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Glucose levels and genetic variants across transcriptional pathways: interaction effects with BMI

International Journal of Obesity volume 34, pages 840–845 (2010)Cite this article

Subjects

Abstract

Objective:

Much of the genetic variation in glucose levels remains to be discovered. Especially, research on gene–environment interactions is scarce. Overweight is one of the main risk factors for hyperglycemia. As transcriptional regulation is important for both weight maintenance and glucose control, we analyzed 353 single nucleotide polymorphisms (SNPs), occurring in transcriptional pathways of glucose and lipid metabolism in interaction with body mass index (BMI) on glucose levels.

Research design and methods:

SNPs were measured in 3244 participants of the Doetichem cohort. Non-fasting glucose levels and BMI were measured twice in 6 years. SNP × BMI interactions were analyzed by mixed models and adjusted for age, sex, time since last meal, and follow-up time. False discovery rate (FDR) <0.2 was used to adjust for multiple testing.

Results:

Two SNPs in the PPARGC1A gene (rs8192678, FDR=0.07; rs3755863, FDR=0.17) showed a significant interaction with BMI. The rare allele of both SNPs was associated with significantly lower glucose levels in subjects with a BMI⩽25 kg m–2 (rs8192678, P=0.02; rs3755863, P=0.03). An inverse association was suggested in subjects with a BMI>28 kg m–2. A small intervention study (n=120) showed similar, though non-significant, results.

Conclusions:

Using a pathway-based approach, we found that BMI significantly modified the association between two SNPs in the PPARGC1A gene and glucose levels. The association between glucose and PPARGC1A was only present in lean subjects. This suggests that the effect of the PPARGC1A gene, which is involved both in fatty acid oxidation and glucose metabolism, is modified by BMI.

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References

  1. Prokopenko I, Langenberg C, Florez JC, Saxena R, Soranzo N, Thorleifsson G et al. Variants in MTNR1B influence fasting glucose levels. Nat Genet 2008; 7: 7.

    Google Scholar 

  2. Boehnke M, Moll PP, Kottke BA, Weidman WH . Partitioning the variability of fasting plasma glucose levels in pedigrees. Genetic and environmental factors. Am J Epidemiol 1987; 125: 679–689.

    Article  CAS  Google Scholar 

  3. Watanabe RM, Valle T, Hauser ER, Ghosh S, Eriksson J, Kohtamaki K et al. Familiality of quantitative metabolic traits in Finnish families with non-insulin-dependent diabetes mellitus. Finland-United States Investigation of NIDDM Genetics (FUSION) Study investigators. Hum Hered 1999; 49: 159–168.

    Article  CAS  Google Scholar 

  4. Henneman P, Aulchenko YS, Frants RR, van Dijk KW, Oostra BA, van Duijn CM . Prevalence and heritability of the metabolic syndrome and its individual components in a Dutch isolate: the Erasmus Rucphen Family study. J Med Genet 2008; 45: 572–577.

    Article  CAS  Google Scholar 

  5. Lazar MA . How obesity causes diabetes: not a tall tale. Science 2005; 307: 373–375.

    Article  CAS  Google Scholar 

  6. Cauchi S, Nead KT, Choquet H, Horber F, Potoczna N, Balkau B et al. The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies. BMC Med Genet 2008; 9: 45.

    Article  Google Scholar 

  7. Wei Q, Jacobs Jr DR, Schreiner PJ, Siscovick DS, Steffes MW, Fornage M . Patterns of association between PPARgamma genetic variation and indices of adiposity and insulin action in African-Americans and whites: the CARDIA Study. J Mol Med 2006; 84: 955–965.

    Article  CAS  Google Scholar 

  8. Carter KW, Hung J, Powell BL, Wiltshire S, Foo BT, Leow YC et al. Association of interleukin-1 gene polymorphisms with central obesity and metabolic syndrome in a coronary heart disease population. Hum Genet 2008; 21: 21.

    Google Scholar 

  9. van Dam RM, Boer JM, Feskens EJ, Seidell JC . Parental history of diabetes modifies the association between abdominal adiposity and hyperglycemia. Diabetes Care 2001; 24: 1454–1459.

    Article  CAS  Google Scholar 

  10. Lu Y, Dolle ME, Imholz S, van ′t Slot R, Verschuren WM, Wijmenga C et al. Multiple genetic variants along candidate pathways influence plasma high-density lipoprotein cholesterol concentrations. J Lipid Res 2008; 49: 2582–2589.

    Article  CAS  Google Scholar 

  11. Wareham NJ, Jakes RW, Rennie KL, Schuit J, Mitchell J, Hennings S et al. Validity and repeatability of a simple index derived from the short physical activity questionnaire used in the European Prospective Investigation into Cancer and Nutrition (EPIC) study. Public Health Nutr 2003; 6: 407–413.

    Article  Google Scholar 

  12. Tietz N . Clinical Guide to Laboratory Tests 3rd edn. WB Saunders Company: Philadelphia, PA, pp 1995; 268–273.

    Google Scholar 

  13. Desvergne B, Michalik L, Wahli W . Transcriptional regulation of metabolism. Physiol Rev 2006; 86: 465–514.

    Article  CAS  Google Scholar 

  14. Hoebee B, Rietveld E, Bont L, Oosten M, Hodemaekers HM, Nagelkerke NJ et al. Association of severe respiratory syncytial virus bronchiolitis with interleukin-4 and interleukin-4 receptor alpha polymorphisms. J Infect Dis 2003; 187: 2–11.

    Article  CAS  Google Scholar 

  15. Fan JB, Chee MS, Gunderson KL . Highly parallel genomic assays. Nat Rev Genet 2006; 7: 632–644.

    Article  CAS  Google Scholar 

  16. van den Berg SW, Dolle ME, Imholz S, van der AD, van ′t Slot R, Wijmenga C et al. Genetic variations in regulatory pathways of fatty acid and glucose metabolism are associated with obesity phenotypes: a population-based cohort study. Int J Obes (Lond) 2009; 33: 1143–1152.

    Article  CAS  Google Scholar 

  17. Roumen C, Corpeleijn E, Feskens EJ, Mensink M, Saris WH, Blaak EE . Impact of 3-year lifestyle intervention on postprandial glucose metabolism: the SLIM study. Diabet Med 2008; 25: 597–605.

    Article  CAS  Google Scholar 

  18. American Diabetes Association. Postprandial blood glucose. Diabetes Care 2001; 24: 775–778.

    Article  Google Scholar 

  19. Allison DB, Cui X, Page GP, Sabripour M . Microarray data analysis: from disarray to consolidation and consensus. Nat Rev Genet 2006; 7: 55–65.

    Article  CAS  Google Scholar 

  20. Benjamini Y, Yekutieli D . Quantitative trait loci analysis using the false discovery rate. Genetics 2005; 171: 783–790.

    Article  CAS  Google Scholar 

  21. Smith NL, Hindorff LA, Heckbert SR, Lemaitre RN, Marciante KD, Rice K et al. Association of genetic variations with nonfatal venous thrombosis in postmenopausal women. JAMA 2007; 297: 489–498.

    Article  CAS  Google Scholar 

  22. Liang H, Ward WF . PGC-1alpha: a key regulator of energy metabolism. Adv Physiol Educ 2006; 30: 145–151.

    Article  Google Scholar 

  23. Nitz I, Ewert A, Klapper M, Doring F . Analysis of PGC-1alpha variants Gly482Ser and Thr612Met concerning their PPARgamma2-coactivation function. Biochem Biophys Res Commun 2007; 353: 481–486.

    Article  CAS  Google Scholar 

  24. Ling C, Del Guerra S, Lupi R, Ronn T, Granhall C, Luthman H . et al. Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion. Diabetologia 2008; 51: 615–622.

    Article  CAS  Google Scholar 

  25. Ling C, Poulsen P, Carlsson E, Ridderstrale M, Almgren P, Wojtaszewski J et al. Multiple environmental and genetic factors influence skeletal muscle PGC-1alpha and PGC-1beta gene expression in twins. J Clin Invest 2004; 114: 1518–1526.

    Article  CAS  Google Scholar 

  26. Barroso I, Luan J, Sandhu MS, Franks PW, Crowley V, Schafer AJ et al. Meta-analysis of the Gly482Ser variant in PPARGC1A in type 2 diabetes and related phenotypes. Diabetologia 2006; 49: 501–505.

    Article  CAS  Google Scholar 

  27. Fanelli M, Filippi E, Sentinelli F, Romeo S, Fallarino M, Buzzetti R et al. The Gly482Ser missense mutation of the peroxisome proliferator-activated receptor gamma coactivator-1alpha (PGC-1alpha) gene associates with reduced insulin sensitivity in normal and glucose-intolerant obese subjects. Dis Markers 2005; 21: 175–180.

    Article  CAS  Google Scholar 

  28. Ridderstrale M, Johansson LE, Rastam L, Lindblad U . Increased risk of obesity associated with the variant allele of the PPARGC1A Gly482Ser polymorphism in physically inactive elderly men. Diabetologia 2006; 49: 496–500.

    Article  CAS  Google Scholar 

  29. Vimaleswaran KS, Luan J, Andersen G, Muller YL, Wheeler E, Brito EC et al. The Gly482Ser genotype at the PPARGC1A gene and elevated blood pressure: a meta-analysis involving 13,949 individuals. J Appl Physiol 2008; 105: 1352–1358.

    Article  Google Scholar 

  30. Stefan N, Thamer C, Staiger H, Machicao F, Machann J, Schick F et al. Genetic variations in PPARD and PPARGC1A determine mitochondrial function and change in aerobic physical fitness and insulin sensitivity during lifestyle intervention. J Clin Endocrinol Metab 2007; 92: 1827–1833.

    Article  CAS  Google Scholar 

  31. Andrulionyte L, Zacharova J, Chiasson JL, Laakso M . Common polymorphisms of the PPAR-gamma2 (Pro12Ala) and PGC-1alpha (Gly482Ser) genes are associated with the conversion from impaired glucose tolerance to type 2 diabetes in the STOP-NIDDM trial. Diabetologia 2004; 47: 2176–2184.

    Article  CAS  Google Scholar 

  32. Goyenechea E, Crujeiras AB, Abete I, Parra D, Martinez JA . Enhanced short-term improvement of insulin response to a low-caloric diet in obese carriers the Gly482Ser variant of the PGC-1alpha gene. Diabetes Res Clin Pract 2008; 82: 190–196.

    Article  CAS  Google Scholar 

  33. Pihlajamaki J, Patti ME . Regulation of PGC-1 in Humans with Insulin Resistance and Type 2 Diabetes: Functional Implications First edn, vol. 5. Elsevier: Oxford, 2006.

    Google Scholar 

Download references

Acknowledgements

The Doetinchem Cohort Study was financially supported by the Ministry of Health, Welfare, and Sport of The Netherlands and the National Institute for Public Health and the Environment. We thank the epidemiologists and fieldworkers of the Municipal Health Service in Doetinchem for their contribution to the data collection for this study. Project director is Dr WMM Verschuren. Logistic management was provided by J Steenbrink and P Vissink, and administrative support by EP van der Wolf. Data management was provided by A Blokstra, AWD van Kessel, and PE Steinberger. Genotyping facilities were provided by C Wijmenga. Technical assistance was provided by HM Hodemaekers and C Strien. The SLIM study was supported by grants from the Dutch Diabetes Research Foundation (DFN 98.901 and DFN 2000.00.020) and the Netherlands Organization for Scientific Research (ZonMW 940-35-034, 2,200.0139). We thank M Mensink and WHM Saris for their contribution.

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Authors and Affiliations

  1. Centre for Nutrition and Health, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands

    C M Povel & J M A Boer

  2. Division of Human Nutrition, Wageningen University, Wageningen, The Netherlands

    C M Povel & E J M Feskens

  3. Laboratory for Health Protection Research, National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands

    S Imholz & M E T Dollé

  4. Department of Human Biology, Maastricht University, Maastricht, The Netherlands

    E E Blaak

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  1. C M Povel
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Correspondence to C M Povel.

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Povel, C., Feskens, E., Imholz, S. et al. Glucose levels and genetic variants across transcriptional pathways: interaction effects with BMI. Int J Obes 34, 840–845 (2010). https://doi.org/10.1038/ijo.2009.302

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