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A study led by the University of Queensland has analysed how genes can affect a child’s body mass index (BMI) over time. The research also explores how these genetic factors may increase the risk of developing diseases such as heart disease or type 2 diabetes later in life.
Childhood overweight and obesity remain major public health challenges in Australia. According to the Australian Institute of Health and Welfare, around one in four Australian children aged 5-17 years live with overweight or obesity. In adults, adiposity status or overall body size is defined using body mass index (BMI), calculated as weight in kilograms divided by height in meters squared. The thresholds are ≥25 kg/m2 for overweight and ≥30 kg/m2 for obesity (≥23 and ≥28 kg/m2, respectively, for Asian populations). In children and adolescents, however, BMI is interpreted using age- and sex-specific growth reference charts rather than fixed cut-offs.
Yet BMI in childhood is dynamic. Body length (or height) and body weight do not increase proportionally during growth, leading to natural fluctuations in BMI. For example, school-aged children experience adiposity rebound around age 6. For clinicians and parents alike, interpreting BMI at a single time point can be challenging.
Current growth assessments primarily depend on population-based growth charts. While these charts are essential tools, they do not consider the biological differences that exist between children. We are increasingly recognising that genetic factors play a significant role in the variation of body size among individuals. However, it remains unclear whether these genetic influences remain consistent from infancy through adolescence or if they change over time.
Understanding this matters. If the biology driving BMI in infancy differs from that in adolescence, then early body size may not necessarily signal lifelong obesity risk in the way we sometimes assume.
What did we find?
In our study, recently published in Nature Communications, we modelled how genetic influences on BMI change from age 1 to 18 years.
Rather than analysing BMI at a single age, which is the standard approach in most genetic studies, we used a longitudinal framework. Using data from 6,291 children in the “Children of the 90s” (also known as Avon Longitudinal Study of Parents and Children) with 65,930 repeated BMI measurements, we applied a random regression model to capture how genetic effects vary across development. This allowed us to estimate how much BMI at different ages is influenced by genetics (ie, heritability), and whether the same genetic factors operate across childhood (ie, genetic correlation).
Regarding heritability, we estimated that common genetic variation explains approximately one quarter of the variation in BMI at different ages, as well as about one quarter of the variation in the rate of BMI change from ages 1 to 18 years.
For genetic correlations between different ages, in simple terms, we asked whether the genes influencing BMI in one-year-olds are the same as those influencing BMI later in life, such as in teenagers and adults. We found that they are not entirely the same. The genetic correlation between BMI in infancy and late adolescence is weak. This indicates that body size in very young children may not reflect the same biological processes that influence adolescent or adult BMI.
Importantly, when we linked these age-specific genetic influences to adult cardiometabolic traits, we found that BMI around age 10, and the overall growth trajectory across childhood (rate of change from 1 to 18 years), showed significant genetic links to later risk factors such as type 2 diabetes and cardiovascular disease.
Strengths of this work include repeated measures across childhood, rigorous longitudinal modelling, and integration with large-scale genetic datasets. By modelling trajectories rather than snapshots, we captured biological change over time.
However, there are limitations. The cohort was predominantly of European ancestry, which limits generalisability to Australia’s diverse population. BMI is also an imperfect proxy for adiposity and does not distinguish fat from lean mass. Finally, genetic effects explain only part of the story — environmental, social and behavioural factors remain crucial.
Implications and next steps
So, what does this mean for Australian clinicians, medical researchers, parents and policy makers?
Firstly, our findings indicate caution in interpreting BMI during infancy as a lifelong risk marker. Variations in early body size may represent temporary biological processes rather than definitive paths toward adult obesity.
Secondly, growth should be seen as a trajectory rather than a single measurement in both research and clinical settings. Future studies should focus on identifying patterns, such as growth trajectories and key developmental phases, that may indicate which children are at greater long-term health risk.
From a policy perspective, I would like to see greater investment in longitudinal child health data collection and method development in Australia. We have excellent local cohort studies, such as the Raine Study, but sustained funding is needed to develop new research methodologies, capture repeated growth data with larger sample sizes, and collect familial, environmental, and genetic data across diverse populations. Strengthening these research infrastructures would support the Australian Government’s priority of “Supporting healthy and thriving communities” by improving understanding of child growth, strengthening population health monitoring, and informing evidence-based policy across the life course.
In the long term, integrating biological information, including genetic variation, into growth evaluations could facilitate more personalised approaches by developing improved predictive tools that acknowledge that children grow differently for biological reasons.
Dr Geng Wang is a postdoctoral research fellow at the Centre for Population & Disease Genomics, Institute for Molecular Bioscience, the University of Queensland.
The statements or opinions expressed in this article reflect the views of the authors and do not necessarily represent the official policy of the AMA, the MJA or InSight+ unless so stated.
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