Accurate Prediction of Three-Dimensional Humanoid Avatars for Anthropometric Modeling.

Objective To evaluate the hypothesis that anthropometric dimensions derived from a person's manifold-regression predicted three-dimensional (3D) humanoid avatar are accurate when compared to their actual circumference, volume, and surface area measurements acquired with a ground-truth 3D optical imaging method. Avatars predicted using this approach, if accurate with respect to anthropometric dimensions, can serve multiple purposes including patient metabolic disease risk stratification in clinical settings. Methods Manifold regression 3D avatar prediction equations were developed on a sample of 570 adults who completed 3D optical scans, dual-energy X-ray absorptiometry (DXA), and bioimpedance analysis (BIA) evaluations. A new prospective sample of 84 adults had ground-truth measurements of 6 body circumferences, 7 volumes, and 7 surface areas with a 20-camera 3D reference scanner. 3D humanoid avatars were generated on these participants with manifold regression including age, weight, height, DXA %fat, and BIA impedances as potential predictor variables. Ground-truth and predicted avatar anthropometric dimensions were quantified with the same software. Results Following exploratory studies, one manifold prediction model was moved forward for presentation that included age, weight, height, and %fat as covariates. Predicted and ground-truth avatars had similar visual appearances; correlations between predicted and ground-truth anthropometric estimates were all high (R 2 s, 0.75-0.99; all p < 0.001) with non-significant mean differences except for arm circumferences (%D ~ 5%; p < 0.05). Concordance correlation coefficients ranged from 0.80-0.99 and small but significant bias (p < 0.05 - 0.01) was present with Bland-Altman plots in 13 of 20 total anthropometric measurements. The mean waist to hip circumference ratio predicted by manifold regression was non-significantly different from ground-truth scanner measurements. Conclusions 3D avatars predicted from demographic, physical, and other accessible characteristics can produce body representations with accurate anthropometric dimensions without a 3D scanner. Combining manifold regression algorithms into established body composition methods such as DXA, BIA, and other accessible methods provides new research and clinical opportunities.

Variations in bioelectrical impedance devices impact raw measures comparisons and subsequent prediction of body composition using recommended estimation equations.

BACKGROUND & AIMS: Bioelectrical impedance analysis (BIA) for body composition estimation is increasingly used in clinical and field settings to guide nutrition and training programs. Due to variations among BIA devices and the proprietary prediction equations used, studies have recommended the use of raw measures of resistance (R) and reactance (Xc) within population-specific equations to predict body composition. OBJECTIVE: We compared raw measures from three BIA devices to assess inter-device variation and the impact of differences on body composition estimations. METHODS: Raw R, Xc, impedance (Z) parameters were measured on a calibrated phantom and athletes using tetrapolar supine (BIASUP4), octapolar supine (BIASUP8), and octapolar standing (BIASTA8) devices. Measures of R and Xc were compared across devices and graphed using BIA vector analysis (BIVA) and raw parameters were entered into recommended athlete-specific equations for predicting fat-free mass (FFM) and appendicular lean soft tissue (ALST). Whole-body FFM and regional ALST were compared across devices and to a criterion five-compartment (5C) model and dual energy X-ray absorptiometry for ALST. RESULTS: Data from 73 (23.2 ± 4.8y) athletes were included in the analyses. Technical differences were observed between Z (range 12.2 - 50.1Ω) measures on the calibrated phantom. Differences in whole-body impedance were apparent due to posture (technological) and electrode placement (biological) factors. This resulted in raw measures for all three devices showing greater dehydration on BIVA compared to published norms for athletes using a separate BIA device. Compared to the 5C FFM, significant differences (p < 0.05) were observed on all three equations for BIASUP8 and BIASTA8, with constant error (CE) from -2.7 to -4.6 kg; no difference was observed for BIASUP4 or when device-specific algorithms were used. Published equations resulted in differences as large as 8.8 kg FFM among BIA devices. For ALST, even after a correction in the error of the published empirical equations, all three devices showed significant (p<0.01) CE from -1.6 to -2.9 kg. CONCLUSIONS: Raw bioimpedance measurements differ among devices due to technical, technological, and biological factors, limiting interchangeability of data across BIA systems. Professionals should be aware of these factors when purchasing systems, comparing data to published reference ranges, or when applying published empirical body composition prediction equations.

Clinical Anthropometrics and Body Composition from 3-Dimensional Optical Imaging.

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

Evaluation of visceral adipose tissue thresholds for elevated metabolic syndrome risk across diverse populations: A systematic review.

Beyond obesity, excess levels of visceral adipose tissue (VAT) significantly contribute to the risk of developing metabolic syndrome (MetS), although thresholds for increased risk vary based on population, regions of interest, and units of measure employed. We sought to determine whether a common threshold exists that is indicative of heightened MetS risk across all populations, accounting for sex, age, BMI, and race/ethnicity. A systematic literature review was conducted in September 2023, presenting threshold values for elevated MetS risk. Standardization equations harmonized the results from DXA, CT, and MRI systems to facilitate a comparison of threshold variations across studies. A total of 52 papers were identified. No single threshold could accurately indicate elevated risk for both males and females across varying BMI, race/ethnicity, and age groups. Thresholds fluctuated from 70 to 165.9 cm2, with reported values consistently lower in females. Generally, premenopausal females and younger adults manifested elevated risks at lower VAT compared to their older counterparts. Notably, Asian populations exhibited elevated risks at lower VAT areas (70-136 cm2) compared to Caucasian populations (85.6-165.9 cm2). All considered studies reported associations of VAT without accommodating covariates. No single VAT area threshold for elevated MetS risk was discernible post-harmonization by technology, units of measure, and region of interest. This review summarizes available evidence for MetS risk assessment in clinical practice. Further exploration of demographic-specific interactions between VAT area and other risk factors is imperative to comprehensively delineate overarching MetS risk.

Long-term sustained effects of the Look AHEAD lifestyle intervention on body composition among adults with type 2 diabetes.

OBJECTIVE: The objective of the study was to test whether there are sustained effects of the Look AHEAD intensive lifestyle intervention (ILI), versus diabetes support and education (DSE), on weight and body composition 12 to 16 years after randomization. METHODS: Participants were a subset of enrollees in the Look AHEAD dual-energy x-ray absorptiometry substudy who completed the final visit, composed of men (DSE = 99; ILI = 94) and women (DSE = 134; ILI = 135) with type 2 diabetes and mean (SD) age 57.2 (6.4) years and BMI 34.9 (5.1) kg/m2 at randomization. Dual-energy x-ray absorptiometry measured total and regional fat and lean masses at randomization, at Years 1, 4, and 8, and at the final visit. Linear mixed-effects regressions were applied with adjustment for group, clinic, sex, age, race/ethnicity, and baseline body composition. RESULTS: Weight and most body compartments were reduced by 2% to 8% (and BMI 4%) in ILI versus DSE in men but not women. ILI-induced loss of lean tissue did not show a lower percent lean mass versus DSE at 16 years after randomization. CONCLUSION: ILI-related changes in weight, fat, and lean mass were detectable 12 to 16 years after randomization in men but, for unknown reasons, not in women. There was no evidence that the intervention led to a disproportionate loss of lean mass by the end of the study.

Advances in body composition: a 100-year journey.

Knowledge of human body composition at the dawn of the twentieth century was based largely on cadaver studies and chemical analyses of isolated organs and tissues. Matters soon changed by the nineteen twenties when the Czech anthropologist Jindrich Matiegka introduced an influential new anthropometric method of fractionating body mass into subcutaneous adipose tissue and other major body components. Today, one century later, investigators can not only quantify every major body component in vivo at the atomic, molecular, cellular, tissue-organ, and whole-body organizational levels, but go far beyond to organ and tissue-specific composition and metabolite estimates. These advances are leading to an improved understanding of adiposity structure-function relations, discovery of new obesity phenotypes, and a mechanistic basis of some weight-related pathophysiological processes and adverse clinical outcomes. What factors over the past one hundred years combined to generate these profound new body composition measurement capabilities in living humans? This perspective tracks the origins of these scientific innovations with the aim of providing insights on current methodology gaps and future research needs.

Design and conduct of a randomized controlled feeding trial in a residential setting with mitigation for COVID-19.

BACKGROUND: Evaluating effects of different macronutrient diets in randomized trials requires well defined infrastructure and rigorous methods to ensure intervention fidelity and adherence. METHODS: This controlled feeding study comprised two phases. During a Run-in phase (14-15 weeks), study participants (18-50 years, BMI, ≥27 kg/m2) consumed a very-low-carbohydrate (VLC) diet, with home delivery of prepared meals, at an energy level to promote 15 ± 3% weight loss. During a Residential phase (13 weeks), participants resided at a conference center. They received a eucaloric VLC diet for three weeks and then were randomized to isocaloric test diets for 10 weeks: VLC (5% energy from carbohydrate, 77% from fat), high-carbohydrate (HC)-Starch (57%, 25%; including 20% energy from refined grains), or HC-Sugar (57%, 25%; including 20% sugar). Outcomes included measures of body composition and energy expenditure, chronic disease risk factors, and variables pertaining to physiological mechanisms. Six cores provided infrastructure for implementing standardized protocols: Recruitment, Diet and Meal Production, Participant Support, Assessments, Regulatory Affairs and Data Management, and Statistics. The first participants were enrolled in May 2018. Participants residing at the conference center at the start of the COVID-19 pandemic completed the study, with each core implementing mitigation plans. RESULTS: Before early shutdown, 77 participants were randomized, and 70 completed the trial (65% of planned completion). Process measures indicated integrity to protocols for weighing menu items, within narrow tolerance limits, and participant adherence, assessed by direct observation and continuous glucose monitoring. CONCLUSION: Available data will inform future research, albeit with less statistical power than originally planned.

Skeletal muscle estimation: A review of techniques and their applications.

Quantifying skeletal muscle size is necessary to identify those at risk for conditions that increase frailty, morbidity, and mortality, as well as decrease quality of life. Although muscle strength, muscle quality, and physical performance have been suggested as important assessments in the screening, prevention, and management of sarcopenic and cachexic individuals, skeletal muscle size is still a critical objective marker. Several techniques exist for estimating skeletal muscle size; however, each technique presents with unique characteristics regarding simplicity/complexity, cost, radiation dose, accessibility, and portability that are important factors for assessors to consider before applying these modalities in practice. This narrative review presents a discussion centred on the theory and applications of current non-invasive techniques for estimating skeletal muscle size in diverse populations. Common instruments for skeletal muscle assessment include imaging techniques such as computed tomography, magnetic resonance imaging, peripheral quantitative computed tomography, dual-energy X-ray absorptiometry, and Brightness-mode ultrasound, and non-imaging techniques like bioelectrical impedance analysis and anthropometry. Skeletal muscle size can be acquired from these methods using whole-body and/or regional assessments, as well as prediction equations. Notable concerns when conducting assessments include the absence of standardised image acquisition/processing protocols and the variation in cut-off thresholds used to define low skeletal muscle size by clinicians and researchers, which could affect the accuracy and prevalence of diagnoses. Given the importance of evaluating skeletal muscle size, it is imperative practitioners are informed of each technique and their respective strengths and weaknesses.

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.

Differences in segmental fat accumulation patterns by sex and ethnicity: An international approach.

Excess fat on the body impacts obesity-related co-morbidity risk; however, the location of fat stores affects the severity of these risks. The purpose of this study was to examine segmental fat accumulation patterns by sex and ethnicity using international datasets. An amalgamated and cross-calibrated dataset of dual x-ray absorptiometry (DXA)-measured variables compiled segmental mass for bone mineral content (BMC), lean mass (LM), and fat mass (FM) for each participant; percentage of segment fat (PSF) was calculated as PSFsegment = (FMsegment/(BMCsegment + LMsegment + FMsegment)) × 100. A total of 30 587 adults (N = 16 490 females) from 13 datasets were included. A regression model was used to examine differences in regional fat mass and PSF. All populations followed the same segmental fat mass accumulation in the ascending order with statistical significance (arms < legs < trunk), except for Hispanic/Latinx males (arms < [legs = trunk]). Relative fat accumulation patterns differed between those with greater PSF in the appendages (Arab, Mexican, Asian, Black, American Caucasian, European Caucasian, and Australasian Caucasian females; Black males) and those with greater PSF in the trunk (Mexican, Asian, American Caucasian, European Caucasian, and Australasian Caucasian males). Greater absolute and relative fat accumulation in the trunk could place males of most ethnicities in this study at a higher risk of visceral fat deposition and associated co-morbidities.

Guidance for assessment of the inflammation etiologic criterion for the GLIM diagnosis of malnutrition: A modified Delphi approach.

BACKGROUND & AIMS: The Global Leadership Initiative on Malnutrition (GLIM) approach to malnutrition diagnosis is based on assessment of three phenotypic (weight loss, low body mass index, and reduced skeletal muscle mass) and two etiologic (reduced food intake/assimilation and disease burden/inflammation) criteria, with diagnosis confirmed by fulfillment of any combination of at least one phenotypic and at least one etiologic criterion. The original GLIM description provided limited guidance regarding assessment of inflammation and this has been a factor impeding further implementation of the GLIM criteria. We now seek to provide practical guidance for assessment of inflammation in support of the etiologic criterion for inflammation. METHODS: A GLIM-constituted working group with 36 participants developed consensus-based guidance through a modified-Delphi review. A multi-round review and revision process served to develop seven guidance statements. RESULTS: The final round of review was highly favorable with 99 % overall "agree" or "strongly agree" responses. The presence of acute or chronic disease, infection or injury that is usually associated with inflammatory activity may be used to fulfill the GLIM disease burden/inflammation criterion, without the need for laboratory confirmation. However, we recommend that recognition of underlying medical conditions commonly associated with inflammation be supported by C-reactive protein (CRP) measurements when the contribution of inflammatory components is uncertain. Interpretation of CRP requires that consideration be given to the method, reference values, and units (mg/dL or mg/L) for the clinical laboratory that is being used. CONCLUSION: Confirmation of inflammation should be guided by clinical judgement based upon underlying diagnosis or condition, clinical signs, or CRP.

Guidance for assessment of the inflammation etiologic criterion for the GLIM diagnosis of malnutrition: A modified Delphi approach.

BACKGROUND: The Global Leadership Initiative on Malnutrition (GLIM) approach to malnutrition diagnosis is based on assessment of three phenotypic (weight loss, low body mass index, and reduced skeletal muscle mass) and two etiologic (reduced food intake/assimilation and disease burden/inflammation) criteria, with diagnosis confirmed by fulfillment of any combination of at least one phenotypic and at least one etiologic criterion. The original GLIM description provided limited guidance regarding assessment of inflammation, and this has been a factor impeding further implementation of the GLIM criteria. We now seek to provide practical guidance for assessment of inflammation. METHODS: A GLIM-constituted working group with 36 participants developed consensus-based guidance through a modified Delphi review. A multiround review and revision process served to develop seven guidance statements. RESULTS: The final round of review was highly favorable, with 99% overall "agree" or "strongly agree" responses. The presence of acute or chronic disease, infection, or injury that is usually associated with inflammatory activity may be used to fulfill the GLIM disease burden/inflammation criterion, without the need for laboratory confirmation. However, we recommend that recognition of underlying medical conditions commonly associated with inflammation be supported by C-reactive protein (CRP) measurements when the contribution of inflammatory components is uncertain. Interpretation of CRP requires that consideration be given to the method, reference values, and units (milligrams per deciliter or milligram per liter) for the clinical laboratory that is being used. CONCLUSION: Confirmation of inflammation should be guided by clinical judgment based on underlying diagnosis or condition, clinical signs, or CRP.

Independent association of thigh muscle fat density with vascular events in Korean adults.

BACKGROUND: We aimed to explore the associations between thigh muscle fat density and vascular events. METHODS: A total of 3,595 adults (mean age, 57.2 years; women, 1,715 [47.7%]) without baseline cardiovascular events from the Korean Atherosclerosis Study-2 were included. Muscle and fat area at the mid-thigh level were measured by computed tomography (CT) using the following Hounsfield Unit range: 0-30 for low density muscle (LDM); 31-100 for normal density muscle (NDM); and - 250 to - 50 for fat. RESULTS: During a median follow-up period of 11.8 (4.3-13.9) years, vascular events occurred in 11.6% of men and 5.9% of women. Individuals with vascular events had a larger LDM area (men: 48.8 ± 15.5 cm2 vs. 44.6 ± 14.5 cm2; women: 39.4 ± 13.2 cm2 vs. 35.0 ± 11.8 cm2, both P < 0.001) compared with those who did not have vascular events during the follow-up of at least 5 years. The LDM/NDM ratio was also independently associated with vascular events after adjusting for cardiometabolic risk factors. Moreover, the LDM/NDM ratio improved the prognostic value for vascular events when added to conventional risk factors. CONCLUSIONS: The current study suggests that a higher thigh muscle fat infiltration is associated with an increased risk of developing vascular events among Korean adults.

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.

Sizing and mending of appendicular muscle mass for hydration during the 12-lead electrocardiogram: True incidence of sarcopenia in heart failure.

BACKGROUND: Our aim was to develop and evaluate a method for the measurement of muscle mass during the 12-channel electrocardiogram (ECG), to determine the incidence of sarcopenia in patients with overhydration and to correct it for congestion. METHODS: A 12-channel ECG that simultaneously provided multifrequency segmental impedance data was used to measure total body water (TBW), extracellular water (ECW), ECW/TBW ratio and appendicular muscle mass (AppMM), validated by whole-body dual-energy X-ray absorptiometry. The mean ECW/TBW ratio was 0.24 ± 0.018 (SD) and 0.25 ± 0.016 for young (age range 20-25 years) healthy males (n = 77) and females (n = 88), respectively. The deviation of the ECW/TBW ratio from this mean was used to correct AppMM for excess ECW ('dry AppMM') in 869 healthy controls and in 765 patients with chronic heart failure (CHF) New York Heart Association classes II-IV. The association of AppMM and dry AppMM with grip strength was also examined in 443 controls and patients. RESULTS: With increasing N-terminal pro-brain natriuretic peptide (NT-proBNP), a continuous decline of AppMM indices is observed, which is more pronounced for dry AppMM indices (for males with NT-proBNP < 125 pg/mL: AppMM index mean = 8.4 ± 1.05, AppMM index dry mean = 8.0 ± 1.46 [n = 201, P < 0.001]; for females with NT-proBNP < 150 pg/mL: AppMM index mean = 6.4 ± 1.0, AppMM index dry mean = 5.8 ± 1.18 [n = 198, P < 0.001]; for males with NT-proBNP > 1000 pg/mL: AppMM index mean = 7.6 ± 0.98, AppMM index dry mean = 6.2 ± 1.11 [n = 137, P < 0.001]; and for females with NT-proBNP > 1000 pg/mL: AppMM index mean = 5.9 ± 0.96, AppMM index dry mean = 4.8 ± 0.94 [n = 109, P < 0.001]). The correlation between AppMM and upper-body AppMM and grip strength (r-value) increased from 0.79 to 0.83 (P < 0.001) and from 0.80 to 0.84 (P < 0.001), respectively, after correction (n = 443). The decline of AppMM with age after correction for ECW is much steeper than appreciated, especially in males: In patients with CHF and sarcopenia, the incidence of sarcopenia may be up to 30% higher after correction for ECW excess according to the European (62% vs. 57%, for males, and 43% vs. 31%, for females) and Foundation for the National Institutes of Health (FNIH) (56% vs. 46%, for males, and 54% vs. 38%, for females) consensus guidelines. CONCLUSIONS: The incidence of sarcopenia in CHF as defined by the European Working Group on Sarcopenia and FNIH consensus may be up to 30% higher after correction for ECW excess. This correction improves the correlation between muscle mass and strength. The presented technology will facilitate, on a large scale, screening for sarcopenia, help identify mechanisms and improve understanding of clinical outcomes.

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, measured quantities of muscle, fat, and bone, is typically assessed through dual energy X-ray absorptiometry (DXA) scans, which requires specialized equipment, trained technicians and involves exposure to radiation. Exterior body shape is dependent on body composition and recent technological advances have made three-dimensional (3D) scanning for body shape accessible and virtually ubiquitous. We developed a model which uses 3D body surface scan inputs to generate DXA scans. When analyzed with commercial software that is used clinically, our model generated images yielded accurate quantities of fat, lean, and bone. Our work highlights the strong relationship between exterior body shape and interior composition. Moreover, it suggests that with enhanced accuracy, such medical imaging models could be more widely adopted in clinical care, making the analysis of body composition more accessible and easier to obtain.

The metabolic load-capacity model and cardiometabolic health in children and youth with obesity.

BACKGROUND: The metabolic load-capacity index (LCI), which represents the ratio of adipose to skeletal muscle tissue-containing compartments, is potentially associated with cardiometabolic diseases. OBJECTIVES: To examine the associations between the LCI and cardiometabolic risk factors in children and youth with obesity. METHODS: This is a cross-sectional study including 10-18 years-old participants with a BMI of ≥95th . LCI by air-displacement plethysmography (ADP) was calculated as fat mass divided by fat-free mass, and LCI by ultrasound (US) as subcutaneous adipose tissue divided by skeletal muscle thickness. Sex-specific medians stratified participants into high versus low LCI. Single (inflammation, insulin resistance, dyslipidemia and hypertension) and clustered cardiometabolic risk factors were evaluated. Linear and logistic regression models tested the associations between these variables, adjusted for sexual maturation. RESULTS: Thirty-nine participants (43.6% males; 59% mid-late puberty) aged 12.5 (IQR: 11.1-13.5) years were included. LCI by ADP was positively associated with markers of inflammation and dyslipidemia; having a higher LCI predicted dyslipidemia in logistic regression. Similarly, LCI by US was positively associated with markers of dyslipidemia and blood pressure. In mid-late pubertal participants, LCI by US was positively associated with markers of insulin resistance and inflammation. CONCLUSIONS: Participants with unfavourable cardiometabolic profile had higher LCI, suggesting its potential use for predicting and monitoring cardiometabolic health in clinical settings.

Proportion of caloric restriction-induced weight loss as skeletal muscle.

OBJECTIVE: This study's objective was to develop models predicting the relative reduction in skeletal muscle (SM) mass during periods of voluntary calorie restriction (CR) and to validate model predictions in longitudinally monitored samples. METHODS: The model development group included healthy nonexercising adults (n = 897) who had whole-body SM mass measured with magnetic resonance imaging. Model predictions of relative SM changes with CR were evaluated in two longitudinal studies, one 12 to 14 weeks in duration (n = 74) and the other 12 months in duration (n = 26). RESULTS: A series of SM prediction models were developed in a sample of 415 males and 482 females. Model-predicted changes in SM mass relative to changes in body weight (i.e., ΔSM/Δbody weight) with a representative model were (mean ± SE) 0.26 ± 0.013 in males and 0.14 ± 0.007 in females (sex difference, p < 0.001). The actual mean proportions of weight loss as SM in the longitudinal studies were 0.23 ± 0.02/0.20 ± 0.06 in males and 0.10 ± 0.02/0.17 ± 0.03 in females, similar to model-predicted values. CONCLUSIONS: Nonelderly males and females with overweight and obesity experience respective reductions in SM mass with voluntary CR in the absence of a structured exercise program of about 2 to 2.5 kg and 1 to 1.5 kg per 10-kg weight loss, respectively. These estimates are predicted to be influenced by interactions between age and body mass index in males, a hypothesis that needs future testing.

Advances in digital anthropometric body composition assessment: neural network algorithm prediction of appendicular lean mass.

Currently available anthropometric body composition prediction equations were often developed on small participant samples, included only several measured predictor variables, or were prepared using conventional statistical regression methods. Machine learning approaches are increasingly publicly available and have key advantages over statistical modeling methods when developing prediction algorithms on large datasets with multiple complex covariates. This study aimed to test the feasibility of predicting DXA-measured appendicular lean mass (ALM) with a neural network (NN) algorithm developed on a sample of 576 participants using 10 demographic (sex, age, 7 ethnic groupings) and 43 anthropometric dimensions generated with a 3D optical scanner. NN-predicted and measured ALM were highly correlated (n = 116; R2, 0.95, p < 0.001, non-significant bias) with small mean, absolute, and root-mean square errors (X ± SD, -0.17 ± 1.64 kg and 1.28 ± 1.04 kg; 1.64). These observations demonstrate the application of NN body composition prediction algorithms to rapidly emerging large and complex digital anthropometric datasets. Clinical Trial Registration: NCT03637855, NCT05217524, NCT03771417, and NCT03706612.