Bennett K. Ng

Clinical anthropometrics and body composition from 3D whole-body surface scans

Background/Objectives:Obesity is a significant worldwide epidemic that necessitates accessible tools for robust body composition analysis. We investigated whether widely available 3D body surface scanners can provide clinically relevant direct anthropometrics (circumferences, areas and volumes) and body composition estimates (regional fat/lean masses).Subjects/Methods:Thirty-nine healthy adults stratified by age, sex and body mass index (BMI) underwent whole-body 3D scans, dual energy X-ray absorptiometry (DXA), air displacement plethysmography and tape measurements. Linear regressions were performed to assess agreement between 3D measurements and criterion methods. Linear models were derived to predict DXA body composition from 3D scan measurements. Thirty-seven external fitness center users underwent 3D scans and bioelectrical impedance analysis for model validation.Results:3D body scan measurements correlated strongly to criterion methods: waist circumference R2=0.95, hip circumference R2=0.92, surface area R2=0.97 and volume R2=0.99. However, systematic differences were observed for each measure due to discrepancies in landmark positioning. Predictive body composition equations showed strong agreement for whole body (fat mass R2=0.95, root mean square error (RMSE)=2.4 kg; fat-free mass R2=0.96, RMSE=2.2 kg) and arms, legs and trunk (R2=0.79-0.94, RMSE=0.5–1.7 kg). Visceral fat prediction showed moderate agreement (R2=0.75, RMSE=0.11 kg).Conclusions:3D surface scanners offer precise and stable automated measurements of body shape and composition. Software updates may be needed to resolve measurement biases resulting from landmark positioning discrepancies. Further studies are justified to elucidate relationships between body shape, composition and metabolic health across sex, age, BMI and ethnicity groups, as well as in those with metabolic disorders.

Body composition by DXA

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.

Accurate body composition measures from whole-body silhouettes

PURPOSE Obesity and its consequences, such as diabetes, are global health issues that burden about 171 × 10(6) adult individuals worldwide. Fat mass index (FMI, kg/m(2)), fat-free mass index (FFMI, kg/m(2)), and percent fat mass may be useful to evaluate under- and overnutrition and muscle development in a clinical or research environment. This proof-of-concept study tested whether frontal whole-body silhouettes could be used to accurately measure body composition parameters using active shape modeling (ASM) techniques. METHODS Binary shape images (silhouettes) were generated from the skin outline of dual-energy x-ray absorptiometry (DXA) whole-body scans of 200 healthy children of ages from 6 to 16 yr. The silhouette shape variation from the average was described using an ASM, which computed principal components for unique modes of shape. Predictive models were derived from the modes for FMI, FFMI, and percent fat using stepwise linear regression. The models were compared to simple models using demographics alone [age, sex, height, weight, and body mass index z-scores (BMIZ)]. RESULTS The authors found that 95% of the shape variation of the sampled population could be explained using 26 modes. In most cases, the body composition variables could be predicted similarly between demographics-only and shape-only models. However, the combination of shape with demographics improved all estimates of boys and girls compared to the demographics-only model. The best prediction models for FMI, FFMI, and percent fat agreed with the actual measures with R(2) adj. (the coefficient of determination adjusted for the number of parameters used in the model equation) values of 0.86, 0.95, and 0.75 for boys and 0.90, 0.89, and 0.69 for girls, respectively. CONCLUSIONS Whole-body silhouettes in children may be useful to derive estimates of body composition including FMI, FFMI, and percent fat. These results support the feasibility of measuring body composition variables from simple cameras such as those found in cell phones.

Dual energy X-ray absorptiometry body composition reference values of limbs and trunk from NHANES 1999–2004 with additional visualization methods

Body Mass Index has traditionally been used as a measure of health, but Fat Mass Index (FMI) and Lean Mass Index (LMI) have been shown to be more predictive of mortality and health risk. Total body FMI and LMI reference curves have particularly been useful in quantifying sarcopenia and sarcopenic obesity. Research has shown regional composition has significant associations to health outcomes. We derived FMI and LMI reference curves of the regions of the body (leg, arm, and trunk) for 15,908 individuals in the 1999–2004 National Health and Nutrition Examination Survey data for each sex and ethnicity using the Lambda-Mu-Sigma (LMS) method and developed software to visualize this regional composition. These reference curves displayed differentiation between males and females during puberty and sharper limb LMI declines during late adulthood for males. For adults ages 30–50, females had 39%, 83%, and 47% larger arm, leg, and trunk FMI values than males, respectively. Males had 49%, 20%, and 15% higher regional LMI values than females for the arms, legs, and trunk respectively. The leg FMI and LMI of black females were 14% and 15% higher respectively than those of Hispanic and white females. White and Hispanic males had 37% higher trunk FMI values than black males. Hispanic females had 20% higher trunk FMI than white and black females. These data underscore the importance of accounting for sex and ethnicity in studies of regional composition. This study is the first to produce regional LMI and FMI reference tables and curves from the NHANES dataset. These reference curves provide a framework useful in studies and research involving sarcopenia, obesity, sarcopenic obesity, and other studies of compositional phenotypes. Further, the software tool we provide for visualizing regional composition will prove useful in monitoring progress in physical therapy, diets, or other attempts to attain healthier compositions.

Clinically applicable optical imaging technology for body size and shape analysis: comparison of systems differing in design

Background/Objectives:Recent advances have extended anthropometry beyond flexible tape measurements to automated three-dimensional optical devices that rapidly acquire hundreds of body surface dimensions. Three new devices were recently introduced that share in common inexpensive optical cameras. The design, and thus potential clinical applicability, of these systems differ substantially leading us to critically evaluate their accuracy and precision.Subjects/Methods:113 adult subjects completed evaluations by the three optical devices (KX-16 (16 stationary cameras), Proscanner (1 vertically oscillating camera), and Styku scanner (1 stationary camera)), air displacement plethysmography (ADP), dual-energy X-ray absorptiometry (DXA) and a flexible tape measure. Optical measurements were compared to reference method estimates that included results acquired by flexible tape, DXA and ADP.Results:Optical devices provided respective circumference and regional volume estimates that overall were well-correlated with those obtained from flexible tape measurements (for example, hip circumference: R2, 0.91, 0.90, 0.96 for the KX-16, Proscanner, and Styku scanner, respectively) and DXA (for example, trunk volume: R2, 0.97, 0.97, and 0.98). Total body volumes measured by the optical devices were highly correlated with those from the ADP system (all R2s, 0.99). Coefficient of variations obtained from duplicate measurements (n, 55) were larger in optical than in reference measurements and significant (P<0.05) bias was present for some optical measurements relative to reference method estimates.Conclusions:Overall, the evaluated optical imaging systems differing in design provided body surface measurements that compared favorably with corresponding reference methods. However, our evaluations uncovered system measurement limitations, such as discrepancies in landmarking, that with correction have the potential to improve future developed devices.

Validation of rapid 4-component body composition assessment with the use of dual-energy X-ray absorptiometry and bioelectrical impedance analysis.

Abstract 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.

Modeling the shape and composition of the human body using dual energy X-ray absorptiometry images

There is growing evidence that body shape and regional body composition are strong indicators of metabolic health. The purpose of this study was to develop statistical models that accurately describe holistic body shape, thickness, and leanness. We hypothesized that there are unique body shape features that are predictive of mortality beyond standard clinical measures. We developed algorithms to process whole-body dual-energy X-ray absorptiometry (DXA) scans into body thickness and leanness images. We performed statistical appearance modeling (SAM) and principal component analysis (PCA) to efficiently encode the variance of body shape, leanness, and thickness across sample of 400 older Americans from the Health ABC study. The sample included 200 cases and 200 controls based on 6-year mortality status, matched on sex, race and BMI. The final model contained 52 points outlining the torso, upper arms, thighs, and bony landmarks. Correlation analyses were performed on the PCA parameters to identify body shape features that vary across groups and with metabolic risk. Stepwise logistic regression was performed to identify sex and race, and predict mortality risk as a function of body shape parameters. These parameters are novel body composition features that uniquely identify body phenotypes of different groups and predict mortality risk. Three parameters from a SAM of body leanness and thickness accurately identified sex (training AUC = 0.99) and six accurately identified race (training AUC = 0.91) in the sample dataset. Three parameters from a SAM of only body thickness predicted mortality (training AUC = 0.66, validation AUC = 0.62). Further study is warranted to identify specific shape/composition features that predict other health outcomes.

Epicardial Catheter Ablation Using High-Intensity Ultrasound Validation in a Swine Model

Background—Epicardial radiofrequency catheter ablation of ventricular tachycardia remains challenging because of the presence of deep myocardial scar and adjacent cardiac structures, such as the coronary arteries, phrenic nerve, and epicardial fat that limit delivery of radiofrequency energy. High-intensity ultrasound (HIU) is an acoustic energy source able to deliver deep lesions through fat, while sparing superficial structures. We developed and tested an epicardial HIU ablation catheter in a closed chest, in vivo swine model. Methods and Results—The HIU catheter is an internally cooled, 14-French, side-facing catheter, integrated with A-mode ultrasound guidance. Swine underwent percutaneous subxyphoid epicardial access and ablation with HIU (n=10 swine) at 15, 20, and 30 W. Compared with irrigated radiofrequency lesions in control swine (n = 5), HIU demonstrated increased lesion depth (HIU 11.6±3.2 mm versus radiofrequency 4.7±1.6 mm; mean±SD) and epicardial sparing (HIU 2.9±2.1 mm versus radiofrequency 0.1±0.2 mm) at all HIU powers, and increased lesion volume at HIU 20 and 30 W (P<0.0001 for all comparisons). HIU ablation over coronary arteries and surrounding epicardial fat resulted in deep lesions with normal angiographic flow. Histological disruption of coronary adventitia, but not media or intima, was noted in 44% of lesions. Conclusions—Compared with radiofrequency, HIU ablation in vivo demonstrates significantly deeper and larger lesions with greater epicardial sparing in a dose-dependent manner. Further development of this catheter may lead to a promising alternative to epicardial radiofrequency ablation.

Reversing the Relationship Between Dual-Energy X-ray Absorptiometry and the 4-Component Model With 1 Sign Flip

We thoroughly enjoyed reading the recent JCD article “An Investigation Into the Differences in Bone Density and Body Composition Measurements Between 2 GE Lunar Densitometers and Their Comparison with a 4-Component Model” from Watson et al (1). We commend the authors for an interesting and practical study comparing dualenergy X-ray absorptiometry (DXA) body composition measurements to a gold-standard 4-component (4-C) model. Watson and colleagues found “a significant difference between 4-C-derived fat mass (FM) and [all DXA FM measurements]” ranging from −0.936 to −2.157 kg. In other words, DXA was found to overestimate FM compared with the 4-C model. However, we noted errors in the authors’ reproduction of the Fuller 4-C equation (2). The correct Fuller equation is:

Can Racial Differences in Resting Metabolic Rate be Explained by Body Composition

TO THE EDITOR: We are writing in regards to the original article “Resting Metabolic Rate Varies by Race and by Sleep Deprivation” authored by Spaeth et al. (1). Our group found this work to be of high interest, and we commend Spaeth et al. for their detailed study design around a broadly applicable hypothesis on the relationship between sleep and metabolism. The article stimulated several thoughts particularly to the authors’ conclusions about the association between resting metabolic rate (RMR) and race.