1 Of all markers of human health, the most intuitive is body shape but based on quantitative evidence. 2 Anthropometry and regional composition measures such as waist circumference (WC), waist to hip ratio 3 (WHR), and visceral adipose tissue area (VAT) are better predictors of obesity-related diseases and mortality 4 risk than body mass index (BMI). Dual-energy X-ray absorptiometry (DXA) can quantify regional adiposity in 5 more detail than the above measures but is underutilized for many reasons including potential harm from 6 ionizing radiation, cost, and training. A study is needed to take advantage of rapid technological developments 7 in the "quantified self movement" to better describe phenotypes of body shape and its relation to metabolic 8 risks. The candidate developed in this proposal is 3D optical whole body scanning. If successful, sophisticated 9 obesity phenotype profiles could be constructed to clarify the underlying associations of body composition with 10 disease, genetics, lifestyle exposures, metabolomics, and be highly assessable using self-assessment 11 technology. Whole body 3D imaging technology is already so accessible that it can be done with video games 12 such as the Microsoft Xbox Kinect, and consumer cameras. 13 The long term goal of the Optical Body Shape and Health Assessment Study is 1) to provide phenotype 14 descriptors of health using body shape, and 2) to provide the tools to visualize and quantify body shape in 15 research, clinical practice, and personal health assessment. Our overall approach is to first derive predictive 16 models of how body shape relates to regional and total body composition (subcutaneous fat, visceral fat, 17 muscle mass, lean mass, and percent fat), and then show how our 3DO body composition estimates are 18 associated to important metabolic risk factors. Our central hypothesis is that 3DO measures of body 19 composition with shape classification better predict metabolic risk factors than anthropometry or DXA body 20 composition alone. Our specific aims are: 1. Identify the unique associations of body shape to body 21 composition indices in a population that represents the variance of sex, age, BMI, and ethnicity found 22 in the US population; 2. Describe the precision and accuracy of 3DO scans to monitor change in body 23 composition and metabolic health interventions; and 3. Estimate the level of association of 3DO to 24 common health indicators including metabolic risk factors by gender, race, age, and BMI. In an 25 exploratory aim, we investigate holistic, high-resolution descriptors of 3D body shape as direct 26 predictors of body composition and metabolic risk using statistical shape models and Latent Class 27 Analysis. By the end of this study, we expect to have models of the shape and composition suitable for self- 28 assessment technologies that are capable of representing over 95% of the shape variance in the US 29 population, and how these models relate to important metabolic status and body composition. The positive 30 impact will be the immediate applicability to clinicians and individuals for personalized risk assessment.