Prediction of muscle mass in arms and legs based on 3D laser-based photonic body scans’ standard dimensions in a homogenous sample of young men

ABSTRACT

Reliably identifying muscle mass from external anthropometric measurements can provide valuable information about a person’s health conditions and related outcomes. A potential tool for easily predicting muscle mass is three-dimensional (3D) body scans, but accurate validation data are missing. The aim of our study was to predict skeletal muscle mass (SMM) as assessed by Bioelectrical Impedance Analysis (BIA) from 3D body scanner data. We aimed to examine which 3D body scan standard parameters of the upper and lower limbs best predict skeletal muscle mass measured by BIA in a cross-sectional and homogenous sample of N = 100 young men. In both arms and legs, Spearman’s rank correlation coefficients with SMM were generally high for girths and volumes, and lower for lengths. The volumes of the forearm (R = 0.80–0.82) and calf (R = 0.87) correlated best with SMM. For the longitudinal follow-up of N = 45 young men, the Wilcoxon signed-rank test showed that, on average, the longitudinally followed-up increased in weight, height, BMI as well as relative/absolute fat mass. The best single predictors for individual differences in SMM were deltas for upper arm girth of both arms (adjusted R2 0.17 and 0.17) and deltas for calf girth of both legs (0.37 and 0.45). Although 3D body scan girth measures can predict SMM in upper and lower limbs satisfying, adding volumes and lengths to the equations increase the precision of the estimations fairly.

KEYWORDS:

• BIA
• surface scans
• Anthroscan
• VITUSbodyscan

Acknowledgments

This paper was part of the medical Master thesis project of Cristine Cavegn. The authors are especially thankful to Andreas Stettbacher (Chief Medical Surgeon), Franz Frey, Alexander Faas, Martino Ghilardi, Marco Müller, and Yvanka Jerkovic from the Swiss Armed Forces for their tremendous (logistic) support. Sabine Güsewell supported the biostatistical analysis. We also thank the IEM collaborators Nikola Koepke, Joël Floris, Lena Öhrström, Gulfirde Akgül, Anne Lehner, Lafi Aldakak, Michael Strässle, Patrick Eppenberger, Claudia Beckmann, and Nakita Frater for helping to collect the data.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, KS, upon reasonable request https://www.iem.uzh.ch/en/people/anthroposcan/kasparstaub.html.

Additional information

Funding

This work was supported by the Mäxi Foundation, Zurich.

Notes on contributors

Cristine Cavegn

Cristine Cavegn is a Medical Student at the University of Zurich.

Frank Rühli

Frank Rühli holds the Chair for Evolutionary Medicine at the Medical Faculty of the University of Zurich, he is the Director of the Institute of Evolutionary Medicine (IEM).

Nicole Bender

Nicole Bender is the Head of the Clinical Evolutionary Medicine Group at the IEM. This author contributed equally to this work with Kaspar Staub: Last authorship.

Kaspar Staub

Kaspar Staub is the Head of the Anthropomterics and ScanLab Group at the IEM. This author contributed equally to this work with Nicole Bender: Last authorship