Acta Diabetologica

, Volume 46, Issue 1, pp 13–21 | Cite as

Comprehensive genetic analysis of the dipeptidyl peptidase-4 gene and cardiovascular disease risk factors in obese individuals

  • Luigi Bouchard
  • Geneviève Faucher
  • André Tchernof
  • Yves Deshaies
  • Stéfane Lebel
  • Frédéric-Simon Hould
  • Picard Marceau
  • Marie-Claude Vohl
Original Article


The incretin system has been shown to stimulate insulin secretion in a glucose dependent manner and currently fosters considerable hope for the treatment of diabetes. Recently, we have shown that the dipeptidylpeptidase-4 (DPP4) gene, which is responsible for incretin inactivation, was overexpressed in omental adipose tissue of obese men with the metabolic syndrome, compared to men not characterized by this condition. Since the cardiovascular disease (CVD) risk profile shows substantial inter-individual variability in obesity, this study aimed at verifying whether DPP4 polymorphisms contribute to explain such a difference. In the first step of this multi-stage study, seven tagging SNPs were genotyped in a sample of 576 obese (BMI > 40 kg/m2) individuals and tested for their association with blood pressure and lipids, as well as diabetes-related phenotypes. Then, in an additional sample of 572 obese individuals (stage 2), SNPs showing trends (P < 0.10) for an association in the first sample were genotyped and reanalyzed. Logistic regressions were used to compute odds ratio for obesity-related metabolic complications. In sample 1, homozygotes for rs17848915 and rs7608798 minor alleles were at lower risk of hyperglycemia/diabetes (P = 0.002) and elevated plasma triglyceride levels (P = 0.030) respectively, whereas rs1558957 heterozygotes were at higher risk to have high plasma triglyceride (P = 0.040), HDL- (P = 0.021), LDL- (P = 0.001) and total-cholesterol (P = 0.003) levels. However, none of these associations was consistently replicated in stage 2. This first comprehensive genetic analysis does not support the notion that DPP4 polymorphisms could modulate the CVD risk profile among obese patients.


Metabolic syndrome Diabetes Candidate gene Polymorphism Association study 



This study was supported by a grant from the Canadian Institutes of Health Research (CIHR) and the Institute of Nutrition, Metabolism and Diabetes (INMD) under its strategic initiative “‘Excellence, Innovation and Advancement in the Study of Obesity and Healthy Body Weight.” The morbidly obese cohort was supported, over the years, by the Laval University Merck Frosst/CIHR Research Chair in Obesity. We express our gratitude to Drs Simon Marceau, Odette Lescelleur, Simon Biron, members of the Laval Hospital biliopancreatic diversion team, who have also sampled adipose tissues for this project. Many thanks are also expressed to Dr Fanny Therrien and Alain Houde, M.Sc., for their help in adipose tissue banking management. We acknowledge the contribution of the Gene Quantification core laboratory of the Centre de Génomique de Québec. Dr Luigi Bouchard is funded by the Laval University Merck Frosst/CIHR Research Chair in Obesity and the Heart and Stroke Foundation of Canada (HSFC)/Sanofi-Aventis research fellowship awards. Dr. André Tchernof is a research scholar from CIHR.


  1. 1.
    Mokdad AH, Ford ES, Bowman BA et al (2003) Prevalence of obesity, diabetes, and obesity-related health risk factors, 2001. JAMA 289:76–79PubMedCrossRefGoogle Scholar
  2. 2.
    Rosamond W, Flegal K, Friday G et al (2007) Heart disease and stroke statistics—2007 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 115:69–171CrossRefGoogle Scholar
  3. 3.
    Drucker DJ, Nauck MA (2006) The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet 368:1696–1705PubMedCrossRefGoogle Scholar
  4. 4.
    Nathan DM (2007) Finding new treatments for diabetes–how many, how fast. how good? N Engl J Med 356:437–440PubMedCrossRefGoogle Scholar
  5. 5.
    Drucker DJ, Philippe J, Mojsov S, Chick WL, Habener JF (1987) Glucagon-like peptide I stimulates insulin gene expression and increases cyclic AMP levels in a rat islet cell line. Proc Natl Acad Sci USA 84:3434–3438PubMedCrossRefGoogle Scholar
  6. 6.
    Drucker DJ (2006) The biology of incretin hormones. Cell Metab 3:153–165PubMedCrossRefGoogle Scholar
  7. 7.
    Miyawaki K, Yamada Y, Yano H et al (1999) Glucose intolerance caused by a defect in the entero-insular axis: a study in gastric inhibitory polypeptide receptor knockout mice. Proc Natl Acad Sci USA 96:14843–14847PubMedCrossRefGoogle Scholar
  8. 8.
    Scrocchi LA, Brown TJ, MaClusky N et al (1996) Glucose intolerance but normal satiety in mice with a null mutation in the glucagon-like peptide 1 receptor gene. Nat Med 2:1254–1258PubMedCrossRefGoogle Scholar
  9. 9.
    Orskov C, Wettergren A, Holst JJ (1993) Biological effects and metabolic rates of glucagonlike peptide-1 7–36 amide and glucagonlike peptide-1 7–37 in healthy subjects are indistinguishable. Diabetes 42:658–661PubMedCrossRefGoogle Scholar
  10. 10.
    De MI, Durinx C, Bal G et al (2000) Natural substrates of dipeptidyl peptidase IV. Adv Exp Med Biol 477:67–87Google Scholar
  11. 11.
    De MI, Lambeir AM, Proost P, Scharpe S (2003) Dipeptidyl peptidase IV substrates. An update on in vitro peptide hydrolysis by human DPPIV. Adv Exp Med Biol 524:3–17Google Scholar
  12. 12.
    Mentlein R (1999) Dipeptidyl-peptidase IV (CD26)—role in the inactivation of regulatory peptides. Regul Pept 85:9–24PubMedCrossRefGoogle Scholar
  13. 13.
    Deacon CF (2004) Therapeutic strategies based on glucagon-like peptide 1. Diabetes 53:2181–2189PubMedCrossRefGoogle Scholar
  14. 14.
    Bouchard L, Tchernof A, Deshaies Y et al (2007) ZFP36: a promising candidate gene for obesity-related metabolic complications identified by converging genomics. Obes Surg 17:372–382PubMedCrossRefGoogle Scholar
  15. 15.
    Konig IR, Ziegler A (2003) Group sequential study designs in genetic-epidemiological case–control studies. Hum Hered 56:63–72PubMedCrossRefGoogle Scholar
  16. 16.
    Sobell JL, Heston LL, Sommer SS (1993) Novel association approach for determining the genetic predisposition to schizophrenia: case–control resource and testing of a candidate gene. Am J Med Genet 48:28–35PubMedCrossRefGoogle Scholar
  17. 17.
    Satagopan JM, Venkatraman ES, Begg CB (2004) Two-stage designs for gene-disease association studies with sample size constraints. Biometrics 60:589–597PubMedCrossRefGoogle Scholar
  18. 18.
    Vohl MC, Sladek R, Robitaille J et al (2004) A survey of genes differentially expressed in subcutaneous and visceral adipose tissue in men. Obes Res 12:1217–1222PubMedCrossRefGoogle Scholar
  19. 19.
    Robitaille J, Despres JP, Perusse L, Vohl MC (2003) The PPAR-gamma P12A polymorphism modulates the relationship between dietary fat intake and components of the metabolic syndrome: results from the Quebec Family Study. Clin Genet 63:109–116PubMedCrossRefGoogle Scholar
  20. 20.
    The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus (1997) Report of the expert committee on the diagnosis and classification of diabetes mellitus. Diabetes Care 20:1183–1197Google Scholar
  21. 21.
    Expert Panel on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (2001) Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA 285:2486–2497Google Scholar
  22. 22.
    Barrett JC, Fry B, Maller J, Daly MJ (2005) Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics 21:263–265PubMedCrossRefGoogle Scholar
  23. 23.
    Laberge AM, Michaud J, Richter A et al (2005) Population history and its impact on medical genetics in Quebec. Clin Genet 68:287–301PubMedCrossRefGoogle Scholar
  24. 24.
    Laberge AM (2007) Prevalence and distribution of genetic diseases in Quebec: impact of the past on the present. Med Sci (Paris) 23:997–1001Google Scholar
  25. 25.
    Aertgeerts K, Ye S, Shi L et al (2004) N-linked glycosylation of dipeptidyl peptidase IV (CD26): effects on enzyme activity, homodimer formation, and adenosine deaminase binding. Protein Sci 13:145–154PubMedCrossRefGoogle Scholar
  26. 26.
    Fulop V, Szeltner Z, Polgar L (2000) Catalysis of serine oligopeptidases is controlled by a gating filter mechanism. EMBO Rep 1:277–281PubMedCrossRefGoogle Scholar
  27. 27.
    Gorrell MD, Gysbers V, McCaughan GW (2001) CD26: a multifunctional integral membrane and secreted protein of activated lymphocytes. Scand J Immunol 54:249–264PubMedCrossRefGoogle Scholar
  28. 28.
    Ajami K, Abbott CA, Obradovic M et al (2003) Structural requirements for catalysis, expression, and dimerization in the CD26/DPIV gene family. Biochemistry 42:694–701PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Luigi Bouchard
    • 1
    • 2
    • 3
  • Geneviève Faucher
    • 1
    • 2
    • 3
  • André Tchernof
    • 2
    • 3
    • 4
  • Yves Deshaies
    • 5
  • Stéfane Lebel
    • 6
  • Frédéric-Simon Hould
    • 6
  • Picard Marceau
    • 6
  • Marie-Claude Vohl
    • 1
    • 2
    • 3
  1. 1.Lipid Research CenterLaval UniversityQuebec CityCanada
  2. 2.Nutraceuticals and Functional Foods InstituteLaval UniversityQuebec CityCanada
  3. 3.Department of Food Science and NutritionLaval UniversityQuebec CityCanada
  4. 4.Molecular Endocrinology and Oncology Research CenterLaval UniversityQuebec CityCanada
  5. 5.Department of Anatomy and Physiology and Laval Hospital Research CenterLaval UniversityQuebec CityCanada
  6. 6.Department of Surgery, Faculty of MedicineLaval UniversityQuebec CityCanada

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