Epidemiology and Population Health

Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study

Abstract

Objective

To investigate whether the association between a genetic profile risk score for obesity (GPRS-obesity) (based on 93 SNPs) and body mass index (BMI) was modified by physical activity (PA), cardiorespiratory fitness, commuting mode, walking pace and sedentary behaviours.

Methods

For the analyses we used cross-sectional baseline data from 310,652 participants in the UK Biobank study. We investigated interaction effects of GPRS-obesity with objectively measured and self-reported PA, cardiorespiratory fitness, commuting mode, walking pace, TV viewing, playing computer games, PC-screen time and total sedentary behaviour on BMI. Body mass index (BMI) was the main outcome measure.

Results

GPRS-obesity was associated with BMI (β:0.54 kg.m−2 per standard deviation (SD) increase in GPRS, [95% CI: 0.53; 0.56]; P = 2.1 × 10−241). There was a significant interaction between GPRS-obesity and objectively measured PA (P[interaction] = 3.3 × 10−11): among inactive individuals, BMI was higher by 0.58 kg.m−2 per SD increase in GPRS-obesity (p = 1.3 × 10−70) whereas among active individuals the relevant BMI difference was less (β:0.33 kg.m−2, p = 6.4 × 10−41). We observed similar patterns for fitness (Unfit β:0.72 versus Fit β:0.36 kg.m−2, P[interaction] = 1.4 × 10−11), walking pace (Slow β:0.91 versus Brisk β:0.38 kg.m−2, P[interaction] = 8.1 × 10−27), discretionary sedentary behaviour (High β:0.64 versus Low β:0.48 kg.m−2, P[interaction] = 9.1 × 10−12), TV viewing (High β:0.62 versus Low β:0.47 kg.m−2, P[interaction] = 1.7 × 10−11), PC-screen time (High β:0.82 versus Low β:0.54 kg.m−2, P[interaction] = 0.0004) and playing computer games (Often β:0.69 versus Low β:0.52 kg.m−2, P[interaction] = 8.9 × 10−10). No significant interactions were found for commuting mode (car, public transport, active commuters).

Conclusions

Physical activity, sedentary behaviours and fitness modify the extent to which a set of the most important known adiposity variants affect BMI. This suggests that the adiposity benefits of high PA and low sedentary behaviour may be particularly important in individuals with high genetic risk for obesity.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

References

  1. 1.

    Bouchard C. Gene-environment interactions in the etiology of obesity: defining the fundamentals. Obesity. 2008;16:S5–S10.

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Huang T, Hu FB. Gene-environment interactions and obesity: recent developments and future directions. BMC Med Genet. 2015;8:530–530.

    Google Scholar 

  3. 3.

    Maes HHM, Neale MC, Eaves LJ. Genetic and environmental factors in relative body weight and human adiposity. Behav Genet. 1997;27:325–51.

    CAS  PubMed  Article  Google Scholar 

  4. 4.

    Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461:747–53.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, et al. Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010;11:446–50.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  6. 6.

    Collaboration NRF. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. The Lancet. 2016;387:1377–96.

    Article  Google Scholar 

  7. 7.

    WHO. Global health risks: mortality and burden of disease attributable to selected major risks. Geneva: World Health Organization; 2009.

  8. 8.

    Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet. 2012;380:2095–128.

    Article  Google Scholar 

  9. 9.

    Hill JO. Understanding and addressing the epidemic of obesity: an energy balance perspective. Endocr Rev. 2006;27:750–61.

    PubMed  Article  Google Scholar 

  10. 10.

    Church TS, Thomas DM, Tudor-Locke C, Katzmarzyk PT, Earnest CP, Rodarte RQ, et al. Trends over 5 decades in US occupation-related physical activity and their associations with obesity. PLoS One. 2011;6:e19657.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Luke A, Cooper RS. Physical activity does not influence obesity risk: time to clarify the public health message. Int J Epidemiol. 2013;42:1831–6.

    PubMed  Article  Google Scholar 

  12. 12.

    Bouchard C, Tremblay A, Després JP, Nadeau A, Lupien PJ, Theriault G, et al. The response to long-term overfeeding in identical-twins. N Engl J Med. 1990;322:1477–82.

    CAS  PubMed  Article  Google Scholar 

  13. 13.

    Mutch DM, Clément K. Unraveling the genetics of human obesity. PLoS Genet. 2006;2:1956–63.

    CAS  Article  Google Scholar 

  14. 14.

    Jou C. The biology and genetics of obesity—a century of inquiries. N Engl J Med. 2014;370:1874–7.

    PubMed  Article  CAS  Google Scholar 

  15. 15.

    Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–U401.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Celis-Morales CA, CFM Marsaux, Livingstone KM, Navas-Carretero S, San-Cristobal R, O’Donovan CB, et al. Physical activity attenuates the effect of the FTO genotype on obesity traits in European adults: the Food4Me study. Obesity (Silver Spring, Md.). 2016;24:962–9.

    Article  Google Scholar 

  17. 17.

    Kilpelaeinen TO, Qi L, Brage S, Sharp SJ, 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. 2011;8:e1001116.

    Article  Google Scholar 

  18. 18.

    Scott RA, Bailey MES, Moran CN, Wilson RH, Fuku N, Tanaka M, et al. FTO genotype and adiposity in children: physical activity levels influence the effect of the risk genotype in adolescent males. Eur J Hum Genet. 2010;18:1339–43.

    PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Richardson AS, North KE, Graff M, Young KL, Mohlke KL, Lange EM et al. The interaction between physical activity and obesity gene variants in association with BMI: Does the obesogenic environment matter? FASEB J. Health Place. 2016;42:159–65.

  20. 20.

    Li S, Zhao J, Luan JA, Luben RN, Rodwell SA, Khaw K-T, et al. Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr. 2010;91:184–90.

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Reddon H, Gerstein HC, Engert JC, Mohan V, Bosch J, Desai D, et al. Physical activity and genetic predisposition to obesity in a multiethnic longitudinal study. Sci Rep. 2016;6:18672–18672.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Qi Q, Li Y, Chomistek AK, Kang JH, Curhan GC, Pasquale LR, et al. Television watching, leisure time physical activity, and the genetic predisposition in relation to body mass index in women and men. Circulation. 2012;126:1821–U70.

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Jessica Tyrrell ARW, Ryan M Ames, Hanieh Yaghootkar, Robin N Beaumont, Samuel E Jones, Marcus A Tuke, et al. Gene–obesogenic environment interactions in the UK Biobank study. Int J Epidemiol. 2017;46:559–75.

  24. 24.

    Bouchard C, Perusse L. Genetics of obesity. Ann Rev Nutr. 1993;13:337–54.

    CAS  Article  Google Scholar 

  25. 25.

    Savva SC, Tornaritis MJ, Kolokotroni O, Chadjigeorgiou C, Kourides Y, Karpathios T, et al. High cardiorespiratory fitness is inversely associated with incidence of overweight in adolescence: a longitudinal study. Scand J Med Sci Sports. 2014;24:982–9.

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Byrd-Williams CE, Shaibi GQ, Sun P, Lane CJ, Ventura EE, Davis JN, et al. Cardiorespiratory fitness predicts changes in adiposity in overweight Hispanic boys. Obesity. 2008;16:1072–7.

    PubMed  Article  Google Scholar 

  27. 27.

    Brien SE, Katzmarzyk PI, Craig CL, Gauvin L. Physical activity, cardiorespiratory fitness and body mass index as predictors of substantial weight gain and obesity: the Canadian physical activity longitudinal study. Can J Public Health. 2007;98:121–4.

    PubMed  Article  Google Scholar 

  28. 28.

    Collins R. What makes UK Biobank special? The Lancet. 2012;379:1173–4.

    Article  Google Scholar 

  29. 29.

    Palmer LJ. UK Biobank: bank on it. Lancet. 2007;369:1980–2.

    PubMed  Article  Google Scholar 

  30. 30.

    Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779.

    PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Guo W, Bradbury KE, Reeves GK, Key TJ. Physical activity in relation to body size and composition in women in UK Biobank. Ann Epidemiol. 2015;25:406–13.

    PubMed  Article  Google Scholar 

  32. 32.

    IPAQ. Guidelines for Data Processing and Analysis of the International Physical Activity Questionnaire (IPAQ)—Short Form,Version 2. 0. In. Version 2 ed: IPAQ, 2004.

  33. 33.

    Celis-Morales CA, Lyall DM, Welsh P, Anderson J, Steell L, Guo Y, et al. Association between active commuting and incident cardiovascular disease, cancer, and mortality: prospective cohort study. Br Med J. 2017;357:j1456.

    Article  Google Scholar 

  34. 34.

    Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA Accelerometer. Med Sci Sports Exercise. 2011;43:1085–93.

    Article  Google Scholar 

  35. 35.

    da Silva ICM, van Hees VT, Ramires VV, Knuth AG, Bielemann RM, Ekelund U, et al. Physical activity levels in three Brazilian birth cohorts as assessed with raw triaxial wrist accelerometry. Int J Epidemiol. 2014;43:1959–68.

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36.

    Celis-Morales C, Lyall DM, Anderson J, Pell JP, Sattar N, Gill J. The association between physical activity and risk of mortality is modulated by grip strength and cardiorespiratory fitness: evidence from 498,135 UK-Biobank participants. Eur Heart J. 2016;38:116–22.

    PubMed Central  PubMed  Google Scholar 

  37. 37.

    Steell L, Ho FK, Sillars A, Petermann-Rocha F, Li H, Lyall DM et al. Dose-response associations of cardiorespiratory fitness with all-cause mortality and incidence and mortality of cancer and cardiovascular and respiratory diseases: the UK Biobank cohort study. Br J Sports Med. 2019. pii: bjsports-2018-099093. https://doi.org/10.1136/bjsports-2018-099093.

  38. 38.

    Galante J, Adamska L, Young A, Young H, Littlejohns TJ, Gallacher J, et al. The acceptability of repeat Internet-based hybrid diet assessment of previous 24-h dietary intake: administration of the Oxford WebQ in UK Biobank. Br J Nutr. 2015;115:681–6.

    PubMed  Article  CAS  Google Scholar 

  39. 39.

    Townsend P, Phillimore M, Beattie A. Health and Deprivation: Inequality and the North, London: Croom Helm Ltd; 1988.

  40. 40.

    WHO. Obesity: preventing and managing the global epidemic. Report of a WHO consultation, World Health Organisation, Geneva, Switzerland. 2000. Report no.: 0512-3054.

  41. 41.

    Purcell S, Neale B, Todd-Brown K, Thomas L, MAR Ferreira, Bender D, et al. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–75.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Breusch Ts, Pagan Ar. A simple test for heteroscedasticity and random coefficient variation. Econometrica. 1979;47:1287–94.

  43. 43.

    Celis-Morales CA, Perez-Bravo F, Ibañez L, Salas C, Bailey ME, Gill JM. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS ONE. 2012;7:e36345.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Li S, Zhao JH, Luan JA, Ekelund U, Luben RN, 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. 2010;7:e1000332.

    PubMed  PubMed Central  Article  Google Scholar 

  45. 45.

    Flint E, Cummins S. Active commuting and obesity in mid-life: cross-sectional, observational evidence from UK Biobank. Lancet Diabetes Endocrinol. 2016;4:420–35.

    PubMed  Article  Google Scholar 

  46. 46.

    Flint E, Webb E, Cummins S. Change in commute mode and body-mass index: prospective, longitudinal evidence from UK Biobank. Lancet Public Health. 2016;1:e46–e55.

    PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    DeFina LF, Haskell WL, Willis BL, Barlow CE, Finley CE, Levine BD, et al. Physical activity versus cardiorespiratory fitness: two (partly) distinct components of cardiovascular health? Prog Cardiovasc Dis. 2015;57:324–9.

    PubMed  Article  Google Scholar 

  48. 48.

    Bouchard C, Rankinen T, Timmons JA. Genomics and genetics in the biology of adaptation to exercise. Compr Physiol. 2011;1:1603–48.

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Williams PT. Physical fitness and activity as separate heart disease risk factors: a meta-analysis. Med Sci Sports Exercise. 2001;33:754–61.

    CAS  Article  Google Scholar 

  50. 50.

    Ganna A, Ingelsson E. 5 year mortality predictors in 498 103 UK Biobank participants: a prospective population-based study. Lancet. 2015;386:533–40.

    PubMed  Article  Google Scholar 

  51. 51.

    van Loon LJC, Goodpaster BH. Increased intramuscular lipid storage in the insulin-resistant and endurance-trained state. Pflugers Arch. 2006;451:606–16.

    CAS  PubMed  Article  Google Scholar 

  52. 52.

    Zurlo F, Lillioja S, Espositodelpuente A, Nyomba BL, Raz I, Saad MF, et al. Low ratio of fat to carbohydrate oxidation as predictor of weight-gain—study of 24-H Rq. Am J Physiol. 1990;259:E650–E657.

    CAS  PubMed  Google Scholar 

  53. 53.

    Seidell JC, Muller DC, Sorkin JD, Andres R. Fasting respiratory exchange ratio and resting metabolic-rate as predictors of weight-gain—the baltimore longitudinal-study on aging. Int J Obesity. 1992;16:667–74.

    CAS  Google Scholar 

  54. 54.

    Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults a systematic review of longitudinal studies, 1996-2011. Am J Prev Med. 2011;41:207–15.

    PubMed  Article  Google Scholar 

  55. 55.

    Ioannidis JPA. Implausible results in human nutrition research. Br Med J 2013;347:f6698.

  56. 56.

    Kipnis V, Midthune D, Freedman L, Bingham S, Day NE, Riboli E, et al. Bias in dietary-report instruments and its implications for nutritional epidemiology. Public Health Nutri. 2002;5(6a):915–−23.

    Article  Google Scholar 

  57. 57.

    Hebert JR, Peterson KE, Hurley TG, Stoddard AM, Cohen N, Field AE, et al. The effect of social desirability trait on self-reported dietary measures among multi-ethnic female health center employees. Ann Epidemiol. 2001;11:417–27.

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, et al. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–96.

    PubMed  Article  Google Scholar 

  59. 59.

    WHO. Obesity and overweight. World Health Organization, Geneva, Switzerland, 2013.

Download references

Acknowledgements

This research has been conducted using the UK Biobank resource. We are grateful to UK Biobank participants.

Funding

The UK Biobank was supported by the Wellcome Trust, Medical Research Council, Department of Health, Scottish Government and the Northwest Regional Development Agency. It has also had funding from the Welsh Assembly Government and the British Heart Foundation. The research was designed, conducted, analysed and interpreted by the authors entirely independently of the funding sources.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Stuart R. Gray.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Celis-Morales, C.A., Lyall, D.M., Petermann, F. et al. Do physical activity, commuting mode, cardiorespiratory fitness and sedentary behaviours modify the genetic predisposition to higher BMI? Findings from a UK Biobank study. Int J Obes 43, 1526–1538 (2019). https://doi.org/10.1038/s41366-019-0381-5

Download citation

Further reading