Gene–environment interactions in obesity: implication for future applications in preventive medicine

Article metrics


Obesity is associated with environmental factors; however, information about gene–environment interactions is lacking. We aimed to elucidate the effects of gene–environment interactions on obesity, specifically between genetic predisposition and various obesity-related lifestyle factors, using data from a population-based prospective cohort study. The genetic risk score (GRS) calculated from East Asian ancestry single-nucleotide polymorphisms was significantly associated with the body mass index (BMI) at baseline (P<0.001). Significant gene–environment interactions were observed for six nutritional factors, alcohol intake, metabolic equivalents-hour per day and the homeostasis model assessment ratio. The GRS altered the effects of lifestyle factors on BMI. Increases in the BMI at baseline per unit intake for each nutritional factor differed depending on the GRS. However, we did not observe significant correlations between the GRS and annual changes in BMI during the follow-up period. This study suggests that the effects of lifestyle factors on obesity differ depending on the genetic risk factors. The approach used to evaluate gene–environment interaction in this study may be applicable to the practice of preventive medicine.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1
Figure 2


  1. 1

    World Health Organization: Fact Sheet No. 311 Obesity and overweight (2015) Accessed 25 July 2015.

  2. 2

    Ng, M., Fleming, T., Robinson, M., Thomson, B., Graetz, N., Margono, C. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781 (2014).

  3. 3

    Taylor, A. E., Ebrahim, S., Ben-Shlomo, Y., Martin, R. M., Whincup, P. H., Yarnell, J. W. et al. Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: a study using data from 4 UK cohorts. Am. J. Clin. Nutr. 91, 547–556 (2010).

  4. 4

    Berrington de Gonzalez, A., Hartge, P., Cerhan, J. R., Flint, A. J., Hannan, L., Maclnnis, R. J. et al. Body-mass index and mortality among 1.46 million white adults. N. Engl. J. Med. 363, 2211–2219 (2010).

  5. 5

    Zheng, W., McLerran, D. F., Rolland, B., Zhang, X., Inoue, M., Matsuo, K. et al. Association between body-mass index and risk of death in more than 1 million Asians. N. Engl. J. Med. 364, 719–729 (2011).

  6. 6

    Tsai, A. G., Williamson, D. F. & Glick, H. A. Direct medical cost of overweight and obesity in the USA: a quantitative systematic review. Obes. Rev. 12, 50–61 (2011).

  7. 7

    Kopelman, P. G. Obesity as a medical problem. Nature 404, 635–643 (2000).

  8. 8

    Qi, L. & Cho, Y. A. Gene-environment interaction and obesity. Nutr. Rev. 66, 684–694 (2008).

  9. 9

    Speliotes, E. K., Willer, C. J., Berndt, S. I., Monda, K. L., Thorleifsson, G., Jackson, A. U. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

  10. 10

    Qi, Q., Chu, A. Y., Kang, J. H., Jenson, M. L., Curhan, G. C., Pasquale, L. R. et al. Sugar-sweetened beverages and genetic risk of obesity. N. Engl. J. Med. 367, 1387–1396 (2012).

  11. 11

    Barrio-Lopez, M. T., Martinez-Gonzalez, M. A., Fernandez-Montero, A., Buenza, J. J., Zazpe, I. & Bes-Rastrollo, M. Prospective study of changes in sugar-sweetened beverage consumption and the incidence of the metabolic syndrome and its components: the SUN cohort. Br. J. Nutr. 110, 1722–1731 (2013).

  12. 12

    Qi, Q. B., Chu, A. Y., Kang, J. H., Huang, J. Y., Rose, L. M., Jensen, M. K. et al. Fried food consumption, genetic risk, and body mass index: gene-diet interaction analysis in three US cohort studies. BMJ 348, g1610 (2014).

  13. 13

    Rukh, G., Sonestedt, E., Melander, O., Hedblad, B., Wirfalt, E., Ericson, U. et al. Genetic susceptibility to obesity and diet intakes: association and interaction analyses in the Malmö Diet and Cancer Study. Genes Nutr. 8, 535–547 (2013).

  14. 14

    Qi, Q., Li, Y., Chomistek, A. K., Kang, J. H., Curhan, G. C., Pasquale, L. R. et al. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation 126, 1821–1827 (2012).

  15. 15

    Li, S., Zhao, J. H., Luan, J., Eklund, U., Luben, R. N., Khaw, K. T. et al. Physical activity attenuates the genetic predisposition to obesity in 20,000 men and women from EPIC-Norfolk prospective population study. PLoS Med. 7, e1000332 (2010).

  16. 16

    Kilpelainen, T. O., Qi, L., Brage, S., Sharp, S. J., Sonestedt, E., Demerath, E. 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. 8, e1001116 (2011).

  17. 17

    Manolio, T. A., Bailey-Wilson, J. E. & Collins, F. S. Genes, environment and the value of prospective cohort studies. Nat. Rev. Genet. 7, 812–820 (2006).

  18. 18

    Karasawa, S., Daimon, M., Sasaki, S., Toriyama, S., Oizumi, T., Susa, S. et al. Association of the common fat mass and obesity associated (FTO) gene polymorphism with obesity in a Japanese population. Endocr. J. 57, 293–301 (2010).

  19. 19

    Kohno, K., Narimatsu, H., Shiono, Y., Suzuki, I., Kato, Y., Fukao, A. et al. Management of erythropoiesis: cross-sectional study of the relationships between erythropoiesis and nutrition, physical features, and adiponectin in 3519 Japanese people. Eur. J. Haematol. 92, 298–307 (2014).

  20. 20

    Konta, T., Hao, Z., Abiko, H., Ishikawa, M., Takahashi, T., Ikeda, A. et al. Prevalence and risk factor analysis of microalbuminuria in Japanese general population: the Takahata study. Kidney Int. 70, 751–756 (2006).

  21. 21

    Lu, Y. & Loos, R. J. Obesity genomics: assessing the transferability of susceptibility loci across diverse populations. Genome Med. 5, 55 (2013).

  22. 22

    WHO Expert Consultation Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 363, 157–163 (2004).

  23. 23

    Okita, K., Iwahashi, H., Kozawa, J., Okauchi, Y., Funahashi, T., Imagawa, A. et al. Homeostasis model assessment of insulin resistance for evaluating insulin sensitivity in patients with type 2 diabetes on insulin therapy. Endocr. J. 60, 283–290 (2013).

  24. 24

    Brinkman, G. L. & Coates, E. O. Jr. The effect of bronchitis, smoking, and occupation on ventilation. Ann. Rev. Respir. Dis. 87, 684–693 (1963).

  25. 25

    Sasaki, S., Yanagibori, R. & Amano, K. Self-administered diet history questionnaire developed for health education: a relative validation of the test-version by comparison with 3-day diet record in women. J. Epidemiol. 8, 203–215 (1998).

  26. 26

    Harada, A., Naito, Y., Inoue, S., Kitabatake, Y., Arao, T. & Ohashi, Y. Validity of a questionnaire for assessment of physical activity in the Japan Arteriosclerosis Longitudinal Study. Med. Sci. Sports Exerc. 35, S340 (2003).

  27. 27

    Liu, E. Y., Li, M. Y., Wang, W. & Li, Y. MaCH-admix: genotype imputation for admixed populations. Genet. Epidemiol. 37, 25–37 (2013).

  28. 28

    Evans, D. M., Visscher, P. M. & Wray, N. R. Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum. Mol. Genet 18, 3525–3531 (2009).

  29. 29

    Wigginton, J. E., Cutler, D. J. & Abecasis, G. R. A note on exact tests of Hardy-Weinberg equilibrium. Am. J. Hum. Genet. 76, 887–893 (2005).

  30. 30

    Wen, W., Cho, Y. S., Zheng, W., Dorajoo, R., Kato, N., Qi, L. et al. Meta-analysis identifies common variants associated with body mass index in east Asians. Nat. Genet. 44, 307–311 (2012).

  31. 31

    Okada, Y., Kubo, M., Ohmiya, H., Takahashi, A., Kumasaka, N., Hosono, N. et al. Common variants at CDKAL1 and KLF9 are associated with body mass index in east Asian populations. Nat. Genet. 44, 302–306 (2012).

  32. 32

    Manolio, T. A. Genomewide association studies and assessment of the risk of disease. N. Engl. J. Med. 363, 166–176 (2010).

  33. 33

    Fall, T. & Ingelsson, E. Genome-wide association studies of obesity and metabolic syndrome. Mol. Cell. Endocrinol. 382, 740–757 (2014).

  34. 34

    Cooper, R. S. Gene-environment interactions and the etiology of common complex disease. Ann. Intern. Med. 139, 437–440 (2003).

  35. 35

    Marti, A., Martinez-Gonzalez, M. A. & Martinez, J. A. Interaction between genes and lifestyle factors on obesity. Proc. Nutr. Soc. 67, 1–8 (2008).

  36. 36

    Howarth, N. C., Saltzman, E. & Roberts, S. B. Dietary fiber and weight regulation. Nutr. Rev. 59, 129–139 (2001).

  37. 37

    Ludwig, D. S., Pereira, M. A., Kroenke, C. H., Hilner, J. E., Van, Horn, L., Slattery, M. L. et al. Dietary fiber, weight gain, and cardiovascular disease risk factors in young adults. JAMA 282, 1539–1546 (1999).

  38. 38

    Slavin, J. L. Dietary fiber and body weight. Nutrition 21, 411–418 (2005).

  39. 39

    Dick, K. J., Nelson, C. P., Tsaprouni, L., Snadling, J. K., Aissi, D., Wahl, S. et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet 383, 1990–1998 (2014).

  40. 40

    Graff, M., Ngwa, J. S., Workalemahu, T., Homuth, G., Schipf, S., Teumer, A. et al. Genome-wide analysis of BMI in adolescents and young adults reveals additional insight into the effects of genetic loci over the life course. Hum. Mol. Genet. 22, 3597–3607 (2013).

  41. 41

    Funatogawa, I., Funatogawa, T., Nakao, M., Karita, K. & Yano, E. Changes in body mass index by birth cohort in Japanese adults: results from the National Nutrition Survey of Japan 1956-2005. Int. J. Epidemiol. 38, 83–92 (2009).

  42. 42

    Yamagata University Genomic Cohort Consortium & Narimatsu, H. Constructing a contemporary gene-environmental cohort: study design of the Yamagata Molecular Epidemiological Cohort Study. J. Hum. Genet. 58, 54–56 (2013).

  43. 43

    Tohoku University Tohoku Medical Megabank Organization. (2015). Accessed 9 September 2015.

  44. 44

    Hamajima, N. . Japan Multi-institutional Collaborative Cohort Study Group The Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study) to detect gene-environment interactions for cancer. Asian Pac. J. Cancer Prev. 8, 317–323 (2007).

  45. 45

    Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindroff, L. A., Hunter, D. J. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).

  46. 46

    Booth, K. M., Pinkston, M. M. & Poston, W. S. C. Obesity and the built environment. J. Am. Diet Assoc. 105, S110–S117 (2005).

Download references


This work was supported by KAKENHI (Grant-in-Aid for Challenging Exploratory Research, grant no.: 25560363) to HN. We would like to thank Editage ( for English language editing.

Author information

Correspondence to Hiroto Narimatsu.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on Journal of Human Genetics website

Supplementary information

Supplementary Figure S1 (JPG 266 kb)

Supplementary Table S1 (DOC 88 kb)

Supplementary Table S2 (XLS 30 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Further reading