an early draft of the manuscript.

Footnotes

  • This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db13-0128/-/DC1.

  • Received January 28, 2013.
  • Accepted June 25, 2013.
  • © 2013 by the American Diabetes Association.

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

REFERENCES

  1. ↵
    1. Tilg H,
    2. Moschen AR
    . Adipocytokines: mediators linking adipose tissue, inflammation and immunity. Nat Rev Immunol 2006;6:772–783pmid:16998510
    OpenUrlCrossRefPubMedWeb of Science
  2. ↵
    1. Li S,
    2. Shin HJ,
    3. Ding EL,
    4. van Dam RM
    . Adiponectin levels and risk of type 2 diabetes: a systematic review and meta-analysis. JAMA 2009;302:179–188pmid:19584347
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    1. Weyer C,
    2. Funahashi T,
    3. Tanaka S,
    4. et al
    . Hypoadiponectinemia in obesity and type 2 diabetes: close association with insulin resistance and hyperinsulinemia. J Clin Endocrinol Metab 2001;86:1930–1935pmid:11344187
    OpenUrlCrossRefPubMedWeb of Science
    1. Esposito K,
    2. Pontillo A,
    3. Di Palo C,
    4. et al
    . Effect of weight loss and lifestyle changes on vascular inflammatory markers in obese women: a randomized trial. JAMA 2003;289:1799–1804pmid:12684358
    OpenUrlCrossRefPubMedWeb of Science
  4. ↵
    1. Matsubara M,
    2. Maruoka S,
    3. Katayose S
    . Inverse relationship between plasma adiponectin and leptin concentrations in normal-weight and obese women. Eur J Endocrinol 2002;147:173–180pmid:12153737
    OpenUrlAbstract
  5. ↵
    1. Xita N,
    2. Tsatsoulis A
    . Adiponectin in diabetes mellitus. Curr Med Chem 2012;19:5451–5458pmid:22876922
    OpenUrlCrossRefPubMed
    1. Wang Y,
    2. Zhou M,
    3. Lam KS,
    4. Xu A
    . Protective roles of adiponectin in obesity-related fatty liver diseases: mechanisms and therapeutic implications. Arq Bras Endocrinol Metabol 2009;53:201–212pmid:19466213
    OpenUrlPubMed
  6. ↵
    1. Nawrocki AR,
    2. Rajala MW,
    3. Tomas E,
    4. et al
    . Mice lacking adiponectin show decreased hepatic insulin sensitivity and reduced responsiveness to peroxisome proliferator-activated receptor gamma agonists. J Biol Chem 2006;281:2654–2660pmid:16326714
    OpenUrlAbstract/FREE Full Text
    1. Manning AK,
    2. Hivert MF,
    3. Scott RA,
    4. et al.,
    5. DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium,
    6. Multiple Tissue Human Expression Resource (MUTHER) Consortium
    . A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 2012;44:659–669pmid:22581228
    OpenUrlCrossRefPubMed
  7. ↵
    1. Gao H,
    2. Fall T,
    3. van Dam RM,
    4. et al
    . Evidence of a causal relationship between adiponectin levels and insulin sensitivity: a Mendelian randomization study. Diabetes 2012;62:1338–1344pmid:23274890
    OpenUrlAbstract/FREE Full Text
  8. ↵
    1. Lawlor DA,
    2. Harbord RM,
    3. Sterne JA,
    4. Timpson N,
    5. Davey Smith G
    . Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27:1133–1163pmid:17886233
    OpenUrlCrossRefPubMed
  9. ↵
    1. Dastani Z,
    2. Hivert MF,
    3. Timpson N,
    4. et al.,
    5. DIAGRAM+ Consortium,
    6. MAGIC Consortium,
    7. GLGC Investigators,
    8. MuTHER Consortium,
    9. DIAGRAM Consortium,
    10. GIANT Consortium,
    11. Global B Pgen Consortium,
    12. Procardis Consortium,
    13. MAGIC investigators,
    14. GLGC Consortium
    . Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals. PLoS Genet 2012;8:e1002607pmid:22479202
    OpenUrlCrossRefPubMed
  10. ↵
    1. Warren LL,
    2. Li L,
    3. Nelson MR,
    4. et al
    . Deep resequencing unveils genetic architecture of ADIPOQ and identifies a novel low-frequency variant strongly associated with adiponectin variation. Diabetes 2012;61:1297–1301pmid:22403302
    OpenUrlAbstract/FREE Full Text
  11. ↵
    1. Voight BF,
    2. Scott LJ,
    3. Steinthorsdottir V,
    4. et al.,
    5. MAGIC investigators,
    6. GIANT Consortium
    . Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet 2010;42:579–589pmid:20581827
    OpenUrlCrossRefPubMedWeb of Science
  12. ↵
    1. Staiger DO,
    2. Stock JH
    . Instrumental variables regression with weak instruments. Econometrica 1997;65:577–586
    OpenUrl
  13. ↵
    1. Stock JH,
    2. Wright JH,
    3. Yogo M
    . A survey of weak instruments and weak identification in generalized method of moments. J Bus Econ Stat 2002;20:518–529
    OpenUrlCrossRefWeb of Science
  14. ↵
    1. Ehret GB,
    2. Munroe PB,
    3. Rice KM,
    4. et al.,
    5. International Consortium for Blood Pressure Genome-Wide Association Studies,
    6. CARDIoGRAM consortium,
    7. CKDGen Consortium,
    8. KidneyGen Consortium,
    9. EchoGen consortium,
    10. CHARGE-HF consortium
    . Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 2011;478:103–109pmid:21909115
    OpenUrlCrossRefPubMedWeb of Science
  15. ↵
    1. Scott RA,
    2. Lagou V,
    3. Welch RP,
    4. et al.,
    5. DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium
    . Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat Genet 2012;44:991–1005pmid:22885924
    OpenUrlCrossRefPubMed
  16. ↵
    1. Willer CJ,
    2. Li Y,
    3. Abecasis GR
    . METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 2010;26:2190–2191pmid:20616382
    OpenUrlAbstract/FREE Full Text
  17. ↵
    1. Viechtbauer W
    . Conducting meta-analyses in R with the metafor package. J Stat Softw 2010;36:1–48
    OpenUrl
  18. ↵
    1. Higgins JP,
    2. Thompson SG
    . Quantifying heterogeneity in a meta-analysis. Stat Med 2002;21:1539–1558pmid:12111919
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    1. DeFronzo RA,
    2. Tobin JD,
    3. Andres R
    . Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979;237:E214–E223pmid:382871
    OpenUrlPubMedWeb of Science
  20. ↵
    1. Knowles JW,
    2. Assimes TL,
    3. Tsao PS,
    4. et al
    . Measurement of insulin-mediated glucose uptake: direct comparison of the modified insulin suppression test and the euglycemic, hyperinsulinemic clamp. Metabolism 2013;62:548–553pmid:23151437
    OpenUrlCrossRefPubMedWeb of Science
  21. ↵
    1. Greenfield MS,
    2. Doberne L,
    3. Kraemer F,
    4. Tobey T,
    5. Reaven G
    . Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes 1981;30:387–392pmid:7014307
    OpenUrlAbstract/FREE Full Text
  22. ↵
    1. Arita Y,
    2. Kihara S,
    3. Ouchi N,
    4. et al
    . Paradoxical decrease of an adipose-specific protein, adiponectin, in obesity. Biochem Biophys Res Commun 1999;257:79–83pmid:10092513
    OpenUrlCrossRefPubMedWeb of Science
  23. ↵
    1. Gui Y,
    2. Silha JV,
    3. Murphy LJ
    . Sexual dimorphism and regulation of resistin, adiponectin, and leptin expression in the mouse. Obes Res 2004;12:1481–1491pmid:15483213
    OpenUrlCrossRefPubMedWeb of Science
  24. ↵
    1. Alkhateeb A,
    2. Al-Azzam S,
    3. Zyadine R,
    4. Abuarqoub D
    . Genetic association of adiponectin with type 2 diabetes in Jordanian Arab population. Gene 2013;512:61-63pmid:23041553
    OpenUrlCrossRefPubMed
    1. Wang B,
    2. Wang C,
    3. Wei D,
    4. et al
    . An association study of SNP + 45 T > G of the AdipoQ gene with type 2 diabetes in Yi and Han people in China. Int J Vitam Nutr Res 2011;81:392–397pmid:22673923
    OpenUrlCrossRefPubMed
    1. Li Y,
    2. Yang Y,
    3. Shi L,
    4. Li X,
    5. Zhang Y,
    6. Yao Y
    . The association studies of ADIPOQ with type 2 diabetes mellitus in Chinese populations. Diabetes Metab Res Rev 2012;28:551–559pmid:22539443
    OpenUrlCrossRefPubMed
    1. Mather KJ,
    2. Christophi CA,
    3. Jablonski KA,
    4. et al.,
    5. Diabetes Prevention Program Research Group
    . Common variants in genes encoding adiponectin (ADIPOQ) and its receptors (ADIPOR1/2), adiponectin concentrations, and diabetes incidence in the Diabetes Prevention Program. Diabet Med 2012;29:1579–1588pmid:22443353
    OpenUrlCrossRefPubMed
    1. Du W,
    2. Li Q,
    3. Lu Y,
    4. et al
    . Genetic variants in ADIPOQ gene and the risk of type 2 diabetes: a case-control study of Chinese Han population. Endocrine 2011;40:413–422pmid:21594755
    OpenUrlCrossRefPubMed
    1. Gong M,
    2. Long J,
    3. Liu Q,
    4. Deng HC
    . Association of the ADIPOQ rs17360539 and rs266729 polymorphisms with type 2 diabetes: a meta-analysis. Mol Cell Endocrinol 2010;325:78–83pmid:20580771
    OpenUrlCrossRefPubMed
    1. Sanghera DK,
    2. Demirci FY,
    3. Been L,
    4. et al
    . PPARG and ADIPOQ gene polymorphisms increase type 2 diabetes mellitus risk in Asian Indian Sikhs: Pro12Ala still remains as the strongest predictor. Metabolism 2010;59:492–501pmid:19846176
    OpenUrlCrossRefPubMedWeb of Science
    1. Melistas L,
    2. Mantzoros CS,
    3. Kontogianni M,
    4. Antonopoulou S,
    5. Ordovas JM,
    6. Yiannakouris N
    . Association of the +45T>G and +276G>T polymorphisms in the adiponectin gene with insulin resistance in nondiabetic Greek women. Eur J Endocrinol 2009;161:845–852pmid:19755407
    OpenUrlAbstract/FREE Full Text
    1. Wang Y,
    2. Zhang D,
    3. Liu Y,
    4. et al
    . Association study of the single nucleotide polymorphisms in adiponectin-associated genes with type 2 diabetes in Han Chinese. J Genet Genomics 2009;36:417–423pmid:19631916
    OpenUrlCrossRefPubMedWeb of Science
    1. Vendramini MF,
    2. Pereira AC,
    3. Ferreira SR,
    4. Kasamatsu TS,
    5. Moisés RS,
    6. Japanese Brazilian Diabetes Study Group
    . Association of genetic variants in the adiponectin encoding gene (ADIPOQ) with type 2 diabetes in Japanese Brazilians. J Diabetes Complications 2010;24:115–120pmid:19269196
    OpenUrlCrossRefPubMed
    1. Bostrom MA,
    2. Freedman BI,
    3. Langefeld CD,
    4. Liu L,
    5. Hicks PJ,
    6. Bowden DW
    . Association of adiponectin gene polymorphisms with type 2 diabetes in an African American population enriched for nephropathy. Diabetes 2009;58:499–504pmid:19056609
    OpenUrlAbstract/FREE Full Text
  25. ↵
    1. Hivert MF,
    2. Manning AK,
    3. McAteer JB,
    4. et al
    . 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;57:3353–3359pmid:18776141
    OpenUrlAbstract/FREE Full Text
  26. ↵
    1. Cook JR,
    2. Semple RK
    . Hypoadiponectinemia—cause or consequence of human “insulin resistance”? J Clin Endocrinol Metab 2010;95:1544–1554pmid:20164291
    OpenUrlCrossRefPubMed
  27. ↵
    1. Semple RK,
    2. Cochran EK,
    3. Soos MA,
    4. et al
    . Plasma adiponectin as a marker of insulin receptor dysfunction: clinical utility in severe insulin resistance. Diabetes Care 2008;31:977–979pmid:18299442
    OpenUrlAbstract/FREE Full Text
    1. Semple RK,
    2. Halberg NH,
    3. Burling K,
    4. et al
    . Paradoxical elevation of high-molecular weight adiponectin in acquired extreme insulin resistance due to insulin receptor antibodies. Diabetes 2007;56:1712–1717pmid:17325257
    OpenUrlAbstract/FREE Full Text
  28. ↵
    1. Semple RK,
    2. Soos MA,
    3. Luan J,
    4. et al
    . Elevated plasma adiponectin in humans with genetically defective insulin receptors. J Clin Endocrinol Metab 2006;91:3219–3223pmid:16705075
    OpenUrlCrossRefPubMedWeb of Science
    1. Antuna-Puente B,
    2. Boutet E,
    3. Vigouroux C,
    4. et al
    . Higher adiponectin levels in patients with Berardinelli-Seip congenital lipodystrophy due to seipin as compared with 1-acylglycerol-3-phosphate-o-acyltransferase-2 deficiency. J Clin Endocrinol Metab 2010;95:1463–1468pmid:20097706
    OpenUrlCrossRefPubMed
  29. ↵
    1. Hattori Y,
    2. Hirama N,
    3. Suzuki K,
    4. Hattori S,
    5. Kasai K
    . Elevated plasma adiponectin and leptin levels in sisters with genetically defective insulin receptors. Diabetes Care 2007;30:e109pmid:17965293
    OpenUrlFREE Full Text
  30. ↵
    1. Basu R,
    2. Pajvani UB,
    3. Rizza RA,
    4. Scherer PE
    . Selective downregulation of the high molecular weight form of adiponectin in hyperinsulinemia and in type 2 diabetes: differential regulation from nondiabetic subjects. Diabetes 2007;56:2174–2177pmid:17513700
    OpenUrlAbstract/FREE Full Text
  31. ↵
    1. Imagawa A,
    2. Funahashi T,
    3. Nakamura T,
    4. et al
    . Elevated serum concentration of adipose-derived factor, adiponectin, in patients with type 1 diabetes. Diabetes Care 2002;25:1665–1666pmid:12196453
    OpenUrlFREE Full Text
    1. Celi F,
    2. Bini V,
    3. Papi F,
    4. et al
    . Circulating adipocytokines in non-diabetic and Type 1 diabetic children: relationship to insulin therapy, glycaemic control and pubertal development. Diabet Med 2006;23:660–665pmid:16759309
    OpenUrlCrossRefPubMedWeb of Science
  32. ↵
    1. Leth H,
    2. Andersen KK,
    3. Frystyk J,
    4. et al
    . Elevated levels of high-molecular-weight adiponectin in type 1 diabetes. J Clin Endocrinol Metab 2008;93:3186–3191pmid:18505763
    OpenUrlCrossRefPubMed
    1. Wood AR,
    2. Hernandez DG,
    3. Nalls MA,
    4. et al
    . Allelic heterogeneity and more detailed analyses of known loci explain additional phenotypic variation and reveal complex patterns of association. Hum Mol Genet 2011;20:4082–4092pmid:21798870
    OpenUrlAbstract/FREE Full Text
  33. ↵
    1. Kubota N,
    2. Terauchi Y,
    3. Yamauchi T,
    4. et al
    . Disruption of adiponectin causes insulin resistance and neointimal formation. J Biol Chem 2002;277:25863–25866
    OpenUrlAbstract/FREE Full Text
    1. Maeda N,
    2. Shimomura I,
    3. Kishida K,
    4. et al
    . Diet-induced insulin resistance in mice lacking adiponectin/ACRP30. Nat Med 2002;8:731–737
    OpenUrlCrossRefPubMedWeb of Science
    1. Berg AH,
    2. Combs TP,
    3. Du X,
    4. Brownlee M,
    5. Scherer PE
    . The adipocyte-secreted protein Acrp30 enhances hepatic insulin action. Nat Med 2001;7:947–953
    OpenUrlCrossRefPubMedWeb of Science
    1. Combs TP,
    2. Pajvani UB,
    3. Berg AH,
    4. et al
    . A transgenic mouse with a deletion in the collagenous domain of adiponectin displays elevated circulating adiponectin and improved insulin sensitivity. Endocrinology 2004;145:367–383
    OpenUrlCrossRefPubMedWeb of Science
    1. Kim JY,
    2. van de Wall E,
    3. Laplante M,
    4. et al
    . Obesity-associated improvements in metabolic profile through expansion of adipose tissue. J Clin Invest 2007;117:2621–2637
    OpenUrlCrossRefPubMedWeb of Science
  34. ↵
    1. Yamauchi T,
    2. Nio Y,
    3. Maki T,
    4. et al
    . Targeted disruption of AdipoR1 and AdipoR2 causes abrogation of adiponectin binding and metabolic actions. Nat Med 2007;13:332–339
    OpenUrlCrossRefPubMedWeb of Science
  35. ↵
    1. Lihn AS,
    2. Ostergard T,
    3. Nyholm B,
    4. Pedersen SB,
    5. Richelsen B,
    6. Schmitz O
    . Adiponectin expression in adipose tissue is reduced in first-degree relatives of type 2 diabetic patients. Am J Physiol Endocrinol Metab 2003;284:E443–E448
    OpenUrlAbstract/FREE Full Text
    1. Ling H,
    2. Waterworth DM,
    3. Stirnadel HA,
    4. et al
    . Genome-wide linkage and association analyses to identify genes influencing adiponectin levels: the GEMS Study. Obesity (Silver Spring) 2009;17:737–744
    OpenUrlCrossRefPubMed
  36. ↵
    1. Kilpelainen TO,
    2. Zillikens MC,
    3. Stancakova A,
    4. et al
    . Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet 2011;43:753–760
    OpenUrlCrossRefPubMed
PreviousNext
Back to top
Diabetes: 62 (10)

In this Issue

October 2013, 62(10)
  • Table of Contents
  • Table of Contents (PDF)
  • About the Cover
  • Index by Author
  • Masthead (PDF)
Sign up to receive current issue alerts
View Selected Citations (0)
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word about Diabetes.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes
(Your Name) has forwarded a page to you from Diabetes
(Your Name) thought you would like to see this page from the Diabetes web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes
Hanieh Yaghootkar, Claudia Lamina, Robert A. Scott, Zari Dastani, Marie-France Hivert, Liling L. Warren, Alena Stancáková, Sarah G. Buxbaum, Leo-Pekka Lyytikäinen, Peter Henneman, Ying Wu, Chloe Y.Y. Cheung, James S. Pankow, Anne U. Jackson, Stefan Gustafsson, Jing Hua Zhao, Christie M. Ballantyne, Weijia Xie, Richard N. Bergman, Michael Boehnke, Fatiha el Bouazzaoui, Francis S. Collins, Sandra H. Dunn, Josee Dupuis, Nita G. Forouhi, Christopher Gillson, Andrew T. Hattersley, Jaeyoung Hong, Mika Kähönen, Johanna Kuusisto, Lyudmyla Kedenko, Florian Kronenberg, Alessandro Doria, Themistocles L. Assimes, Ele Ferrannini, Torben Hansen, Ke Hao, Hans Häring, Joshua W. Knowles, Cecilia M. Lindgren, John J. Nolan, Jussi Paananen, Oluf Pedersen, Thomas Quertermous, Ulf Smith, the GENESIS Consortium, the RISC Consortium, Terho Lehtimäki, Ching-Ti Liu, Ruth J.F. Loos, Mark I. McCarthy, Andrew D. Morris, Ramachandran S. Vasan, Tim D. Spector, Tanya M. Teslovich, Jaakko Tuomilehto, Ko Willems van Dijk, Jorma S. Viikari, Na Zhu, Claudia Langenberg, Erik Ingelsson, Robert K. Semple, Alan R. Sinaiko, Colin N.A. Palmer, Mark Walker, Karen S.L. Lam, Bernhard Paulweber, Karen L. Mohlke, Cornelia van Duijn, Olli T. Raitakari, Aurelian Bidulescu, Nick J. Wareham, Markku Laakso, Dawn M. Waterworth, Debbie A. Lawlor, James B. Meigs, J. Brent Richards, Timothy M. Frayling
Diabetes Oct 2013, 62 (10) 3589-3598; DOI: 10.2337/db13-0128

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Add to Selected Citations
Share

Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes
Hanieh Yaghootkar, Claudia Lamina, Robert A. Scott, Zari Dastani, Marie-France Hivert, Liling L. Warren, Alena Stancáková, Sarah G. Buxbaum, Leo-Pekka Lyytikäinen, Peter Henneman, Ying Wu, Chloe Y.Y. Cheung, James S. Pankow, Anne U. Jackson, Stefan Gustafsson, Jing Hua Zhao, Christie M. Ballantyne, Weijia Xie, Richard N. Bergman, Michael Boehnke, Fatiha el Bouazzaoui, Francis S. Collins, Sandra H. Dunn, Josee Dupuis, Nita G. Forouhi, Christopher Gillson, Andrew T. Hattersley, Jaeyoung Hong, Mika Kähönen, Johanna Kuusisto, Lyudmyla Kedenko, Florian Kronenberg, Alessandro Doria, Themistocles L. Assimes, Ele Ferrannini, Torben Hansen, Ke Hao, Hans Häring, Joshua W. Knowles, Cecilia M. Lindgren, John J. Nolan, Jussi Paananen, Oluf Pedersen, Thomas Quertermous, Ulf Smith, the GENESIS Consortium, the RISC Consortium, Terho Lehtimäki, Ching-Ti Liu, Ruth J.F. Loos, Mark I. McCarthy, Andrew D. Morris, Ramachandran S. Vasan, Tim D. Spector, Tanya M. Teslovich, Jaakko Tuomilehto, Ko Willems van Dijk, Jorma S. Viikari, Na Zhu, Claudia Langenberg, Erik Ingelsson, Robert K. Semple, Alan R. Sinaiko, Colin N.A. Palmer, Mark Walker, Karen S.L. Lam, Bernhard Paulweber, Karen L. Mohlke, Cornelia van Duijn, Olli T. Raitakari, Aurelian Bidulescu, Nick J. Wareham, Markku Laakso, Dawn M. Waterworth, Debbie A. Lawlor, James B. Meigs, J. Brent Richards, Timothy M. Frayling
Diabetes Oct 2013, 62 (10) 3589-3598; DOI: 10.2337/db13-0128
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Jump to section

  • Article
    • Abstract
    • RESEARCH DESIGN AND METHODS
    • RESULTS
    • DISCUSSION
    • ACKNOWLEDGMENTS
    • Footnotes
    • REFERENCES
  • Figures & Tables
  • Suppl Material
  • Info & Metrics
  • PDF

Related Articles

Cited By...

More in this TOC Section

Original Research

  • Pathogenesis study based on high throughput single-cell sequencing analysis reveals novel transcriptional landscape and heterogeneity of retinal cells in type 2 diabetic mice
  • Unique human and mouse β-cell senescence-associated secretory phenotype (SASP) reveal conserved signaling pathways and heterogeneous factors
  • Effects of Gastric Bypass Surgery on the Brain; Simultaneous Assessment of Glucose Uptake, Blood Flow, Neural Activity and Cognitive Function during Normo- and Hypoglycemia
Show more Original Research

Genetics/Genomes/Proteomics/Metabolomics

  • Low-Frequency Genetic Variant in the Hepatic Glucokinase Gene Is Associated With Type 2 Diabetes and Insulin Resistance in Chinese Population
  • Baseline Assessment of Circulating MicroRNAs Near Diagnosis of Type 1 Diabetes Predicts Future Stimulated Insulin Secretion
  • Polycystic Ovary Syndrome and Risk of Type 2 Diabetes, Coronary Heart Disease, and Stroke
Show more Genetics/Genomes/Proteomics/Metabolomics

Similar Articles

Navigate

  • Current Issue
  • Online Ahead of Print
  • Scientific Sessions Abstracts
  • Collections
  • Archives
  • Submit
  • Subscribe
  • Email Alerts
  • RSS Feeds

More Information

  • About the Journal
  • Instructions for Authors
  • Journal Policies
  • Reprints and Permissions
  • Advertising
  • Privacy Policy: ADA Journals
  • Copyright Notice/Public Access Policy
  • Contact Us

Other ADA Resources

  • Diabetes Care
  • Clinical Diabetes
  • Diabetes Spectrum
  • Scientific Sessions Abstracts
  • Standards of Medical Care in Diabetes
  • BMJ Open - Diabetes Research & Care
  • Professional Books
  • Diabetes Forecast

 

  • DiabetesJournals.org
  • Diabetes Core Update
  • ADA's DiabetesPro
  • ADA Member Directory
  • Diabetes.org

© 2021 by the American Diabetes Association. Diabetes Print ISSN: 0012-1797, Online ISSN: 1939-327X.