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BMI peak in infancy as a predictor for later BMI in the Uppsala Family Study

International Journal of Obesity volume 33, pages 929–937 (2009)Cite this article

Abstract

Background:

The development of obesity through childhood, often characterized by using body mass index (BMI), has received much recent interest because of the rapidly increasing levels of obesity worldwide. However, the extent to which the BMI trajectory in the first year of life (the BMI ‘peak’ in particular) is associated with BMI in later childhood has received little attention.

Subjects:

The Uppsala Family Study includes 602 families, comprising mother, father and two consecutive singleton offspring, both of whom were delivered at the Uppsala Academic Hospital, Sweden, between 1987 and 1995. The children's postnatal growth data, including serial measurements of height and weight (from which BMI was calculated), were obtained from health records. All children had a physical examination when they were aged between 5 and 13 years, at which height and weight were again recorded and used to calculate age- and sex-adjusted BMI z-scores.

Methods:

Subject-specific growth curves were fitted to the infant BMI data using penalized splines with random coefficients, and from these the location of the BMI peak for each participant was estimated. A multilevel modelling approach was used to assess the relationships between the BMI peak and BMI z-score in later childhood.

Results:

The BMI peak occurred, on average, slightly later in female children, with a higher BMI peak in male children. Considered separately, both age and BMI at BMI peak were positively associated with later BMI z-score. Considered jointly, both dimensions of BMI peak retained their positive associations.

Conclusions:

The growth trajectory associated with higher childhood BMI appears to include a later and/or higher BMI peak in infancy.

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Acknowledgements

We thank Ilona Koupil, Torsten Tuvemo and Ann-Christine Synvanen for agreeing to providing access to these data and Rawya Mohsen for organizing the data. The original data collection was funded by the UK Medical Research Council.

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Authors and Affiliations

  1. Department of Epidemiology and Population Health, London School of Hygiene and Tropical Medicine, London, UK

    R J Silverwood, B L De Stavola & D A Leon

  2. MRC Centre of Epidemiology for Child Health, UCL Institute of Child Health, London, UK

    T J Cole

Authors
  1. R J Silverwood
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  2. B L De Stavola
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  3. T J Cole
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  4. D A Leon
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Corresponding author

Correspondence to R J Silverwood.

Annex

Annex

Consider initially the fitting of a growth curve for a single subject. Let yj be the (log transformed) BMI for this subject at age xj, j=1,…,n. Let k1, …, kK be a set of K distinct knots in the range of xj and define

Then the spline model of degree p with K knots at k1, …, kK is defined as

where β1, …, βp and u1, …, uK are to be estimated and . As unconstrained fitting of u1, …, uK will result in a ‘wiggly’ fit14, a constraint such as ∑k=1Kuk2<C for some constant C may be imposed. The resulting model is referred to as a penalized spline model.

It can be shown14, using the principle of ‘best linear unbiased prediction’ (BLUP)22, that the penalized spline model (1) can be represented as a mixed model with . The model can then be considered in the general linear mixed model form,

where

Subject-specific growth curves are obtained by the inclusion of subject-specific random (spline) parameters which model the deviation of a given individual's curve from the population average (spline) curve. Now consider all subjects in the data set so that yij is the (log transformed) BMI for subject i, i=1, …, m, at age xij, j=1, …, ni. Then the penalized spline model of degree p can be extended to give

where , where ∑ is an unstructured (p+1) × (p+1) covariance matrix, , and all terms are independent of one another apart from ai0, …, aip for a given i. The present analysis used cubic penalized spline models, with both cubic population average curves and cubic subject-specific deviations from these (that is, (2) with p=3), to model BMI growth through infancy.

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Silverwood, R., De Stavola, B., Cole, T. et al. BMI peak in infancy as a predictor for later BMI in the Uppsala Family Study. Int J Obes 33, 929–937 (2009). https://doi.org/10.1038/ijo.2009.108

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  • DOI: https://doi.org/10.1038/ijo.2009.108

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