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The body size and composition assessment is commonly included in the routine management of healthy athletes as well as of different types of patients to personalize the training or rehabilitation strategy. The digital anthropometric analyses described in the following protocol can be performed with recently introduced systems. These new tools and approaches have the potential to be widely used in clinical settings because they are very simple to operate and enable the rapid collection of accurate and reproducible data. One system consists of a rotating platform with a weight measurement plate, three infrared cameras, and a tablet built into a tower, while the other system consists of a tablet mounted on a holder. After image capture, the software of both systems generates a de-identified three-dimensional humanoid avatar with associated anthropometric and body composition variables. The measurement procedures are simple: a subject can be tested in a few minutes and a comprehensive report (including the three-dimensional scan and body size, shape, and composition measurements) is automatically generated.

Objective: The objective of the study was to test whether there are sustained effects of the Look AHEAD intensive lifestyle intervention (ILI), versus diabetes support and education (DSE), on weight and body composition 12 to 16 years after randomization., Methods: Participants were a subset of enrollees in the Look AHEAD dual-energy x-ray absorptiometry substudy who completed the final visit, composed of men (DSE = 99; ILI = 94) and women (DSE = 134; ILI = 135) with type 2 diabetes and mean (SD) age 57.2 (6.4) years and BMI 34.9 (5.1) kg/m 2 at randomization. Dual-energy x-ray absorptiometry measured total and regional fat and lean masses at randomization, at Years 1, 4, and 8, and at the final visit. Linear mixed-effects regressions were applied with adjustment for group, clinic, sex, age, race/ethnicity, and baseline body composition., Results: Weight and most body compartments were reduced by 2% to 8% (and BMI 4%) in ILI versus DSE in men but not women. ILI-induced loss of lean tissue did not show a lower percent lean mass versus DSE at 16 years after randomization., Conclusion: ILI-related changes in weight, fat, and lean mass were detectable 12 to 16 years after randomization in men but, for unknown reasons, not in women. There was no evidence that the intervention led to a disproportionate loss of lean mass by the end of the study., (© 2024 The Obesity Society.)

Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machine learning approaches are increasingly publicly available and have key advantages over statistical modeling methods when developing prediction algorithms on large datasets with multiple complex covariates. This study aimed to test the feasibility of predicting DXA-measured appendicular lean mass (ALM) with a neural network (NN) algorithm developed on a sample of 576 participants using 10 demographic (sex, age, 7 ethnic groupings) and 43 anthropometric dimensions generated with a 3D optical scanner. NN-predicted and measured ALM were highly correlated (n = 116; R 2 , 0.95, p < 0.001, non-significant bias) with small mean, absolute, and root-mean square errors (X ± SD, -0.17 ± 1.64 kg and 1.28 ± 1.04 kg; 1.64). These observations demonstrate the application of NN body composition prediction algorithms to rapidly emerging large and complex digital anthropometric datasets. Clinical Trial Registration: NCT03637855, NCT05217524, NCT03771417, and NCT03706612., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background & Aims: The multicompartment approach to body composition modeling provides a more precise quantification of body compartments in healthy and clinical populations. We sought to develop and validate a simplified and accessible multicompartment body composition model using 3-dimensional optical (3DO) imaging and bioelectrical impedance analysis (BIA)., Methods: Samples of adults and collegiate-aged student-athletes were recruited for model calibration. For the criterion multicompartment model (Wang-5C), participants received measures of scale weight, body volume (BV) via air displacement, total body water (TBW) via deuterium dilution, and bone mineral content (BMC) via dual energy x-ray absorptiometry. The candidate model (3DO-5C) used stepwise linear regression to derive surrogate measures of BV using 3DO, TBW using BIA, and BMC using demographics. Test-retest precision of the candidate model was assessed via root mean square error (RMSE). The 3DO-5C model was compared to criterion via mean difference, concordance correlation coefficient (CCC), and Bland-Altman analysis. This model was then validated using a separate dataset of 20 adults., Results: 67 (31 female) participants were used to build the 3DO-5C model. Fat-free mass (FFM) estimates from Wang-5C (60.1 ± 13.4 kg) and 3DO-5C (60.3 ± 13.4 kg) showed no significant mean difference (-0.2 ± 2.0 kg; 95 % limits of agreement [LOA] -4.3 to +3.8) and the CCC was 0.99 with a similar effect in fat mass that reflected the difference in FFM measures. In the validation dataset, the 3DO-5C model showed no significant mean difference (0.0 ± 2.5 kg; 95 % LOA -3.6 to +3.7) for FFM with almost perfect equivalence (CCC = 0.99) compared to the criterion Wang-5C. Test-retest precision (RMSE = 0.73 kg FFM) supports the use of this model for more frequent testing in order to monitor body composition change over time., Conclusions: Body composition estimates provided by the 3DO-5C model are precise and accurate to criterion methods when correcting for field calibrations. The 3DO-5C approach offers a rapid, cost-effective, and accessible method of body composition assessment that can be used broadly to guide nutrition and exercise recommendations in athletic settings and clinical practice., Competing Interests: Conflicts of interest TK is an employee of Hologic, Inc. however, the sponsors had no role in the in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results., (Published by Elsevier Ltd.)

Background: Athletes vary in hydration status due to ongoing training regimes, diet demands, and extreme exertion. With water being one of the largest body composition compartments, its variation can cause misinterpretation of body composition assessments meant to monitor strength and training progress. In this study, we asked what accessible body composition approach could best quantify body composition in athletes with a variety of hydration levels., Methods: The Da Kine Study recruited collegiate and intramural athletes to undergo a variety of body composition assessments including air-displacement plethysmography (ADP), deuterium-oxide dilution (D 2 O), dual-energy X-ray absorptiometry (DXA), underwater-weighing (UWW), 3D-optical (3DO) imaging, and bioelectrical impedance (BIA). Each of these methods generated 2- or 3-compartment body composition estimates of fat mass (FM) and fat-free mass (FFM) and was compared to equivalent measures of the criterion 6-compartment model (6CM) that accounts for variance in hydration. Body composition by each method was used to predict abdominal and thigh strength, assessed by isokinetic/isometric dynamometry., Results: In total, 70 (35 female) athletes with a mean age of 21.8 ± 4.2 years were recruited. Percent hydration (Body Water 6CM /FFM 6CM ) had substantial variation in both males (63-73 %) and females (58-78 %). ADP and DXA FM and FF M had moderate to substantial agreement with the 6C model (Lin's Concordance Coefficient [CCC] = 0.90-0.95) whereas the other measures had lesser agreement (CCC <0.90) with one exception of 3DO FFM in females (CCC = 0.91). All measures of FFM produced excellent precision with %CV < 1.0 %. However, FM measures in general had worse precision (% CV < 2.0 %). Increasing quartiles (significant p < 0.001 trend) of 6CM FFM resulted in increasing strength measures in males and females. Moreover, the stronger the agreement between the alternative methods to the 6CM, the more robust their correlation with strength, irrespective of hydration status., Conclusion: The criterion 6CM showed the best association to strength regardless of the hydration status of the athletes for both males and females. Simpler methods showed high precision for both FM and FFM and those with the strongest agreement to the 6CM had the highest strength associations., Summary Box: This study compared various body composition analysis methods in 70 athletes with varying states of hydration to the criterion 6-compartment model and assessed their relationship to muscle strength. The results showed that accurate and precise estimates of body composition can be determined in athletes, and a more accurate body composition measurement produces better strength estimates. The best laboratory-based techniques were air displacement plethysmography and dual-energy x-ray absorptiometry, while the commercial methods had moderate-poor agreement. Prioritizing accurate body composition assessment ensures better strength estimates in athletes., Competing Interests: Conflict of interest JS received an investigator-initiated grant from Hologic, Inc. to fund the larger study and other grants on body composition from Hologic and GE Healthcare. SH is on the Medical Advisory Board for Tanita Corporation. TK is a current employee at Hologic. The remaining authors report no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background/objectives: Skeletal muscle mass (SMM) estimation is important but challenging in clinical settings. Criterion methods, such as magnetic resonance imaging (MRI), are often inaccessible. However, surrogate methods, such as dual-energy X-ray absorptiometry (DXA) and multi-frequency bioelectrical impedance analysis (MFBIA), can use MRI-based equations to estimate SMM, although the agreement between these methods is unclear., Subjects/methods: Total and segmental SMM were estimated with DXA and MFBIA using MRI-based equations in 313 healthy adults (120 M, 193 F; age 30.2 ± 13.0 y; BMI 24.6 ± 4.0 kg/m 2 ). DXA total SMM was estimated using the Kim and McCarthy equations, and segmental SMM was estimated using the McCarthy equations. Relationships between DXA and MFBIA SMM were examined using Deming regression, Lin's concordance correlation coefficient (CCC), equivalence testing, Bland-Altman analysis, and related tests., Results: Strong linear relationships were observed for total (R 2 0.95, CCC 0.96-0.97), leg (R 2 0.90, CCC 0.85) and arm (R 2 0.93, CCC 0.93) SMM in the entire sample. Kim equation SMM demonstrated statistical equivalence with MFBIA for total SMM, but the Deming regression slope differed from 1 and proportional bias was present. McCarthy equation total SMM exhibited a regression slope that did not differ from 1, and no proportional bias was present in the entire sample. However, equivalence with MFBIA was not observed. Systematically higher leg and arm SMM values were observed with DXA as compared to MFBIA., Conclusions: While DXA and MFBIA total SMM generally exhibited strong agreement, higher appendicular SMM by DXA highlights technical differences between methods., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: The obesity epidemic brought a need for accessible methods to monitor body composition, as excess adiposity has been associated with cardiovascular disease, metabolic disorders, and some cancers. Recent 3-dimensional optical (3DO) imaging advancements have provided opportunities for assessing body composition. However, the accuracy and precision of an overall 3DO body composition model in specific subgroups are unknown., Objectives: This study aimed to evaluate 3DO's accuracy and precision by subgroups of age, body mass index, and ethnicity., Methods: A cross-sectional analysis was performed using data from the Shape Up! Adults study. Each participant received duplicate 3DO and dual-energy X-ray absorptiometry (DXA) scans. 3DO meshes were digitally registered and reposed using Meshcapade. Principal component analysis was performed on 3DO meshes. The resulting principal components estimated DXA whole-body and regional body composition using stepwise forward linear regression with 5-fold cross-validation. Duplicate 3DO and DXA scans were used for test-retest precision. Student's t tests were performed between 3DO and DXA by subgroup to determine significant differences., Results: Six hundred thirty-four participants (females = 346) had completed the study at the time of the analysis. 3DO total fat mass in the entire sample achieved R 2 of 0.94 with root mean squared error (RMSE) of 2.91 kg compared to DXA in females and similarly in males. 3DO total fat mass achieved a % coefficient of variation (RMSE) of 1.76% (0.44 kg), whereas DXA was 0.98% (0.24 kg) in females and similarly in males. There were no mean differences for total fat, fat-free, percent fat, or visceral adipose tissue by age group (P > 0.068). However, there were mean differences for underweight, Asian, and Black females as well as Native Hawaiian or other Pacific Islanders (P < 0.038)., Conclusions: A single 3DO body composition model produced accurate and precise body composition estimates that can be used on diverse populations. However, adjustments to specific subgroups may be warranted to improve the accuracy in those that had significant differences. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults)., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background/objectives: Fat-free mass (FFM) often serves as a body composition outcome variable in weight loss studies. An important assumption is that the proportions of components that make up FFM remain stable following weight loss; some body composition models rely on these "constants". This exploratory study examined key FFM component proportions before and following weight loss in two studies of participants with overweight and obesity., Subjects/methods: 201 men and women consumed calorie-restricted moderate- or very-low carbohydrate diets leading to 10-18% weight loss in 9-15 weeks. Measured total body fat, lean mass, bone mineral, total body water (TBW), and body weight at baseline and follow-up were used to derive FFM and its chemical proportions using a four-component model., Results: A consistent finding in both studies was a non-significant reduction in bone mineral and a corresponding increase (p < 0.001) in bone mineral/FFM; FFM density increased significantly in one group of women and in all four participant groups combined (both, p < 0.05). FFM hydration (TBW/FFM) increased in all groups of men and women, one significantly (p < 0.01), and in the combined sample (borderline, p < 0.10). The proportion of FFM as protein decreased across all groups, two significantly (p < 0.05-0.01) and in the combined sample (p < 0.05)., Conclusion: FFM relative proportions of chemical components may not be identical before and after short-term weight loss, an observation impacting some widely used body composition models and methods. Caution is thus needed when applying FFM as a safety signal or to index metabolic evaluations in clinical trials when these body composition approaches are used., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

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 (R 2 ) 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)., (Copyright © 2023 American Society for Nutrition. All rights reserved.)

Background: Skeletal muscle is a large and clinically relevant body component that has been difficult and impractical to quantify outside of specialized facilities. Advances in smartphone technology now provide the opportunity to quantify multiple body surface dimensions such as circumferences, lengths, surface areas, and volumes., Objectives: This study aimed to test the hypothesis that anthropometric body measurements acquired with a smartphone application can be used to accurately estimate an adult's level of muscularity., Methods: Appendicular lean mass (ALM) measured by DXA served as the reference for muscularity in a sample of 322 adults. Participants also had digital anthropometric dimensions (circumferences, lengths, and regional and total body surface areas and volumes) quantified with a 20-camera 3D imaging system. Least absolute shrinkage and selection operator (LASSO) regression procedures were used to develop the ALM prediction equations in a portion of the sample, and these models were tested in the remainder of the sample. Then, the accuracy of the prediction models was cross-validated in a second independent sample of 53 adults who underwent ALM estimation by DXA and the same digital anthropometric estimates acquired with a smartphone application., Results: LASSO models included multiple significant demographic and 3D digital anthropometric predictor variables. Evaluation of the models in the testing sample indicated respective RMSEs in women and men of 1.56 kg and 1.53 kg and R 2 's of 0.74 and 0.90, respectively. Cross-validation of the LASSO models in the smartphone application group yielded RMSEs in women and men of 1.78 kg and 1.50 kg and R 2 's of 0.79 and 0.95; no significant differences or bias between measured and predicted ALM values were observed., Conclusions: Smartphone image capture capabilities combined with device software applications can now provide accurate renditions of the adult muscularity phenotype outside of specialized laboratory facilities. Am J Clin Nutr 2023;x:xx. This trial was registered at clinicaltrials.gov as NCT03637855 (https://clinicaltrials.gov/ct2/show/NCT03637855), NCT05217524 (https://clinicaltrials.gov/ct2/show/NCT05217524), and NCT03771417 (https://clinicaltrials.gov/ct2/show/NCT03771417)., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

Background: Novel advancements in wearable technologies include continuous measurement of body composition via smart watches. The accuracy and stability of these devices are unknown., Objectives: This study evaluated smart watches with integrated bioelectrical impedance analysis (BIA) sensors for their ability to measure and monitor changes in body composition., Methods: Participants recruited across BMIs received duplicate body composition measures using 2 wearable bioelectrical impedance analysis (W-BIA) model smart watches in sitting and standing positions, and multiple versions of each watch were used to evaluate inter- and intramodel precision. Duplicate laboratory-grade octapolar bioelectrical impedance analysis (8-BIA) and criterion DXA scans were acquired to compare estimates between the watches and laboratory methods. Test-retest precision and least significant changes assessed the ability to monitor changes in body composition., Results: Of 109 participants recruited, 75 subjects completed the full manufacturer-recommended protocol. No significant differences were observed between W-BIA watches in position or between watch models. Significant fat-free mass (FFM) differences (P < 0.05) were observed between both W-BIA and 8-BIA when compared to DXA, though the systematic biases to the criterion were correctable. No significant difference was observed between the W-BIA and the laboratory-grade BIA technology for FFM (55.3 ± 14.5 kg for W-BIA versus 56.0 ± 13.8 kg for 8-BIA; P > 0.05; Lin's concordance correlation coefficient = 0.97). FFM was less precise on the watches than DXA {CV, 0.7% [root mean square error (RMSE) = 0.4 kg] versus 1.3% (RMSE = 0.7 kg) for W-BIA}, requiring more repeat measures to equal the same confidence in body composition changes over time as DXA., Conclusions: After systematic correction, smart-watch BIA devices are capable of stable, reliable, and accurate body composition measurements, with precision comparable to but lower than that of laboratory measures. These devices allow for measurement in environments not accessible to laboratory systems, such as homes, training centers, and geographically remote locations., (Copyright © 2022 American Society for Nutrition.)

Background: Many predictors of morbidity caused by metabolic disease are associated with body shape. 3D optical (3DO) scanning captures body shape and has been shown to accurately and precisely predict body composition variables associated with mortality risk. 3DO is safer, less expensive, and more accessible than criterion body composition assessment methods such as dual-energy X-ray absorptiometry (DXA). However, 3DO scanning has not been standardized across manufacturers for pose, mesh resolution, and post processing methods., Purpose: We introduce a scanner-agnostic algorithm that automatically fits a topologically consistent human mesh to 3DO scanned point clouds and predicts clinically important body metrics using a standardized body shape model. Our models transform raw scans captured by any 3DO scanner into fixed topology meshes with anatomical consistency, standardizing the outputs of 3DO scans across manufacturers and allowing for the use of common prediction models across scanning devices., Methods: A fixed-topology body mesh template was automatically registered to 848 training scans from three different 3DO systems. Participants were between 18 and 89 years old with body mass index ranging from 14 to 52 kg/m 2 . Scans were registered by first performing a coarse nearest neighbor alignment between the template and the input scan with an anatomically constrained principal component analysis (PCA) domain deformation using a device and gender specific bootstrap basis trained on 70 seed scans each. The template mesh was then optimized to fit the target with a smooth per-vertex surface-to-surface deformation. A combined unified PCA model was created from the superset of all automatically fit training scans including all three devices. Body composition predictions to DXA measurements were learned from the training mesh PCA coefficients using linear regression. Using this final unified model, we tested the accuracy of our body composition models on a withheld sample of 562 scans by fitting a PCA parameterized template mesh to each raw scan and predicting the expected body composition metrics from the principal components using the learned regression model., Results: We achieved coefficients of determination (R 2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R 2 was as high as 0.94 (total fat and lean, trunk fat), and all root-mean-squared errors were below 3.0 kg. All predicted body composition variables were not significantly different from reference DXA measurements except for visceral fat and female trunk fat. Repeatability precision as measured by the coefficient of variation (%CV) was around 2-3x worse than DXA precision, with visceral fat %CV below 2x DXA %CV and female total fat mass at 5x., Conclusions: Our method provides an accurate, automated, and scanner agnostic framework for standardizing 3DO scans and a low cost, radiation-free alternative to criterion radiology imaging for body composition analysis. We published a web-app version of this work at https://shapeup.shepherdresearchlab.org/3do-bodycomp-analyzer/ that accepts mesh file uploads and returns templated meshes with body composition predictions for demo purposes., (© 2022 American Association of Physicists in Medicine.)

Background: Deuterium oxide (D2O) dilution is the criterion method for total body water (TBW) measurement, but results may vary depending on the specimen type, analysis method, and analyzing laboratory. Bioelectrical impedance (BIA) estimates TBW, but results may vary by device make and model., Objectives: We investigated the accuracy and precision of TBW estimates and how measurement conditions affected the accuracy of body composition using multicompartment body composition models., Methods: Eighty collegiate athletes received duplicate TBW measures acquired from 3 BIA devices (S10, SFB7, and SOZO) and from unique D2O combinations of specimen type (saliva, urine), analysis methodology [Fourier transform infrared spectrophotometry (FTIR), isotope-ratio mass spectrometry (IRMS)], and 3 different laboratories. TBW measures were substituted into 2-compartment (2C) and 5-compartment (5C) body composition models. Criterion measures were compared using Lin's concordance correlation coefficient cutoff of poor (<0.90), moderate (0.90-0.95), substantial (0.95-0.99), and almost perfect (>0.99)., Results: Fifty-one participants (26 female) completed the protocol. Using IRMS saliva as the criterion TBW, all other measures produced a substantial or almost perfect agreement, except for SFB7 (poor) and SOZO (moderate). The 2C body composition measures using D2O and BIA produced poor agreement except for moderate agreement for lab 3 FTIR saliva. The 5C body composition measures using D2O produced a substantial agreement, whereas the BIA device S10 and SOZO had a moderate agreement, while the SFB7 had a poor agreement to the criterion. Test-retest precision varied between techniques from 0.3% to 1.2% for TBW., Conclusions: Small differences in TBW measurement led to significant differences in 2C models. The 5C models partially mitigate differences seen in 2C models when different TBW measures are used. Interchanging TBW measures in multicompartment models can be problematic and should be performed with these considerations., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Body mass is the primary metabolic compartment related to a vast number of clinical indices and predictions. The extent to which skeletal muscle (SM), a major body mass component, varies between people of the same sex, weight, height, and age is largely unknown. The current study aimed to explore the magnitude of muscularity variation present in adults and to examine if variation in muscularity associates with other body composition and metabolic measures., Methods: Muscularity was defined as the difference (residual) between a person's actual and model-predicted SM mass after controlling for their weight, height, and age. SM prediction models were developed using data from a convenience sample of 492 healthy non-Hispanic (NH) White adults (ages 18-80 years) who had total body SM and SM surrogate, appendicular lean soft tissue (ALST), measured with magnetic resonance imaging and dual-energy X-ray absorptiometry, respectively; residual SM (SM R ) and ALST were expressed in kilograms and kilograms per square meter. ALST mass was also evaluated in a population sample of 8623 NH-White adults in the 1999-2006 National Health and Nutrition Examination Survey. Associations between muscularity and variation in the residual mass of other major organs and tissues and resting energy expenditure were evaluated in the convenience sample., Results: The SM, on average, constituted the largest fraction of body weight in men and women up to respective BMIs of 35 and 25 kg/m 2 . SM in the convenience sample varied widely with a median of 31.2 kg and an SM R inter-quartile range/min/max of 3.35 kg/-10.1 kg/9.0 kg in men and 21.1 kg and 2.59 kg/-7.2 kg/7.5 kg in women; per cent of body weight as SM at 25th and 75th percentiles for men were 33.1% and 39.6%; corresponding values in women were 24.2% and 30.8%; results were similar for SM R indices and for ALST measures in the convenience and population samples. Greater muscularity in the convenience sample was accompanied by a smaller waist circumference (men/women: P < 0.001/=0.085) and visceral adipose tissue (P = 0.014/0.599), larger liver (P = 0.065/<0.001), kidneys (P = 0.051/<0.009), and bone mineral (P < 0.001/<0.001), and larger magnitude resting energy expenditure (P < 0.001/<0.001) than predicted for the same sex, age, weight, and height., Conclusions: Muscle mass is the largest body compartment in most adults without obesity and is widely variable in mass across people of similar body size and age; and high muscularity is accompanied by distinct body composition and metabolic characteristics. This previously unrecognized heterogeneity in muscularity in the general population has important clinical and research implications., (© 2022 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Objective: Three-dimensional optical (3DO) imaging devices for acquiring anthropometric measurements are proliferating in healthcare facilities, although applicability in young children has not been evaluated; small body size and movement may limit device accuracy. The current study aim was to critically test three commercial 3DO devices in young children., Methods: The number of successful scans and circumference measurements at six anatomic sites were quantified with the 3DO devices in 64 children, ages 5-8 years. Of the scans available for processing, 3DO and flexible tape-measure measurements made by a trained anthropometrist were compared., Results: Sixty of 181 scans (33.1%) could not be processed for technical reasons. Of processed scans, mean 3DO-tape circumference differences tended to be small (~1-9%) and varied across systems; correlations and bias estimates also varied in strength across anatomic sites and systems (e.g., regression R 2 s, 0.54-0.97, all p < 0.01). Overall findings differed across devices; best results were for a multi-camera stationary system and less so for two rotating single- or dual-camera systems., Conclusions: Available 3DO devices for quantifying anthropometric dimensions in adults vary in applicability in young children according to instrument design. These findings suggest the need for 3DO devices designed specifically for small and/or young children., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: The accurate assessment of total body and regional body circumferences, volumes, and compositions are critical to monitor physical activity and dietary interventions, as well as accurate disease classifications including obesity, metabolic syndrome, sarcopenia, and lymphedema. We assessed body composition and anthropometry estimates provided by a commercial 3-dimensional optical (3DO) imaging system compared to criterion measures., Methods: Participants of the Shape Up! Adults study were recruited for similar sized stratifications by sex, age (18-40, 40-60, >60 years), BMI (under, normal, overweight, obese), and across five ethnicities (non-Hispanic [NH] Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander). All participants received manual anthropometry assessments, duplicate whole-body 3DO (Styku S100), and dual-energy X-ray absorptiometry (DXA) scans. 3DO estimates provided by the manufacturer for anthropometry and body composition were compared to the criterion measures using concordance correlation coefficient (CCC) and Bland-Altman analysis. Test-retest precision was assessed by root mean square error (RMSE) and coefficient of variation., Results: A total of 188 (102 female) participants were included. The overall fat free mass (FFM) as measured by DXA (54.1 ± 15.2 kg) and 3DO (55.3 ± 15.0 kg) showed a small mean difference of 1.2 ± 3.4 kg (95% limits of agreement -7.0 to +5.6) and the CCC was 0.97 (95% CI: 0.96-0.98). The CCC for FM was 0.95 (95% CI: 0.94-0.97) and the mean difference of 1.3 ± 3.4 kg (95% CI: -5.5 to +8.1) reflected the difference in FFM measures. 3DO anthropometry and body composition measurements showed high test-retest precision for whole body volume (1.1 L), fat mass (0.41 kg), percent fat (0.60%), arm and leg volumes, (0.11 and 0.21 L, respectively), and waist and hip circumferences (all <0.60 cm). No group differences were observed when stratified by body mass index, sex, or race/ethnicity., Conclusions: The anthropometric and body composition estimates provided by the 3DO scanner are precise and accurate to criterion methods if offsets are considered. This method offers a rapid, broadly available, and automated method of body composition assessment regardless of body size. Further studies are recommended to examine the relationship between measurements obtained by 3DO scans and metabolic health in healthy and clinical populations., Competing Interests: Conflict of Interest All authors report no conflict of interest., (Published by Elsevier Ltd.)

Objective: The aim of this study was to investigate whether digitally re-posing three-dimensional optical (3DO) whole-body scans to a standardized pose would improve body composition accuracy and precision regardless of the initial pose., Methods: Healthy adults (n = 540), stratified by sex, BMI, and age, completed whole-body 3DO and dual-energy X-ray absorptiometry (DXA) scans in the Shape Up! Adults study. The 3DO mesh vertices were represented with standardized templates and a low-dimensional space by principal component analysis (stratified by sex). The total sample was split into a training (80%) and test (20%) set for both males and females. Stepwise linear regression was used to build prediction models for body composition and anthropometry outputs using 3DO principal components (PCs)., Results: The analysis included 472 participants after exclusions. After re-posing, three PCs described 95% of the shape variance in the male and female training sets. 3DO body composition accuracy compared with DXA was as follows: fat mass R 2 = 0.91 male, 0.94 female; fat-free mass R 2 = 0.95 male, 0.92 female; visceral fat mass R 2 = 0.77 male, 0.79 female., Conclusions: Re-posed 3DO body shape PCs produced more accurate and precise body composition models that may be used in clinical or nonclinical settings when DXA is unavailable or when frequent ionizing radiation exposure is unwanted., (© 2021 The Obesity Society.)

Background: Adaptive thermogenesis (AT) is currently defined as the fat-free mass (FFM)-independent change in resting energy expenditure (REE) in response to caloric restriction (CR) or overfeeding (OF). So far, the impact of changes in the anatomical and molecular composition of FFM on AT has not been addressed., Objectives: To assess the impact of changes in FFM composition on AT., Methods: FFM was assessed in 32 healthy young men during controlled 21-d CR and 14 d of subsequent OF. Anatomical (i.e., the organ/tissue level) and molecular (i.e., water, mineral, and protein content and thus body density) composition of FFM were characterized. REE was measured by indirect calorimetry., Results: With CR, body weight and REE decreased by 4.2 ± 0.9 kg and 173 ± 107 kcal/d, respectively, with corresponding increases of 3.5 ± 1.2 kg and 194 ± 110 kcal/d during OF (P < 0.001 for all changes). Changes in FFM explained 56.7% and 66.7% of weight loss and weight gain, respectively. Weight changes were associated with changes in various anatomical (i.e., masses of skeletal muscle, liver, kidneys, and brain) and molecular components (total body water, protein, and bone minerals) of FFM. After adjustments for changes in FFM only, AT was 116 ± 127 (P < 0.001) and 27 ± 115 kcal/d (NS) with CR and OF, respectively. Adjustments for FFM and its anatomical and molecular composition reduced AT in response to CR to 83 ± 116 and 122 ± 123 kcal/d (P < 0.05 and P < 0.001) whereas during OF, AT became significant at 87 ± 146 kcal/d (anatomical; P < 0.05) and 86 ± 118 kcal/d (molecular; P < 0.001)., Conclusions: Adjusting changes in REE with under- and overfeeding for the corresponding changes in the anatomical and molecular composition of FFM decreased AT after CR and increased AT after OF, but overall adjusted AT was likely not large enough in magnitude to be able to prevent weight loss or resist weight gain., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: Reference ranges for lean mass (LM) and fat mass (FM) are essential in identifying soft tissue disorders; however, no such reference ranges exist for the most commonly used Hologic dual-energy X-ray absorptiometry (DXA) machine in Australia., Methods: Cross-sectional study of community-dwelling adults (aged 18-88 years) who underwent a Hologic DXA scan at one of three commercialized densitometry centres in Australia. Age-specific and sex-specific percentile curves were generated for LM [LM, appendicular lean mass (ALM), ALM adjusted for height squared (ALM/h 2 ), and ALM adjusted for body mass index (ALM/BMI)] and FM [FM, FM adjusted for height squared (FM/h 2 ), appendicular fat mass, and android and gynoid fat] parameters using the LMS statistical method. Cutpoints equivalent to T-scores of -1, -2, and -2.5 standard deviations below the young mean reference group (20-29 years) were also generated for LM parameters., Results: A total of 15 479 community-dwelling adults (54% men) with a median age of 33 years (interquartile range: 28, 42) were included. LM, ALM, and ALM/h 2 remained stable until age 50, after which these parameters started to decline in both sexes. Compared with age 50, median percentiles of LM, ALM, and ALM/h 2 declined by -5.9 kg, -3.7 kg, and -0.86 kg/m 2 in men and by -2.5 kg, -1.8 kg, and -0.10 kg/m 2 in women at age 70, respectively. Adjusting ALM for BMI (rather than height squared) resulted in different trends, with ALM/BMI decreasing from as early as age 20. Compared with age 20, median percentiles of ALM/BMI at age 40 declined by -0.10 kg/kg/m 2 in men and by -0.06 kg/kg/m 2 in women; and at age 70, ALM/BMI declined by -0.25 kg/kg/m 2 in men and by -0.20 kg/kg/m 2 in women. Cutpoints equivalent to T-scores of -1, -2, and -2.5 standard deviations for ALM/BMI were 1.01, 0.86, and 0.77 kg/kg/m 2 in men and 0.70, 0.59, and 0.53 kg/kg/m 2 in women, respectively. All FM parameters progressively increased from age 20 and continued up until age 70., Conclusions: We developed reference ranges for LM and FM parameters from Hologic DXA machines in a large cohort of Australian adults, which will assist researchers and clinicians in identifying soft tissue disorders such as obesity, sarcopenia, and cachexia., (© 2021 The Authors. Journal of Cachexia, Sarcopenia and Muscle published by John Wiley & Sons Ltd on behalf of Society on Sarcopenia, Cachexia and Wasting Disorders.)

Accurate and precise body composition estimates, notably of total body adiposity, are a vital component of in vivo physiology and metabolic studies. The reference against which other body composition approaches are usually validated or calibrated is the family of methods referred to as multicomponent "body density" models. These models quantify three to six components by combining measurements of body mass, body volume, total body water, and osseous mineral mass. Body mass is measured with calibrated scales, volume with underwater weighing or air-displacement plethysmography, total body water with isotope dilution, and osseous mineral mass by dual-energy X-ray absorptiometry. Body density is then calculated for use in model as body mass/volume. Studies over the past decade introduced a new approach to quantifying body volume that relies on dual-energy X-ray absorptiometry measurements, an advance that simplifies multicomponent density model development by eliminating the need for underwater weighing or air-displacement plethysmography systems when these technologies are unavailable and makes these methods more accessible to research and clinical programs. This review critically examines these new dual-energy X-ray approaches for quantifying body volume and density, explores their shortcomings, suggests alternative derivation approaches, and introduces ideas for potential future research studies., (© 2021 World Obesity Federation.)

Purpose: Anthropometry is a method for quantifying body size and shape often used to derive body composition and health risk prediction models. Recent technology advancements led to development of three-dimensional (3D) optical scanners that can overcome most of the limitations associated with manual anthropometric data collection. However, each of the currently available devices offers proprietary measurements that do not match conventional anthropometric definitions. The aim of the current study was to develop and then evaluate the precision and accuracy of new "universal" 3D optical analysis software that calculates digital anthropometric volumes using identical standard landmarks across scanners., Methods: Dual-energy x-ray absorptiometry (DXA) and air displacement plethysmography (ADP) total body and regional volume and fat mass reference measurements and 3D optical scans from two proprietary devices were collected from 356 participants to evaluate the robustness of total body and regional volume and fat mass measurements calculated by the developed software. Linear regression modeling with threefold cross validation was used to evaluate total body and regional fat masses from 3D scans., Results: Total body and regional volumes measured by DXA and ADP had strong associations with corresponding estimates from the commercial 3D optical scanners coupled with the universal software (e.g., R 2  = 0.98 for Styku and R 2  = 1.00 for SS20, for both DXA and ADP comparisons). Regional body volumes also had strong correlation between DXA and the 3DO scanners (e.g., for arm, leg and trunk, respective R 2 s of 0.75, 0.86, and 0.97 for Styku and 0.79, 0.89, and 0.98 for SS20). Similarly, there were strong associations between DXA- measured total body and regional fat mass and 3D optical estimates calculated by the universal software (e.g., for total body, arm, leg and trunk, respective R 2 s of 0.86, 0.72, 0.77, and 0.88 for Styku and 0.84, 0.76, 0.78, and 0.85 for SS20). Absolute differences in volumes and fat mass between the reference methods and the universal software values revealed underlying proprietary scanner differences that can be improved when designing future devices., Conclusions: The current study suggests that, when compared against values calculated using DXA and ADP, the universal software was able to measure total and regional body volumes reliably from scans obtained by two different scanners. The universal software, with future refinements, combined with potential optical scanner design improvements, creates new opportunities for developing large multicenter anthropometric databases with uniformly defined body dimensions that can be used for modeling health risks., Clinical Trial Registration Id: Shape Up! Adults Study, NCT0363785., (© 2021 American Association of Physicists in Medicine.)

Background: Calf circumference (CC) is used in geriatric studies as a simple and practical skeletal muscle (SM) marker for diagnosing low SM and sarcopenia. Currently applied CC cutoff points were developed in samples including older participants; values representative of the full adult lifespan are lacking., Objectives: We aimed to develop CC cutoff points and to identify relevant confounding factors from the large and diverse NHANES 1999-2006 population sample., Methods: Demographic, anthropometric, and imaging data (DXA, appendicular lean mass) from the adult (age ≥18 y) NHANES sample were partitioned into subgroups according to sex, age, ethnicity, and race. Adults aged 18-39 y and BMI (in kg/m2) 18.5-24.9 were set as a reference population; CC cutoff points were derived at 1 and 2 SDs below the mean., Results: The sample included 17,789 participants, 51.3% males and 48.7% females, with respective ages (mean ± SD) of 43.3 ± 16.1 y and 45.5 ± 16.9 y. CC was strongly correlated with appendicular lean mass, r = 0.84 and 0.86 for males and females (both P < 0.001), respectively. Significant differences in mean CC were present across sex, ethnic, self-reported race, and BMI groups. Adjusting CC for adiposity using BMI revealed a decrease in CC beginning after the second decade in males and third decade in females. Rounded CC cutoff values for moderately and severely low CC were 34 cm and 32 cm (males), and 33 cm and 31 cm (females), respectively. Our findings support the use of BMI-adjusted CC values for participants outside the normal-weight BMI range (18-24.9)., Conclusions: This study defined CC values in a diverse population sample along with a BMI-adjustment approach that helps to remove the confounding effects of adiposity and thereby improves CC as a useful clinical estimate of SM mass., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Importance: Antibody blockade of activin type II receptor (ActRII) signaling stimulates skeletal muscle growth. Previous clinical studies suggest that ActRII inhibition with the monoclonal antibody bimagrumab also promotes excess adipose tissue loss and improves insulin resistance., Objective: To evaluate the efficacy and safety of bimagrumab on body composition and glycemic control in adults with type 2 diabetes and overweight and obesity., Design, Setting, and Participants: This double-masked, placebo-controlled, 48-week, phase 2 randomized clinical trial was conducted among adults with type 2 diabetes, body mass index between 28 and 40, and glycated hemoglobin (HbA1c) levels between 6.5% and 10.0% at 9 US and UK sites. The trial was conducted from February 2017 to May 2019. Only participants who completed a full treatment regimen were included in analysis., Interventions: Patients were randomized to intravenous infusion of bimagrumab (10 mg/kg up to 1200 mg in 5% dextrose solution) or placebo (5% dextrose solution) treatment every 4 weeks for 48 weeks; both groups received diet and exercise counseling., Main Outcomes and Measures: The primary end point was least square mean change from baseline to week 48 in total body fat mass (FM); secondary and exploratory end points were lean mass (LM), waist circumference (WC), HbA1c level, and body weight (BW) changes from baseline to week 48., Results: A total of 75 patients were randomized to bimagrumab (n = 37; 23 [62.2%] women) or placebo (n = 38; 12 [31.6%] women); 58 (77.3%) completed the 48-week study. Patients at baseline had a mean (SD) age of 60.4 (7.7) years; mean (SD) BMI of 32.9 (3.4); mean (SD) BW of 93.6 (14.9) kg; mean (SD) FM of 35.4 (7.5) kg; and mean (SD) HbA1c level of 7.8% (1.0%). Changes at week 48 for bimagrumab vs placebo were as follows: FM, -20.5% (-7.5 kg [80% CI, -8.3 to -6.6 kg]) vs -0.5% (-0.18 kg [80% CI, -0.99 to 0.63 kg]) (P < .001); LM, 3.6% (1.70 kg [80% CI, 1.1 to 2.3 kg]) vs -0.8% (-0.4 kg [80% CI, -1.0 to 0.1 kg]) (P < .001); WC, -9.0 cm (80% CI, -10.3 to -7.7 cm) vs 0.5 cm (80% CI, -0.8 to 1.7 cm) (P < .001); HbA1c level, -0.76 percentage points (80% CI, -1.05 to -0.48 percentage points) vs -0.04 percentage points (80% CI, -0.23 to 0.31 percentage points) (P = .005); and BW, -6.5% (-5.9 kg [80% CI, -7.1 to -4.7 kg]) vs -0.8% (-0.8 kg [80% CI, -1.9 to 0.3 kg]) (P < .001). Bimagrumab's safety and tolerability profile was consistent with prior studies., Conclusions and Relevance: In this phase 2 randomized clinical trial, ActRII blockade with bimagrumab led to significant loss of FM, gain in LM, and metabolic improvements during 48 weeks in patients with overweight or obesity who had type 2 diabetes. ActRII pathway inhibition may provide a novel approach for the pharmacologic management of excess adiposity and accompanying metabolic disturbances., Trial Registration: ClinicalTrials.gov number: NCT03005288.

Purpose: Total and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera., Methods: Duplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets., Results: Results were reported on a held-out set. Body composition prediction achieved R 2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests., Conclusion: Total and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible., (© 2020 American Association of Physicists in Medicine.)

Accurate measurement of body composition is required to improve health outcomes in children and adolescents with overweight or obesity. This systematic review aimed to summarize the reliability and validity of field and laboratory body composition techniques employed in pediatric obesity studies to facilitate technique selection for research and clinical practice implementation. A systematic search in MEDLINE (via PubMed), EMBASE, CINAHL, and SPORTDiscus from inception up to December 2019 was conducted, using a combination of the following concepts: body composition, pediatric overweight/obesity, and reliability/validity. The search strategy resulted in 66 eligible articles reporting reliability (19.7%), agreement between body composition techniques cross sectionally (80.3%), and/or diagnostic test accuracy (10.6%) in children and adolescents with overweight or obesity (mean age range = 7.0-16.5 years). Skinfolds, air-displacement plethysmography (ADP), dual-energy X-ray absorptiometry (DXA), and ultrasound presented as reliable techniques. DXA, ADP, and isotope dilution showed similar and the best agreement with reference standards. Compared with these laboratory techniques, the validity of estimating body composition by anthropometric equations, skinfolds, and BIA was inferior. In conclusion, the assessment of body composition by laboratory techniques cannot be replaced by field techniques due to introduction of measurement errors, which potentially conceal actual changes in body components., (© 2020 World Obesity Federation.)

Body composition estimates are widely used in clinical research and field studies as measures of energy-nutrient balance, functionality and health. Despite their broad relevance and multiple applications, important gaps remain in techniques available for accurately and precisely quantifying body composition in infants and children from birth through 5 years. Identifying these gaps and highlighting research needs in this age group were the topics of a National Institutes of Health workshop held in Bethesda, MD, USA, 30-31 May 2019. Experts reviewed available methods (multicompartment models, air-displacement plethysmography, dual-energy X-ray absorptiometry, weight-length and height indices, bioimpedance analysis, anthropometry-skinfold techniques, quantitative magnetic resonance, optical imaging, omics and D3-creatine dilution), their limitations in this age range and high priority research needs. A summary of their individual and collective workshop deliberations is provided in this report., (© 2020 World Obesity Federation.)

The increasing prevalence and adaptability of 3D optical scan (3DO) technology has invoked many recent studies which use 3DO scanning as a convenient and inexpensive means for predicting body composition and health risks. The Shape Up studies seek a device-agnostic solution for body composition estimation based on principal component analysis (PCA). This paper reports a progress made on Shape Up's previous work which served as a criterion analysis for PCA-based body composition and health risk prediction. This study presents proof-of-concept for a novel automated landmark detection step that allows for a fully automated PCA-based approach to body composition estimation that facilitates a practical device-agnostic PCA-based solution to body composition estimation from 3DO scans. Our results show that replacing expensive and time-consuming manual point placement with the proposed automated landmarks will not diminish the quality of body composition estimates allowing for a more practical pipeline that can be used in real-world settings.

Background/objectives: Three-dimensional optical (3DO) imaging systems that rapidly and accurately provide body shape and composition information are increasingly available in research and clinical settings. Recently, relatively low-cost and space efficient 3DO systems with the ability to report and track individual assessments were introduced to the consumer market for home use. This study critically evaluated the first 3DO imaging device intended for personal operation, the Naked Body Scanner (NBS), against reference methods., Participants/methods: Circumferences at six standardized anatomic sites were measured with a flexible tape in 90 participants ranging in age (5-74 years), ethnicity, and adiposity. Regression analysis and Bland-Altman plots compared these direct measurements and dual-energy X-ray absorptiometry (DXA) %fat estimates to corresponding NBS values. Method precision was analyzed from duplicate anthropometric and NBS measurements in a subgroup of 51 participants., Results: The NBS exhibited greater variation in test-retest reliability (CV, 0.4-2.7%) between the six measured anatomic locations when compared with manually measured counterparts (0.2-0.4%). All six device-derived circumferences correlated with flexible tape references (R 2 s, 0.84-0.97; p < 0.0001). Measurement bias was apparent for three anatomic sites while mean differences were present for five. The NBS's %fat estimates also correlated with DXA results (R 2  = 0.73, p < 0.0001) with no significant bias., Conclusions: This system opens a new era of digital home-based assessments that can be incorporated into weight loss or exercise interventions accessible to clinical investigators as well as individual users.

Estimates of body composition have been derived using 3-dimensional optical imaging (3DO), but no equations to date have been calibrated using a 4-component (4C) model criterion. This investigation reports the development of a novel body fat prediction formula using anthropometric data from 3DO imaging and a 4C model. Anthropometric characteristics and body composition of 179 participants were measured via 3DO (Size Stream ® SS20) and a 4C model. Machine learning was used to identify significant anthropometric predictors of body fat (BF%), and stepwise/lasso regression analyses were employed to develop new 3DO-derived BF% prediction equations. The combined equation was externally cross-validated using paired 3DO and DXA assessments (n = 158), producing a R 2 value of 0.78 and a constant error of (X ± SD) 0.8 ± 4.5%. 3DO BF% estimates demonstrated equivalence with DXA based on equivalence testing with no proportional bias in the Bland-Altman analysis. Machine learning methods may hold potential for enhancing 3DO-derived BF% estimates.

One century ago Harris and Benedict published a short report critically examining the relations between body size, body shape, age, and basal metabolic rate. At the time, basal metabolic rate was a vital measurement in diagnosing diseases such as hypothyroidism. Their conclusions and basal metabolic rate prediction formulas still resonate today. Using the Harris-Benedict approach as a template, we systematically examined the relations between body size, body shape, age, and skeletal muscle mass (SM), the main anatomic feature of sarcopenia. The sample consisted of 12,330 non-Hispanic (NH) white and NH black participants in the US National Health and Nutrition Survey who had complete weight, height, waist circumference, age, and dual-energy X-ray (DXA) absorptiometry data. A conversion formula was used to derive SM from DXA-measured appendicular lean soft tissue mass. Weight, height, waist circumference, and age alone and in combination were significantly correlated with SM (all, p < 0.001). Advancing analyses through the aforementioned sequence of predictor variables allowed us to establish how at the anatomic level these body size, body shape, and age measures relate to SM much in the same way the Harris-Benedict equations provide insights into the structural origins of basal heat production. Our composite series of SM prediction equations should prove useful in modeling efforts and in generating hypotheses aimed at understanding how SM relates to body size and shape across the adult lifespan., (Copyright © 2020 Heymsfield, Stanley, Pietrobelli and Heo.)

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., (Copyright © American Society for Nutrition 2019.)

Objective: This study aimed to explore the accuracy and precision of three-dimensional optical (3DO) whole-body scanning for automated anthropometry and estimating total and regional body composition., Methods: Healthy children and adolescents (n = 181, ages 5-17 years) were recruited for the Shape Up! Kids study. Each participant underwent whole-body dual-energy x-ray absorptiometry and 3DO scans; multisite conventional tape measurements served as the anthropometric criterion measure. 3DO body shape was described using automated body circumference, length, and volume measures. 3DO estimates were compared with criterion measures using simple linear regression by the stepwise selection method., Results: Of the 181 participants, 112 were used for the training set, 49 were used for the test set, and 20 were excluded for technical reasons. 3DO body composition estimates were strongly associated with dual-energy x-ray absorptiometry measures for percent body fat, fat mass, and fat-free mass (R 2 : 0.83, 0.96, and 0.98, respectively). 3DO provided reliable measurements of fat mass (coefficient of variation, 3.30; root mean square error [RMSE], 0.53), fat-free mass (coefficient of variation, 1.34; RMSE, 0.53 kg), and percent body fat (RMSE = 1.2%)., Conclusions: 3DO surface scanning provides accurate and precise anthropometric and body composition estimates in children and adolescents with high precision. 3DO is a safe, accessible, and practical method for evaluating body shape and composition in research and clinical settings., (© 2019 The Obesity Society.)

Background: Low appendicular skeletal muscle mass (ASM) is associated with negative outcomes, but its assessment requires proper limb muscle evaluation. We aimed to verify how anthropometric circumferences are correlated to ASM and to develop new prediction equations based on calf circumference and other anthropometric measures, using dual-energy X-ray absorptiometry (DEXA) as the reference method., Methods: DEXA and anthropometric information from 15,293 adults surveyed in the 1999-2006 NHANES were evaluated. ASM was defined by the sum of the lean soft tissue from the limbs. Anthropometric data included BMI and calf, arm, thigh, and waist circumferences. Correlations were assessed by Pearson's correlation, and multivariable linear regression produced 4 different ASM prediction equations. The concordance and the overall 95% limits of agreement between measured and estimated ASM were assessed using Lin's coefficient and Bland-Altman's approach., Results: Calf and thigh circumferences were highly correlated with ASM, independent of age and ethnicity. Among the models, the best performance came from the equation constituted solely by calf circumference, sex, race, and age as independent variables, which was able to explain almost 90% of the DEXA-measured ASM variation. The inclusion of different anthropometric parameters in the model increased collinearity without improving estimates. Concordance between the four developed equations and DEXA-measured ASM was high (Lin's concordance coefficient >0.90)., Conclusion: Despite the good performance of the four developed equations in predicting ASM, the best results came from the equation constituted only by calf circumference, sex, race, and age. This equation allows satisfactory ASM estimation from a single anthropometric measurement., (© 2019 American Society for Parenteral and Enteral Nutrition.)

Skeletal muscle is recognized as a tissue with high metabolic capacity given its key roles in glucose and lipid metabolism. Although low muscle mass has been associated with metabolic disorders in adults, it is not clear if this body composition phenotype is related to metabolic health status earlier in life. In this review, we aim to clarify whether having low muscle mass is associated with increased risk of metabolic dysregulation in the pediatric population. Fifteen original articles investigating the relationship between body composition measures of muscle mass and single or clustered metabolic risk factors in children and adolescents were critically evaluated. Despite a growing body of evidence supporting low muscle mass as a risk factor for metabolic health in children and adolescents, conflicting associations were reported. Differences in body composition techniques, muscle mass indices, and clinical methods used to assess metabolic biomarkers may have contributed to a lack of a consistent conclusion. Moreover, most studies did not control for potential biological and lifestyle confounders. Future studies using precise, reproducible techniques to evaluate body composition and metabolic biomarkers are required to determine the implications of low muscle mass on metabolic health during childhood and adolescence., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Objective: Chronic positive energy balance leads to obesity, and the "excess" weight is usually described as consisting solely of adipose tissue (AT) or its two components, fat and fat-free mass (nonfat cell mass, extracellular fluid). This study aimed to clarify the nature of "obesity" tissue., Methods: A total of 333 adults had AT, skin, skeletal muscle, bone, heart, liver, kidney, spleen, brain, and residual mass measured or derived using magnetic resonance imaging and dual-energy x-ray absorptiometry. First, associations between these components and AT were examined by developing multiple regression models. Next, obesity-tissue composition was developed by deriving mean component mass differences between participant groups with normal weight (BMI < 25 kg/m 2 ) and those with obesity (BMI > 29.9 kg/m 2 ); respective resting energy expenditures and metabolizable energy and protein contents were calculated., Results: AT significantly predicted organ-tissue mass in 17 of 18 multiple regression models. In addition to AT and skeletal muscle, the following associations were found: skin, liver, and bone were main contributors to obesity-tissue composition; liver, kidneys, and heart to resting energy expenditure; and skin, liver, and bone to metabolizable energy and protein contents. A pronounced sexual dimorphism was present in all three models., Conclusions: Obesity is characterized not only by excess AT but by increases in the masses of other "companion" organs and tissues and their related metabolic properties., (© 2019 The Obesity Society.)

Objectives: The scaling of structural components to body size is well studied in mammals, although comparable human observations in a large and diverse sample are lacking. The current study aimed to fill this gap by examining the scaling relationships between total body (TB) and regional bone and skeletal muscle (SM) mass with body size, as defined by stature, in a nationally representative sample of the US population., Methods: Subjects were 17,126 non-Hispanic (NH) white, NH black, and Mexican American men and women, aged ≥18 years, evaluated in the National Health and Nutrition Examination Survey who had TB and regional bone mineral (BMin) and lean soft tissue (LST) mass measured by dual-energy X-ray absorptiometry. BMin and appendicular LST served as surrogate bone and SM mass measures, respectively. The allometric model, BMin or LST = α(height) β , in a logarithmic form was used to generate scaling exponents., Results: The findings were similar across all gender and race groups: body mass scaled to height with powers of ~2.0 (mean β ± SE, 1.94 ± 0.08-2.29 ± 0.09) while TB and appendicular BMin and appendicular LST scaled to height with consistently larger powers than those for body mass (eg, all P < .05 in NH white men and women); the largest BMin and LST scaling powers to height were observed in the lower extremities., Conclusions: Bone and SM mass, notably those of the lower extremities, increase as proportions of body mass with greater adult height. Metabolic and biomechanical implications emerge from these observations, the first of their kind in a representative adult US population sample., (© 2019 Wiley Periodicals, Inc.)

Body mass in humans and animals is strongly associated with the rate of heat production as defined by resting energy expenditure (REE). Beginning with the ancient Greeks up to the present time, philosophers and scientists have endeavored to understand the nature and sources of bodily heat. Today we recognize that body mass consists of organs and tissues, each of which produces a specified amount of heat at rest. An individual organ's REE can now be estimated in vivo as the product of its assumed mass-specific metabolic rate and its imaging-derived mass; whole-body REE reflects the sum of organ and tissue metabolic rates. The sizes of organs and total body mass in adults are governed by two main factors, a person's stature or height, and their level of adiposity. With greater body size, as represented by adult height independent of adiposity, organs remain stable or increase in mass according to distinct "scaling" patterns. Similarly, with greater relative adiposity organs adaptively accommodate to the increase in imposed mechanical and metabolic loading conditions. Through a detailed analysis of these stature and adiposity effects, we show how classical statistical REE prediction models can be mechanistically understood at the anatomic body composition level.

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.

Abnormalities in body composition can occur at any body weight. Low muscle mass is a predictor of poor morbidity and mortality and occurs in several populations. This narrative review provides an overview of the importance of low muscle mass on health outcomes for patients in inpatient, outpatient and long-term care clinical settings. A one-year glimpse at publications that showcases the rapidly growing research of body composition in clinical settings is included. Low muscle mass is associated with outcomes such as higher surgical and post-operative complications, longer length of hospital stay, lower physical function, poorer quality of life and shorter survival. As such, the potential clinical benefits of preventing and reversing this condition are likely to impact patient outcomes and resource utilization/health care costs. Clinically viable tools to measure body composition are needed for routine screening and intervention. Future research studies should elucidate the effectiveness of multimodal interventions to counteract low muscle mass for optimal patient outcomes across the healthcare continuum. Key messages Low muscle mass is associated with several negative outcomes across the healthcare continuum. Techniques to identify and counteract low muscle mass in clinical settings are needed.

Background/objectives: Body mass index (BMI) is a proxy for obesity that is commonly used in spite of its limitation in estimating body fatness. Trained observers with repeated exposure to different body types can estimate body fat (BF) of individuals compared to criterion methods with reasonable accuracy. The purpose of this study was to develop and validate a computer algorithm to provide a valid estimate %BF using digital photographs., Subjects/methods: Our sample included 97 children and 226 adults (age in years: 11.3±3.3; 38.1±11.6, respectively). Measured height and weight were used (BMI in kg/m2: 20.4±4.4; 28.7±6.6 for children and adults, respectively). Dual x-ray absorptiometry (DXA) was the criterion method. Body volume (BVPHOTO) and body shape (BSPHOTO) were derived from two digital images. Final support vector regression (SVR) models were trained using age, sex, race, BMI for % BFNOPHOTO, plus BVPHOTO and BSPHOTO for %BFPHOTO. Separate validation models were used to evaluate the learning algorithm in children and adults. The differences in correlations between %BFDXA, %BFNOPHOTO and %BFPHOTO were tested using the Fisher's Z-score transformation., Results: Mean BFDXA and BFPHOTO were 27.0%±9.2 vs. 26.7%± 7.4 in children and 32.9± 10.4% vs. 32.8%±9.3 in adults. SVR models produced %BFPHOTO values strongly correlated with %BFDXA. Our final model produced correlations of rDP = 0.80 and rDP = 0.87 in children and adults, respectively for %BFPHOTO vs. %BFDXA. The correlation between %BFNOPHOTO and %BFDXA was moderate, yet statistically significant in both children rDB = 0.70; p <0.0001 and adults rDB = 0.86; p<0.0001. However, the correlations for rDP were statistically higher than rDB (%BFDXA vs. %BFNOPHOTO) in both children and adults (children: Z = 5.95, p<0.001; adults: Z = 3.27, p<0.0001)., Conclusions: Our photographic method produced valid estimates of BF in both children and adults. Further research is needed to create norms for subgroups by sex, race/ethnicity, and mobility status., Competing Interests: No honoraria, grants, or other forms of payment were given to anyone to produce this manuscript. However, Dr. Allison has received grants, honoraria, donations, and consulting fees from numerous food, beverage, pharmaceutical companies, and other commercial, government, and nonprofit entities with interests in obesity. None of Dr. Allison’s sponsors were involved in the design, data collection, analysis, interpretation, or decision to publish the results presented in this manuscript. The remaining authors of this manuscript have no competing interests to disclose. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Background: The 4-component (4C) model is a criterion method for human body composition that separates the body into fat, water, mineral, and protein, but requires 4 measurements with significant cost and time requirements that preclude wide clinical use. A simplified model integrating only 2 measurements-dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA)-and 10 min of patient time has been proposed., Objective: We aimed to validate a rapid, simplified 4C DXA + BIA body composition model in a clinical population., Design: This was a cross-sectional observational study of 31 healthy adults. Participants underwent whole-body DXA, segmental BIA, air displacement plethysmography (ADP), and total body water (TBW) measurement by deuterium (D2O) dilution. 4C composition was calculated through the use of the Lohman model [DXA mineral mass, D2O TBW, ADP body volume (BV), scale weight] and the simplified model (DXA mineral mass and BV, BIA TBW, scale weight). Accuracy of percentage of fat (%Fat) and protein measurements was assessed via linear regression. Test-retest precision was calculated with the use of duplicate DXA and BIA measurements., Results: Of 31 participants, 23 were included in the analysis. TBWBIA showed good test-retest precision (%CV = 5.2 raw; 1.1 after outlier removal) and high accuracy to TBWD2O [TBWD2O = 0.956*TBWBIA, R2= 0.92, root mean squared error (RMSE) = 2.2 kg]. %Fat estimates from DXA, ADP, D2O, and BIA all showed high correlation with the Lohman model. However, only the 4C simplified model provides high accuracy for both %Fat (R2 = 0.96, RMSE = 2.33) and protein mass (R2= 0.76, RMSE = 1.8 kg). %Fat precision from 4C DXA + BIA was comparable with DXA (root mean square-SD = 0.8 and 0.6 percentage units, respectively)., Conclusions: This work validates a simplified 4C method that measures fat, water, mineral, and protein in a 10-min clinic visit. This model has broad clinical application to monitor many conditions including over/dehydration, malnutrition, obesity, sarcopenia, and cachexia.

Background: Estimating energy requirements forms an integral part of developing diet and activity interventions. Current estimates often rely on a product of physical activity level (PAL) and a resting metabolic rate (RMR) prediction. PAL estimates, however, typically depend on subjective self-reported activity or a clinician's best guess. Energy-requirement models that do not depend on an input of PAL may provide an attractive alternative., Methods: Total daily energy expenditure (TEE) measured by doubly labeled water (DLW) and a metabolic chamber from 119 subjects obtained from a database of pre-intervention measurements measured at Pennington Biomedical Research Center were used to develop a metabolic ward and free-living models that predict energy requirements. Graded models, including different combinations of input variables consisting of age, height, weight, waist circumference, body composition, and the resting metabolic rate were developed. The newly developed models were validated and compared to three independent databases., Results: Sixty-four different linear and nonlinear regression models were developed. The adjusted R 2 for models predicting free-living energy requirements ranged from 0.65 with covariates of age, height, and weight to 0.74 in models that included body composition and RMR. Independent validation R 2 between actual and predicted TEE varied greatly across studies and between genders with higher coefficients of determination, lower bias, slopes closer to 1, and intercepts closer to zero, associated with inclusion of body composition and RMR covariates. The models were programmed into a user-friendly web-based app available at: http://www.pbrc.edu/research-and-faculty/calculators/energy-requirements/ (Video Demo for Reviewers at: https://www.youtube.com/watch?v=5UKjJeQdODQ ) CONCLUSIONS: Energy-requirement equations that do not require knowledge of activity levels and include all available input variables can provide more accurate baseline estimates. The models are clinically accessible through the web-based application.

As yet, genome-wide association studies (GWAS) have not added much to our understanding of the mechanisms of body weight control and of the etiology of obesity. This shortcoming is widely attributed to the complexity of the issues. The appeal of this explanation notwithstanding, we surmise that (i) an oversimplification of the phenotype (namely by the use of crude anthropometric traits) and (ii) a lack of sound concepts of body weight control and, thus, a lack of a clear research focus have impeded better insights most. The idea of searching for polygenetic mechanisms underlying common forms of obesity was born out of the impressive findings made for monogenetic forms of extreme obesity. In the case of common obesity, however, observational studies on normal weight and overweight subjects never provided any strong evidence for a tight internal control of body weight. In addition, empirical studies of weight changes in normal weight and overweight subjects revealed an intra-individual variance that was similar to inter-individual variance suggesting the absence of tight control of body weight. Not least, this lack of coerciveness is reflected by the present obesity epidemic. Finally, data on detailed body composition highlight that body weight is too heterogeneous a phenotype to be controlled as a single entity. In summary GWAS of obesity using crude anthropometric traits have likely been misled by popular heritability estimates that may have been inflated in the first place. To facilitate more robust and useful insights into the mechanisms of internal control of human body weight and, consequently, the genetic basis of obesity, we argue in favor of a broad discussion between scientists from the areas of integrative physiologic and of genomics. This discussion should aim at better conceived studies employing biologically more meaningful phenotypes based on in depth body composition analysis. To advance the scientific community-including the editors of our top journals-needs a re-launch of future GWAS of obesity.

This paper gives a brief overview of common non-invasive techniques for body composition analysis and a more in-depth review of a body composition assessment method based on fat-referenced quantitative MRI. Earlier published studies of this method are summarized, and a previously unpublished validation study, based on 4753 subjects from the UK Biobank imaging cohort, comparing the quantitative MRI method with dual-energy X-ray absorptiometry (DXA) is presented. For whole-body measurements of adipose tissue (AT) or fat and lean tissue (LT), DXA and quantitative MRIs show excellent agreement with linear correlation of 0.99 and 0.97, and coefficient of variation (CV) of 4.5 and 4.6 per cent for fat (computed from AT) and LT, respectively, but the agreement was found significantly lower for visceral adipose tissue, with a CV of >20 per cent. The additional ability of MRI to also measure muscle volumes, muscle AT infiltration and ectopic fat, in combination with rapid scanning protocols and efficient image analysis tools, makes quantitative MRI a powerful tool for advanced body composition assessment., Competing Interests: Competing interests: MB, JW, TR and ODL are employees and stockholders of AMRA Medical AB., (© American Federation for Medical Research (unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Body composition measurements from DXA have been available since DXA technology was developed 30years ago, but are historically underutilized. Recently, there have been rapid developments in body composition assessment including the analysis and publication of representative data for the US, official usage guidance from the International Society for Clinical Densitometry, and development of regional body composition measures with clinical utility. DXA body composition is much more than whole body percent fat. In this paper celebrating 30years of DXA for body composition, we will review the principles of DXA soft tissue analysis, practical clinical and research applications, and what to look for in the future., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Dual-energy X-ray absorptiometry (DXA) is widely used in body composition measurement and evaluation. Because of its numerous applications, the probability of instrument discrepancies has increased dramatically. This study quantitatively compares 2 different DXA systems. In this study, 96 subjects (60 female and 36 male, aged 19-82 years) were recruited and scanned using a General Electric Lunar iDXA and a Hologic Discovery scanner. Four measurements (percent fat, total mass, bone mineral density [BMD], and bone mineral content [BMC]) were quantitatively compared in the whole body and in specific anatomic regions (arms, legs, trunk, android, gynoid, head, ribs, and pelvis). A simple linear regression of each measurement was performed to examine the correlation between the 2 systems. Percent fat, total mass, BMC, and BMD were highly correlated between the 2 DXA systems, with correlation r values greater than 0.854 for both the whole body and the individual anatomic regions except for BMC and BMD in ribs. The high correlation between the 2 DXA systems with systematic differences enabled development of calibration equations for extending the multisystem measurements to advanced quantitative analyses., (Copyright © 2016 The International Society for Clinical Densitometry. Published by Elsevier Inc. All rights reserved.)

Background: The phase angle (PA) has been used as a prognostic marker in several clinical situations. Nevertheless, its biological meaning is not completely understood., Objective: We verified how body-composition components could explain the PA., Design: The trial was a cross-sectional study involving 1442 participants (women: 58.5%; Caucasian: 40.2%) from body-composition studies. Labeled tritium dilution and total-body potassium were used to estimate total-body water (TBW) and intracellular water (ICW), respectively. Extracellular water (ECW) and the ECW:ICW ratio were estimated from the difference and the ratio of these values. Fat-free mass (FFM) and fat mass (FM) were estimated with the use of dual-energy X-ray absorptiometry, underwater weighing (UWW), and TBW. The PA was estimated with the use of a single-frequency bioelectrical impedance analysis system. Correlations between the PA and all body-composition variables were evaluated. A multivariate linear regression analysis was performed to adjust for the effects of body-composition variables on the PA variability. All analyses were performed separately by sex., Results: Compared with men, women exhibited significantly larger ECW:ICW ratios and FM. The highest positive correlation was shown between the PA and FFM obtained with the use of UWW (both sexes). The highest negative correlation was shown between the PA and ECW:ICW ratios for both sexes. Age, race, height, ECW:ICW, and FFM from UWW were significant PA determinants in a multivariate linear regression model. Even after adjustment for all significant covariates, the explained PA variance was low (adjusted R(2) = 0.539 and 0.421 in men and women, respectively). The greatest impact on the total PA prediction in both men and women were age, FFM, and height., Conclusions: Age is the most significant PA predictor in men and women followed by FFM and height. The ECW:ICW contribution may explain the association of the PA observed in the clinical setting and in people who are obese., (© 2016 American Society for Nutrition.)