Can waist circumference provide a new “third” dimension to BMI when predicting percentage body fat in children? Insights using allometric modelling
AuthorsNevill, Alan M.
Gomes, Thayse Natacha
Katzmarzyk, Peter T
Duncan, Michael J.
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AbstractIntroduction: Body mass index (BMI) is often criticised for not being able to distinguish between lean and fat tissue. Waist circumference (WC), adjusted for stature, is proposed as an alternative weight-status index, as it is more sensitive to changes in central adiposity. Purpose: To combine the three dimensions of height, mass and WC to provide a simple, meaningful and more accurate index associated with percentage body fat (BF%). Methods: We employed a four independent sample design. Sample 1 consisted of 551 children (320 boys) (Mean ± S.D. of age = 7.2 ± 2.0 years), recruited from London, UK. Samples 2, 3 and 4 consisted of 5387 children (2649 boys) aged 7-17 years recruited from schools in Portugal. Allometric modelling was used to identify the most effective anthropometric index associated with BF%. The data from sample 2, 3 and 4 were used to confirm and cross validate the model derived in sample 1. Results: The allometric models from all four samples identified a positive mass exponent and a negative height exponent that was approximately twice that of the mass exponent and a waist circumference exponent that was approximately half the mass exponent. Consequently, the body-shape index most strongly associated with BF% was BMI√WC. The √WC component of the new index can simply be interpreted as a WC “weighting” of the traditional BMI. Conclusions: Compared to using BMI and WC in isolation, BMI√WC could provide a more effective and equally non-invasive proxy for BF% in children that can be used in public and community health settings.
CitationNevill AM, Bryant E, Wilkinson K, Gomes TN, Chaves R, Pereira S, Katzmarzyk PT, Maia J, Duncan MJ. Can waist circumference provide a new “third” dimension to BMI when predicting percentage body fat in children? Insights using allometric modelling, Pediatric Obesity. 2018;e12491. https://doi.org/10.1111/ijpo.12491
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