Group-based trajectory modelling for BMI trajectories in childhood: A systematic review

Mattsson, M., Maher, G. M., Boland, F., Fitzgerald, A. P., Murray, D. M. & Biesma, R., 1-Jan-2019, In : Obesity Reviews. 18 p.

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  • Group‐based trajectory modelling for BMI trajectories in childhood

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  • Molly Mattsson
  • Gillian M. Maher
  • Fiona Boland
  • Anthony P. Fitzgerald
  • Deirdre M. Murray
  • Regien Biesma

Childhood obesity is an important public health issue. We aimed to systematically review studies that used group-based trajectory modelling approaches to investigate body mass index (BMI) trajectories in early childhood, explore associated determinants, and the association with body composition outcomes. Five databases were searched systematically for studies using group-based trajectory modelling approaches to track BMI trajectories from birth. Fourteen studies using latent class growth analysis or growth mixture modelling to track BMI trajectories were identified. Three or four trajectories were identified in most studies. High maternal pre-pregnancy BMI was the most frequently identified risk factor for membership of a rapid gain trajectory. Significant associations between rapid weight gain and stable high trajectories and body measures at follow-up were identified by several studies. Relatively similar trajectories were identified across studies. Trajectories characterized by rapid weight gain were associated with several predictors, as well as body measures at follow-up, however not with great consistency. Similar associations with body measure outcomes were found for stable high and rapid gain trajectories, suggesting that long-term outcomes do not differ greatly between children with consistently high BMI and children with rapid increases in BMI. As the shape and timing of the trajectories differed between studies, it is difficult to draw conclusions.

Original languageEnglish
Number of pages18
JournalObesity Reviews
Publication statusPublished - 1-Jan-2019


  • BMI trajectories, growth mixture modelling, latent class growth analysis

ID: 82373293