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BioMed Research International
Volume 2013, Article ID 431825, 7 pages
Research Article

Creation of an Adiposity Index for Children Aged 6–8 Years: The Gateshead Millennium Study

1Institute of Health and Society, Newcastle University, Sir James Spence Institute, Royal Victoria Infirmary, Newcastle upon Tyne NE1 4LP, UK
2Department of Statistics, Glasgow University, Glasgow G12 8QW, UK
3Human Nutrition Research Centre, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
4Department of Psychology, Durham University, Durham DH1 3LE, UK
5PEACH Unit, Faculty of Medicine, Glasgow University, Glasgow G12 8QQ, UK

Received 4 April 2013; Accepted 9 August 2013

Academic Editor: Nina Cecilie Øverby

Copyright © 2013 Mark S. Pearce et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Objective. A number of measures of childhood adiposity are in use, but all are relatively imprecise and prone to bias. We constructed an adiposity index (AI) using a number of different measures. Methods. Detailed body composition data on 460 of the Gateshead Millennium Study cohort at the age of 6–8 years were analysed. The AI was calculated using factor analysis on age plus thirteen measures of adiposity and/or size. Correlations between these variables, the AI, and more traditional measures of adiposity in children were investigated. Results. Based on the factor loading sizes, the first component, taken to be the AI, consisted mainly of measures of fat-mass (the skinfold measurements, fat mass score, and waist circumference). The second comprised variables measuring frame size, while the third consisted mainly of age. The AI had a high correlation with body mass index (BMI) (rho = 0.81). Conclusions. While BMI is practical for assessing adiposity in children, the AI combines a wider range of data related to adiposity than BMI alone and appears both valid and valuable as a research tool for studies of childhood adiposity. Further research is necessary to investigate the utility of AI for research in other samples of children and also in adults.