Hindawi Publishing Corporation BioMed Research International Volume 2013, Article ID 431825, 7 pages http://dx.doi.org/10.1155/2013/431825 Research Article Creation of an Adiposity Index for Children Aged 6–8 Years: The Gateshead Millennium Study Mark S. Pearce, 1 Peter W. James, 1 Maria Franco-Villoria, 2 Kathryn N. Parkinson, 1,3 Angela R. Jones, 1,3 Laura Basterfield, 1,3 Robert F. Drewett, 4 Charlotte M. Wright, 5 and Ashley J. Adamson 1,3 1 Institute of Health and Society, Newcastle University, Sir James Spence Institute, Royal Victoria Inirmary, Newcastle upon Tyne NE1 4LP, UK 2 Department of Statistics, Glasgow University, Glasgow G12 8QW, UK 3 Human Nutrition Research Centre, Newcastle University, Newcastle upon Tyne NE1 7RU, UK 4 Department of Psychology, Durham University, Durham DH1 3LE, UK 5 PEACH Unit, Faculty of Medicine, Glasgow University, Glasgow G12 8QQ, UK Correspondence should be addressed to Mark S. Pearce; mark.pearce@ncl.ac.uk Received 4 April 2013; Accepted 9 August 2013 Academic Editor: Nina Cecilie Øverby Copyright © 2013 Mark S. Pearce et al. his 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 diferent measures. Methods. Detailed body composition data on 460 of the Gateshead Millennium Study cohort at the age 6–8 of years were analysed. he 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 irst component, taken to be the AI, consisted mainly of measures of fat-mass (the skinfold measurements, fat mass score, and waist circumference). he second comprised variables measuring frame size, while the third consisted mainly of age. he 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. 1. Introduction It is well known that the prevalence of childhood obesity has increased rapidly in most parts of the world [1]. While the recent evidence suggesting a levelling of in the incidence of childhood overweight and obesity is promising [2], the prevalence of excess weight in children continues to be an urgent public health challenge. Childhood obesity is known to be an important risk factor for future morbidity and risk of early mortality [3]. Obese children are also more likely to experience psychological or psychiatric problems than nonobese children, and the risk of psychological morbidity increases with age [3, 4]. While the presence of high rates of childhood obesity at population levels is evident, it is less clear how to best identify children with excess adiposity at an individual level. here are a number of measures of childhood adiposity currently in use, all of which are relatively imprecise and prone to bias. By deinition, obesity is excess body fatness and should ideally be deined on the basis of a measure of body fatness. However, all gold standard methods of measurement are expensive or invasive so that simpler proxy measures of excess fatness are usually required [5]. Body mass index (BMI) (weight (kg)/height (m) 2 ) is widely accepted as a convenient measure of a person’s fatness, but it does not separate fat from lean mass and thus provides a screening but not a diagnostic test [6]. his is a particular issue in children where BMI alone cannot accurately distinguish healthy, muscular