Accurate body composition measures from whole-body silhouettes
Obesity and its consequences, such as diabetes, are global health issues that burden about 171 × 106 adult individuals worldwide. Fat mass index (FMI, kg/m2), fat-free mass index (FFMI, kg/m2), 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.
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)].
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 R2 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.
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.