Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies

Abstract

Background Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status. Objectives The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling. Methods Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA. Results This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female). Conclusions 3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings. This trial was registered at clinicaltrials.gov as NCT03637855.

How shape-based anthropometry can complement traditional anthropometric techniques: a cross-sectional study

“…In contrast, the approach used in our study has been shown to identify differences in scale-invariant shape features that cannot be captured using traditional anthropometric techniques. There have been recent studies which have also used principal components analysis (PCA) to detect variations in torso 3D scan data similar to our study, such as Ruto et al 43 and Ng et al 44 . However, the torso scan data in both these investigations were not scaled to uniform size, so as a result some variations observed within these studies were related to differences in overall body height and size, as well as variations in scale-invariant body shape.…”

Novel body fat estimation using machine learning and 3-dimensional optical imaging

“…This proof-of-concept investigation identified and validated a novel 3DO body fat prediction equation that accounts for divergent anthropometric characteristics. The combined equation was found to have acceptable validity and minimal group-level error compared to DXA, mirroring the results of Ng and colleagues, 1 who reported comparable validity of a 3DO-derived prediction equation relative to DXA fat mass estimates in a multi-ethnic, mixed-sex cohort. Taken together, these findings suggest that device-specific 3DO BF% prediction equations could serve as a suitable alternative to DXA to estimate body composition in groups of healthy individuals.…”

“…In recent years, the use of commercially available 3-dimensional optical imaging (3DO) devices has garnered considerable interest as a potentially useful, non-invasive method to evaluate automated anthropometry and body composition for adults 1 , 2 and children. 3 These devices utilize infrared and visible light to create a 3-dimensional representation of a subject’s body, allowing for rapid, automatic assessment of anthropometric characteristics.…”

“…A unique feature of the present investigation is the use of the 4C model to develop the 3DO BF% prediction equation, as previous studies have not developed or validated 3DO-derived estimates using a multi-compartment model. 1 , 6 , 10 , 11 Furthermore, the novel application of machine learning techniques to determine key anthropometric factors associated with body fat variation revealed that the optimal 3DO-derived variables to predict percent body fat differ substantially between lean and obese individuals. However, limitations of the present investigation are the small sample size for machine learning techniques, which likely did not capture the full variety of body shapes and compositions, and the necessity of utilizing few regressors to avoid overfitting.…”

Multiple measures derived from 3D photonic body scans improve predictions of fat and muscle mass in young Swiss men

“…As regards the potential use of multiple measures for a more precise prediction of body composition, previous studies only considered a limited number of predefined measures [42], and only few of them used automatic variable selection procedures to identify the best predictors [43][44][45]. Because some of the 150 standard measurements are strongly correlated among each other, model selection procedures and other techniques such as 3D surface geometry may have to account for these correlations [46][47][48][49][50][51]. Still, further research is needed to identify which of the 150 standard measurements are most relevant for the prediction of body composition, or whether multiple (and partly strongly correlated) measurements are relevant, and how they should be selected or combined to obtain the most reliable predictions.…”