Monitoring Body Composition Change for Intervention Studies with Advancing 3D Optical Imaging Technology in Comparison to Dual-Energy X-Ray Absorptiometry

Author:

Wong Michael C.ORCID,Bennett Jonathan P.,Leong Lambert T.,Tian Isaac Y.,Liu Yong E.,Kelly Nisa N.,McCarthy Cassidy,Wong Julia MW,Ebbeling Cara B.,Ludwig David S.,Irving Brian A.,Scott Matthew C.,Stampley James,Davis Brett,Johannsen Neil,Matthews Rachel,Vincellette Cullen,Garber Andrea K.,Maskarinec Gertraud,Weiss Ethan,Rood Jennifer,Varanoske Alyssa N.,Pasiakos Stefan M.,Heymsfield Steven B.,Shepherd John A.ORCID

Abstract

ABSTRACTBackgroundRecent three-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown.ObjectiveTo evaluate 3DO’s ability to monitor body composition changes across multiple intervention studies.MethodsA 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 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 (PCs), which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression.ResultsThe analysis included 133 participants (45 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range 3-23 weeks. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.86, 0.73, and 0.70 with 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. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA.ConclusionsAs compared to 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.

Publisher

Cold Spring Harbor Laboratory

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