J Korean Diabetes.  2016 Dec;17(4):266-270. 10.4093/jkd.2016.17.4.266.

Application of Nutrigenomics in Diabetes

• Paik JK

• KMID: 2362985
• DOI: http://doi.org/10.4093/jkd.2016.17.4.266

Abstract

Diabetes mellitus (DM) is considered a global pandemic and its incidence continues to grow worldwide. The most common treatments for controlling diabetes focus on glucose control as a means to reduce long-term complications. Major changes in diet have taken place over the past 10,000 years since the beginning of the Agricultural Revolution: however, human genes have not changed. We now live in a nutritional environment that differs from that for which our genetic constitution was selected. Nutrients and dietary patterns are central issues in the prevention, development and treatment of DM. Nutritional genomics studies generally focus on dietary patterns according to genetic variations, the role of gene-nutrient interactions, gene-diet-phenotype interactions and epigenetic modifications caused by nutrients; these studies facilitate an understanding of the early molecular events that occur in DM and contribute to the identification of better biomarkers and diagnostic tools for the disease. In particular, this approach will help develop tailored diets that maximize the use of nutrients and other functional ingredients present in food, which will aid in the prevention and delay of DM and its complications. Here, we provide an understanding of the role of gene variants and nutrient interactions, and discuss the importance of nutrients and dietary patterns on gene expression.

Keyword

Diabetes mellitus; Nutrigenomics; Phenotype; Single nucleotide polymorphism

MeSH Terms

Biomarkers
Constitution and Bylaws
Diabetes Mellitus
Diet
Epigenomics
Gene Expression
Genetic Variation
Glucose
Humans
Incidence
Nutrigenomics*
Pandemics
Phenotype
Polymorphism, Single Nucleotide
Biomarkers
Glucose

Reference

References

1. Simopoulos AP. Genetic variation and dietary response: Nutrigenetics/Nutrigenomics. Asia Pacific J Clin Nutr. 2002; 11:S117–28.

2. van Ommen B, Stierum R. Nutrigenomics: exploiting systems biology in the nutrition and health arena. Curr Opin Biotechnol. 2002; 13:517–21.

3. Elliott R, Ong TJ. Nutritional genomics. BMJ. 2002; 324:1438–42.

4. Chávez A, Muñoz de Chávez M. Nutrigenomics in public health nutrition: short-term perspectives. Eur J Clin Nutr. 2003; 57(Suppl 1):S97–100.

5. Kritchevsky D. Diet and cancer: what's next? J Nutr. 2003; 133(11 Suppl 1):3827S–9S.

6. Ordovas JM, Corella D. Nutritional genomics. Annu Rev Genomics Hum Genet. 2004; 5:71–118.

7. Müller M, Kersten S. Nutrigenomics: goals and strategies. Nat Rev Genet. 2003; 4:315–22.

8. DeBusk R. Diet-related disease, nutritional genomics, and food and nutrition professionals. J Am Diet Assoc. 2009; 109:410–3.

9. Diamond J. The double puzzle of diabetes. Nature. 2003; 423:599–602.

10. 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 metaanalysis. Am J Epidemiol. 2010; 171:645–55.

11. Lazar MA. PPAR gamma, 10 years later. Biochimie. 2005; 87:9–13.

12. Lamri A, Abi Khalil C, Jaziri R, Velho G, Lantieri O, Vol S, Froguel P, Balkau B, Marre M, Fumeron F. 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; 36:218–24.

13. Luan J, Browne PO, Harding AH, Halsall DJ, O'Rahilly S, Chatterjee VK, Wareham NJ. Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes. 2001; 50:686–9.

14. Ruchat SM, Elks CE, Loos RJ, Vohl MC, Weisnagel SJ, Rankinen T, Bouchard C, Pérusse L. Evidence of interaction between type 2 diabetes susceptibility genes and dietary fat intake for adiposity and glucose homeostasis-related phenotypes. J Nutrigenet Nutrigenomics. 2009; 2:225–34.

15. 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; 89:1256–62.

16. Ortega-Azorín C, Sorlí JV, Asensio EM, Coltell O, Martínez-González MÁ, Salas-Salvadó J, Covas MI, Arós F, Lapetra J, Serra-Majem L, Gómez-Gracia E, Fiol M, Sáez-Tormo G, Pintó X, Muñoz MA, Ros E, Ordovás JM, Estruch R, Corella D. 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.

17. Steemburgo T, Azevedo MJ, Gross JL, Milagro FI, Campión J, Martínez JA. The rs9939609 polymorphism in the FTO gene is associated with fat and fiber intakes in patients with type 2 diabetes. J Nutrigenet Nutrigenomics. 2013; 6:97–106.

18. Kang R, Kim M, Chae JS, Lee SH, Lee JH. Consumption of whole grains and legumes modulates the genetic effect of the APOA5–1131C variant on changes in triglyceride and apolipoprotein A-V concentrations in patients with impaired fasting glucose or newly diagnosed type 2 diabetes. Trials. 2014; 15:100.

19. Kim M, Chae JS, Kim M, Lee SH, Lee JH. Effects of a 3-year dietary intervention on age-related changes in triglyceride and apolipoprotein A-V levels in patients with impaired fasting glucose or new-onset type 2 diabetes as a function of the APOA5–1131 T > C polymorphism. Nutr J. 2014; 13:40.