Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Epidemiology and Population Health

Longitudinal association of a body mass index (BMI) genetic risk score with growth and BMI changes across the life course: The Cardiovascular Risk in Young Finns Study

Abstract

Background

The role of genetic risk scores associated with adult body mass index (BMI) on BMI levels across the life course is unclear. We examined if a 97 single nucleotide polymorphism weighted genetic risk score (wGRS97) associated with age-related progression in BMI at different life stages and distinct developmental trajectories of BMI across the early life course.

Methods

2188 Cardiovascular Risk in Young Finns Study participants born pre-1980 who had genotype data and objective measurements of height and weight collected up to 8 times from age 6 to 49 years. Associations were examined using Individual Growth Curve analysis, Latent Class Growth Mixture Modelling, and Poisson modified regression.

Results

The wGRS97 associated with BMI from age 6 years with peak effect sizes observed at age 30 years (females: 1.14 kg/m2; males: 1.09 kg/m2 higher BMI per standard deviation increase in wGRS97). The association between wGRS97 and BMI became stronger with age in childhood but slowed in adolescence, especially in females, and weakened at age 35–40 years. A higher wGRS97 associated with an increased BMI velocity in childhood and adulthood, but not with BMI change in adulthood. Compared with belonging to a ‘normal stable’ life-course trajectory group (normal BMI from childhood to adulthood), a one standard deviation higher wGRS97 associated with a 13–127% increased risk of belonging to a less favourable life-course BMI trajectory group.

Conclusions

Individuals with genetic susceptibility to higher adult BMI have higher levels and accelerated rates of increase in BMI in childhood/adolescence, and are at increased risk of having a less favourable life-course BMI trajectory.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Sex-specific marginal effects and 95% confidence intervals (shaded) of the wGRS97 on BMI levels as a function of age.
Fig. 2: Smoothed averaged BMI trajectory in the ‘High genetic risk group’ (75th wGRS97 percentile) and ‘Low genetic risk’ group (25th wGRS97 percentile), with adult overweight and obese cut-offs.
Fig. 3: Predicted probabilities of belonging to each identified life-course BMI trajectory as a function of the standardised deviation genetic risk score (wGRS97z).

Similar content being viewed by others

References

  1. Ludwig DS. Childhood obesity-the shape of things to come. N Engl J Med. 2007;357:2325–7.

    Article  CAS  Google Scholar 

  2. Whitaker RC, Wright JA, Pepe MS, Seidel KD, Dietz WH. Predicting obesity in young adulthood from childhood and parental obesity. N Engl J Med. 1997;337:869–73.

    Article  CAS  Google Scholar 

  3. Juhola J, Magnussen CG, Viikari JS, Kahonen M, Hutri-Kahonen N, Jula A, et al. Tracking of serum lipid levels, blood pressure, and body mass index from childhood to adulthood: the Cardiovascular Risk in Young Finns Study. J Pediatr. 2011;159:584–90.

    Article  CAS  Google Scholar 

  4. Buscot M-J, Thomson RJ, Juonala M, Sabin MA, Burgner DP, Lehtimäki T, et al. Distinct child-to-adult body mass index trajectories are associated with different levels of adult cardiometabolic risk. Eur Heart J. 2018;39:2263–70.

    Article  Google Scholar 

  5. Buscot MJ, Thomson RJ, Juonala M, Sabin MA, Burgner DP, Lehtimaki T, et al. BMI trajectories associated with resolution of elevated youth BMI and incident adult obesity. Pediatrics. 2018;141.

  6. Rokholm B, Silventoinen K, Tynelius P, Gamborg M, Sorensen TI, Rasmussen F. Increasing genetic variance of body mass index during the Swedish obesity epidemic. PLoS ONE. 2011;6:e27135.

    Article  CAS  Google Scholar 

  7. Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24.

    Article  CAS  Google Scholar 

  8. 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;518:197–206.

    Article  CAS  Google Scholar 

  9. Munthali RJ, Sahibdeen V, Kagura J, Hendry LM, Norris SA, Ong KK, et al. Genetic risk score for adult body mass index associations with childhood and adolescent weight gain in an African population. Genes Nutr. 2018;13:24.

    Article  Google Scholar 

  10. Graff M, Ngwa JS, 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. 2013;22:3597–607.

    Article  CAS  Google Scholar 

  11. Graff M, North KE, Mohlke KL, Lange LA, Luo J, Harris KM, et al. Estimation of genetic effects on BMI during adolescence in an ethnically diverse cohort: The National Longitudinal Study of Adolescent Health. Nutr Diabetes. 2012;2:e47.

    Article  CAS  Google Scholar 

  12. Graff M, North KE, Richardson AS, Young KL, Mazul AL, Highland HM, et al. BMI loci and longitudinal BMI from adolescence to young adulthood in an ethnically diverse cohort. Int J Obes. 2017;41:759–68.

    Article  CAS  Google Scholar 

  13. Warrington NM, Howe LD, Paternoster L, Kaakinen M, Herrala S, Huikari V, et al. A genome-wide association study of body mass index across early life and childhood. Int J Epidemiol. 2015;44:700–12.

    Article  Google Scholar 

  14. Day FR, Loos RJ. Developments in obesity genetics in the era of genome-wide association studies. J Nutrigenet Nutrigenom. 2011;4:222–38.

    Article  Google Scholar 

  15. Song M, Zheng Y, Qi L, Hu FB, Chan AT, Giovannucci EL. Longitudinal analysis of genetic susceptibility and bmi throughout adult life. Diabetes. 2018;67:248–55.

    Article  CAS  Google Scholar 

  16. Dietz WH. Critical periods in childhood for the development of obesity. Am J Clin Nutr. 1994;59:955–9.

    Article  CAS  Google Scholar 

  17. Dietz WH. Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics. 1998;101:518–25.

    CAS  PubMed  Google Scholar 

  18. Hardy R, Wills AK, Wong A, Elks CE, Wareham NJ, Loos RJ, et al. Life course variations in the associations between FTO and MC4R gene variants and body size. Hum Mol Genet. 2010;19:545–52.

    Article  CAS  Google Scholar 

  19. Raitakari OT, Juonala M, Ronnemaa T, Keltikangas-Jarvinen L, Rasanen L, Pietikainen M. Cohort profile: the cardiovascular risk in Young Finns Study. Int J Epidemiol. 2008;37:1220–6.

    Article  Google Scholar 

  20. Teo YY, Inouye M, Small KS, Gwilliam R, Deloukas P, Kwiatkowski DP, et al. A genotype calling algorithm for the Illumina BeadArray platform. Bioinformatics. 2007;23:2741–6.

    Article  CAS  Google Scholar 

  21. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.

    Article  Google Scholar 

  22. Consortium TGP. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061–73.

    Article  Google Scholar 

  23. Freedman DS, Lawman HG, Skinner AC, McGuire LC, Allison DB, Ogden CL. Validity of the WHO cutoffs for biologically implausible values of weight, height, and BMI in children and adolescents in NHANES from 1999 through 2012. Am J Clin Nutr. 2015;102:1000–6.

    Article  CAS  Google Scholar 

  24. Shi J, Korsiak J, Roth DE. New approach for the identification of implausible values and outliers in longitudinal childhood anthropometric data. Ann Epidemiol. 2018;28:204–11.e3.

    Article  Google Scholar 

  25. Mirman D. Growth curve analysis and visualization using R. Boca Raton: Chapman and Hall/CRC; 2014.

    Google Scholar 

  26. Mirman D, Dixon JA, Magnuson JS. Statistical and computational models of the visual world paradigm: Growth curves and individual differences. J Mem Lang. 2008;59:475–94.

    Article  Google Scholar 

  27. Singer JD, Willett JB. Applied longitudinal data analysis: modeling change and event occurence. New York: Oxford University Press; 2003.

    Book  Google Scholar 

  28. Team RC. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. http://www.R-project.org/.

  29. Hohenadel MG, Baier LJ, Piaggi P, Muller YL, Hanson RL, Krakoff J, et al. The impact of genetic variants on BMI increase during childhood versus adulthood. Int J Obes. 2016;40:1301–9.

    Article  CAS  Google Scholar 

  30. Fulford AJ, Ong KK, Elks CE, Prentice AM, Hennig BJ. Progressive influence of body mass index-associated genetic markers in rural Gambians. J Med Genet. 2015;52:375–80.

    Article  CAS  Google Scholar 

  31. Warrington NM, Howe LD, Wu YY, Timpson NJ, Tilling K, Pennell CE. et al. Association of a body mass index genetic risk score with growth throughout childhood and adolescence. PLoS ONE. 2013;8:e79547.

    Article  Google Scholar 

  32. Felix JF, Bradfield JP, Monnereau C, van der Valk RJ, Stergiakouli E, Chesi A, et al. Genome-wide association analysis identifies three new susceptibility loci for childhood body mass index. Hum Mol Genet. 2016;25:389–403.

    Article  CAS  Google Scholar 

  33. Seyednasrollah F, Makela J, Pitkanen N, Juonala M, Hutri-Kahonen N, Lehtimaki T, et al. Prediction of adulthood obesity using genetic and childhood clinical risk factors in the Cardiovascular Risk in Young Finns Study. Circ Cardiovasc Genet. 2017;10.

  34. Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4:133–42.

    Article  Google Scholar 

Download references

Acknowledgements

The YFS has been financially supported by the Academy of Finland: grants 286284, 134309 (Eye), 126925, 121584, 124282, 129378 (Salve), 117787 (Gendi), and 41071 (Skidi); the Social Insurance Institution of Finland; Competitive State Research Financing of the Expert Responsibility area of Kuopio, Tampere and Turku University Hospitals (grant X51001); Juho Vainio Foundation; Paavo Nurmi Foundation; Finnish Foundation for Cardiovascular Research; Finnish Cultural Foundation; The Sigrid Juselius Foundation; Tampere Tuberculosis Foundation; Emil Aaltonen Foundation; Yrjö Jahnsson Foundation; Signe and Ane Gyllenberg Foundation; Diabetes Research Foundation of Finnish Diabetes Association; and EU Horizon 2020 (grant 755320 for TAXINOMISIS); and European Research Council (grant 742927 for MULTIEPIGEN project); Tampere University Hospital Supporting Foundation. CGM is supported by National Heart Foundation of Australia Future Leader Fellowship (100849).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marie-Jeanne Buscot.

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

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buscot, MJ., Wu, F., Juonala, M. et al. Longitudinal association of a body mass index (BMI) genetic risk score with growth and BMI changes across the life course: The Cardiovascular Risk in Young Finns Study. Int J Obes 44, 1733–1742 (2020). https://doi.org/10.1038/s41366-020-0611-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-020-0611-x

Search

Quick links