SEARCHCROSS-SECTIONALepidemiology, public health

cross-sectional·epidemiology, public health, nutrition, research methods, observational study·PMC10564621

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

European Journal of Clinical Nutrition · 5 authors, 2 centres

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TL;DR

An Anthroscan VITUS 3D photonic scanner provided body volumes, circumferences, and lengths.

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An Anthroscan VITUS 3D photonic scanner provided body volumes, circumferences, and lengths. Backward elimination identified the most predictive 3D scan variables, and linear regression models were compared against standard anthropometric predictors (BMI, WC, WHtR). Among standard measures, waist circumference performed best for estimating RFM and VAT in both sexes. 3D scan models consistently yielded higher explained variation than standard anthropometric models, with increases of 5% (RFM in men) to 10% (SMI in men) in r². Adding age and lifestyle variables further improved explained variation.

STUDY TYPE

cross-sectional

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epidemiology

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ORIGINAL PAPER

PMC10564621

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

European Journal of Clinical Nutrition

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ABSTRACT

Abstract Background 3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age. Methods 105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)). Results Among the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs. Conclusions 3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans. Subject terms: Epidemiology, Epidemiology

CITATION EXPORT

Manuel Guarnieri Lopez, Katarina L Matthes, Cynthia Sob et al.. Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study. European Journal of Clinical Nutrition. (2023). https://doi.org/10.1038/s41430-023-01309-4

AUTHORS

5 · 2 CENTRES

Manuel Guarnieri Lopez, Katarina L Matthes, Cynthia Sob, Nicole Bender, Kaspar Staub

1Institute of Evolutionary Medicine, University of Zurich, Zurich, Switzerland
2Institute for Environmental Decisions, Consumer Behavior, ETH Zurich, Zurich, Switzerland

Manuel Guarnieri Lopez, Katarina L Matthes, Cynthia Sob et al.. Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study. European Journal of Clinical Nutrition. (2023). https://doi.org/10.1038/s41430-023-01309-4