Quantifying body shape in pediatric clinical research

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Project Summary/Abstract. Excess adiposity is associated with metabolic changes that significantly increase the risk of developing 13 types of cancer. It is estimated that up to 20% of cancer cases are caused by obesity and that obesity prevention can play a significant role in the reduction of cancer incidence. Among obese adolescents, the most rapid weight gain has been shown to occur between 2 and 6 years of age. Despite clear connections between these factors and obesity risk, the study of obesity in early childhood is limited by the lack of body composition technologies appropriate for this age range. The long term goal of the Shape Up! Keiki is 1) to provide pediatric phenotype descriptors of health derived from detailed body shape scans from high-speed and high depth resolution 3D cameras, and 2) to provide the tools to visualize and quantify body shape in research and clinical practice. Our approach addresses technology issues that have hindered body composition research in this age range including participants' inability to hold still, follow directions, small body size, and rapid fluid shifts. To develop our body composition models, we will recruit 360 ethnically-diverse children from birth to 5 years stratified by sex and BMI-Z. Our central hypothesis is that optical estimates of body composition suitably represent a 5-compartment (5C) body composition model for studies of adiposity and health in young children and are superior to that of simple anthropometry and demographics. Our specific aims and subaims are as follows: 1) identify the statistical shape descriptors from 3DO scans that best represent 5-compartment body composition in an ethnically-diverse pediatric population, 1(a) identify the relationships that best link 3DO shape descriptors of body subregions (arms, legs, trunk), and matching volumes and body composition measures, 1(b) calibrate automated 3DO anthropometry to clinically relevant girths and lengths, Exploratory) identify accessible combinations of 3DO and TBW that can be calibrated to criterion 5C measures of fat and hydration, 2) identify the factors that define the precision of accessible 3D optical body composition estimates to monitor change in body composition and metabolic health interventions, 3) contrast the association of body shape, 3DO, and 5C criterion body composition to pediatric health indicators including clinically relevant exposures (SES, nursing duration, birth method, nutrition) and development. The rationale for this study is that early life access to accurate body composition data will enable identification of factors that increase obesity, metabolic disease, and cancer risk, and provide a means to target interventions to those that would benefit. The expected outcome is that our findings would be immediately applicable to accessible gaming and imaging sensors found on modern computers.