Bowen Xie1, Jesus I Avila1, Bennett K Ng1, Bo Fan1, Victoria Loo2, Vicente Gilsanz3, Thomas Hangartner4, Heidi J Kalkwarf5, Joan Lappe6, Sharon Oberfield7, Karen Winer8, Babette Zemel9, John A Shepherd1. 1. Department of Radiology and Biomedical Imaging, University of California, San Francisco, California 94115-0628. 2. Department of Political Science, Yale University, New Haven, Connecticut CT 06520-8301. 3. Children's Hospital Los Angeles, Los Angeles, California CA 90027. 4. Department of Biomedical, Industrial, & Human Factors Engineering, Wright State University, Dayton, Ohio OH 45435. 5. Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio OH 45229. 6. Department of Endocrinology, Creighton University, Omaha, Nebraska NE 68131. 7. Department of Pediatrics, Columbia University, New York, New York NY 10032. 8. The Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland MD 20892-7510. 9. Children's Hospital of Philadelphia, Philadelphia, Pennsylvania PA 19104.
Abstract
PURPOSE: Obesity and its consequences, such as diabetes, are global health issues that burden about 171 × 10(6) adult individuals worldwide. Fat mass index (FMI, kg/m(2)), fat-free mass index (FFMI, kg/m(2)), and percent fat mass may be useful to evaluate under- and overnutrition and muscle development in a clinical or research environment. This proof-of-concept study tested whether frontal whole-body silhouettes could be used to accurately measure body composition parameters using active shape modeling (ASM) techniques. METHODS: Binary shape images (silhouettes) were generated from the skin outline of dual-energy x-ray absorptiometry (DXA) whole-body scans of 200 healthy children of ages from 6 to 16 yr. The silhouette shape variation from the average was described using an ASM, which computed principal components for unique modes of shape. Predictive models were derived from the modes for FMI, FFMI, and percent fat using stepwise linear regression. The models were compared to simple models using demographics alone [age, sex, height, weight, and body mass index z-scores (BMIZ)]. RESULTS: The authors found that 95% of the shape variation of the sampled population could be explained using 26 modes. In most cases, the body composition variables could be predicted similarly between demographics-only and shape-only models. However, the combination of shape with demographics improved all estimates of boys and girls compared to the demographics-only model. The best prediction models for FMI, FFMI, and percent fat agreed with the actual measures with R(2) adj. (the coefficient of determination adjusted for the number of parameters used in the model equation) values of 0.86, 0.95, and 0.75 for boys and 0.90, 0.89, and 0.69 for girls, respectively. CONCLUSIONS: Whole-body silhouettes in children may be useful to derive estimates of body composition including FMI, FFMI, and percent fat. These results support the feasibility of measuring body composition variables from simple cameras such as those found in cell phones.
PURPOSE: Obesity and its consequences, such as diabetes, are global health issues that burden about 171 × 10(6) adult individuals worldwide. Fat mass index (FMI, kg/m(2)), fat-free mass index (FFMI, kg/m(2)), and percent fat mass may be useful to evaluate under- and overnutrition and muscle development in a clinical or research environment. This proof-of-concept study tested whether frontal whole-body silhouettes could be used to accurately measure body composition parameters using active shape modeling (ASM) techniques. METHODS: Binary shape images (silhouettes) were generated from the skin outline of dual-energy x-ray absorptiometry (DXA) whole-body scans of 200 healthy children of ages from 6 to 16 yr. The silhouette shape variation from the average was described using an ASM, which computed principal components for unique modes of shape. Predictive models were derived from the modes for FMI, FFMI, and percent fat using stepwise linear regression. The models were compared to simple models using demographics alone [age, sex, height, weight, and body mass index z-scores (BMIZ)]. RESULTS: The authors found that 95% of the shape variation of the sampled population could be explained using 26 modes. In most cases, the body composition variables could be predicted similarly between demographics-only and shape-only models. However, the combination of shape with demographics improved all estimates of boys and girls compared to the demographics-only model. The best prediction models for FMI, FFMI, and percent fat agreed with the actual measures with R(2) adj. (the coefficient of determination adjusted for the number of parameters used in the model equation) values of 0.86, 0.95, and 0.75 for boys and 0.90, 0.89, and 0.69 for girls, respectively. CONCLUSIONS: Whole-body silhouettes in children may be useful to derive estimates of body composition including FMI, FFMI, and percent fat. These results support the feasibility of measuring body composition variables from simple cameras such as those found in cell phones.
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