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. 2023 Apr;117(4):802-813.
doi: 10.1016/j.ajcnut.2023.02.006. Epub 2023 Feb 14.

Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry

Michael C Wong  1 , Jonathan P Bennett  1 , Lambert T Leong  2 , Isaac Y Tian  3 , Yong E Liu  2 , Nisa N Kelly  2 , Cassidy McCarthy  4 , Julia M W Wong  5 , Cara B Ebbeling  5 , David S Ludwig  5 , Brian A Irving  6 , Matthew C Scott  7 , James Stampley  6 , Brett Davis  6 , Neil Johannsen  7 , Rachel Matthews  6 , Cullen Vincellette  6 , Andrea K Garber  8 , Gertraud Maskarinec  2 , Ethan Weiss  8 , Jennifer Rood  4 , Alyssa N Varanoske  9 , Stefan M Pasiakos  10 , Steven B Heymsfield  4 , John A Shepherd  11
Affiliations

Monitoring body composition change for intervention studies with advancing 3D optical imaging technology in comparison to dual-energy X-ray absorptiometry

Michael C Wong et al. Am J Clin Nutr. 2023 Apr.

Abstract

Background: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown.

Objectives: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies.

Methods: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis.

Results: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R2) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA.

Conclusions: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363).

Keywords: DXA; body composition; interventions; monitoring; three-dimensional optical imaging; weight loss.

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Figures

FIGURE 1
FIGURE 1
Histograms of the time (weeks) between the baseline and follow-up DXA scans for females (top) and males (bottom). The numbers over the bars represent the count. Multiple numbers over a multicolor bar represent the count in the corresponding order.
FIGURE 2
FIGURE 2
Average baseline (left) and follow-up (right) body shapes for females (top) and males (bottom) by the study.
FIGURE 3
FIGURE 3
Scatter plot comparisons between 3DO and DXA body composition at baseline, follow-up, and adjusted change for females. Blue, horizontal lines and orange, vertical lines are signifying the amount of change needed to pass the least significant change for DXA and 3DO, respectively. Purple (zone 4) and green glyphs (zone 1) represent agreement of significant or nonsignificant change by 3DO and DXA. The orange glyphs (zone 3) represent significant change detected by 3DO but not DXA and vice versa for the blue glyphs (zone 2). The zones and glyphs are consistent for the proceeding-adjusted change plots.
FIGURE 4
FIGURE 4
Scatter plot comparisons between 3DO and DXA body composition at baseline, follow-up, and adjusted change for males. Blue, horizontal lines and orange, vertical lines are signifying the amount of change needed to pass the least significant change for DXA and 3DO, respectively. Purple (zone 4) and green glyphs (zone 1) represent agreement of significant or nonsignificant change by 3DO and DXA. The orange glyphs (zone 3) represent significant change detected by 3DO but not DXA and vice versa for the blue glyphs (zone 2). The zones and glyphs are consistent for the proceeding adjusted change plots.
FIGURE 5
FIGURE 5
Histograms to show the frequency of percent FFM change relative to the total weight change in the female (top) and male (bottom) samples.
See this image and copyright information in PMC

References

    1. Wong M.C., McCarthy C., Fearnbach N., Yang S., Shepherd J., Heymsfield S.B. Emergence of the obesity epidemic: six decade visualization with humanoid avatars. Am. J. Clin. Nutr. 2022;115(4):1189–1193. - PMC - PubMed
    1. Heymsfield S.B., Wadden T.A. Mechanisms, pathophysiology, and management of obesity. N. Engl. J. Med. 2017;376(3):254–266. - PubMed
    1. Bennett J., Wong M.C., McCarthy C., Fearnbach N., Queen K., Shepherd J., et al. Emergence of the adolescent obesity epidemic in the United States: five-decade visualization with humanoid avatars. Int. J. Obes (Lond). 2022;46(9):1587–1590. - PMC - PubMed
    1. Hales C.M., Carroll M.D., Fryar C.D., Ogden C.L. CDC; 2020. Prevalence of obesity and severe obesity among adults: United States, 2017-2018.https://www.cdc.gov/nchs/data/databriefs/db360-h.pdf Internet. Available from: - PubMed
    1. Arem H., Irwin M. A review of web-based weight loss interventions in adults. Obes. Rev. 2011;12(5):e236–e243. - PMC - PubMed

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