Prediction of Android and Gynoid Body Adiposity via a Three-dimensional Stereovision Body Imaging System and Dual-Energy X-ray Absorptiometry

Abstract

Objective Current methods for measuring regional body fat are expensive and inconvenient compared to the relative cost-effectiveness and ease-of-use of a stereovision body imaging (SBI) system. The primary goal of this research is to develop prediction models for android and gynoid fat by body measurements assessed via SBI and dual-energy x-ray absorptiometry (DXA). Subsequently, mathematical equations for prediction of total and regional (trunk, leg) body adiposity were established via parameters measured by SBI and DXA. Methods A total of 121 participants were randomly assigned into primary and cross-validation groups. Body measurements were obtained via traditional anthropometrics, SBI, and DXA. Multiple regression analysis was conducted to develop mathematical equations by demographics and SBI assessed body measurements as independent variables and body adiposity (fat mass and percent fat) as dependent variables. The validity of the prediction models was evaluated by a split sample method and Bland-Altman analysis. Results The R2 of the prediction equations for fat mass and percent body fat were 93.2% and 76.4% for android, and 91.4% and 66.5% for gynoid, respectively. The limits of agreement for the fat mass and percent fat were − 0.06 ± 0.87 kg and − 0.11 ± 1.97 % for android and − 0.04 ± 1.58 kg and − 0.19 ± 4.27 % for gynoid. Prediction values for fat mass and percent fat were 94.6% and 88.9% for total body, 93.9% and 71.0% for trunk, and 92.4% and 64.1% for leg, respectively. Conclusions The three-dimensional (3D) SBI produces reliable parameters that can predict android and gynoid, as well as total and regional (trunk, leg) fat mass.

Clinical anthropometrics and body composition from 3D whole-body surface scans

“…Body composition models from 3D features calibrated using DXA data validated well, similar to those reported by Lee et al , 7,18 especially in light of the different body composition measurement methods used (DXA calibration data and BIA validation data in our study and DXA and MRI data in Lee’s studies). In particular, our whole-body fat mass prediction model showed strong fit to the calibration data (R 2 = 0.95), matching the equivalent Lee model (R 2 = 0.95).…”

“…In particular, our whole-body fat mass prediction model showed strong fit to the calibration data (R 2 = 0.95), matching the equivalent Lee model (R 2 = 0.95). 7 Our visceral fat prediction model showed moderately strong association (R 2 = 0.75), similar to Lee (R 2 = 0.72). 13 …”

“…Where available, we derived linear regression equations using the parameters described by Lee et al . 7,13 The ratio of waist girth to waist width was used as a surrogate for ‘central obesity depth’ defined by Lee et al . Waist girth and width were likewise used as surrogates for ‘central obesity width’ as defined by Lee et al .…”

“…Lee et al . 7 demonstrated accurate prediction of whole-body and regional fat mass and percent fat from regional volume and length measures from an eight-camera structured illumination scanner. Development of low-cost light-coding technology has enabled more affordable 3D scanners such as the TC 2 KX-16 (Cary, NC, USA) 8 and the Fit3D Proscanner (Redwood City, CA, USA).…”

A Smartphone Application for Personal Assessments of Body Composition and Phenotyping

“…Lee et al [34] reported that inclusion of the 3D-determined waist-to-hip ratio significantly improved multiple regression equations to predict MRI-estimated visceral AT but neither total abdominal AT nor subcutaneous AT. They also used 3D imaging to assess fat patterning of adults [35]. Compared to the usual demographic information (e.g., gender, age, and ethnicity) and standard anthropometric measurements (e.g., weight, height, and waist circumference), the inclusion of 3D body image data (e.g., regional volumes, circumferences and sagittal thicknesses) improved the precision, relative to DXA, to 2 kg and 0.2% for android and 3.2 kg and 0.4% for gynoid AT mass and % fat, respectively.…”