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Erschienen in:
01.03.2015 | Original Article
Anthropometric models of bone mineral content and areal bone mineral density based on the bone mineral density in childhood study
Erschienen in: Osteoporosis International | Ausgabe 3/2015
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Summary
New models describing anthropometrically adjusted normal values of bone mineral density and content in children have been created for the various measurement sites. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.
Introduction
Previous descriptions of children’s bone mineral measurements by age have focused on segmenting diverse populations by race and sex without adjusting for anthropometric variables or have included the effects of a single anthropometric variable.
Methods
We applied multivariate semi-metric smoothing to the various pediatric bone-measurement sites using data from the Bone Mineral Density in Childhood Study to evaluate which of sex, race, age, height, weight, percent body fat, and sexual maturity explain variations in the population’s bone mineral values. By balancing high adjusted R
2 values with clinical needs, two models are examined.
Results
At the spine, whole body, whole body sub head, total hip, hip neck, and forearm sites, models were created using sex, race, age, height, and weight as well as an additional set of models containing these anthropometric variables and percent body fat. For bone mineral density, weight is more important than percent body fat, which is more important than height. For bone mineral content, the order varied by site with body fat being the weakest component. Including more anthropometrics in the model reduces the overlap of the critical groups, identified as those individuals with a Z-score below −2, from the standard sex, race, and age model.
Conclusions
If body fat is not available, the simpler model including height and weight should be used. The inclusion of multiple explanatory variables in the models provides the opportunity to calculate Z-scores that are adjusted with respect to the relevant anthropometric parameters.