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A machine learning approach relating 3D body scans to body composition in humans

European Journal of Clinical Nutrition volume 73, pages 200–208 (2019)Cite this article

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Abstract

A long-standing question in nutrition and obesity research involves quantifying the relationship between body fat and anthropometry. To date, the mathematical formulation of these relationships has relied on pairing easily obtained anthropometric measurements such as the body mass index (BMI), waist circumference, or hip circumference to body fat. Recent advances in 3D body shape imaging technology provides a new opportunity for quickly and accurately obtaining hundreds of anthropometric measurements within seconds, however, there does not yet exist a large diverse database that pairs these measurements to body fat. Herein, we leverage 3D scanned anthropometry obtained from a population of United States Army basic training recruits to derive four subpopulations of homogenous body shape archetypes using a combined principal components and cluster analysis. While the Army database was large and diverse, it did not have body composition measurements. Therefore, these body shape archetypes were paired to an alternate smaller sample of participants from the Pennington Biomedical Research Center in Baton Rouge, LA that were not only similarly imaged by the same 3D scanning machine, but also had concomitant measures of body composition by dual-energy X-ray absorptiometry body composition. With this enhanced ability to obtain anthropometry through 3D scanning quickly of large populations, our machine learning approach for pairing body shapes from large datasets to smaller datasets that also contain state-of-the-art body composition measurements can be extended to pair other health outcomes to 3D body shape anthropometry.

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Acknowledgements

Funding:

This work was partially supported by two National Institutes of Health NORC Center Grants P30DK072476, Pennington/Louisiana; and P30DK040561, Harvard; and R01DK109008, Shape UP! Adults.

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Authors and Affiliations

  1. Department of Mathematical Sciences, United States Military Academy, West Point, NY, USA

    James D. Pleuss, Kevin Talty, Steven Morse, Patrick Kuiper, Michael Scioletti & Diana M. Thomas

  2. Pennington Biomedical Research Center, Baton Rouge, LA, USA

    Steven B. Heymsfield

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  1. James D. Pleuss
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Correspondence to Diana M. Thomas.

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Pleuss, J.D., Talty, K., Morse, S. et al. A machine learning approach relating 3D body scans to body composition in humans. Eur J Clin Nutr 73, 200–208 (2019). https://doi.org/10.1038/s41430-018-0337-1

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