Children and Adolescents' Anthropometrics Body Composition from 3-D Optical Surface Scans

The applicability of a commercial 3DO body scanner in measuring body composition in Chinese adults with overweight and obesity: a secondary analysis based on a weight-loss clinical trial

BACKGROUND

A commercial three-dimensional optical (3DO) scanning system was reported to be used in body composition assessment. However, the applicability in Chinese adults has yet to be well-studied.

METHODS

This secondary analysis was based on a 16-week weight-loss clinical trial with an optional extension to 24 weeks. Waist and hip circumference and body composition were measured by 3DO scanning at each follow-up visit during the study. Bioelectrical impedance analysis (BIA) was also performed to confirm the reliability of 3DO scanning at each visit. We used Lin's concordance correlation coefficients (CCC) to evaluate the correlation between the two methods above-mentioned. Bland-Altman analysis was also performed to evaluate the agreement and potential bias between different methods.

RESULTS

A total number of 70 Chinese adults overweight and obese (23 men and 47 women, aged 31.8 ± 5.8 years) were included in the analysis, which resulted in 350 3DO scans and corresponding 350 BIA measurements. The percent body fat, fat mass, and fat-free mass were 33.9 ± 5.4%, 26.7 ± 4.6 kg, and 50.3 ± 8.7 kg before the trial by 3DO scanning. And they were 30.5 ± 5.8%, 22.5 ± 4.7 kg, and 49.4 ± 8.3 kg after 16 weeks of the trial. Compared with BIA, 3DO scanning performed best in the assessment of fat-free mass (CCC = 0.89, 95%CI: 0.86, 0.90), then followed by fat mass (CCC = 0.76, 95%CI: 0.71, 0.80) and percent body fat (CCC = 0.70, 95%CI: 0.64, 0.75). Subgroup analysis showed that 3DO scanning and BIA correlated better in women than that in men, and correlated better in measuring fat-free mass in participants with larger body weight (BMI ≥28.0 kg/m2) than those with smaller body weight (<28.0 kg/m2).

CONCLUSIONS

3DO scanning is an effective technology to monitor changes in body composition in Chinese adults overweight and obese. However its accuracy and reliability in different ethnicities needs further exploration.

Mobile phone applications for 3-dimensional scanning and digital anthropometry: a precision comparison with traditional scanners

BACKGROUND

The precision of digital anthropometry through 3-dimensional (3D) scanning has been established for relatively large, expensive, non-portable systems. The comparative performance of modern mobile applications is unclear.

SUBJECTS/METHODS

Forty-six adults (age: 23.3 ± 5.3 y; BMI: 24.4 ± 4.1 kg/m2) were assessed in duplicate using: (1) a mobile phone application capturing two individual 2D images, (2) a mobile phone application capturing serial images collected during a subject's complete rotation, (3) a traditional scanner with a time of flight infrared sensor collecting visual data from a subject being rotated on a mechanical turntable, and (4) a commercial measuring booth with structured light technology using 20 infrared depth sensors positioned in the booth. The absolute and relative technical error of measurement (TEM) and intraclass correlation coefficient (ICC) for each method were established.

RESULTS

Averaged across circumferences, the absolute TEM, relative TEM, and ICC were (1) 0.9 cm, 1.5%, and 0.975; (2) 0.5 cm, 0.9%, and 0.986; (3) 0.8 cm, 1.5%, and 0.974; and (4) 0.6 cm, 1.1%, and 0.985. For total body volume, these values were (1) 2.2 L, 3.0%, and 0.978; (2) 0.8 L, 1.1%, and 0.997; (3) 0.7 L, 0.9%, and 0.998; and (4) 0.8 L, 1.1%, and 0.996, with segmental volumes demonstrating higher relative errors.

CONCLUSION

A 3D scanning mobile phone application involving full rotation of subjects in front of a smartphone camera exhibited similar reliability to larger, less portable, more expensive 3D scanners. In contrast, larger errors were observed for a mobile scanning application utilizing two 2D images, although the technical errors were acceptable for some applications.

Patient perceptions of three-dimensional (3D) surface imaging technology and traditional methods used to assess anthropometry

Background

Obesity and overweight are commonplace, yet attrition rates in weight management clinics are high. Traditional methods of body measurement may be a deterrent due to invasive and time-consuming measurements and negative experiences of how data are presented back to individuals. Emerging new technologies, such as three-dimensional (3D) surface imaging technology, might provide a suitable alternative. This study aimed to understand acceptability of traditional and 3D surface imaging-based body measures, and whether perceptions differ between population groups.

Methods

This study used a questionnaire to explore body image, body measurement and shape, followed by a qualitative semi-structured interview and first-hand experience of traditional and 3D surface imaging-based body measures.

Results

49 participants responded to the questionnaire and 26 participants attended for the body measurements and interview over a 2-month period. There were 3 main themes from the qualitative data 1) Use of technology, 2) Participant experience, expectations and perceptions and 3) Perceived benefits and uses.

Conclusion

From this study, 3D-surface imaging appeared to be acceptable to patients as a method for anthropometric measurements, which may reduce anxiety and improve attrition rates in some populations. Further work is required to understand the scalability, and the role and implications of these technologies in weight management practice. (University Research Ethics Committee reference number ER41719941).

Predictors of visceral and subcutaneous adipose tissue and muscle density: The ShapeUp! Kids study

BACKGROUND AND AIMS

Body fat distribution, i.e., visceral (VAT), subcutaneous adipose tissue (SAT) and intramuscular fat, is important for disease prevention, but sex and ethnic differences are not well understood. Our aim was to identify anthropometric, demographic, and lifestyle predictors for these outcomes.

METHODS AND RESULTS

The cross-sectional ShapeUp!Kids study was conducted among five ethnic groups aged 5-18 years. All participants completed questionnaires, anthropometric measurements, and abdominal MRI scans. VAT and SAT areas at four lumbar levels and muscle density were assessed manually. General linear models were applied to estimate coefficients of determination (R2) and to compare the fit of VAT and SAT prediction models. After exclusions, the study population had 133 male and 170 female participants. Girls had higher BMI-z scores, waist circumference (WC), and SAT than boys but lower VAT/SAT and muscle density. SAT, VAT, and VAT/SAT but not muscle density differed significantly by ethnicity. R2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R2 increased from 0.85 to 0.88 (girls) and 0.62 to 0.71 (boys) when WC was added while VAT models improved from 0.62 to 0.65 (girls) and 0.57 to 0.62 (boys). VAT values were significantly lower among Blacks than Whites with little difference for the other groups.

CONCLUSION

This analysis in a multiethnic population identified BMI-z scores and WC as the major predictors of MRI-derived SAT and VAT and highlights the important ethnic differences that need to be considered in diverse populations.

3D Convolutional Deep Learning for Nonlinear Estimation of Body Composition from Whole-Body Morphology

Total and regional body composition are strongly correlated with metabolic syndrome and have been estimated non-invasively from 3D optical scans using linear parameterizations of body shape and linear regression models. Prior works produced accurate and precise predictions on many, but not all, body composition targets relative to the reference dual X-Ray absorptiometry (DXA) measurement. Here, we report the effects of replacing linear models with nonlinear parameterization and regression models on the precision and accuracy of body composition estimation in a novel application of deep 3D convolutional graph networks to human body composition modeling. We assembled an ensemble dataset of 4286 topologically standardized 3D optical scans from four different human body shape databases, DFAUST, CAESAR, Shape Up! Adults, and Shape Up! Kids and trained a parameterized shape model using a graph convolutional 3D autoencoder (3DAE) in lieu of linear PCA. We trained a nonlinear Gaussian process regression (GPR) on the 3DAE parameter space to predict body composition via correlations to paired DXA reference measurements from the Shape Up! scan subset. We tested our model on a set of 424 randomly withheld test meshes and compared the effects of nonlinear computation against prior linear models. Nonlinear GPR produced up to 20% reduction in prediction error and up to 30% increase in precision over linear regression for both sexes in 10 tested body composition variables. Deep shape features produced 6-8% reduction in prediction error over linear PCA features for males only and a 4-14% reduction in precision error for both sexes. Our best performing nonlinear model predicting body composition from deep features outperformed prior work using linear methods on all tested body composition prediction metrics in both precision and accuracy. All coefficients of determination (R2) for all predicted variables were above 0.86. We show that GPR is a more precise and accurate method for modeling body composition mappings from body shape features than linear regression. Deep 3D features learned by a graph convolutional autoencoder only improved male body composition accuracy but improved precision in both sexes. Our work achieved lower estimation RMSEs than all previous work on 10 metrics of body composition.

Development and validation of a rapid multicompartment body composition model using 3-dimensional optical imaging and bioelectrical impedance analysis

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.

Generative deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans

BACKGROUND

Body shape, an intuitive health indicator, is deterministically driven by body composition. We developed and validated a deep learning model that generates accurate dual-energy X-ray absorptiometry (DXA) scans from three-dimensional optical body scans (3DO), enabling compositional analysis of the whole body and specified subregions. Previous works on generative medical imaging models lack quantitative validation and only report quality metrics.

METHODS

Our model was self-supervised pretrained on two large clinical DXA datasets and fine-tuned using the Shape Up! Adults study dataset. Model-predicted scans from a holdout test set were evaluated using clinical commercial DXA software for compositional accuracy.

RESULTS

Predicted DXA scans achieve R2 of 0.73, 0.89, and 0.99 and RMSEs of 5.32, 6.56, and 4.15 kg for total fat mass (FM), fat-free mass (FFM), and total mass, respectively. Custom subregion analysis results in R2s of 0.70-0.89 for left and right thigh composition. We demonstrate the ability of models to produce quantitatively accurate visualizations of soft tissue and bone, confirming a strong relationship between body shape and composition.

CONCLUSIONS

This work highlights the potential of generative models in medical imaging and reinforces the importance of quantitative validation for assessing their clinical utility.

Body composition in healthy singleton term infants using the three-dimensional photonic scanning method: A multicenter cross-sectional study

OBJECTIVES

Body composition is an integral part of the nutritional assessment during infancy as it is closely related to future health. The three-dimensional photonic body surface scanning (3-DPS) method is a promising new technique for measuring body composition in children because of its advantages of easy operation, low cost, and no exposure to radiation. Using 3-DPS, this study aimed to illustrate the growth trajectories of body composition indicators during infancy according to sex and age.

METHODS

This was a multicenter cross-sectional study. The body compositions of 9644 singleton term infants from four centers in Shandong Province, China, were assessed using 3-DPS. The data of 8769 healthy infants (52.0% boys), whose z scores of weight-for-length, length-for-age, and weight-for-age, according to World Health Organization standards, were in the range of -2 to 2, -3 to 3, and -3 to 3, respectively, were sampled to construct percentile curves of fat mass (FM), fat-free mass (FFM), FM percentage (FM%), FM index (FMI), and FFM index (FFMI) with the generalized additive model for location, scale, and shape method.

RESULTS

Percentile charts for FM, FFM, FM%, FMI, and FFMI were developed based on age and sex. FM and FFM presented consistent trajectories with that of weight, with the fastest growth occurring at 1 to 3 mo of age. FM%, FMI, and FFMI increased with age, peaked at 6 mo, and gradually declined, which was consistent with the body mass index trend. All indicators, except for FFMI, were always significantly higher in boys than in girls ages 1 to 12 mo, indicating that sex differences in body composition existed mainly in FM rather than in lean body mass.

CONCLUSIONS

The body composition of healthy singleton term infants during infancy varies with age; boys may have more FM accumulation than girls.

Infant Body Composition in an Asian Pacific Islander Population

BACKGROUND

Normative infant body composition data using air displacement plethysmography (ADP) are from primarily Caucasian populations. Racial differences may exist.

OBJECTIVES

To describe body composition in Asian and Pacific Islander infants and compare them to previously published data on Caucasian infants.

DESIGN

Body composition was measured using ADP with the PEA POD® Infant Body Composition System in 249 healthy full-term newborns in a predominately Asian and Pacific Islander population in Hawaii within the first 3 days of life and compared to published data on Caucasian infants with multiple t-tests adjusted for false discovery rate.

RESULTS

There were no differences in percent body fat between Asian, Pacific Islander, or mixed race Asian Pacific Islander infants. Both Asian and Pacific Islander infants had significantly higher percent body fat than Caucasians from Italy in Europe (13.2% and 11.8% vs 8.9%, p < 0.01 among males, 15.3% and 15.6% vs 8.7%, p < 0.01 among females) but not when compared to Caucasians from New York.

CONCLUSIONS

Racial and geographical differences in body composition exist at birth between Asian and Pacific Islanders and other Caucasian cohorts. Previously published ADP nomograms must be interpreted with caution. Future studies are needed to investigate the impact of environmental, perinatal, and genetic factors on infant body composition and its relationship to future cardiometabolic morbidity. Efforts to address racial disparities in cardiometabolic disease measures must also address pre-conceptual maternal health, which may have long-term implications on future body composition in offspring.

Associations between 3D surface scanner derived anthropometric measurements and body composition in a cross-sectional study

BACKGROUND

3D laser-based photonic scanners are increasingly used in health studies to estimate body composition. However, too little is known about whether various 3D body scan measures estimate body composition better than single standard anthropometric measures, and which body scans best estimate it. Furthermore, little is known about differences by sex and age.

METHODS

105 men and 96 women aged between 18 and 90 years were analysed. Bioelectrical Impedance Analysis was used to estimate whole relative fat mass (RFM), visceral adipose tissue (VAT) and skeletal muscle mass index (SMI). An Anthroscan VITUSbodyscan was used to obtain 3D body scans (e.g. volumes, circumferences, lengths). To reduce the number of possible predictors that could predict RFM, VAT and SMI backward elimination was performed. With these selected predictors linear regression on the respective body compositions was performed and the explained variations were compared with models using standard anthropometric measurements (Body Mass Index (BMI), waist circumference (WC) and waist-to-height-ratio (WHtR)).

RESULTS

Among the models based on standard anthropometric measures, WC performed better than BMI and WHtR in estimating body composition in men and women. The explained variations in models including body scan variables are consistently higher than those from standard anthropometrics models, with an increase in explained variations between 5% (RFM for men) and 10% (SMI for men). Furthermore, the explained variation of body composition was additionally increased when age and lifestyle variables were added. For each of the body composition variables, the number of predictors differed between men and women, but included mostly volumes and circumferences in the central waist/chest/hip area and the thighs.

CONCLUSIONS

3D scan models performed better than standard anthropometric measures models to predict body composition. Therefore, it is an advantage for larger health studies to look at body composition more holistically using 3D full body surface scans.

Cross-sectional assessment of body composition and detection of malnutrition risk in participants with low body mass index and eating disorders using 3D optical surface scans

BACKGROUND

New recommendations for the assessment of malnutrition and sarcopenia include body composition, specifically reduced muscle mass. Three-dimensional optical imaging (3DO) is a validated, accessible, and affordable alternative to dual X-ray absorptiometry (DXA).

OBJECTIVE

Identify strengths and weaknesses of 3DO for identification of malnutrition in participants with low body mass index (BMI) and eating disorders.

DESIGN

Participants were enrolled in the cross-sectional Shape Up! Adults and Kids studies of body shape, metabolic risk, and functional assessment and had BMI of <20 kg/m2 in adults or <85% of median BMI (mBMI) in children and adolescents. A subset was referred for eating disorders evaluation. Anthropometrics, scans, strength testing, and questionnaires were completed in clinical research centers. Lin's Concordance Correlation Coefficient (CCC) assessed agreement between 3DO and DXA; multivariate linear regression analysis examined associations between weight history and body composition.

RESULTS

Among 95 participants, mean ± SD BMI was 18.3 ± 1.4 kg/m2 in adult women (N = 56), 19.0 ± 0.6 in men (N = 14), and 84.2% ± 4.1% mBMI in children (N = 25). Concordance was excellent for fat-free mass (FFM, CCC = 0.97) and strong for appendicular lean mass (ALM, CCC = 0.86) and fat mass (FM, CCC = 0.87). By DXA, 80% of adults met the low FFM index criterion for malnutrition, and 44% met low ALM for sarcopenia; 52% of children and adolescents were <-2 z-score for FM. 3DO identified 95% of these cases. In the subset, greater weight loss predicted lower FFM, FM, and ALM by both methods; a greater percentage of weight regained predicted a higher percentage of body fat.

CONCLUSIONS

3DO can accurately estimate body composition in participants with low BMI and identify criteria for malnutrition and sarcopenia. In a subset, 3DO detected changes in body composition expected with weight loss and regain secondary to eating disorders. These findings support the utility of 3DO for body composition assessment in patients with low BMI, including those with eating disorders. This trial was registered at clinicaltrials.gov as NCT03637855.

Accuracy and Precision of 3-dimensional Optical Imaging for Body Composition by Age, BMI, and Ethnicity

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 R2 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).

Automated body composition estimation from device-agnostic 3D optical scans in pediatric populations

BACKGROUND

Excess adiposity in children is strongly correlated with obesity-related metabolic disease in adulthood, including diabetes, cardiovascular disease, and 13 types of cancer. Despite the many long-term health risks of childhood obesity, body mass index (BMI) Z-score is typically the only adiposity marker used in pediatric studies and clinical applications. The effects of regional adiposity are not captured in a single scalar measurement, and their effects on short- and long-term metabolic health are largely unknown. However, clinicians and researchers rarely deploy gold-standard methods for measuring compartmental fat such as magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA) on children and adolescents due to cost or radiation concerns. Three-dimensional optical (3DO) scans are relatively inexpensive to obtain and use non-invasive and radiation-free imaging techniques to capture the external surface geometry of a patient's body. This 3D shape contains cues about the body composition that can be learned from a structured correlation between 3D body shape parameters and reference DXA scans obtained on a sample population.

STUDY AIM

This study seeks to introduce a radiation-free, automated 3D optical imaging solution for monitoring body shape and composition in children aged 5-17.

METHODS

We introduce an automated, linear learning method to predict total and regional body composition of children aged 5-17 from 3DO scans. We collected 145 male and 206 female 3DO scans on children between the ages of 5 and 17 with three scanners from independent manufacturers. We used an automated shape templating method first introduced on an adult population to fit a topologically consistent 60,000 vertex (60 k) mesh to 3DO scans of arbitrary scanning source and mesh topology. We constructed a parameterized body shape space using principal component analysis (PCA) and estimated a regression matrix between the shape parameters and their associated DXA measurements. We automatically fit scans of 30 male and 38 female participants from a held-out test set and predicted 12 body composition measurements.

RESULTS

The coefficient of determination (R2) between 3DO predicted body composition and DXA measurements was at least 0.85 for all measurements with the exception of visceral fat on 3D scan predictions. Precision error was 1-4 times larger than that of DXA. No predicted variable was significantly different from DXA measurement except for male trunk lean mass.

CONCLUSION

Optical imaging can quickly, safely, and inexpensively estimate regional body composition in children aged 5-17. Frequent repeat measurements can be taken to chart changes in body adiposity over time without risk of radiation overexposure.

Prediction of fat mass from anthropometry at ages 7 to 9 years in Samoans: a cross-sectional study in the Ola Tuputupua'e cohort

BACKGROUND/OBJECTIVE

With increasing obesity prevalence in children globally, accurate and practical methods for quantifying body fat are critical for effective monitoring and prevention, particularly in high-risk settings. No population is at higher risk of obesity than Pacific Islanders, including children living in the independent nation of Samoa. We developed and validated sex-specific prediction models for fat mass in Samoan children.

SUBJECTS/METHODS

Dual X-ray absorptiometry (DXA) assessments of fat mass and weight, height, circumferences, and skinfolds were obtained from 356 children aged 7-9 years old in the Ola Tuputupua'e "Growing Up" study. Sex-specific models were developed from a randomly selected model development sample (n = 118 females, n = 120 males) using generalized linear regressions. In a validation sample (n = 59 females; n = 59 males), Lin's concordance and Bland-Altman limits-of-agreement (LoA) of DXA-derived and predicted fat mass from this study and other published models were examined to assess precision and accuracy.

RESULTS

Models to predict fat mass in kilograms were: e^[(-0.0034355 * Age8 - 0.0059041 * Age9 + 1.660441 * ln (Weight (kg))-0.0087281 * Height (cm) + 0.1393258 * ln[Suprailiac (mm)] - 2.661793)] for females and e^[-0.0409724 * Age8 - 0.0549923 * Age9 + 336.8575 * [Weight (kg)]-2 - 22.34261 * ln (Weight (kg)) [Weight (kg)]-1 + 0.0108696 * Abdominal (cm) + 6.811015 * Subscapular (mm)-2 - 8.642559 * ln (Subscapular (mm)) Subscapular (mm)-2 - 1.663095 * Tricep (mm)-1 + 3.849035]for males, where Age8 = Age9 = 0 for children at age 7 years, Age8 = 1 and Age9 = 0 at 8 years, Age8 = 0 and Age9 = 1 at 9 years. Models showed high predictive ability, with substantial concordance (ρC > 0.96), and agreement between DXA-derived and model-predicted fat mass (LoA female = -0.235, 95% CI:-2.924-2.453; male = -0.202, 95% CI:-1.977-1.572). Only one of four existing models, developed in a non-Samoan sample, accurately predicted fat mass among Samoan children.

CONCLUSIONS

We developed models that predicted fat mass in Samoans aged 7-9 years old with greater precision and accuracy than the majority of existing models that were tested. Monitoring adiposity in children with these models may inform future obesity prevention and interventions.

Smartphone prediction of skeletal muscle mass: model development and validation in adults

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 R2'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 R2'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).

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Machine learning-based obesity classification considering 3D body scanner measurements

Obesity can cause various diseases and is a serious health concern. BMI, which is currently the popular measure for judging obesity, does not accurately classify obesity; it reflects the height and weight but ignores the characteristics of an individual's body type. In order to overcome the limitations of classifying obesity using BMI, we considered 3-dimensional (3D) measurements of the human body. The scope of our study was limited to Korean subjects. In order to expand 3D body scan data clinically, 3D body scans, Dual-energy X-ray absorptiometry, and Bioelectrical Impedance Analysis data was collected pairwise for 160 Korean subjects. A machine learning-based obesity classification framework using 3D body scan data was designed, validated through Accuracy, Recall, Precision, and F1 score, and compared with BMI and BIA. In a test dataset of 40 people, BMI had the following values: Accuracy: 0.529, Recall: 0.472, Precision: 0.458, and F1 score: 0.462, while BIA had the following values: Accuracy: 0.752, Recall: 0.742, Precision: 0.751, and F1 score: 0.739. Our proposed model had the following values: Accuracy: 0.800, Recall: 0.767, Precision: 0.842, and F1 score: 0.792. Thus, our accuracy was higher than BMI as well as BIA. Our model can be used for obesity management through 3D body scans.

Digital Anthropometry: A Systematic Review on Precision, Reliability and Accuracy of Most Popular Existing Technologies

Digital anthropometry (DA) has been recently developed for body composition evaluation and for postural analysis. The aims of this review are to examine the current state of DA technology, as well as to verify the methods for identifying the best technology to be used in the field of DA by evaluating the reliability and accuracy of the available technologies on the market, and lay the groundwork for future technological developments. A literature search was performed and 28 studies met the inclusion criteria. The reliability and accuracy of DA was high in most studies, especially in the assessment of patients with obesity, although they varied according to the technology used; a good correlation was found between DA and conventional anthropometry (CA) and body composition estimates. DA is less time-consuming and less expensive and could be used as a screening tool before more expensive imaging techniques or as an alternative to other less affordable techniques. At present, DA could be useful in clinical practice, but the heterogeneity of the available studies (different devices used, laser technologies, population examined, etc.) necessitates caution in the interpretation of the obtained results. Furthermore, the need to develop integrated technologies for analyzing body composition according to multi-compartmental models is increasingly evident.

A device-agnostic shape model for automated body composition estimates from 3D optical scans

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/m2 . 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 (R2 ) above 0.8 on all nine fat and lean predictions except female visceral fat (0.77). R2 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.

The Differential Effects of Adiposity and Fitness on Functional Connectivity in Preadolescent Children

PURPOSE

Childhood obesity is a global health concern, with >340 million youth considered overweight or obese. In addition to contributing greatly to health care costs, excess adiposity associated with obesity is considered a major risk factor for premature mortality from cardiovascular and metabolic diseases and is also negatively associated with cognitive and brain health. A complementary line of research highlights the importance of cardiorespiratory fitness, a by-product of engaging in physical activity, on an abundance of health factors, including cognitive and brain health.

METHODS

This study investigated the relationship among excess adiposity (visceral adipose tissue [VAT], subcutaneous abdominal adipose tissue), total abdominal adipose tissue, whole-body percent fat [WB%FAT], body mass index (BMI), and fat-free cardiorespiratory fitness (FF-V̇O 2max ) on resting-state functional connectivity (RSFC) in 121 ( f = 68) children (7-11 yr) using a data-driven whole-brain multivoxel pattern analysis.

RESULTS

Multivoxel pattern analysis revealed brain regions that were significantly associated with VAT, BMI, WB%FAT, and FF-V̇O 2 measures. Yeo's (2011) RSFC-based seven-network cerebral cortical parcellation was used for labeling the results . Post hoc seed-to-voxel analyses found robust negative correlations of VAT and BMI with areas involved in the visual, somatosensory, dorsal attention, ventral attention, limbic, frontoparietal, and default mode networks. Further, positive correlations of FF-V̇O 2 were observed with areas involved in the ventral attention and frontoparietal networks. These novel findings indicate that negative health factors in childhood may be selectively and negatively associated with the 7 Yeo-defined functional networks, yet positive health factors (FF-V̇O 2 ) may be positively associated with these networks.

CONCLUSIONS

These novel results extend the current literature to suggest that BMI and adiposity are negatively associated with, and cardiorespiratory fitness (corrected for fat-free mass) is positively associated with, RSFC networks in children.

Three-dimensional optical body shape and features improve prediction of metabolic disease risk in a diverse sample of adults

OBJECTIVE

This study examined whether body shape and composition obtained by three-dimensional optical (3DO) scanning improved the prediction of metabolic syndrome (MetS) prevalence compared with BMI and demographics.

METHODS

A diverse ambulatory adult population underwent whole-body 3DO scanning, blood tests, manual anthropometrics, and blood pressure assessment in the Shape Up! Adults study. MetS prevalence was evaluated based on 2005 National Cholesterol Education Program criteria, and prediction of MetS involved logistic regression to assess (1) BMI, (2) demographics-adjusted BMI, (3) 85 3DO anthropometry and body composition measures, and (4) BMI + 3DO + demographics models. Receiver operating characteristic area under the curve (AUC) values were generated for each predictive model.

RESULTS

A total of 501 participants (280 female) were recruited, with 87 meeting the criteria for MetS. Compared with the BMI model (AUC = 0.819), inclusion of age, sex, and race increased the AUC to 0.861, and inclusion of 3DO measures further increased the AUC to 0.917. The overall integrated discrimination improvement between the 3DO + demographics and the BMI model was 0.290 (p < 0.0001) with a net reclassification improvement of 0.214 (p < 0.0001).

CONCLUSIONS

Body shape measures from an accessible 3DO scan, adjusted for demographics, predicted MetS better than demographics and/or BMI alone. Risk classification in this population increased by 29% when using 3DO scanning.

Acceptability and Experiences with the Use of 3D Scans to Measure Anthropometry of Young Children in Surveys and Surveillance Systems from the Perspective of Field Teams and Caregivers

Background

Portable systems using three-dimensional (3D) scan data to calculate young child anthropometry measurements in population-based surveys and surveillance systems lack acceptability data from field workers and caregivers.

Objective

The aim was to assess acceptability and experiences with 3D scans measuring child aged 0-59 mo anthropometry in population-based surveys and surveillance systems in Guatemala, Kenya, and China (0-23 mo only) among field teams and caregivers of young children as secondary objectives of an external effectiveness evaluation.

Methods

Manual data were collected twice and 12 images captured per child by anthropometrist/expert and assistant (AEA) field teams (individuals/country, n = 15/Guatemala, n = 8/Kenya, n = 6/China). Caregivers were interviewed after observing their child's manual and scan data collection. Mixed methods included an administered caregiver interview (Guatemala, n = 465; Kenya, n = 496; China, n = 297) and self-administered AEA questionnaire both with closed- and open-ended questions, and 6 field team focus group discussions (FGDs; Guatemala, n = 2; Kenya, n = 3; China, n = 1). Qualitative data were coded by 2 authors and quantitative data produced descriptive statistics. Mixed-method results were compared and triangulated.

Results

Most AEAs were female with secondary or higher education. Approximately 80-90% of caregivers were the child's mother. To collect all anthropometry data, 62.1% of the 29 AEAs preferred scan, while 31% preferred manual methods. In FGDs, a key barrier for manual and scan methods was lack of child cooperation. Across countries, approximately 30% to almost 50% of caregivers said their child was bothered by each manual and scan method, while ≥95% of caregivers were willing to have their child measured by scans in the future.

Conclusions

Use of 3D scans to calculate anthropometry measurements was generally at least as acceptable as manual anthropometry measurement among AEA field workers and caregivers of young children aged <60 mo, and in some cases preferred.

Emergence of the adolescent obesity epidemic in the United States: five-decade visualization with humanoid avatars

BACKGROUND/OBJECTIVES

Body size and shape have increased over the past several decades with one in five adolescents now having obesity according to objective anthropometric measures such as weight, height, and body mass index (BMI). The gradual physical changes and their consequences may not be fully appreciated upon visual inspection by those managing the long-term health of adolescents. This study aimed to develop humanoid avatars representing the gradual changes in adolescent body size and shape over the past five decades and to align avatars with key BMI percentile cut points for underweight, normal weight, overweight, and obesity.

PARTICIPANTS/METHODS

Participants included 223 children and adolescents between the ages of 5 and 18 years approximately representative of the race/ethnicity and BMI of the noninstitutionalized US population. Each participant completed a three-dimensional whole-body scan, and the collected data was used to develop manifold regression models for generating humanoid male and female avatars from specified ages, weights, and heights. Secular changes in the mean weights and heights of adolescents were acquired from six U.S. National Health and Nutrition Surveys beginning in 1971-1974 and ending in 2015-2018. Male and female avatars at two representative ages, 10 and 15 years, were developed for each survey and at the key BMI percentile cut points based on data from the 2015-2018 survey.

RESULTS

The subtle changes in adolescent Americans' body size and shape over the past five decades are represented by 24 male and female 10- and 15-year-old avatars and 8 corresponding BMI percentile cut points.

CONCLUSIONS

The current study, the first of its kind, aligns objective physical examination weights and heights with the visual appearance of adolescents. Aligning the biometric and visual information may help improve awareness and appropriate clinical management of adolescents with excess adiposity passing through health care systems.

TRIAL REGISTRATION

ClinicalTrials.Gov NCT03706612.

Subcutaneous and visceral fat assessment by DXA and MRI in older adults and children

OBJECTIVE

Given the importance of body fat distribution in chronic disease development, feasible methods to assess body fat are essential. This study compared dual-energy x-ray absorptiometry (DXA) in measuring visceral and subcutaneous adipose tissue (VAT and SAT) with magnetic resonance imaging (MRI).

METHODS

VAT and SAT were assessed using similar DXA and MRI protocols among 1,795 elderly participants of the Adiposity Phenotype Study (APS) and 309 children/adolescents in Shape Up! Kids (SKids). Spearman correlations, Bland-Altman plots, and coefficients of determination (R2 ) assessed agreement between DXA and MRI measures.

RESULTS

DXA overestimated SAT values in APS (315 vs. 229 cm2 ) and SKids (212 vs. 161 cm2 ), whereas DXA underestimated VAT measures (141 vs. 167 cm2 ) in adults only. The correlations between DXA and MRI values were stronger for SAT than VAT (APS: r = 0.92 vs. 0.88; SKids: 0.90 vs. 0.74). Bland-Altman plots confirmed better agreement for SAT than VAT despite differences by sex, ethnicity, and weight status with respective R2 values for SAT and VAT of 0.88 and 0.84 (APS) and 0.81 and 0.69 (SKids).

CONCLUSION

These findings indicate that SAT by DXA reflects MRI measures in children and older adults, whereas agreement for VAT is weaker for individuals with low VAT levels.

Emergence of the obesity epidemic: 6-decade visualization with humanoid avatars

BACKGROUND

Visualizations of the emerging obesity epidemic, such as with serial US color prevalence maps, provide graphic images that extend informative public health messages beyond those in written communications. Advances in low-cost 3D optical technology now allow for development of large image databases that include participants varying in race/ethnicity, body mass, height, age, and circumferences. When combined with contemporary statistical methods, these data sets can be used to create humanoid avatar images with prespecified anthropometric features.

OBJECTIVES

The current study aimed to develop a humanoid avatar series with characteristics of representative US adults extending over the past 6 decades.

METHODS

3D optical scans were conducted on a demographically diverse sample of 570 healthy adults. Image data were converted to principal components and manifold regression equations were then developed with body mass, height, age, and waist circumference as covariates. Humanoid avatars were generated for representative adults with these 4 characteristics as reported in CDC surveys beginning in 1960-1962 up to 2015-2018.

RESULTS

There was a curvilinear increase in adult US population body mass, waist circumference, and BMI in males and females across the 9 surveys spanning 6 decades. A small increase in average adult population age was present between 1960 and 2018; height changes were inconsistent. A series of 4 avatars developed at ∼20-y intervals for representative males and females reveal the changes in body size and shape consistent with the emergence of the obesity epidemic. An additional series of developed avatars portray the shapes and sizes of males and females at key BMI cutoffs.

CONCLUSIONS

New mathematical approaches and accessible 3D optical technology combined with increasingly available large and diverse data sets across the life span now make unique visualization of body size and shape possible on a previously unattainable scale. This study is registered at https://clinicaltrials.gov/ct2/show/NCT03637855 as NCT03637855.

Assessment of clinical measures of total and regional body composition from a commercial 3-dimensional optical body scanner

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.

A pose-independent method for accurate and precise body composition from 3D optical scans

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 R2 = 0.91 male, 0.94 female; fat-free mass R2 = 0.95 male, 0.92 female; visceral fat mass R2 = 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.

Anthropometric Indicators as a Tool for Diagnosis of Obesity and Other Health Risk Factors: A Literature Review

Obesity is characterized by the accumulation of an excessive amount of fat mass (FM) in the adipose tissue, subcutaneous, or inside certain organs. The risk does not lie so much in the amount of fat accumulated as in its distribution. Abdominal obesity (central or visceral) is an important risk factor for cardiovascular diseases, diabetes, and cancer, having an important role in the so-called metabolic syndrome. Therefore, it is necessary to prevent, detect, and appropriately treat obesity. The diagnosis is based on anthropometric indices that have been associated with adiposity and its distribution. Indices themselves, or a combination of some of them, conform to a big picture with different values to establish risk. Anthropometric indices can be used for risk identification, intervention, or impact evaluation on nutritional status or health; therefore, they will be called anthropometric health indicators (AHIs). We have found 17 AHIs that can be obtained or estimated from 3D human shapes, being a noninvasive alternative compared to X-ray-based systems, and more accessible than high-cost equipment. A literature review has been conducted to analyze the following information for each indicator: definition; main calculation or obtaining methods used; health aspects associated with the indicator (among others, obesity, metabolic syndrome, or diabetes); criteria to classify the population by means of percentiles or cutoff points, and based on variables such as sex, age, ethnicity, or geographic area, and limitations.

Test-retest reliability and validity of body composition methods in adults

Cost-effective and efficient body composition measurement devices that are reliable and valid are necessary for identifying health risk as well as for understanding the effectiveness of lifestyle interventions. The objective of this study was to evaluate the test-retest reliability and validity of three body composition measurement devices. Forty-nine adults (mean age (SD) = 31.5 (10.7) y; BMI = 23.5 (3.0) kg/m² ) completed a reference air displacement plethysmography (ADP) measure, and duplicate measures using skinfold callipers (Lange), ultrasound (BodyMetrix A-mode) and a 3-dimensional photonic scanner (3DPS; Fit3D ProScanner). Skinfold thickness was measured at seven sites using callipers and ultrasound; percent body fat (%BF) was then estimated using population-specific algorithms. The 3DPS was used to measure body circumferences, and then %BF was estimated using its beta-software. While skinfold callipers showed poor absolute reliability (mean differences (Δ) [95% CI] = 0.54% [0.22, 0.87], standard error of measurement (SEM) = 0.63%), ultrasound and the 3DPS showed excellent absolute (Δ = 0.17% [-0.25, 0.58], SEM = 0.78%; and Δ = -0.01% [-0.43, 0.40], SEM = 0.67%, respectively) and relative reliability (ICC_2,1  = 0.988 [0.979, 0.993]; and ICC_2,1  = 0.983 [0.968, 0.991], respectively). Compared to ADP (n = 43), skinfold callipers underestimated %BF (Δ = -4.53 [-7.72, -1.34]; p = 0.003), while ultrasound (Δ = -0.32 [-3.51, 2.87]; p = 0.99) and the 3DPS (Δ = 1.06 [-2.12. 4.26]; p = 0.77) were not significantly different. Bland-Altman plots showed a minimal bias of ultrasound [95% limit of agreement (LOA) = -7.87, 7.23] and the 3DPS [95% LOA = -6.66, 8.79]. In conclusion, estimating %BF from subcutaneous fat measurements using ultrasound and body circumferences using a 3DPS may be reliable and valid methods that require minimal technician expertise.

Digital anthropometric evaluation of young children: comparison to results acquired with conventional anthropometry

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 R²s, 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.

Estimating somatotype from a single-camera 3D body scanning system

Somatotype is an approach to quantify body physique (shape and body composition). Somatotyping by manual measurement (the anthropometric method) or visual rating (the photoscopic method) needs technical expertize to minimize intra- and inter-observer errors. This study aims to develop machine learning models which enable automatic estimation of Heath-Carter somatotypes using a single-camera 3D scanning system. Single-camera 3D scanning was used to obtain 3D imaging data and computer vision techniques to extract features of body shape. Machine learning models were developed to predict participants' somatotypes from the extracted shape features. These predicted somatotypes were compared against manual measurement procedures. Data were collected from 46 participants and used as the training/validation set for model developing, whilst data collected from 17 participants were used as the test set for model evaluation. Evaluation tests showed that the 3D scanning methods enable accurate (mean error < 0.5; intraclass correlation coefficients >0.8) and precise (test-retest root mean square error < 0.5; intraclass correlation coefficients >0.8) somatotype predictions. This study shows that the 3D scanning methods could be used as an alternative to traditional somatotyping approaches after the current models improve with the large datasets.

Total body and regional surface area: Quantification with low-cost three-dimensional optical imaging systems

OBJECTIVES

Body surface area (SA) is a widely used physical measure incorporated into multiple thermophysiology and evolutionary biology models currently estimated in humans either with empirical prediction equations or costly whole-body laser imaging systems. The introduction of low-cost 3D scanners provides a new opportunity to quantify total body (TB) and regional SA, although a critical question prevails: can these devices acquire the quality of depth information and process this initial data to form a mesh that has the fidelity needed to generate accurate SA estimates?

MATERIALS AND METHODS

This question was answered by comparing SA estimates calculated using images from four commercial 3D scanners in 108 adults to corresponding estimates acquired with a whole-body laser system. This was accomplished by processing initial mesh data from all devices, including the laser system, with the same universal software adapted specifically for repairing mesh gaps, identifying landmarks, and generating SA measurements.

RESULTS

TB SA measured on all four 3D scanners was highly correlated with corresponding laser system estimates (R² s, 0.98-0.99; all p < 0.001) with some small but significant mean differences (-0.19 to 0.06 m² ); root-mean square errors (RMSEs) were small (0.02-0.03 m² ); and significant bias was present for one device. Qualitatively similar results (e.g., R² s, 0.78-0.95; mean Δs, -0.05 to 0.02 m² ; RMSEs, 0.01-0.03 m² ) were present for trunk, arm, and leg SA comparisons.

DISCUSSION

The current study observations demonstrate that low-cost and practical 3D optical scanners are capable of accurately quantifying TB and regional SA, thus opening new opportunities for evaluating human phenotypes and related physiological characteristics.

DXA-Derived Adiposity and Lean Indices for Management of Cardiometabolic and Musculoskeletal Frailty: Data Interpretation Tricks and Reporting Tips

The proper assessment and follow-up of obesity and sarcopenia are relevant for the proper management of the complications of cardiometabolic and musculoskeletal frailty. A total body dual-energy X-ray absorptiometry (DXA) scan should be systematically incorporated in the rehabilitative routine management of patients with obesity and sarcopenia. In the former patients, the total body DXA can be used to assess the fat tissue amount and distribution, while in the latter patients, it can be used to quantify the reduction of appendicular lean mass and to investigate the inter-limb lean mass asymmetry. This tutorial article provides an overview of different DXA-derived fat and lean indices and describes a step-by-step procedure on how to produce a complete DXA report. We suggest that the systematic incorporation of these indices into routine examinations of the patients with obesity and sarcopenia can be useful for identifying the patients at risk for cardiometabolic and neuromuscular impairment-related comorbidities and for evaluating the effectiveness of pharmacological and rehabilitative interventions.

Fully Automated Pipeline for Body Composition Estimation from 3D Optical Scans using Principal Component Analysis: A Shape Up Study

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.

Predictors of liver fat among children and adolescents from five different ethnic groups

Objectives

As rates of obesity around the world have increased, so has the detection of high level of liver fat in children and adolescents. This may put them at risk for cardiovascular disease later in life. This analysis of a cross‐sectional population‐based study of children and adolescents evaluated demographic and lifestyle determinants of percent liver fat.

Methods

Healthy participants (123 girls and 99 boys aged 5–17 years) recruited by convenience sampling in three locations completed questionnaires, anthropometric measurements, and dual X‐ray absorptiometry and magnetic resonance imaging (MRI) assessment. General linear models were applied to estimate the association of demographic, anthropometric, and dietary factors as well as physical activity with MRI‐based percent liver fat.

Results

The strongest predictor of liver fat was body mass index (BMI; p < 0.0001); overweight and obesity were associated with 0.5% and 1% higher liver fat levels. The respective adjusted mean percent values were 2.9 (95% CI 2.7, 3.1) and 3.4 (95% CI 3.2, 3.6) as compared to normal weight (2.4; 95% CI 2.3, 2.6). Mean percent liver fat was highest in Whites and African Americans, intermediate in Hispanic, and lowest among Asians and Native Hawaiians/Pacific Islanders (p < 0.0001). Age (p = 0.67), sex (p = 0.28), physical activity (p = 0.74), and diet quality (p = 0.70) were not significantly related with liver fat.

Conclusions

This study in multiethnic children and adolescents confirms the strong relationship of BMI with percent liver fat even in a population with low liver fat levels without detecting an association with age, sex, and dietary or physical activity patterns.

Predicting 3D body shape and body composition from conventional 2D photography

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 R² 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.

Assessment of body composition in pediatric overweight and obesity: A systematic review of the reliability and validity of common techniques

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.

Body Composition Measurements from Birth through 5 Years: Challenges, Gaps, and Existing & Emerging Technologies-A National Institutes of Health workshop

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.

Distinct phenotypic characteristics of normal-weight adults at risk of developing cardiovascular and metabolic diseases

BACKGROUND

The normal-weight BMI range (18.5-24.9 kg/m2) includes adults with body shape and cardiometabolic disease risk features of excess adiposity, although a distinct phenotype developed on a large and diverse sample is lacking.

OBJECTIVE

To identify demographic, behavioral, body composition, and health-risk biomarker characteristics of people in the normal-weight BMI range who are at increased risk of developing cardiovascular and metabolic diseases based on body shape.

METHODS

Six nationally representative waist circumference index (WCI, weight/height0.5) prediction formulas, with BMI and age as covariates, were developed using data from 17,359 non-Hispanic (NH) white, NH black, and Mexican-American NHANES 1999-2006 participants. These equations were then used to predict WCI in 5594 NHANES participants whose BMI was within the normal weight range. Men and women in each race/Hispanic-origin group were then separated into high, medium, and low tertiles based on the difference (residual) between measured and predicted WCI. Characteristics were compared across tertiles; P values for significance were adjusted for multiple comparisons.

RESULTS

Men and women in the high WCI residual tertile, relative to their BMI and age-equivalent counterparts in the low tertile, had significantly lower activity levels; higher percent trunk and total body fat (e.g. NH white men, X ± SE, 25.3 ± 0.2% compared with 20.4 ± 0.2%); lower percent appendicular lean mass (skeletal muscle) and bone mineral content; and higher plasma insulin and triglycerides, higher homeostatic model assessment of insulin resistance (e.g. NH white men, 1.45 ± 0.07 compared with 1.08 ± 0.06), and lower plasma HDL cholesterol. Percent leg fat was also significantly higher in men but lower in women. Similar patterns of variable statistical significance were present within sex and race/ethnic groups.

CONCLUSIONS

Cardiometabolic disease risk related to body shape in people who are normal weight according to BMI is characterized by a distinct phenotype that includes potentially modifiable behavioral health risk factors.

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

INTRODUCTION

Digital tools like 3D laser-based photonic scanners, which can assess external anthropometric measurements for population based studies, and predict body composition, are gaining in importance. Here we focus on a) systematic deviation between manually determined and scanned standard measurements, b) differences regarding the strength of association between these standard measurements and body composition, and c) improving these predictions of body composition by considering additional scan measurements.

METHODS

We analysed 104 men aged 19-23. Bioelectrical Impedance Analysis was used to estimate whole body fat mass, visceral fat mass and skeletal muscle mass (SMM). For the 3D body scans, an Anthroscan VITUSbodyscan was used to automatically obtain 90 body shape measurements. Manual anthropometric measurements (height, weight, waist circumference) were also taken.

RESULTS

Scanned and manually measured height, waist circumference, waist-to-height-ratio, and BMI were strongly correlated (Spearman Rho>0.96), however we also found systematic differences. When these variables were used to predict body fat or muscle mass, explained variation and prediction standard errors were similar between scanned and manual measurements. The univariable predictions performed well for both visceral fat (r2 up to 0.92) and absolute fat mass (AFM, r2 up to 0.87) but not for SMM (r2 up to 0.54). Of the 90 body scanner measures used in the multivariable prediction models, belly circumference and middle hip circumference were the most important predictors of body fat content. Stepwise forward model selection using the AIC criterion showed that the best predictive power (r2 up to 0.99) was achieved with models including 49 scanner measurements.

CONCLUSION

The use of a 3D full body scanner produced results that strongly correlate to manually measured anthropometric measures. Predictions were improved substantially by including multiple measurements, which can only be obtained with a 3D body scanner, in the models.

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

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 R² 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.