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

Background/Objectives: Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses). Subjects/Methods: Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation. Results: 3D body scan measurements correlated strongly to criterion methods: waist circumference R 2 =0.95, hip circumference R 2 =0.92, surface area R 2 =0.97 and volume R 2 =0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R 2 =0.95, root mean square error (RMSE)=2.4 kg; fat-free mass R 2 =0.96, RMSE=2.2 kg) and arms, legs and trunk (R 2 =0.79-0.94, RMSE=0.5–1.7 kg). Visceral fat prediction showed moderate agreement (R 2 =0.75, RMSE=0.11 kg). Conclusions: 3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders.