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Abstract
Abstract
Background: Genetic factors are important determinants of overweight. We examined whether there are differential effect
sizes depending on children’s body composition.
Methods: We analysed data of n = 4,837 children recorded in the Avon Longitudinal Study of Parents and Children
(ALSPAC), applying quantile regression with sex- and age-specific standard deviation scores (SDS) of body mass index (BMI)
or with body fat mass index and fat-free mass index at 9 years as outcome variables and an ‘‘obesity-risk-allele score’’ based
on eight genetic variants known to be associated with childhood BMI as the explanatory variable.
Results: The quantile regression coefficients increased with increasing child’s BMI-SDS and fat mass index percentiles,
indicating larger effects of the genetic factors at higher percentiles. While the associations with BMI-SDS were of similar size
in medium and high BMI quantiles (40th percentile and above), effect sizes with fat mass index increased over the whole fat
mass index distribution. For example, the fat mass index of a normal-weight (50th percentile) child was increased by
0.13 kg/m2 (95% confidence interval (CI): 0.09, 0.16) per additional allele, compared to 0.24 kg/m2 per allele (95% CI: 0.15,
0.32) in children at the 90th percentile. The genetic associations with fat-free mass index were weaker and the quantile
regression effects less pronounced than those on fat mass index.
Conclusions: Genetic risk factors for childhood overweight appear to have greater effects on fatter children. Interaction of
known genetic factors with environmental or unknown genetic factors might provide a potential explanation of these findings.
| Translated title of the contribution | Genetic markers of obesity risk: stronger associations with body composition in overweight compared to normal-weight children |
|---|---|
| Original language | English |
| Pages (from-to) | 1 - 4 |
| Number of pages | 4 |
| Journal | PLOS ONE |
| DOIs | |
| Publication status | Published - Apr 2011 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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