Skip to main content

Advertisement

Log in

Gene-Diet Interactions in Type 2 Diabetes

  • Diabetes and Obesity (MR Carnethon, Section Editor)
  • Published:
Current Nutrition Reports Aims and scope Submit manuscript

Abstract

Type 2 diabetes (T2D) is thought to arise from an interaction between susceptibility genes and a diabetogenic environment. This review summarizes progress pertaining specifically to gene-diet interactions. Recent efforts have been population-based and have focused on established genetic and dietary risk factors for T2D. TCF7L2 × carbohydrate-quality and IRS1 × macronutrient-composition interactions are promising factors, but most studies of gene-diet interactions are conflicting or need follow-up. T2D genetic risk scores are powerful predictors of developing T2D, but whether they can be combined with dietary risk factors merits further study. Lack of statistical power, imprecise diet measures, and conceptual issues surrounding replication all challenge our efforts to characterize interactions. Collaborations are needed for optimal study designs in both hypothesis-testing and hypothesis-generating contexts. Continued investment in studies of gene-diet interactions may lead to novel mechanistic insights into T2D, opportunities for risk stratification, and ultimately to personalized nutrition to prevent the disease.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

Papers of particular interest published recently have been highlighted as: • Of importance •• Of major importance

  1. Hogan P, Dall T, Nikolov P. Economic costs of diabetes in the US in 2002. Diabetes Care. 2003;26(3):917–32.

    PubMed  Google Scholar 

  2. International Diabetes Federation. IDF Diabetes Atlas, 6th edn. Brussels, Belgium: http://www.idf.org/diabetesatlas. Accessed 15 July 2014.

  3. DeFronzo RA. Pathogenesis of type 2 diabetes: metabolic and molecular implications for identifying diabetes genes. Diabetes Rev. 1997;5(3):177–269.

    Google Scholar 

  4. Permutt MA, Wasson J, Cox N. Genetic epidemiology of diabetes. J Clin Invest. 2005;115(6):1431–9.

    CAS  PubMed Central  PubMed  Google Scholar 

  5. Poulsen P, Kyvik KO, Vaag A, Beck-Nielsen H. Heritability of type II (non-insulin-dependent) diabetes mellitus and abnormal glucose tolerance–a population-based twin study. Diabetologia. 1999;42(2):139–45.

    CAS  PubMed  Google Scholar 

  6. Kaprio J, Tuomilehto J, Koskenvuo M, et al. Concordance for type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetes mellitus in a population-based cohort of twins in Finland. Diabetologia. 1992;35(11):1060–7.

    CAS  PubMed  Google Scholar 

  7. Morris AP, Voight BF, Teslovich TM, et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–90. This article is the latest in a series of successful genome-wide association studies of type 2 diabetes that demonstrate the power of an agnostic system-wide approach to susceptibility loci discovery.

    CAS  PubMed Central  PubMed  Google Scholar 

  8. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–16.

    CAS  PubMed Central  PubMed  Google Scholar 

  9. Strawbridge RJ, Dupuis J, Prokopenko I, et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60(10):2624–34.

    CAS  PubMed Central  PubMed  Google Scholar 

  10. Florez JC. Newly identified loci highlight beta cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia. 2008;51(7):1100–10.

    CAS  PubMed  Google Scholar 

  11. Ley SH, Hamdy O, Mohan V, Hu FB. Prevention and management of type 2 diabetes: dietary components and nutritional strategies. Lancet. 2014;383(9933):1999–2007. This article provides a comprehensive review of dietary factors implicated in the development and management of type 2 diabetes. The work draws on several meta-analyses, large-scale association studies, intervention trials, and other credible literature and resources.

    CAS  PubMed  Google Scholar 

  12. Koloverou E, Esposito K, Giugliano D, Panagiotakos D. The effect of Mediterranean diet on the development of type 2 diabetes mellitus: a meta-analysis of 10 prospective studies and 136,846 participants. Metabolism. 2014;63(7):903–11.

    CAS  PubMed  Google Scholar 

  13. Barker DJ. Intrauterine programming of adult disease. Mol Med Today. 1995;1(9):418–23.

    CAS  PubMed  Google Scholar 

  14. Burdge GC, Lillycrop KA. Nutrition, epigenetics, and developmental plasticity: implications for understanding human disease. Annu Rev Nutr. 2010;30:315–39.

    CAS  PubMed  Google Scholar 

  15. Patel MS, Srinivasan M. Metabolic programming: causes and consequences. J Biol Chem. 2002;277(3):1629–32.

    CAS  PubMed  Google Scholar 

  16. Loopstra-Masters RC, Liese AD, Haffner SM, Wagenknecht LE, Hanley AJ. Associations between the intake of caffeinated and decaffeinated coffee and measures of insulin sensitivity and beta cell function. Diabetologia. 2011;54(2):320–8.

    CAS  PubMed  Google Scholar 

  17. Guerrero-Romero F, Rodriguez-Moran M. Magnesium improves the beta-cell function to compensate variation of insulin sensitivity: double-blind, randomized clinical trial. Eur J Clin Invest. 2011;41(4):405–10.

    CAS  PubMed  Google Scholar 

  18. Yue F, Zhang X, Zhang H, Jiang X, Gao L, Zhao J. Association of alcohol consumption with the impaired beta-cell function independent of body mass index among Chinese men. Endocr J. 2012;59(5):425–33.

    CAS  PubMed  Google Scholar 

  19. Dimas AS, Lagou V, Barker A, et al. Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity. Diabetes. 2014;63(6):2158–71. This article examines the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. This knowledge may inform the design of future gene-diet interaction studies.

    CAS  PubMed  Google Scholar 

  20. Wing RR, Goldstein MG, Acton KJ, et al. Behavioral science research in diabetes: lifestyle changes related to obesity, eating behavior, and physical activity. Diabetes Care. 2001;24(1):117–23.

    CAS  PubMed  Google Scholar 

  21. Hu FB. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34(6):1249–57.

    PubMed Central  PubMed  Google Scholar 

  22. Neel JV. Diabetes mellitus: a “thrifty” genotype rendered detrimental by “progress”? Am J Hum Genet. 1962;14:353–62.

    CAS  PubMed Central  PubMed  Google Scholar 

  23. Thomas D. Gene–environment-wide association studies: emerging approaches. Nat Rev Genet. 2010;11(4):259–72.

    CAS  PubMed Central  PubMed  Google Scholar 

  24. Rothman KJ, Greenland S, editors. Modern epidemiology. Philadelphia: Lippincott Williams and Wilkins; 1998.

    Google Scholar 

  25. Cornelis MC, Hu FB. Gene-environment interactions in the development of type 2 diabetes: recent progress and continuing challenges. Annu Rev Nutr. 2012.

  26. Albert PS, Ratnasinghe D, Tangrea J, Wacholder S. Limitations of the case-only design for identifying gene-environment interactions. Am J Epidemiol. 2001;154(8):687–93.

    CAS  PubMed  Google Scholar 

  27. Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343–50.

    CAS  PubMed  Google Scholar 

  28. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393–403.

    CAS  PubMed  Google Scholar 

  29. Franks PW, Mesa JL, Harding AH, Wareham NJ. Gene-lifestyle interaction on risk of type 2 diabetes. Nutr Metab Cardiovasc Dis. 2007;17(2):104–24.

    PubMed  Google Scholar 

  30. Voight BF, Scott LJ, Steinthorsdottir V, et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat Genet. 2010;42(7):579–89.

    CAS  PubMed Central  PubMed  Google Scholar 

  31. Gouda HN, Sagoo GS, Harding AH, Yates J, Sandhu MS, Higgins JP. The association between the peroxisome proliferator-activated receptor-gamma2 (PPARG2) Pro12Ala gene variant and type 2 diabetes mellitus: a HuGE review and meta-analysis. Am J Epidemiol. 2010;171(6):645–55.

    PubMed Central  PubMed  Google Scholar 

  32. Tonjes A, Scholz M, Loeffler M, Stumvoll M. Association of Pro12Ala polymorphism in peroxisome proliferator-activated receptor gamma with Pre-diabetic phenotypes: meta-analysis of 57 studies on nondiabetic individuals. Diabetes Care. 2006;29(11):2489–97.

    PubMed  Google Scholar 

  33. Lazar MA. PPAR gamma, 10 years later. Biochimie. 2005;87(1):9–13.

    CAS  PubMed  Google Scholar 

  34. Luan J, Browne PO, Harding AH, et al. Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes. 2001;50(3):686–9.

    CAS  PubMed  Google Scholar 

  35. Memisoglu A, Hu FB, Hankinson SE, et al. Interaction between a peroxisome proliferator-activated receptor gamma gene polymorphism and dietary fat intake in relation to body mass. Hum Mol Genet. 2003;12(22):2923–9.

    CAS  PubMed  Google Scholar 

  36. Soriguer F, Morcillo S, Cardona F, et al. Pro12Ala polymorphism of the PPARG2 gene is associated with type 2 diabetes mellitus and peripheral insulin sensitivity in a population with a high intake of oleic acid. J Nutr. 2006;136(9):2325–30.

    CAS  PubMed  Google Scholar 

  37. Ylonen SK, Salminen I, Lyssenko V, et al. The Pro12Ala polymorphism of the PPAR-gamma2 gene affects associations of fish intake and marine n-3 fatty acids with glucose metabolism. Eur J Clin Nutr. 2008;62(12):1432–9.

    CAS  PubMed  Google Scholar 

  38. Robitaille J, Despres JP, Perusse L, Vohl MC. 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. 2003;63(2):109–16.

    CAS  PubMed  Google Scholar 

  39. Lamri A, Abi Khalil C, Jaziri R, et al. Dietary fat intake and polymorphisms at the PPARG locus modulate BMI and type 2 diabetes risk in the D.E.S.I.R. prospective study. Int J Obes (Lond). 2011.

  40. Lamri A, Abi Khalil C, Jaziri R, et al. Dietary fat intake and polymorphisms at the PPARG locus modulate BMI and type 2 diabetes risk in the D.E.S.I.R. prospective study. Int J Obes (Lond). 2012;36(2):218–24.

    CAS  Google Scholar 

  41. Nelson TL, Fingerlin TE, Moss LK, Barmada MM, Ferrell RE, Norris JM. Association of the peroxisome proliferator-activated receptor gamma gene with type 2 diabetes mellitus varies by physical activity among non-Hispanic whites from Colorado. Metabolism. 2007;56(3):388–93.

    CAS  PubMed  Google Scholar 

  42. Patel CJ, Chen R, Kodama K, Ioannidis JP, Butte AJ. Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus. Hum Genet. 2013;132(5):495–508.

    CAS  PubMed Central  PubMed  Google Scholar 

  43. Pisabarro RE, Sanguinetti C, Stoll M, Prendez D. High incidence of type 2 diabetes in peroxisome proliferator-activated receptor gamma2 Pro12Ala carriers exposed to a high chronic intake of trans fatty acids and saturated fatty acids. Diabetes Care. 2004;27(9):2251–2.

    CAS  PubMed  Google Scholar 

  44. Lindi VI, Uusitupa MI, Lindstrom J, et al. Association of the Pro12Ala polymorphism in the PPAR-gamma2 gene with 3-year incidence of type 2 diabetes and body weight change in the Finnish Diabetes Prevention Study. Diabetes. 2002;51(8):2581–6.

    CAS  PubMed  Google Scholar 

  45. Florez JC, Jablonski KA, Sun MW, et al. Effects of the type 2 diabetes-associated PPARG P12A polymorphism on progression to diabetes and response to troglitazone. J Clin Endocrinol Metab. 2007;92(4):1502–9.

    CAS  PubMed Central  PubMed  Google Scholar 

  46. Jablonski KA, McAteer JB, de Bakker PI, et al. Common variants in 40 genes assessed for diabetes incidence and response to metformin and lifestyle intervention in the diabetes prevention program. Diabetes. 2010;59(10):2672–81.

    CAS  PubMed Central  PubMed  Google Scholar 

  47. Cornelis MC, Qi L, Kraft P, Hu FB. TCF7L2, dietary carbohydrate, and risk of type 2 diabetes in US women. Am J Clin Nutr. 2009;89(4):1256–62.

    CAS  PubMed Central  PubMed  Google Scholar 

  48. Fisher E, Boeing H, Fritsche A, Doering F, Joost HG, Schulze MB. Whole-grain consumption and transcription factor-7-like 2 ( TCF7L2) rs7903146: gene-diet interaction in modulating type 2 diabetes risk. Br J Nutr. 2009;101(4):478–81.

    CAS  PubMed  Google Scholar 

  49. Wirstrom T, Hilding A, Gu HF, Ostenson CG, Bjorklund A. Consumption of whole grain reduces risk of deteriorating glucose tolerance, including progression to prediabetes. Am J Clin Nutr. 2013;97(1):179–87.

    PubMed  Google Scholar 

  50. Hindy G, Sonestedt E, Ericson U, et al. Role of TCF7L2 risk variant and dietary fibre intake on incident type 2 diabetes. Diabetologia. 2012;55(10):2646–54.

    CAS  PubMed Central  PubMed  Google Scholar 

  51. Villegas R, Goodloe RJ, McClellan Jr BE, Boston J, Crawford DC. Gene-carbohydrate and gene-fiber interactions and type 2 diabetes in diverse populations from the National Health and Nutrition Examination Surveys (NHANES) as part of the Epidemiologic Architecture for Genes Linked to Environment (EAGLE) study. BMC Genet. 2014;15:69.

    PubMed Central  PubMed  Google Scholar 

  52. Nettleton JA, McKeown NM, Kanoni S, et al. Interactions of dietary whole-grain intake with fasting glucose- and insulin-related genetic loci in individuals of European descent: a meta-analysis of 14 cohort studies. Diabetes Care. 2010;33(12):2684–91.

    PubMed Central  PubMed  Google Scholar 

  53. Wang J, Li L, Zhang J, et al. Association of rs7903146 (IVS3C/T) and rs290487 (IVS3C/T) polymorphisms in TCF7L2 with type 2 diabetes in 9,619 Han Chinese population. PLoS ONE. 2013;8(3):e59053.

    CAS  PubMed Central  PubMed  Google Scholar 

  54. Pulizzi N, Lyssenko V, Jonsson A, et al. Interaction between prenatal growth and high-risk genotypes in the development of type 2 diabetes. Diabetologia. 2009;52(5):825–9.

    CAS  PubMed  Google Scholar 

  55. van Hoek M, Langendonk JG, de Rooij SR, Sijbrands EJ, Roseboom TJ. Genetic variant in the IGF2BP2 gene may interact with fetal malnutrition to affect glucose metabolism. Diabetes. 2009;58(6):1440–4.

    PubMed Central  PubMed  Google Scholar 

  56. Florez JC, Jablonski KA, Bayley N, et al. TCF7L2 polymorphisms and progression to diabetes in the Diabetes Prevention Program. N Engl J Med. 2006;355(3):241–50.

    CAS  PubMed Central  PubMed  Google Scholar 

  57. Rutter GA. Think zinc: new roles for zinc in the control of insulin secretion. Islets. 2010;2(1):49–50.

    PubMed  Google Scholar 

  58. Myers SA, Nield A, Myers M. Zinc transporters, mechanisms of action and therapeutic utility: implications for type 2 diabetes mellitus. J Nutr Metab. 2012;2012:173712.

    PubMed Central  PubMed  Google Scholar 

  59. Beletate V, El Dib RP, Atallah AN. Zinc supplementation for the prevention of type 2 diabetes mellitus. Cochrane Database Syst Rev. 2007;1, CD005525.

    PubMed  Google Scholar 

  60. Shan Z, Bao W, Zhang Y, et al. Interactions between zinc transporter-8 gene (SLC30A8) and plasma zinc concentrations for impaired glucose regulation and type 2 diabetes. Diabetes. 2014;63(5):1796–803.

    CAS  PubMed  Google Scholar 

  61. Kanoni S, Nettleton JA, Hivert MF, et al. Total zinc intake may modify the glucose-raising effect of a zinc transporter (SLC30A8) variant: a 14-cohort meta-analysis. Diabetes. 2011;60(9):2407–16.

    CAS  PubMed Central  PubMed  Google Scholar 

  62. Billings LK, Jablonski KA, Ackerman RJ, et al. The influence of rare genetic variation in SLC30A8 on diabetes incidence and beta-cell function. J Clin Endocrinol Metab. 2014;99(5):E926–30.

    CAS  PubMed  Google Scholar 

  63. Zeggini E, Weedon MN, Lindgren CM, et al. Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science. 2007;316(5829):1336–41.

    CAS  PubMed Central  PubMed  Google Scholar 

  64. Marzo N, Mora C, Fabregat ME, et al. Pancreatic islets from cyclin-dependent kinase 4/R24C (Cdk4) knockin mice have significantly increased beta cell mass and are physiologically functional, indicating that Cdk4 is a potential target for pancreatic beta cell mass regeneration in Type 1 diabetes. Diabetologia. 2004;47(4):686–94.

  65. Mettus RV, Rane SG. Characterization of the abnormal pancreatic development, reduced growth and infertility in Cdk4 mutant mice. Oncogene. 2003;22(52):8413–21.

    CAS  PubMed  Google Scholar 

  66. Moore AF, Jablonski KA, McAteer JB, et al. Extension of type 2 diabetes genome-wide association scan results in the diabetes prevention program. Diabetes. 2008;57(9):2503–10.

    CAS  PubMed Central  PubMed  Google Scholar 

  67. Almind K, Inoue G, Pedersen O, Kahn CR. A common amino acid polymorphism in insulin receptor substrate-1 causes impaired insulin signaling. Evidence from transfection studies. J Clin Invest. 1996;97(11):2569–75.

    CAS  PubMed Central  PubMed  Google Scholar 

  68. Laukkanen O, Pihlajamaki J, Lindstrom J, et al. Common polymorphisms in the genes regulating the early insulin signalling pathway: effects on weight change and the conversion from impaired glucose tolerance to Type 2 diabetes. The Finnish Diabetes Prevention Study. Diabetologia. 2004;47(5):871–7.

    CAS  PubMed  Google Scholar 

  69. Marin C, Perez-Martinez P, Delgado-Lista J, et al. The insulin sensitivity response is determined by the interaction between the G972R polymorphism of the insulin receptor substrate 1 gene and dietary fat. Mol Nutr Food Res. 2011;55(2):328–35.

    CAS  PubMed  Google Scholar 

  70. Kilpelainen TO, Zillikens MC, Stancakova A, et al. Genetic variation near IRS1 associates with reduced adiposity and an impaired metabolic profile. Nat Genet. 2011;43(8):753–60.

    CAS  PubMed Central  PubMed  Google Scholar 

  71. Rung J, Cauchi S, Albrechtsen A, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet. 2009;41(10):1110–5.

    CAS  PubMed  Google Scholar 

  72. Ericson U, Rukh G, Stojkovic I, et al. Sex-specific interactions between the IRS1 polymorphism and intakes of carbohydrates and fat on incident type 2 diabetes. Am J Clin Nutr. 2013;97(1):208–16.

    CAS  PubMed  Google Scholar 

  73. Zheng JS, Arnett DK, Parnell LD, et al. Modulation by dietary fat and carbohydrate of IRS1 association with type 2 diabetes traits in two populations of different ancestries. Diabetes Care. 2013;36(9):2621–7.

    CAS  PubMed Central  PubMed  Google Scholar 

  74. Qi Q, Xu M, Wu H, et al. IRS1 genotype modulates metabolic syndrome reversion in response to 2-year weight-loss diet intervention: the POUNDS LOST trial. Diabetes Care. 2013;36(11):3442–7.

    CAS  PubMed  Google Scholar 

  75. Qi Q, Bray GA, Smith SR, Hu FB, Sacks FM, Qi L. Insulin receptor substrate 1 gene variation modifies insulin resistance response to weight-loss diets in a 2-year randomized trial: the Preventing Overweight Using Novel Dietary Strategies (POUNDS LOST) trial. Circulation. 2011;124(5):563–71.

    CAS  PubMed Central  PubMed  Google Scholar 

  76. Speliotes EK, Willer CJ, Berndt SI, et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet. 2010;42(11):937–48.

    CAS  PubMed Central  PubMed  Google Scholar 

  77. Saxena R, Hivert MF, Langenberg C, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42(2):142–8.

    CAS  PubMed Central  PubMed  Google Scholar 

  78. Meyre D. Is FTO, a type 2 diabetes susceptibility gene? Diabetologia. 2012;55(4):873–6.

    CAS  PubMed  Google Scholar 

  79. Lyssenko V, Eliasson L, Kotova O, et al. Pleiotropic effects of GIP on islet function involve osteopontin. Diabetes. 2011;60(9):2424–33.

    CAS  PubMed Central  PubMed  Google Scholar 

  80. Ortega-Azorin C, Sorli JV, Asensio EM, et al. Associations of the FTO rs9939609 and the MC4R rs17782313 polymorphisms with type 2 diabetes are modulated by diet, being higher when adherence to the Mediterranean diet pattern is low. Cardiovasc Diabetol. 2012;11:137.

    CAS  PubMed Central  PubMed  Google Scholar 

  81. Sonestedt E, Lyssenko V, Ericson U, et al. Genetic variation in the glucose-dependent insulinotropic polypeptide receptor modifies the association between carbohydrate and fat intake and risk of type 2 diabetes in the Malmo Diet and Cancer cohort. J Clin Endocrinol Metab. 2012;97(5):E810–8.

    CAS  PubMed  Google Scholar 

  82. Qi Q, Bray GA, Hu FB, Sacks FM, Qi L. Weight-loss diets modify glucose-dependent insulinotropic polypeptide receptor rs2287019 genotype effects on changes in body weight, fasting glucose, and insulin resistance: the Preventing Overweight Using Novel Dietary Strategies trial. Am J Clin Nutr. 2012;95(2):506–13.

    CAS  PubMed Central  PubMed  Google Scholar 

  83. Lappalainen TJ, Tolppanen AM, Kolehmainen M, et al. The common variant in the FTO gene did not modify the effect of lifestyle changes on body weight: the Finnish Diabetes Prevention Study. Obesity (Silver Spring). 2009;17(4):832–6.

    CAS  Google Scholar 

  84. Cornelis MC, Qi L, Zhang C, et al. Joint effects of common genetic variants on the risk for type 2 diabetes in U.S. men and women of European ancestry. Ann Intern Med. 2009;150(8):541–50.

    PubMed Central  PubMed  Google Scholar 

  85. Qi L, Cornelis MC, Zhang C, van Dam RM, Hu FB. Genetic predisposition, Western dietary pattern, and the risk of type 2 diabetes in men. Am J Clin Nutr. 2009;89(5):1453–8.

    CAS  PubMed Central  PubMed  Google Scholar 

  86. Langenberg C, Sharp SJ, Franks PW, et al. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med. 2014;11(5):e1001647. This is a recent study from a series of large-scale gene-environment interaction studies coordinated by consortia. See the Table 1 entry for this study. No interactions were observed between the T2D GRS and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score. The relative effect of the T2D GRS was reportedly greater in younger and leaner participants. However, the investigators emphasize that on an absolute risk scale this subgroup would not be a logical target for preventive interventions.

    PubMed Central  PubMed  Google Scholar 

  87. Li Y, Qi Q, Workalemahu T, Hu FB, Qi L. Birth weight, genetic susceptibility, and adulthood risk of type 2 diabetes. Diabetes Care. 2012;35(12):2479–84.

    PubMed Central  PubMed  Google Scholar 

  88. Bookman EB, McAllister K, Gillanders E, et al. Gene-environment interplay in common complex diseases: forging an integrative model-recommendations from an NIH workshop. Genet Epidemiol. 2011.

  89. Hunter DJ. Gene-environment interactions in human diseases. Nat Rev Genet. 2005;6(4):287–98.

    CAS  PubMed  Google Scholar 

  90. Kraft P, Hunter D. Integrating epidemiology and genetic association: the challenge of gene-environment interaction. Philos Trans R Soc Lond B Biol Sci. 2005;360(1460):1609–16.

    CAS  PubMed Central  PubMed  Google Scholar 

  91. Moffitt TE, Caspi A, Rutter M. Strategy for investigating interactions between measured genes and measured environments. Arch Gen Psychiatry. 2005;62(5):473–81.

    CAS  PubMed  Google Scholar 

  92. Smith PG, Day NE. The design of case-control studies: the influence of confounding and interaction effects. Int J Epidemiol. 1984;13(3):356–65.

    CAS  PubMed  Google Scholar 

  93. Palla L, Higgins JP, Wareham NJ, Sharp SJ. Challenges in the use of literature-based meta-analysis to examine gene-environment interactions. Am J Epidemiol. 2010;171(11):1225–32.

    PubMed  Google Scholar 

  94. Moore SC, Gunter MJ, Daniel CR, et al. Common genetic variants and central adiposity among Asian-Indians. Obesity (Silver Spring). 2011.

  95. Kilpelainen TO, Qi L, Brage S, et al. Physical activity attenuates the influence of FTO variants on obesity risk: a meta-analysis of 218,166 adults and 19,268 children. PLoS Med. 2011;8(11):e1001116.

    PubMed Central  PubMed  Google Scholar 

  96. Scott RA, Chu AY, Grarup N, et al. No interactions between previously associated 2-hour glucose gene variants and physical activity or BMI on 2-hour glucose levels. Diabetes. 2012;61(5):1291–6.

    CAS  PubMed Central  PubMed  Google Scholar 

  97. Wild CP. The exposome: from concept to utility. Int J Epidemiol. 2012;41(1):24–32.

    PubMed  Google Scholar 

  98. Martin Sanchez F, Gray K, Bellazzi R, Lopez-Campos G. Exposome informatics: considerations for the design of future biomedical research information systems. J Am Med Inform Assoc. 2014;21(3):386–90.

    PubMed  Google Scholar 

  99. Zheng JS, Parnell LD, Smith CE, et al. Circulating 25-hydroxyvitamin D, IRS1 variant rs2943641, and insulin resistance: replication of a gene-nutrient interaction in 4 populations of different ancestries. Clin Chem. 2014;60(1):186–96.

    CAS  PubMed Central  PubMed  Google Scholar 

  100. Willett WC. Nutritional epidemiology. New York: Oxford University Press; 1998.

    Google Scholar 

  101. Stover PJ, Harlan WR, Hammond JA, Hendershot T, Hamilton CM. PhenX: a toolkit for interdisciplinary genetics research. Curr Opin Lipidol. 2010;21(2):136–40.

    CAS  PubMed  Google Scholar 

  102. Anuradha CV. Phytochemicals targeting genes relevant for type 2 diabetes. Can J Physiol Pharmacol. 2013;91(6):397–411. This article provides a comprehensive review of the literature pertaining to phytochemicals/extracts that may target diabetogenic genes. A prospective on nutritional therapy of T2D is also provided.

    CAS  PubMed  Google Scholar 

  103. Shin SY, Fauman EB, Petersen AK, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50. This study provides the most comprehensive exploration of genetic loci influencing human metabolism thus far. The investigators report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. Some of these loci overlap with those associated with T2D (see Table 2 in the current paper). Links between these loci and metabolites may, thus, provide mechanistic insight into the role of these loci (or metabolites) in T2D development.

    CAS  PubMed  Google Scholar 

  104. Catalogue of Published Genome-Wide Association Studies. www.genome.gov/gwastudies. Accessed 1 July 2014.

  105. Thomas DC, Lewinger JP, Murcray CE, Gauderman WJ. Invited commentary: GE-Whiz! Ratcheting gene-environment studies up to the whole genome and the whole exposome. Am J Epidemiol. 2012;175(3):203–7. discussion 208–209. This Invited Commentary discusses recent approaches to genome-wide environment interaction studies with special reference to findings from two recent studies in the same issue of the journal (Cornelis et al. 2012 and Mukherjee et al. 2012).

    PubMed Central  PubMed  Google Scholar 

  106. Franks PW. Gene x environment interactions in type 2 diabetes. Curr Diab Rep. 2011;11(6):552–61.

    PubMed  Google Scholar 

  107. Khoury MJ, Wacholder S. Invited commentary: from genome-wide association studies to gene-environment-wide interaction studies–challenges and opportunities. Am J Epidemiol. 2009;169(2):227–30. discussion 234-225.

    PubMed Central  PubMed  Google Scholar 

  108. Mukherjee B, Ahn J, Gruber SB, Chatterjee N. Testing gene-environment interaction in large-scale case-control association studies: possible choices and comparisons. Am J Epidemiol. 2012;175(3):177–90. This article presents a comparative simulation study of power and type I error properties of several statistical methods for genome-wide environment interaction testing. Importance of the findings are reviewed by Thomas et al. 2012 in the same issue of the Journal.

    PubMed Central  PubMed  Google Scholar 

  109. Cornelis MC, Tchetgen Tchetgen EJ, Liang L, et al. Gene-environment interactions in genome-wide association studies: a comparative study of tests applied to empirical studies of type 2 diabetes. Am J Epidemiol. 2012;175(3):191–202. This study compares the performance of several statistical methods for testing gene-environment interactions in the context of GWAS using two case-control GWAS of type 2 diabetes. The importance of the findings are reviewed by Thomas et al. 2012 in the same issue of the Journal.

    PubMed Central  PubMed  Google Scholar 

  110. Ritchie MD, Motsinger AA. Multifactor dimensionality reduction for detecting gene-gene and gene-environment interactions in pharmacogenomics studies. Pharmacogenomics. 2005;6(8):823–34.

    CAS  PubMed  Google Scholar 

  111. Hung RJ, Brennan P, Malaveille C, et al. Using hierarchical modeling in genetic association studies with multiple markers: application to a case-control study of bladder cancer. Cancer Epidemiol Biomarkers Prev. 2004;13(6):1013–21.

    CAS  PubMed  Google Scholar 

  112. Patel CJ, Chen R, Butte AJ. Data-driven integration of epidemiological and toxicological data to select candidate interacting genes and environmental factors in association with disease. Bioinformatics. 2012;28(12):i121–6.

    CAS  PubMed Central  PubMed  Google Scholar 

  113. Kraft P, Yen YC, Stram DO, Morrison J, Gauderman WJ. Exploiting gene-environment interaction to detect genetic associations. Hum Hered. 2007;63(2):111–9.

    CAS  PubMed  Google Scholar 

  114. Zaitlen N, Lindstrom S, Pasaniuc B, et al. Informed conditioning on clinical covariates increases power in case-control association studies. PLoS Genet. 2012;8(11):e1003032.

    CAS  PubMed Central  PubMed  Google Scholar 

  115. Grarup N, Andersen G. Gene-environment interactions in the pathogenesis of type 2 diabetes and metabolism. Curr Opin Clin Nutr Metab Care. 2007;10(4):420–6.

    CAS  PubMed  Google Scholar 

  116. Ordovas JM, Tai ES. Why study gene-environment interactions? Curr Opin Lipidol. 2008;19(2):158–67.

    CAS  PubMed  Google Scholar 

  117. Nemoto M, Sasaki T, Deeb SS, Fujimoto WY, Tajima N. Differential effect of PPARgamma2 variants in the development of type 2 diabetes between native Japanese and Japanese Americans. Diabetes Res Clin Pract. 2002;57(2):131–7.

    CAS  PubMed  Google Scholar 

  118. Fisher E, Schreiber S, Joost HG, Boeing H, Doring F. A two-step association study identifies CAV2 rs2270188 single nucleotide polymorphism interaction with fat intake in type 2 diabetes risk. J Nutr. 2011;141(2):177–81.

    CAS  PubMed  Google Scholar 

  119. Florez JC, Jablonski KA, McAteer J, et al. Testing of diabetes-associated WFS1 polymorphisms in the Diabetes Prevention Program. Diabetologia. 2008;51(3):451–7.

    CAS  PubMed Central  PubMed  Google Scholar 

  120. Pollin TI, Jablonski KA, McAteer JB, et al. Triglyceride response to an intensive lifestyle intervention is enhanced in carriers of the GCKR Pro446Leu polymorphism. J Clin Endocrinol Metab. 2011;96(7):E1142–7.

    PubMed Central  PubMed  Google Scholar 

  121. Su Y, Simmen FA, Xiao R, Simmen RC. Expression profiling of rat mammary epithelial cells reveals candidate signaling pathways in dietary protection from mammary tumors. Physiol Genomics. 2007;30(1):8–16.

    CAS  PubMed  Google Scholar 

  122. Richard D, Oszust F, Guillaume C, et al. Infusion of docosahexaenoic acid protects against myocardial infarction. Prostaglandins Leukot Essent Fat Acids. 2014;90(4):139–43.

    CAS  Google Scholar 

  123. Hall E, Volkov P, Dayeh T, et al. Effects of palmitate on genome-wide mRNA expression and DNA methylation patterns in human pancreatic islets. BMC Med. 2014;12:103.

    PubMed Central  PubMed  Google Scholar 

  124. Tsiftsoglou AS, Vizirianakis IS, Strouboulis J. Erythropoiesis: model systems, molecular regulators, and developmental programs. IUBMB Life. 2009;61(8):800–30.

    CAS  PubMed  Google Scholar 

  125. Park JJ, Berggren JR, Hulver MW, Houmard JA, Hoffman EP. GRB14, GPD1, and GDF8 as potential network collaborators in weight loss-induced improvements in insulin action in human skeletal muscle. Physiol Genomics. 2006;27(2):114–21.

    CAS  PubMed  Google Scholar 

  126. Lu J, Zeng Y, Hou W, et al. The soybean peptide aglycin regulates glucose homeostasis in type 2 diabetic mice via IR/IRS1 pathway. J Nutr Biochem. 2012;23(11):1449–57.

    CAS  PubMed  Google Scholar 

  127. Montagut G, Blade C, Blay M, et al. Effects of a grapeseed procyanidin extract (GSPE) on insulin resistance. J Nutr Biochem. 2010;21(10):961–7.

    CAS  PubMed  Google Scholar 

  128. Balage M, Dupont J, Mothe-Satney I, Tesseraud S, Mosoni L, Dardevet D. Leucine supplementation in rats induced a delay in muscle IR/PI3K signaling pathway associated with overall impaired glucose tolerance. J Nutr Biochem. 2011;22(3):219–26.

    CAS  PubMed  Google Scholar 

  129. Qin B, Polansky MM, Anderson RA. Cinnamon extract regulates plasma levels of adipose-derived factors and expression of multiple genes related to carbohydrate metabolism and lipogenesis in adipose tissue of fructose-fed rats. Horm Metab Res. 2010;42(3):187–93.

    CAS  PubMed  Google Scholar 

  130. Banas SM, Rouch C, Kassis N, Markaki EM, Gerozissis K. A dietary fat excess alters metabolic and neuroendocrine responses before the onset of metabolic diseases. Cell Mol Neurobiol. 2009;29(2):157–68.

    CAS  PubMed  Google Scholar 

  131. Alkharfy KM, Al-Daghri NM, Yakout SM, Hussain T, Mohammed AK, Krishnaswamy S. Influence of vitamin D treatment on transcriptional regulation of insulin-sensitive genes. Metab Syndr Relat Disord. 2013;11(4):283–8.

    CAS  PubMed  Google Scholar 

  132. Babacanoglu C, Yildirim N, Sadi G, Pektas MB, Akar F. Resveratrol prevents high-fructose corn syrup-induced vascular insulin resistance and dysfunction in rats. Food Chem Toxicol. 2013;60:160–7.

    CAS  PubMed  Google Scholar 

  133. Toyoshima Y, Ohne Y, Takahashi SI, Noguchi T, Kato H. Dietary protein deprivation decreases the serine phosphorylation of insulin receptor substrate-1 in rat skeletal muscle. J Mol Endocrinol. 2004;32(2):519–31.

    CAS  PubMed  Google Scholar 

  134. Murata M, Kaji H, Iida K, Okimura Y, Chihara K. Dual action of eicosapentaenoic acid in hepatoma cells: up-regulation of metabolic action of insulin and inhibition of cell proliferation. J Biol Chem. 2001;276(33):31422–8.

    CAS  PubMed  Google Scholar 

  135. Moon JH, Lee JY, Kang SB, et al. Dietary monounsaturated fatty acids but not saturated fatty acids preserve the insulin signaling pathway via IRS-1/PI3K in rat skeletal muscle. Lipids. 2010;45(12):1109–16.

    CAS  PubMed  Google Scholar 

  136. Lennon R, Pons D, Sabin MA, et al. Saturated fatty acids induce insulin resistance in human podocytes: implications for diabetic nephropathy. Nephrol Dial Transplant. 2009;24(11):3288–96.

    CAS  PubMed  Google Scholar 

  137. Solinas G, Naugler W, Galimi F, Lee MS, Karin M. Saturated fatty acids inhibit induction of insulin gene transcription by JNK-mediated phosphorylation of insulin-receptor substrates. Proc Natl Acad Sci U S A. 2006;103(44):16454–9.

    CAS  PubMed Central  PubMed  Google Scholar 

  138. Ho MM, Yoganathan P, Chu KY, Karunakaran S, Johnson JD, Clee SM. Diabetes genes identified by genome-wide association studies are regulated in mice by nutritional factors in metabolically relevant tissues and by glucose concentrations in islets. BMC Genet. 2013;14:10.

    CAS  PubMed Central  PubMed  Google Scholar 

  139. Yoshimura M, Pearson S, Kadota Y, Gonzalez CE. Identification of ethanol responsive domains of adenylyl cyclase. Alcohol Clin Exp Res. 2006;30(11):1824–32.

    CAS  PubMed  Google Scholar 

  140. Rabbani M, Nelson EJ, Hoffman PL, Tabakoff B. Role of protein kinase C in ethanol-induced activation of adenylyl cyclase. Alcohol Clin Exp Res. 1999;23(1):77–86.

    CAS  PubMed  Google Scholar 

  141. Socha P, Grote V, Gruszfeld D, et al. Milk protein intake, the metabolic-endocrine response, and growth in infancy: data from a randomized clinical trial. Am J Clin Nutr. 2011;94(6 Suppl):1776S–84S.

    CAS  PubMed  Google Scholar 

  142. Dreja T, Jovanovic Z, Rasche A, et al. Diet-induced gene expression of isolated pancreatic islets from a polygenic mouse model of the metabolic syndrome. Diabetologia. 2010;53(2):309–20.

    CAS  PubMed Central  PubMed  Google Scholar 

  143. Pendse J, Ramachandran PV, Na J, et al. A Drosophila functional evaluation of candidates from human genome-wide association studies of type 2 diabetes and related metabolic traits identifies tissue-specific roles for dHHEX. BMC Genomics. 2013;14:136.

    CAS  PubMed Central  PubMed  Google Scholar 

  144. Nojima K, Sugimoto K, Ueda H, Babaya N, Ikegami H, Rakugi H. Analysis of hepatic gene expression profile in a spontaneous mouse model of type 2 diabetes under a high sucrose diet. Endocr J. 2013;60(3):261–74.

    CAS  PubMed  Google Scholar 

  145. Lu P, Bar-Yoseph F, Levi L, et al. High beta-palmitate fat controls the intestinal inflammatory response and limits intestinal damage in mucin Muc2 deficient mice. PLoS ONE. 2013;8(6):e65878.

    CAS  PubMed Central  PubMed  Google Scholar 

  146. Kadegowda AK, Khan MJ, Piperova LS, et al. Trans-10, cis 12-conjugated linoleic acid-induced milk fat depression is associated with inhibition of PPARgamma signaling and inflammation in murine mammary tissue. J Lipids. 2013;2013:890343.

    PubMed Central  PubMed  Google Scholar 

  147. Ables GP, Perrone CE, Orentreich D, Orentreich N. Methionine-restricted C57BL/6J mice are resistant to diet-induced obesity and insulin resistance but have low bone density. PLoS ONE. 2012;7(12):e51357.

  148. Jung CH, Ahn J, Jeon TI, Kim TW, Ha TY. Syzygium aromaticum ethanol extract reduces high-fat diet-induced obesity in mice through downregulation of adipogenic and lipogenic gene expression. Exp Ther Med. 2012;4(3):409–14.

    PubMed Central  PubMed  Google Scholar 

  149. Jung CH, Cho I, Ahn J, Jeon TI, Ha TY. Quercetin reduces high-fat diet-induced fat accumulation in the liver by regulating lipid metabolism genes. Phytother Res. 2013;27(1):139–43.

    CAS  PubMed  Google Scholar 

  150. Matsuo T, Nakata Y, Katayama Y, et al. PPARG genotype accounts for part of individual variation in body weight reduction in response to calorie restriction. Obesity (Silver Spring). 2009;17(10):1924–31.

    CAS  Google Scholar 

  151. Bitto A, Altavilla D, Bonaiuto A, et al. Effects of aglycone genistein in a rat experimental model of postmenopausal metabolic syndrome. J Endocrinol. 2009;200(3):367–76.

    CAS  PubMed  Google Scholar 

  152. AlSaleh A, Sanders TA, O’Dell SD. Effect of interaction between PPARG, PPARA and ADIPOQ gene variants and dietary fatty acids on plasma lipid profile and adiponectin concentration in a large intervention study. Proc Nutr Soc. 2012;71(1):141–53.

    CAS  PubMed  Google Scholar 

  153. Ruiz-Narvaez EA, Kraft P, Campos H. Ala12 variant of the peroxisome proliferator-activated receptor-gamma gene (PPARG) is associated with higher polyunsaturated fat in adipose tissue and attenuates the protective effect of polyunsaturated fat intake on the risk of myocardial infarction. Am J Clin Nutr. 2007;86(4):1238–42.

    PubMed  Google Scholar 

  154. Jiang DF, Li WT, Yang HL, Zhang ZZ, Chen D, Sun C. Long-term effects of evodiamine on expressions of lipogenesis and lipolysis genes in mouse adipose and liver tissues. Genet Mol Res. 2014;13(1):1038–46.

    CAS  PubMed  Google Scholar 

  155. Hiller B, Angulo J, Olivera M, Nuernberg G, Nuernberg K. How selected tissues of lactating holstein cows respond to dietary polyunsaturated fatty acid supplementation. Lipids. 2013;48(4):357–67.

    CAS  PubMed  Google Scholar 

  156. Takeda E, Arai H, Muto K, et al. Gene expression in low glycemic index diet - impact on metabolic control. Forum Nutr. 2007;60:127–39.

    CAS  PubMed  Google Scholar 

  157. Yin L, Unger EL, Jellen LC, et al. Systems genetic analysis of multivariate response to iron deficiency in mice. Am J Physiol. 2012;302(11):R1282–96.

    CAS  Google Scholar 

  158. Gong M, Garige M, Hirsch K, Lakshman MR. Liver Galbeta1,4GlcNAc alpha2,6-sialyltransferase is down-regulated in human alcoholics: possible cause for the appearance of asialoconjugates. Metabolism. 2007;56(9):1241–7.

    CAS  PubMed Central  PubMed  Google Scholar 

  159. Garige M, Gong M, Rao MN, Zhang Y, Lakshman MR. Mechanism of action of ethanol in the down-regulation of Gal(beta)1, 4GlcNAc alpha2,6-sialyltransferase messenger RNA in human liver cell lines. Metabolism. 2005;54(6):729–34.

    CAS  PubMed  Google Scholar 

  160. Sakurai T, Kitadate K, Nishioka H, et al. Oligomerised lychee fruit-derived polyphenol attenuates cognitive impairment in senescence-accelerated mice and endoplasmic reticulum stress in neuronal cells. Br J Nutr. 2013;110(9):1549–58.

    CAS  PubMed  Google Scholar 

  161. Han H, Hu J, Lau MY, Feng M, Petrovic LM, Ji C. Altered methylation and expression of ER-associated degradation factors in long-term alcohol and constitutive ER stress-induced murine hepatic tumors. Front Genet. 2013;4:224.

    PubMed Central  PubMed  Google Scholar 

  162. Okamura T, Yanobu-Takanashi R, Takeuchi F, et al. Deletion of CDKAL1 affects high-fat diet-induced fat accumulation and glucose-stimulated insulin secretion in mice, indicating relevance to diabetes. PLoS ONE. 2012;7(11):e49055.

    CAS  PubMed Central  PubMed  Google Scholar 

  163. Lin W, Burks CA, Hansen DR, Kinnamon SC, Gilbertson TA. Taste receptor cells express pH-sensitive leak K+ channels. J Neurophysiol. 2004;92(5):2909–19.

    CAS  PubMed  Google Scholar 

  164. Muller YL, Piaggi P, Hoffman D, et al. Common genetic variation in the glucokinase gene (GCK) is associated with type 2 diabetes and rates of carbohydrate oxidation and energy expenditure. Diabetologia. 2014;57(7):1382–90.

    CAS  PubMed Central  PubMed  Google Scholar 

  165. Klupa T, Solecka I, Nowak N, et al. The influence of dietary carbohydrate content on glycaemia in patients with glucokinase maturity-onset diabetes of the young. J Int Med Res. 2011;39(6):2296–301.

    CAS  PubMed  Google Scholar 

  166. Bouchard-Mercier A, Rudkowska I, Lemieux S, Couture P, Vohl MC. An interaction effect between glucokinase gene variation and carbohydrate intakes modulates the plasma triglyceride response to a fish oil supplementation. Genes Nutr. 2014;9(3):395.

    PubMed Central  PubMed  Google Scholar 

  167. Zhang Y, Li R, Li Y, Chen W, Zhao S, Chen G. Vitamin A status affects obesity development and hepatic expression of key genes for fuel metabolism in Zucker fatty rats. Biochem Cell Biol. 2012;90(4):548–57.

    CAS  PubMed  Google Scholar 

  168. Sakamoto E, Seino Y, Fukami A, et al. Ingestion of a moderate high-sucrose diet results in glucose intolerance with reduced liver glucokinase activity and impaired glucagon-like peptide-1 secretion. J Diabetes Investig. 2012;3(5):432–40.

    CAS  PubMed Central  PubMed  Google Scholar 

  169. Kim JY, Song EH, Lee HJ, et al. Chronic ethanol consumption-induced pancreatic {beta}-cell dysfunction and apoptosis through glucokinase nitration and its down-regulation. J Biol Chem. 2010;285(48):37251–62.

    CAS  PubMed Central  PubMed  Google Scholar 

  170. Chen G, Zhang Y, Lu D, Li NQ, Ross AC. Retinoids synergize with insulin to induce hepatic Gck expression. Biochem J. 2009;419(3):645–53.

    CAS  PubMed Central  PubMed  Google Scholar 

  171. Jang WY, Bae KB, Kim SH, et al. Overexpression of Jazf1 reduces body weight gain and regulates lipid metabolism in high fat diet. Biochem Biophys Res Commun. 2014;444(3):296–301.

    PubMed  Google Scholar 

  172. Collins JF, Hu Z. Promoter analysis of intestinal genes induced during iron-deprivation reveals enrichment of conserved SP1-like binding sites. BMC Genomics. 2007;8:420.

    PubMed Central  PubMed  Google Scholar 

  173. Rank G, Sutton R, Marshall V, et al. Novel roles for erythroid Ankyrin-1 revealed through an ENU-induced null mouse mutant. Blood. 2009;113(14):3352–62.

    CAS  PubMed Central  PubMed  Google Scholar 

  174. Seo HJ, Kim HC, Klein TA, et al. Molecular detection and genotyping of Japanese encephalitis virus in mosquitoes during a 2010 outbreak in the Republic of Korea. PLoS ONE. 2013;8(2):e55165.

    CAS  PubMed Central  PubMed  Google Scholar 

  175. Wijesekara N, Chimienti F, Wheeler MB. Zinc, a regulator of islet function and glucose homeostasis. Diabetes Obes Metab. 2009;11 Suppl 4:202–14.

    CAS  PubMed  Google Scholar 

  176. Siavoshian S, Blottiere HM, Cherbut C, Galmiche JP. Butyrate stimulates cyclin D and p21 and inhibits cyclin-dependent kinase 2 expression in HT-29 colonic epithelial cells. Biochem Biophys Res Commun. 1997;232(1):169–72.

    CAS  PubMed  Google Scholar 

  177. Wang L, Mear JP, Kuan CY, Colbert MC. Retinoic acid induces CDK inhibitors and growth arrest specific (Gas) genes in neural crest cells. Dev Growth Differ. 2005;47(3):119–30.

    CAS  PubMed  Google Scholar 

  178. Toyokuni S. Mysterious link between iron overload and CDKN2A/2B. J Clin Biochem Nutr. 2011;48(1):46–9.

    CAS  PubMed Central  PubMed  Google Scholar 

  179. Han M, Serrano MC, Lastra-Vicente R, et al. Folate rescues lithium-, homocysteine- and Wnt3A-induced vertebrate cardiac anomalies. Dis Model Mech. 2009;2(9–10):467–78.

    CAS  PubMed Central  PubMed  Google Scholar 

  180. Perlman RK, Rosner MR. Identification of zinc ligands of the insulin-degrading enzyme. J Biol Chem. 1994;269(52):33140–5.

    CAS  PubMed  Google Scholar 

  181. Brandimarti P, Costa-Junior JM, Ferreira SM, et al. Cafeteria diet inhibits insulin clearance by reduced insulin-degrading enzyme expression and mRNA splicing. J Endocrinol. 2013;219(2):173–82.

    CAS  PubMed  Google Scholar 

  182. Du J, Zhang L, Liu S, Wang Z. Palmitic acid and docosahexaenoic acid opposingly regulate the expression of insulin-degrading enzyme in neurons. Die Pharm. 2010;65(3):231–2.

    CAS  Google Scholar 

  183. Bellia F, Grasso G. The role of copper(II) and zinc(II) in the degradation of human and murine IAPP by insulin-degrading enzyme. J Mass Spectrom. 2014;49(4):274–9.

    CAS  PubMed  Google Scholar 

  184. Beildeck ME, Islam M, Shah S, Welsh J, Byers SW. Control of TCF-4 expression by VDR and vitamin D in the mouse mammary gland and colorectal cancer cell lines. PLoS ONE. 2009;4(11):e7872.

    PubMed Central  PubMed  Google Scholar 

  185. Winbo A, Sandstrom O, Palmqvist R, Rydberg A. Iron-deficiency anaemia, gastric hyperplasia, and elevated gastrin levels due to potassium channel dysfunction in the Jervell and Lange-Nielsen Syndrome. Cardiol Young. 2013;23(3):325–34.

    PubMed  Google Scholar 

  186. Piron J, Choveau FS, Amarouch MY, et al. KCNE1-KCNQ1 osmoregulation by interaction of phosphatidylinositol-4,5-bisphosphate with Mg2+ and polyamines. J Physiol. 2010;588(Pt 18):3471–83.

    CAS  PubMed Central  PubMed  Google Scholar 

  187. Iriti M, Varoni EM, Vitalini S. Melatonin in traditional Mediterranean diets. J Pineal Res. 2010;49(2):101–5.

    CAS  PubMed  Google Scholar 

  188. Bediz CS, Baltaci AK, Mogulkoc R. Both zinc deficiency and supplementation affect plasma melatonin levels in rats. Acta Physiol Hung. 2003;90(4):335–9.

    CAS  PubMed  Google Scholar 

  189. Fournier I, Ploye F, Cottet-Emard JM, Brun J, Claustrat B. Folate deficiency alters melatonin secretion in rats. J Nutr. 2002;132(9):2781–4.

    CAS  PubMed  Google Scholar 

  190. Reiter RJ, Manchester LC, Tan DX. Melatonin in walnuts: influence on levels of melatonin and total antioxidant capacity of blood. Nutrition. 2005;21(9):920–4.

    CAS  PubMed  Google Scholar 

  191. Stamateris RE, Sharma RB, Hollern DA, Alonso LC. Adaptive beta-cell proliferation increases early in high-fat feeding in mice, concurrent with metabolic changes, with induction of islet cyclin D2 expression. Am J Physiol Endocrinol Metab. 2013;305(1):E149–59.

    CAS  PubMed Central  PubMed  Google Scholar 

  192. Jaholkowski P, Mierzejewski P, Zatorski P, et al. Increased ethanol intake and preference in cyclin D2 knockout mice. Genes Brain Behav. 2011;10(5):551–6.

    CAS  PubMed  Google Scholar 

  193. Zancai P, Cariati R, Rizzo S, Boiocchi M, Dolcetti R. Retinoic acid-mediated growth arrest of EBV-immortalized B lymphocytes is associated with multiple changes in G1 regulatory proteins: p27Kip1 up-regulation is a relevant early event. Oncogene. 1998;17(14):1827–36.

    CAS  PubMed  Google Scholar 

  194. Li Y, Glozak MA, Smith SM, Rogers MB. The expression and activity of D-type cyclins in F9 embryonal carcinoma cells: modulation of growth by RXR-selective retinoids. Exp Cell Res. 1999;253(2):372–84.

    CAS  PubMed  Google Scholar 

  195. Ma Y, Feng Q, Sekula D, Diehl JA, Freemantle SJ, Dmitrovsky E. Retinoid targeting of different D-type cyclins through distinct chemopreventive mechanisms. Cancer Res. 2005;65(14):6476–83.

    CAS  PubMed  Google Scholar 

  196. Giannini G, Di Marcotullio L, Ristori E, et al. HMGI(Y) and HMGI-C genes are expressed in neuroblastoma cell lines and tumors and affect retinoic acid responsiveness. Cancer Res. 1999;59(10):2484–92.

    CAS  PubMed  Google Scholar 

  197. Song YH, Ray K, Liebhaber SA, Cooke NE. Vitamin D-binding protein gene transcription is regulated by the relative abundance of hepatocyte nuclear factors 1alpha and 1beta. J Biol Chem. 1998;273(43):28408–18.

    CAS  PubMed  Google Scholar 

  198. Manzardo AM, Gunewardena S, Wang K, Butler MG. Exon microarray analysis of human dorsolateral prefrontal cortex in alcoholism. Alcohol Clin Exp Res. 2014;38(6):1594–601.

    CAS  PubMed  Google Scholar 

  199. Kwatra D, Subramaniam D, Ramamoorthy P, et al. Methanolic extracts of bitter melon inhibit colon cancer stem cells by affecting energy homeostasis and autophagy. Evid Based Complement Alternat Med. 2013;2013:702869.

    PubMed Central  PubMed  Google Scholar 

  200. Shah M, Stebbins JL, Dewing A, Qi J, Pellecchia M, Ronai ZA. Inhibition of Siah2 ubiquitin ligase by vitamin K3 (menadione) attenuates hypoxia and MAPK signaling and blocks melanoma tumorigenesis. Pigment Cell Melanoma Res. 2009;22(6):799–808.

    CAS  PubMed Central  PubMed  Google Scholar 

  201. Barbachano A, Ordonez-Moran P, Garcia JM, et al. SPROUTY-2 and E-cadherin regulate reciprocally and dictate colon cancer cell tumourigenicity. Oncogene. 2010;29(34):4800–13.

    CAS  PubMed  Google Scholar 

  202. Park SH, Lee H, Park KK, Kim HW, Park T. Taurine-responsive genes related to signal transduction as identified by cDNA microarray analyses of HepG2 cells. J Med Food. 2006;9(1):33–41.

    CAS  PubMed  Google Scholar 

  203. Johnstone KA, Diakogiannaki E, Dhayal S, Morgan NG, Harries LW. Dysregulation of Hnf1b gene expression in cultured beta-cells in response to cytotoxic fatty acid. JOP. 2011;12(1):6–10.

    PubMed  Google Scholar 

  204. van Angelen AA, San-Cristobal P, Pulskens WP, Hoenderop JG, Bindels RJ. The impact of dietary magnesium restriction on magnesiotropic and calciotropic genes. Nephrol Dial Transplant. 2013;28(12):2983–93.

    PubMed  Google Scholar 

  205. Adalat S, Woolf AS, Johnstone KA, et al. HNF1B mutations associate with hypomagnesemia and renal magnesium wasting. J Am Soc Nephrol. 2009;20(5):1123–31.

    CAS  PubMed Central  PubMed  Google Scholar 

  206. De Miranda J, Panizzutti R, Foltyn VN, Wolosker H. Cofactors of serine racemase that physiologically stimulate the synthesis of the N-methyl-D-aspartate (NMDA) receptor coagonist D-serine. Proc Natl Acad Sci U S A. 2002;99(22):14542–7.

    PubMed Central  PubMed  Google Scholar 

  207. Olney JJ, Navarro M, Thiele TE. Targeting central melanocortin receptors: a promising novel approach for treating alcohol abuse disorders. Front Neurosci. 2014;8:128.

    PubMed Central  PubMed  Google Scholar 

  208. Shelkar GP, Kale AD, Singh U, Singru PS, Subhedar NK, Kokare DM. Alpha-melanocyte stimulating hormone modulates ethanol self-administration in posterior ventral tegmental area through melanocortin-4 receptors. Addict Biol. 2014.

  209. Navarro M, Lerma-Cabrera JM, Carvajal F, Lowery EG, Cubero I, Thiele TE. Assessment of voluntary ethanol consumption and the effects of a melanocortin (MC) receptor agonist on ethanol intake in mutant C57BL/6J mice lacking the MC-4 receptor. Alcohol Clin Exp Res. 2011;35(6):1058–66.

    CAS  PubMed Central  PubMed  Google Scholar 

  210. Panaro BL, Cone RD. Melanocortin-4 receptor mutations paradoxically reduce preference for palatable foods. Proc Natl Acad Sci U S A. 2013;110(17):7050–5.

    CAS  PubMed Central  PubMed  Google Scholar 

  211. Schwinkendorf DR, Tsatsos NG, Gosnell BA, Mashek DG. Effects of central administration of distinct fatty acids on hypothalamic neuropeptide expression and energy metabolism. Int J Obes (Lond). 2011;35(3):336–44.

    CAS  Google Scholar 

  212. Naitoh R, Miyawaki K, Harada N, et al. Inhibition of GIP signaling modulates adiponectin levels under high-fat diet in mice. Biochem Biophys Res Commun. 2008;376(1):21–5.

    CAS  PubMed  Google Scholar 

  213. Li Q, Zhong W, Qiu Y, et al. Preservation of hepatocyte nuclear factor-4alpha contributes to the beneficial effect of dietary medium chain triglyceride on alcohol-induced hepatic lipid dyshomeostasis in rats. Alcohol Clin Exp Res. 2013;37(4):587–98.

    CAS  PubMed Central  PubMed  Google Scholar 

  214. Kang X, Zhong W, Liu J, et al. Zinc supplementation reverses alcohol-induced steatosis in mice through reactivating hepatocyte nuclear factor-4alpha and peroxisome proliferator-activated receptor-alpha. Hepatology. 2009;50(4):1241–50.

    CAS  PubMed Central  PubMed  Google Scholar 

  215. Yamaguchi N, Miyamoto S, Ogura Y, Goda T, Suruga K. Hepatocyte nuclear factor-4alpha regulates human cellular retinol-binding protein type II gene expression in intestinal cells. Am J Physiol Gastrointest Liver Physiol. 2009;296(3):G524–33.

    CAS  PubMed  Google Scholar 

  216. Rajas F, Gautier A, Bady I, Montano S, Mithieux G. Polyunsaturated fatty acyl coenzyme A suppress the glucose-6-phosphatase promoter activity by modulating the DNA binding of hepatocyte nuclear factor 4 alpha. J Biol Chem. 2002;277(18):15736–44.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgments

Thanks to Pablo Larrea for assistance with extracting studies and relevant details referenced in this review.

Funding source

Marilyn C. Cornelis is supported, in part, by the American Diabetes Association Grant 7-13-JF-15.

Compliance with Ethics Guidelines

Conflict of Interest

Marilyn C. Cornelis declares that she has no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marilyn C. Cornelis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cornelis, M.C. Gene-Diet Interactions in Type 2 Diabetes. Curr Nutr Rep 3, 302–323 (2014). https://doi.org/10.1007/s13668-014-0095-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13668-014-0095-1

Keywords

Navigation