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:

Pediatrics

Eating behavior trajectories in the first 10 years of life and their relationship with BMI

Abstract

Background

Child eating behaviors are highly heterogeneous and their longitudinal impact on childhood weight is unclear. The objective of this study was to characterize eating behaviors during the first 10 years of life and evaluate associations with BMI at age 11 years.

Method

Data were parental reports of eating behaviors from 15 months to age 10 years (n = 12,048) and standardized body mass index (zBMI) at age 11 years (n = 4884) from the Avon Longitudinal Study of Parents and Children. Latent class growth analysis was used to derive latent classes of over-, under-, and fussy-eating. Linear regression models for zBMI at 11 years on each set of classes were fitted to assess associations with eating behavior trajectories.

Results

We identified four classes of overeating; “low stable” (70%), “low transient” (15%), “late increasing” (11%), and “early increasing” (6%). The “early increasing” class was associated with higher zBMI (boys: β = 0.83, 95% CI: 0.65, 1.02; girls: β = 1.1; 0.92, 1.28) compared with “low stable.” Six classes were found for undereating; “low stable” (25%), “low transient” (37%), “low decreasing” (21%), “high transient” (11%), “high decreasing” (4%), and “high stable” (2%). The latter was associated with lower zBMI (boys: β = −0.79; −1.15, −0.42; girls: β = −0.76; −1.06, −0.45). Six classes were found for fussy eating; “low stable” (23%), “low transient” (15%), “low increasing” (28%), “high decreasing” (14%), “low increasing” (13%), and “high stable” (8%). The “high stable” class was associated with lower zBMI (boys: β = −0.49; −0.68–0.30; girls: β = −0.35; −0.52, −0.18).

Conclusions

Early increasing overeating during childhood is associated with higher zBMI at age 11. High persistent levels of undereating and fussy eating are associated with lower zBMI. Longitudinal trajectories of eating behaviors may help identify children potentially at risk of adverse weight outcomes.

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

Access options

Buy this article

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

Fig. 1: Prevalence of eating behaviors across the eight assessment waves.
Fig. 2: Assigned classes of overeating, undereating, and fussy eating using posterior probabilities.
Fig. 3: Trajectories of parental reports of overeating behaviors from 15 to 116 months for boys and girls (6186 boys and 5817 girls).

Similar content being viewed by others

References

  1. Ashcroft J, Semmler C, Carnell S, van Jaarsveld CHM, Wardle J. Continuity and stability of eating behaviour traits in children. Eur J Clin Nutr. 2008;62:985–90.

    CAS  PubMed  Google Scholar 

  2. Derks IPM, Bolhuis K, Sijbrands EJG, Gaillard R, Hillegers MHJ, Jansen PW. Predictors and patterns of eating behaviors across childhood: results from The Generation R study. Appetite. 2019;141:104295.

    PubMed  Google Scholar 

  3. Farrow C, Blissett J. Stability and continuity of parentally reported child eating behaviours and feeding practices from 2 to 5 years of age. Appetite. 2012;58:151–6.

    CAS  PubMed  Google Scholar 

  4. Emmett PM, Hays NP, Taylor CM. Factors associated with maternal worry about her young child exhibiting choosy feeding behaviour. Int J Environ Res Public Health. 2018;15:1236

    PubMed Central  Google Scholar 

  5. Cardona Cano S, Tiemeier H, Van Hoeken D, Tharner A, Jaddoe VW, Hofman A, et al. Trajectories of picky eating during childhood: a general population study. Int J Eat Disord. 2015;48:570–9.

    PubMed  Google Scholar 

  6. Taylor CM, Steer CD, Hays NP, Emmett PM. Growth and body composition in children who are picky eaters: a longitudinal view. Eur J Clin Nutr. 2018;73:869–78.

    PubMed  PubMed Central  Google Scholar 

  7. Braet C, Claus L, Goossens L, Moens E, Van Vlierberghe L, Soetens B. Differences in eating style between overweight and normal-weight youngsters. J Health Psychol. 2008;13:733–43.

    PubMed  Google Scholar 

  8. Hajna S, LeBlanc PJ, Faught BE, Merchant AT, Cairney J, Hay J, et al. Associations between family eating behaviours and body composition measures in peri-adolescents: results from a community-based study of school-aged children. Can J Public Health. 2014;105:E15–21.

    PubMed  PubMed Central  Google Scholar 

  9. Domoff SE, Miller AL, Kaciroti N, Lumeng JC. Validation of the Children’s Eating Behaviour Questionnaire in a low-income preschool-aged sample in the United States. Appetite. 2015;95:415–20.

    PubMed  PubMed Central  Google Scholar 

  10. Jansen PW, Roza SJ, Jaddoe VW, Mackenbach JD, Raat H, Hofman A, et al. Children’s eating behavior, feeding practices of parents and weight problems in early childhood: results from the population-based Generation R Study. Int J Behav Nutr Phys Act. 2012;9:130.

    PubMed  PubMed Central  Google Scholar 

  11. Webber L, Hill C, Saxton J, Van Jaarsveld CH, Wardle J. Eating behaviour and weight in children. Int J Obes. 2009;33:21–8.

    CAS  Google Scholar 

  12. Sanchez U, Weisstaub G, Santos JL, Corvalan C, Uauy R. GOCS cohort: children's eating behavior scores and BMI. Eur J Clin Nutr. 2016;70:925–8.

    CAS  PubMed  Google Scholar 

  13. Svensson V, Lundborg L, Cao Y, Nowicka P, Marcus C, Sobko T. Obesity related eating behaviour patterns in Swedish preschool children and association with age, gender, relative weight and parental weight-factorial validation of the Children’s Eating Behaviour Questionnaire. Int J Behav Nutr Phys Act. 2011;8:134.

    PubMed  PubMed Central  Google Scholar 

  14. Cao YT, Svensson V, Marcus C, Zhang J, Zhang JD, Sobko T. Eating behaviour patterns in Chinese children aged 12-18 months and association with relative weight-factorial validation of the Children’s Eating Behaviour Questionnaire. Int J Behav Nutr Phys Act. 2012;9:5.

    PubMed  PubMed Central  Google Scholar 

  15. Parkinson KN, Drewett RF, Le Couteur AS, Adamson AJ. TGMSC do maternal ratings of appetite in infants predict later Child Eating Behaviour Questionnaire scores and body mass index? Appetite. 2010;54:186–90.

    PubMed  Google Scholar 

  16. van Jaarsveld CHM, Llewellyn CH, Johnson L, Wardle J. Prospective associations between appetitive traits and weight gain in infancy. Am J Clin Nutr. 2011;94:1562–7.

    PubMed  Google Scholar 

  17. Steinsbekk S, Wichstrom L. Predictors of change in BMI from the age of 4 to 8. J Pediatr Psychol. 2015;40:1056–64.

    PubMed  Google Scholar 

  18. Antoniou EE, Roefs A, Kremers SP, Jansen A, Gubbels JS, Sleddens EF, et al. Picky eating and child weight status development: a longitudinal study. J Hum Nutr Diet. 2016;29:298–307.

    CAS  PubMed  Google Scholar 

  19. de Barse LM, Tiemeier H, Leermakers ET, Voortman T, Jaddoe VW, Edelson LR, et al. Longitudinal association between preschool fussy eating and body composition at 6 years of age: The Generation R Study. Int J Behav Nutr Phys Act. 2015;12:153.

    PubMed  PubMed Central  Google Scholar 

  20. Mallan KM, Fildes A, Magarey AM, Daniels LA. The relationship between number of fruits, vegetables, and noncore foods tried at age 14 months and food preferences, dietary intake patterns, fussy eating behavior, and weight status at age 3.7 years. J Acad Nutr Diet. 2016;116:630–7.

    PubMed  Google Scholar 

  21. Carruth BR, Skinner JD. Revisiting the picky eater phenomenon: neophobic behaviors of young children. J Am Coll Nutr. 2000;19:771–80.

    CAS  PubMed  Google Scholar 

  22. Boyd A, Golding J, Macleod J, Lawlor DA, Fraser A, Henderson J, et al. Cohort profile: the ‘children of the 90s’-the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol. 2013;42:111–27.

    PubMed  Google Scholar 

  23. Fraser A, Macdonald-Wallis C, Tilling K, Boyd A, Golding J, Smith GD, et al. Cohort profile: The Avon Longitudinal Study of Parents and Children: ALSPAC mothers cohort. Int J Epidemiol. 2013;42:97–110.

    PubMed  Google Scholar 

  24. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320:1240–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Clogg C. Latent class models: recent developments and prospects for the future. In: Arminger G, Clogg C, Sobel M, editors. Handbook of statistical modeling for the social and behavioral sciences. New York, NY: Plenum Press; 1995. p. 311–59.

  26. Berlin KS, Parra GR, Williams NA. An introduction to latent variable mixture modeling (part 2): longitudinal latent class growth analysis and growth mixture models. J Pediatr Psychol. 2014;39:188–203.

    PubMed  Google Scholar 

  27. Asparouhov T, Muthen B. Auxiliary variables in mixture modeling: three-step approaches using Mplus. Struct Equ Model. 2014;21:329–41.

    Google Scholar 

  28. Goodman LA. On the assignment of individuals to latent classes. Sociol Methodol. 2007;37:1–22.

    Google Scholar 

  29. Muthen LK, Muthen B. Mplus user’s guide. 8th ed. Los Angeles, CA: Muthén & Muthén; 1997–2017.

  30. StataCorp. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC; 2017.

  31. Northstone K, Emmett P, Nethersole F. Pregnancy ASTALSo, childhood. The effect of age of introduction to lumpy solids on foods eaten and reported feeding difficulties at 6 and 15 months. J Hum Nutr Diet. 2001;14:43–54.

    CAS  PubMed  Google Scholar 

  32. Syrad H, Llewellyn CH, Johnson L, Boniface D, Jebb SA, van Jaarsveld CHM, et al. Meal size is a critical driver of weight gain in early childhood. Sci Rep. 2016;6:28368

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Huang TT, Howarth NC, Lin BH, Roberts SB, McCrory MA. Energy intake and meal portions: associations with BMI percentile in U.S. children. Obes Res. 2004;12:1875–85.

    PubMed  Google Scholar 

  34. McConahy KL, Smiciklas-Wright H, Birch LL, Mitchell DC, Picciano MF. Food portions are positively related to energy intake and body weight in early childhood. J Pediatr. 2002;140:340–7.

    PubMed  Google Scholar 

  35. Carnell S, Wardle J. Measuring behavioural susceptibility to obesity: validation of the child eating behaviour questionnaire. Appetite. 2007;48:104–13.

    PubMed  Google Scholar 

  36. Syrad H, Johnson L, Wardle J, Llewellyn CH. Appetitive traits and food intake patterns in early life. Am J Clin Nutr. 2016;103:231–5.

    CAS  PubMed  Google Scholar 

  37. van Jaarsveld CHM, Boniface D, Llewellyn CH, Wardle J. Appetite and growth A longitudinal sibling analysis. JAMA Pediatr. 2014;168:345–50.

    PubMed  Google Scholar 

  38. Tharner A, Jansen PW, Kiefte-de Jong JC, Moll HA, van der Ende J, Jaddoe VW, et al. Toward an operative diagnosis of fussy/picky eating: a latent profile approach in a population-based cohort. Int J Behav Nutr Phys Act. 2014;11:14.

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Llewellyn C, Wardle J. Behavioral susceptibility to obesity: gene-environment interplay in the development of weight. Physiol Behav. 2015;152:494–501.

    CAS  PubMed  Google Scholar 

  41. Llewellyn CH, Trzaskowski M, van Jaarsveld CHM, Plomin R, Wardle J. Satiety mechanisms in genetic risk of obesity. JAMA Pediatr. 2014;168:338–44.

    PubMed  PubMed Central  Google Scholar 

  42. Konttinen H, Llewellyn C, Wardle J, Silventoinen K, Joensuu A, Mannisto S, et al. Appetitive traits as behavioural pathways in genetic susceptibility to obesity: a population-based cross-sectional study. Sci Rep. 2015;5:14726.

    PubMed  PubMed Central  Google Scholar 

  43. de Lauzon-Guillain B, Clifton EA, Day FR, Clement K, Brage S, Forouhi NG, et al. Mediation and modification of genetic susceptibility to obesity by eating behaviors. Am J Clin Nutr. 2017;106:996–1004.

    PubMed  PubMed Central  Google Scholar 

  44. Jacob R, Drapeau V, Tremblay A, Provencher V, Bouchard C, Perusse L. The role of eating behavior traits in mediating genetic susceptibility to obesity. Am J Clin Nutr. 2018;108:445–52.

    PubMed  Google Scholar 

  45. Vilela S, Hetherington MM, Oliveira A, Lopes C. Tracking diet variety in childhood and its association with eating behaviours related to appetite: the generation XXI birth cohort. Appetite. 2018;123:241–8.

    PubMed  Google Scholar 

  46. de Barse LM, Tiemeier H, Leermakers ETM, Voortman T, Jaddoe VWV, Edelson LR, et al. Longitudinal association between preschool fussy eating and body composition at 6 years of age: the Generation R Study. International J Behav Nutr Phys Act. 2015;12:8.

    Google Scholar 

  47. APA. Diagnostic and statistical manual of mental disorders (DSM-V). Arlington, VA: APA Press; 2013.

  48. Bryant‐Waugh R. Avoidant restrictive food intake disorder: an illustrative case example. 1 online resource p. New York, N.Y: Wiley; 2013.

  49. Fisher MM, Rosen DS, Ornstein RM, Mammel KA, Katzman DK, Rome ES, et al. Characteristics of avoidant/restrictive food intake disorder in children and adolescents: a “new disorder” in DSM-5. J Adolesc Health. 2014;55:49–52.

    PubMed  Google Scholar 

  50. Herle M, De Stavola B, Hübel C, Abdulkadir M, Ferreira DS, Loos RJF, et al. A longitudinal study of eating behaviours in childhood and later eating disorder behaviours and diagnoses. British J Psychiatr. 2020;216:113–9.

    Google Scholar 

  51. Smith AD, Herle M, Fildes A, Cooke L, Steinsbekk S, Llewellyn CH. Food fussiness and food neophobia share a common etiology in early childhood. J Child Psychol Psyc. 2017;58:189–96.

    Google Scholar 

  52. Blissett J, Meyer C, Haycraft E. Maternal mental health and child feeding problems in a non-clinical group. Eating Behaviors. 2007;8:311–8.

    CAS  PubMed  Google Scholar 

  53. Ram N, Grimm KJ. Growth mixture modeling: a method for identifying differences in longitudinal change among unobserved groups. Int J Behav Dev. 2009;33:565–76.

    PubMed  PubMed Central  Google Scholar 

  54. Bray BC, Lanza ST, Tan X. Eliminating bias in classify-analyze approaches for latent class analysis. Struct Equ Model. 2015;22:1–11.

    Google Scholar 

Download references

Acknowledgements

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.

Funding

This work was supported by the UK Medical Research Council and the Medical Research Foundation (ref: MR/R004803/1). The UK Medical Research Council and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf). CMB acknowledges funding from the Swedish Research Council (VR Dnr: 538–2013–8864). The funders were not involved in the design or conduct of the study; collection, management, analysis, or interpretation of the data; or preparation, review, or approval of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

MH, BDS, CMB, RB-W, and NM designed the research; MH and BDS performed statistical analyses; all authors wrote and revised the manuscript for important intellectual content; NM had primary responsibility for final content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nadia Micali.

Ethics declarations

Conflict of interest

CMB reports: Shire (grant recipient, Scientific Advisory Board member) and Pearson and Walker (author, royalty recipient). The other authors declare that they have no conflict of interest.

Ethical approval

Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. All procedures were performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments.

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

Herle, M., Stavola, B.D., Hübel, C. et al. Eating behavior trajectories in the first 10 years of life and their relationship with BMI. Int J Obes 44, 1766–1775 (2020). https://doi.org/10.1038/s41366-020-0581-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-020-0581-z

This article is cited by

Search

Quick links