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The 2-component molecular-level model dividing body mass into fat and fat-free mass (FFM) is a cornerstone of contemporary body composition research across multiple disciplines. Confusion prevails, however, as the term lean body mass (LBM) is frequently used interchangeably with FFM in scientific discourse. Are LBM and FFM the same or different body components? Captain Albert R. Behnke originated the LBM concept in 1942 and he argued that his "physiological" LBM component included "essential" fat or structural lipids whereas FFM is a chemical entity "free" of fat. Classical experimental animal and human studies conducted during Behnke's era laid the foundation for the widely used body density and total body water 2-component molecular-level body composition models. Refined body composition models, organization of lipids into structural and functional groupings, and lipid extraction methods all have advanced since Behnke's era. Our review provides an in-depth analysis of these developments with the aim of clarifying distinctions between the chemical composition of LBM and FFM. Our retrospective analysis reveals that FFM, derived experimentally as the difference between body weight and extracted neutral or nonpolar lipids (mainly triglycerides), includes polar or structural lipids (that is, Behnke's "essential" fat). Accordingly, LBM as originally proposed by Behnke has the same chemical composition as FFM, thus answering a longstanding ambiguity in the body composition literature. Bringing body composition science into the modern era mandates the use of the chemically correct term FFM with the elimination of the duplicative term LBM that today has value primarily in a historical context. Avoiding the use of the term LBM additionally limits confusion surrounding similar widely used body composition terms such as lean mass, lean soft tissue mass, and lean muscle mass., Competing Interests: Conflict of interest SBH serves on the Medical Advisory Boards of Tanita Corporation, Novo Nordisk, Abbott, Novartis, Versanis, and Medifast. GMT has received support for his research laboratory, in the form of research grants or equipment loan or donation, from manufacturers of body composition assessment devices, including Size Stream LLC; Naked Labs Inc.; Prism Labs Inc.; RJL Systems; MuscleSound; and Biospace, Inc. None of these entities played a role in the present work., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background & Aims: Body shape expressed as the trunk-to-leg volume ratio is associated with diabetes and mortality due to the associations between higher adiposity and lower lean mass with Metabolic Syndrome (MetS) risk. Reduced appendicular muscle mass is associated with malnutrition risk and age-related frailty, and is a risk factor for poor treatment outcomes related to MetS and other clinical conditions (e.g.; cancer). These measures are traditionally assessed by dual-energy X-ray absorptiometry (DXA), which can be difficult to access in clinical settings. The Shape Up! Adults trial (SUA) demonstrated the accuracy and precision of 3-dimensional optical imaging (3DO) for body composition as compared to DXA and other criterion measures. Here we assessed whether trunk-to-leg volume estimates derived from 3DO are associated with MetS risk in a similar way as when measured by DXA. We further explored if estimations of appendicular lean mass (ALM) could be made using 3DO to further improve the accessibility of measuring this important frailty and disease risk factor., Methods: SUA recruited participants across sex, age (18-40, 40-60, >60 years), BMI (under, normal, overweight, obese), and race/ethnicity (non-Hispanic [NH] Black, NH White, Hispanic, Asian, Native Hawaiian/Pacific Islander) categories. Each participant had whole-body DXA and 3DO scans, and measures of cardiovascular health. The 3DO measures of trunk and leg volumes were calibrated to DXA to express equivalent trunk-to-leg volume ratios. We expressed each blood measure and overall MetS risk in quartile gradations of trunk-to-leg volume previously defined by National Health and Nutrition Examination Survey (NHANES). Finally, we utilized 3DO measures to estimate DXA ALM using ten-fold cross-validation of the entire dataset., Results: Participants were 502 (273 female) adults, mean age = 46.0 ± 16.5y, BMI = 27.6 ± 7.1 kg/m 2 and a mean DXA trunk-to-leg volume ratio of 1.47 ± 0.22 (females: 1.43 ± 0.23; males: 1.52 ± 0.20). After adjustments for age and sex, each standard deviation increase in trunk-to-leg volume by 3DO was associated with a 3.3 (95% odds ratio [OR] = 2.4-4.2) times greater risk of MetS, with individuals in the highest quartile of trunk-to-leg at 27.4 (95% CI: 9.0-53.1) times greater risk of MetS compared to the lowest quartile. Risks of elevated blood biomarkers as related to high 3DO trunk-to-leg volume ratios were similar to previously published comparisons using DXA trunk-to-leg volume ratios. Estimated ALM by 3DO was correlated to DXA (r 2  = 0.96, root mean square error = 1.5 kg) using ten-fold cross-validation., Conclusion: Using thresholds of trunk-to-leg associated with MetS developed on a sample of US-representative adults, trunk-to-leg ratio by 3DO after adjustments for offsets showed significant associations to blood parameters and MetS risk. 3DO scans provide a precise and accurate estimation of ALM across the range of body sizes included in the study sample. The development of these additional measures improves the clinical utility of 3DO for the assessment of MetS risk as well as the identification of low muscle mass associated with poor cardiometabolic and functional health., Competing Interests: Conflict of interest None., (Published by Elsevier Ltd.)

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/m 2 ) 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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David S. Ludwig Dr. Ludwig has received royalties for books that recommend a carbohydrate-modified diet. David B. Allison In the last thirty-six months, Dr. Allison has received personal payments or promises for same from: Alkermes, Inc.; Amin Talati Wasserman for KSF Acquisition Corp (Glanbia); General Mills; Kaleido Biosciences; Law Offices of Ronald Marron; Novo Nordisk Fonden; and Zero Longevity Science (as stock options). Donations to a foundation have been made on his behalf by the Northarvest Bean Growers Association. He has served as an unpaid consultant/advisor to the USDA Agricultural Research Service. Dr. Allison’s institution, Indiana University, and the Indiana University Foundation have received funds or donations to support his research or educational activities from: Alliance for Potato Research and Education; American Egg Board; Arnold Ventures; Eli Lilly and Company; Mars, Inc.; National Cattlemen’s Beef Association; National Pork Board; Pfizer, Inc.; Soleno Therapeutics; WW (formerly Weight Watchers); and numerous other for-profit and non-profit organizations to support the work of the School of Public Health and the university more broadly. Steven B. Heymsfield Dr. Heymsfield reports personal fees from Medifast Corporation, Tanita Corporation, Novo Nordisk, and Amgen, outside the submitted work. Tapan Mehta Dr. Mehta has received fees from Novo Nordisk, Heart Rhythm Clinical and Research Solutions, The Obesity Journal, and PLOS One., (Copyright © 2024 Elsevier Inc. All rights reserved.)

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., Competing Interests: Conflicts of interest M. Isabel T. D. Correia reported support from Abbott, Danone, Fresenius, Nestlé for lectures and educational materials. David Evans disclosed support from Abbott Nutrition for research grants, and consulting and speaking honoraria; Fresenius Kabi for consulting and speaking honoraria; Coram/CVS Optioncare for consulting and speaking honoraria; and Alcresta Therapeutics for consulting and speaking honoraria. M. Cristina Gonzalez disclosed receiving honoraria and/or paid consultancy from Abbott Nutrition, Nutricia, and Nestle Health Science Brazil. Steven Heymsfield reported serving on the medical advisory boards of Tanita Corporation, Medifast, Abbott, and Novo Nordisk. Ainsley Malone disclosed that she is an employee of ASPEN and that she has received an honorarium from the Abbott Nutrition Health Institute. Kris M. Mogensen disclosed serving on the Baxter Advisory Board for parenteral nutrition and indirect calorimetry. Alison Steiber reported that she is an employee of the Academy of Nutrition and Dietetics and that she has received grant funding from the Academy of Nutrition and Dietetics Foundation and the Administration for Community Living. All other authors reported no disclosures., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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 (R 2 ) 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. R 2 values were higher for SAT than VAT across groups and improved slightly after adding WC. For SAT, R 2 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., (Copyright © 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.)

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

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

Background: 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/m 2 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/m 2 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., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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 (R 2 ) 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., (Copyright © 2023 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

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

Background: The impact of body composition (BC) abnormalities on COVID-19 outcomes remains to be determined., Objectives: We summarized the evidence on BC abnormalities and their relationship with adverse clinical outcomes in patients with COVID-19., Methods: A systematic search was conducted up until 26 September, 2022 for observational studies using BC techniques to quantify skeletal muscle mass (or related compartments), muscle radiodensity or echo intensity, adipose tissue (AT; or related compartments), and phase angle (PhA) in adults with COVID-19. Methodological quality of studies was assessed using the Newcastle-Ottawa Scale. A synthesis without meta-analysis was conducted to summarize the prevalence of BC abnormalities and their significant associations with clinical outcomes., Results: We included 62 studies (69.4% low risk of bias) with 12-1138 participants, except 3 studies with ≤490,301 participants. Using CT and different cutoff values, prevalence ranged approximately from 22% to 90% for low muscle mass, 12% to 85% for low muscle radiodensity, and 16% to 70% for high visceral AT. Using BIA, prevalence of high FM was 51%, and low PhA was 22% to 88%. Mortality was inversely related to PhA (3/4 studies) and positively related to intra- and intermuscular AT (4/5 studies), muscle echo intensity (2/2 studies), and BIA-estimated FM (2/2 studies). Intensive care unit (ICU) admission was positively related to visceral AT (6/7 studies) and total AT (2/3 studies). Disease severity and hospitalization outcomes were positively related to intra- and intermuscular AT (2/2 studies). Inconsistent associations were found for the rest of the BC measures and hospitalization outcomes., Conclusions: Abnormalities in BC were prevalent in patients with COVID-19. Although conflicting associations were observed among certain BC abnormalities and clinical outcomes, higher muscle echo intensity (reflective of myosteatosis) and lower PhA were more consistently associated with greater mortality risk. Likewise, high intra- and intermuscular AT and visceral AT were associated with mortality and ICU admission, respectively. This trial was registered at PROSPERO as CRD42021283031., (Copyright © 2023 American Society for Nutrition. Published by Elsevier Inc. All rights reserved.)

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

Background: Recent 3-dimensional optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise in clinical measures made by DXA. However, the sensitivity for monitoring body composition change over time with 3DO body shape imaging is unknown., Objectives: This study aimed to evaluate the ability of 3DO in monitoring body composition changes across multiple intervention studies., Methods: A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at the baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Using an established statistical shape model, each 3DO mesh was transformed into principal components, which were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus the baseline) were compared with those of DXA using a linear regression analysis., Results: The analysis included 133 participants (45 females) in 6 studies. The mean (SD) length of follow-up was 13 (5) wk (range: 3-23 wk). Agreement between 3DO and DXA (R 2 ) for changes in total FM, total FFM, and appendicular lean mass were 0.86, 0.73, and 0.70, with root mean squared errors (RMSEs) of 1.98 kg, 1.58 kg, and 0.37 kg, in females and 0.75, 0.75, and 0.52 with RMSEs of 2.31 kg, 1.77 kg, and 0.52 kg, in males, respectively. Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA., Conclusions: Compared with DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allows users to self-monitor on a frequent basis throughout interventions. This trial was registered at clinicaltrials.gov as NCT03637855 (Shape Up! Adults; https://clinicaltrials.gov/ct2/show/NCT03637855); NCT03394664 (Macronutrients and Body Fat Accumulation: A Mechanistic Feeding Study; https://clinicaltrials.gov/ct2/show/NCT03394664); NCT03771417 (Resistance Exercise and Low-Intensity Physical Activity Breaks in Sedentary Time to Improve Muscle and Cardiometabolic Health; https://clinicaltrials.gov/ct2/show/NCT03771417); NCT03393195 (Time Restricted Eating on Weight Loss; https://clinicaltrials.gov/ct2/show/NCT03393195), and NCT04120363 (Trial of Testosterone Undecanoate for Optimizing Performance During Military Operations; https://clinicaltrials.gov/ct2/show/NCT04120363)., (Copyright © 2023 American Society for Nutrition. All rights reserved.)

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

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

Introduction: Loss of skeletal muscle mass and function (sarcopenia) is common in individuals with obesity due to metabolic changes associated with a sedentary lifestyle, adipose tissue derangements, comorbidities (acute and chronic diseases), and during the ageing process. Co-existence of excess adiposity and low muscle mass/function is referred to as sarcopenic obesity (SO), a condition increasingly recognized for its clinical and functional features that negatively influence important patient-centred outcomes. Effective prevention and treatment strategies for SO are urgently needed, but efforts are hampered by the lack of an universally established SO Definition and diagnostic criteria. Resulting inconsistencies in the literature also negatively affect the ability to define prevalence as well as clinical relevance of SO for negative health outcomes., Aims and Methods: The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) launched an initiative to reach expert consensus on a Definition and diagnostic criteria for SO. The jointly appointed international expert panel proposes that SO is defined as the co-existence of excess adiposity and low muscle mass/function. The diagnosis of SO should be considered in at-risk individuals who screen positive for a co-occurring elevated body mass index or waist circumference, and markers of low skeletal muscle mass and function (risk factors, clinical symptoms, or validated questionnaires). Diagnostic procedures should initially include assessment of skeletal muscle function, followed by assessment of body composition where presence of excess adiposity and low skeletal muscle mass or related body compartments confirm the diagnosis of SO. Individuals with SO should be further stratified into Stage I in the absence of clinical complications, or Stage II if cases are associated with complications linked to altered body composition or skeletal muscle dysfunction., Conclusions: ESPEN and EASO, as well as the expert international panel, advocate that the proposed SO Definition and diagnostic criteria be implemented into routine clinical practice. The panel also encourages prospective studies in addition to secondary analysis of existing datasets, to study the predictive value, treatment efficacy, and clinical impact of this SO definition., Competing Interests: Conflict of interest None of the authors display any conflict of interest in the production of this manuscript., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

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., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Society for Nutrition.)

Background: The aim of obesity treatment is to promote loss of fat relative to lean mass. However, body composition changes with calorie restriction differ among individuals., Objectives: The goal of this study was to test the hypothesis that insulin secretion predicts body composition changes among young and middle-age adults with high BMI (in kg/m2) following major weight loss., Methods: Exploratory analyses were conducted with pre-randomization data from 2 large feeding trials: the Framingham, Boston, Bloomington, Birmingham, and Baylor study (FB4; n = 82, 43.9% women, BMI ≥27) and the Framingham State Food Study [(FS)2; n = 161, 69.6% women, BMI ≥25]. Participants in the 2 trials consumed calorie-restricted moderate-carbohydrate or very-low-carbohydrate diets to produce 12-18% weight loss in ∼14 wk or 10-14% in ∼10 wk, respectively. We determined insulin concentration 30 min after a 75-g oral glucose load (insulin-30) as a measure of insulin secretion and HOMA-IR as a measure of insulin resistance at baseline. Body composition was determined by DXA at baseline and post-weight loss. Associations were analyzed using general linear models with adjustment for covariates., Results: In FB4, higher insulin-30 was associated with a smaller decrease in fat mass (0.441 kg per 100 μIU/mL increment in baseline insulin-30; P = 0.005; -1.20-kg mean difference between the first compared with the fifth group of insulin-30) and a larger decrease in lean mass (-0.465 kg per 100 μIU/mL; P = 0.004; 1.27-kg difference). Participants with higher insulin-30 lost a smaller proportion of weight loss as fat (-3.37% per 100 μIU/mL; P = 0.003; 9.20% difference). Greater HOMA-IR was also significantly associated with adverse body composition changes. Results from (FS)2 were qualitatively similar but of a smaller magnitude., Conclusions: Baseline insulin dynamics predict substantial individual differences in body composition following weight loss. These findings may inform understanding of the pathophysiological basis for weight regain and the design of more effective obesity treatment. Registered at clinicaltrials.gov as NCT03394664 and NCT02068885., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

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

According to a commonly held view, the obesity pandemic is caused by overconsumption of modern, highly palatable, energy-dense processed foods, exacerbated by a sedentary lifestyle. However, obesity rates remain at historic highs, despite a persistent focus on eating less and moving more, as guided by the energy balance model (EBM). This public health failure may arise from a fundamental limitation of the EBM itself. Conceptualizing obesity as a disorder of energy balance restates a principle of physics without considering the biological mechanisms that promote weight gain. An alternative paradigm, the carbohydrate-insulin model (CIM), proposes a reversal of causal direction. According to the CIM, increasing fat deposition in the body-resulting from the hormonal responses to a high-glycemic-load diet-drives positive energy balance. The CIM provides a conceptual framework with testable hypotheses for how various modifiable factors influence energy balance and fat storage. Rigorous research is needed to compare the validity of these 2 models, which have substantially different implications for obesity management, and to generate new models that best encompass the evidence., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Society for Nutrition.)

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

Background and Aims: Considering the applicability of phase angle (PhA) as a marker of muscle mass and function, we aimed to investigate whether PhA is a predictor of muscle abnormalities and function in patients with cancer., Methods: In a sample of patients with colorectal cancer (CRC), PhA was obtained from measurements of resistance and reactance from bioelectrical impedance analysis. Computerized tomography imaging at the third lumbar vertebra was used to evaluate muscle abnormalities by quantifying skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD). Muscle function was assessed by handgrip strength (HGS) and gait speed (GS)., Results: This cross-sectional study included 190 participants (X±SD), mean age 60.5 ± 11.3 years; 57% men; 78% had cancer stages III to IV. PhA was highly correlated with SMI (r = 0.70) and moderately correlated with HGS (r = 0.54). PhA explained 48% of the SMI variability (R 2  = 0.485), 21% of the SMD variability (R 2  = 0.214), 26% of HGS (R 2  = 0.261) and 9.8% of GS (R 2  = 0.098). In the multivariate model adjusted for age, sex, body mass index, performance status, comorbidities and cancer stage, 1-degree decrease in PhA was associated with low SMI (Odds Ratio (OR) = 6.56, 95% CI: 2.90-14.86) and with low SMI and HGS combined (OR = 11.10, 95% CI: 2.61-47.25). In addition, Receiving Operating Characteristics curve analysis showed that PhA had a good diagnostic accuracy for detecting low SMI, low SMI and SMD combined, low SMD and HGS and low SMI and HGS combined (AUC = 0.81, 95% CI: 0.74-0.88; AUC = 0.88, 95% CI: 0.81-0.95; AUC = 0.80, 95% CI: 0.70-0.91; AUC = 0.82, 95% CI: 0.74-0.89; respectively)., Conclusions: PhA was a predictor of muscle abnormalities and function and had a good diagnostic accuracy for detecting low muscle mass, low muscle mass and radiodensity, low muscle radiodensity and strength, and low muscle mass and strength in patients with CRC., Competing Interests: Conflict of interest CMP reports grants from Campus Alberta Innovation Program during the conduct of the study; personal fees from Consultancy, personal fees from Payment for lectures including service on speakers’ bureaus, outside the submitted work. SBH reports personal fees from Tanita Corporation, Medical Advisory Board, outside the submitted work., (Copyright © 2021 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

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

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., (Copyright © The Author(s) on behalf of the American Society for Nutrition 2020.)

Background: Sarcopenic obesity is a clinical and functional condition characterized by the coexistence of excess fat mass and sarcopenia. Currently, different definitions of sarcopenic obesity exist and its diagnostic criteria and cut-offs are not universally established. Therefore, the prevalence and sensitivity of this condition for any disease risk prediction is affected significantly., Aim: This work was conducted under the auspices of the European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO). An international expert panel performed a systematic review as an initial step to analyze and summarize the available scientific literature on the definitions and the diagnostic criteria for sarcopenic obesity proposed and/or applied in human studies to date., Methods: The present systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The search was conducted in April 2018 in three databases (PubMed, Scopus, Web of Science). Human studies conducted in both sexes, irrespective of ethnicity, and published from 2007 to 2018 were included; cohorts of individuals with obesity and acute or chronic conditions and treatments reported to negatively influence skeletal muscle mass and function independently of obesity were excluded from final analyses. The quality of the studies was evaluated using the Newcastle-Ottawa Scale (NOS) adapted for cross sectional studies., Results: The electronic search retrieved 2335 papers of which 75 met the eligibility criteria. A marked heterogeneity in definitions and approaches to diagnose sarcopenic obesity was observed. This was mainly due to differences in the definitions of obesity and sarcopenia, in the methodologies used to assess body composition and physical function, and in the reference values for the variables that have been used (different cut-offs, interquartile analysis, diverse statistical stratification methods). This variability may be attributable, at least in part, to the availability of the methodologies in the different settings, to the variability in specialties and backgrounds of the researcher, and to the different settings (general population, clinical settings, etc.) where studies were performed., Conclusion: The results of the current work support the need for consensus proposals on: 1) definition of sarcopenic obesity; 2) diagnostic criteria both at the level of potential gold-standards and acceptable surrogates with wide clinical applicability, and with related cut-off values; 3) methodologies to be used in actions 1 and 2. First steps should be aimed at reaching consensus on plausible proposals that would need subsequent validation based on homogeneous studies and databases, possibly based on analyses of existing cohorts, to help define the prevalence of the condition, its clinical and functional relevance as well as most effective prevention and treatment strategies., Competing Interests: Conflict of interest The authors declare no conflict of interest., (Copyright © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

We read the recent article in Psychology of Sport and Exercise by Liu et al. ("A randomized controlled trial of coordination exercise on cognitive function in obese adolescents") with great interest. Our interest in the article stemmed from the extraordinary differences in obesity-related outcomes reported in response to a rope-jumping intervention. We requested the raw data from the authors to confirm the results and, after the journal editors reinforced our request, the authors graciously provided us with their data. We share our evaluation of the original data herein, which includes concerns that weight and BMI loss by the intervention appears extraordinary in both magnitude and aspects of the distributions. We request that the authors address our findings by providing explanations of the extraordinary data or correcting any errors that may have occurred in the original report, as appropriate.

Background: Three-dimensional optical (3DO) body scanning has been proposed for automatic anthropometry. However, conventional measurements fail to capture detailed body shape. More sophisticated shape features could better indicate health status., Objectives: The objectives were to predict DXA total and regional body composition, serum lipid and diabetes markers, and functional strength from 3DO body scans using statistical shape modeling., Methods: Healthy adults underwent whole-body 3DO and DXA scans, blood tests, and strength assessments in the Shape Up! Adults cross-sectional observational study. Principal component analysis was performed on registered 3DO scans. Stepwise linear regressions were performed to estimate body composition, serum biomarkers, and strength using 3DO principal components (PCs). 3DO model accuracy was compared with simple anthropometric models and precision was compared with DXA., Results: This analysis included 407 subjects. Eleven PCs for each sex captured 95% of body shape variance. 3DO body composition accuracy to DXA was: fat mass R2 = 0.88 male, 0.93 female; visceral fat mass R2 = 0.67 male, 0.75 female. 3DO body fat test-retest precision was: root mean squared error = 0.81 kg male, 0.66 kg female. 3DO visceral fat was as precise (%CV = 7.4 for males, 6.8 for females) as DXA (%CV = 6.8 for males, 7.4 for females). Multiple 3DO PCs were significantly correlated with serum HDL cholesterol, triglycerides, glucose, insulin, and HOMA-IR, independent of simple anthropometrics. 3DO PCs improved prediction of isometric knee strength (combined model R2 = 0.67 male, 0.59 female; anthropometrics-only model R2 = 0.34 male, 0.24 female)., Conclusions: 3DO body shape PCs predict body composition with good accuracy and precision comparable to existing methods. 3DO PCs improve prediction of serum lipid and diabetes markers, and functional strength measurements. The safety and accessibility of 3DO scanning make it appropriate for monitoring individual body composition, and metabolic health and functional strength in epidemiological settings.This trial was registered at clinicaltrials.gov as NCT03637855., (Copyright © American Society for Nutrition 2019.)

Background: The Goldberg cutoffs are used to decrease bias in self-reported estimates of energy intake (EISR). Whether the cutoffs reduce and eliminate bias when used in regressions of health outcomes has not been assessed., Objective: We examined whether applying the Goldberg cutoffs to data used in nutrition studies could reliably reduce or eliminate bias., Methods: We used data from the Comprehensive Assessment of Long-Term Effects of Reducing Intake of Energy (CALERIE), the Interactive Diet and Activity Tracking in American Association of Retired Persons (IDATA) study, and the National Diet and Nutrition Survey (NDNS). Each data set included EISR, energy intake estimated from doubly labeled water (EIDLW) as a reference method, and health outcomes including baseline anthropometric, biomarker, and behavioral measures and fitness test results. We conducted 3 linear regression analyses using EISR, a plausible EISR based on the Goldberg cutoffs (EIG), and EIDLW as an explanatory variable for each analysis. Regression coefficients were denoted ${\hat{\beta }&#95;{\rm SR}}$, ${\hat{\beta }&#95;{\rm G}}$, and ${\hat{\beta }&#95;{\rm DLW}}$, respectively. Using the jackknife method, bias from ${\hat{\beta }&#95;{\rm SR}}$ compared with ${\hat{\beta }&#95;{\rm DLW}}$ and remaining bias from ${\hat{\beta }&#95;{\rm G}}$ compared with ${\hat{\beta }&#95;{\rm DLW}}$ were estimated. Analyses were repeated using Pearson correlation coefficients., Results: The analyses from CALERIE, IDATA, and NDNS included 218, 349, and 317 individuals, respectively. Using EIG significantly decreased the bias only for a subset of those variables with significant bias: weight (56.1%; 95% CI: 28.5%, 83.7%) and waist circumference (WC) (59.8%; 95% CI: 33.2%, 86.5%) with CALERIE, weight (20.8%; 95% CI: -6.4%, 48.1%) and WC (17.3%; 95% CI: -20.8%, 55.4%) with IDATA, and WC (-9.5%; 95% CI: -72.2%, 53.1%) with NDNS. Furthermore, bias significantly remained even after excluding implausible data for various outcomes. Results obtained with Pearson correlation coefficient analyses were qualitatively consistent., Conclusions: Some associations between EIG and outcomes remained biased compared with associations between EIDLW and outcomes. Use of the Goldberg cutoffs was not a reliable method for eliminating bias., (Copyright © American Society for Nutrition 2019.)

Skeletal muscle plays major roles in metabolism and overall health across the lifecycle. Emerging evidence indicates that prenatal (maternal diet during pregnancy and genetic defects) and postnatal factors (physical activity, hormones, dietary protein, and obesity) influence muscle mass acquisition and strength early in life. As a consequence, low muscle mass and strength contributes to several adverse health outcomes during childhood. Specifically, studies demonstrated inverse associations of muscle mass and strength to single and clustered metabolic risk factors. The literature also consistently reports that low muscle mass and strength are associated with reduced bone parameters during growth, increasing the risk of osteoporosis in old age. Furthermore, muscle mass gains are associated with improved neurodevelopment in the first years of life. Given these negative implications of low muscle mass and strength on health, it is crucial to track muscle mass and strength development from childhood to adolescence. Several body composition techniques are currently available for estimation of muscle mass, all with unique advantages and disadvantages. The value of ultrasound as a technique to measure muscle mass is emerging in pediatric research with potential for translating the research findings to clinical settings. For the assessment of muscle strength, the handgrip strength test has been widely employed but without a standardized protocol. Although further research is needed to define normative data and cut points for the low muscle mass and strength phenotype, the use of such non-invasive medical monitoring is a promising strategy to identify early abnormalities and prevent low muscle mass in adulthood., (Copyright © 2019 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

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

Background: While many people with overweight or obesity can lose weight temporarily, most have difficulty maintaining weight loss over the long term. Studies of dietary composition typically focus on weight loss, rather than weight-loss maintenance, and rely on nutrition education and dietary counseling, rather than controlled feeding protocols. Variation in initial weight loss and insufficient differentiation among treatments confound interpretation of results and compromise conclusions regarding the weight-independent effects of dietary composition. The aim of the present study was to evaluate three test diets differing in carbohydrate-to-fat ratio during weight-loss maintenance., Design and Dietary Interventions: Following weight loss corresponding to 12±2% of baseline body weight on a standard run-in diet, 164 participants aged 18 to 65years were randomly assigned to one of three test diets for weight-loss maintenance through 20weeks (test phase). We fed them high-carbohydrate (60% of energy from carbohydrate, 20% fat), moderate-carbohydrate (40% carbohydrate, 40% fat), and low-carbohydrate (20% carbohydrate, 60% fat) diets, controlled for protein content (20% of energy). During a 2-week ad libitum feeding phase following the test phase, we assessed the effect of the test diets on body weight., Outcomes: The primary outcome was total energy expenditure, assessed by doubly-labeled water methodology. Secondary outcomes included resting energy expenditure and physical activity, chronic disease risk factors, and variables to inform an understanding of physiological mechanisms by which dietary carbohydrate-to-fat ratio might influence metabolism. Weight change during the ad libitum feeding phase was conceptualized as a proxy measure of hunger., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Background: Attempts to model cumulative intake curves with quadratic functions have not simultaneously taken gustatory stimulation, satiation, and maximal food intake into account., Objective: Our aim was to develop a dynamic model for cumulative intake curves that captures gustatory stimulation, satiation, and maximal food intake., Design: We developed a first-principles model describing cumulative intake that universally describes gustatory stimulation, satiation, and maximal food intake using 3 key parameters: 1) the initial eating rate, 2) the effective duration of eating, and 3) the maximal food intake. These model parameters were estimated in a study (n = 49) where eating rates were deliberately changed. Baseline data was used to determine the quality of model's fit to data compared with the quadratic model. The 3 parameters were also calculated in a second study consisting of restrained and unrestrained eaters. Finally, we calculated when the gustatory stimulation phase is short or absent., Results: The mean sum squared error for the first-principles model was 337.1 ± 240.4 compared with 581.6 ± 563.5 for the quadratic model, or a 43% improvement in fit. Individual comparison demonstrated lower errors for 94% of the subjects. Both sex (P = 0.002) and eating duration (P = 0.002) were associated with the initial eating rate (adjusted R 2 = 0.23). Sex was also associated (P = 0.03 and P = 0.012) with the effective eating duration and maximum food intake (adjusted R 2 = 0.06 and 0.11). In participants directed to eat as much as they could compared with as much as they felt comfortable with, the maximal intake parameter was approximately double the amount. The model found that certain parameter regions resulted in both stimulation and satiation phases, whereas others only produced a satiation phase., Conclusions: The first-principles model better quantifies interindividual differences in food intake, shows how aspects of food intake differ across subpopulations, and can be applied to determine how eating behavior factors influence total food intake., (© 2017 American Society for Nutrition.)

Background: Ideal body weight (IBW) equations and body mass index (BMI) ranges have both been used to delineate healthy or normal weight ranges, although these 2 different approaches are at odds with each other. In particular, past IBW equations are misaligned with BMI values, and unlike BMI, the equations have failed to recognize that there is a range of ideal or target body weights., Objective: For the first time, to our knowledge, we merged the concepts of a linear IBW equation and of defining target body weights in terms of BMI., Design: With the use of calculus and approximations, we derived an easy-to-use linear equation that clinicians can use to calculate both IBW and body weight at any target BMI value. We measured the empirical accuracy of the equation with the use of NHANES data and performed a comparative analysis with past IBW equations., Results: Our linear equation allowed us to calculate body weights for any BMI and height with a mean empirical accuracy of 0.5-0.7% on the basis of NHANES data. Moreover, we showed that our body weight equation directly aligns with BMI values for both men and women, which avoids the overestimation and underestimation problems at the upper and lower ends of the height spectrum that have plagued past IBW equations., Conclusions: Our linear equation increases the sophistication of IBW equations by replacing them with a single universal equation that calculates both IBW and body weight at any target BMI and height. Therefore, our equation is compatible with BMI and can be applied with the use of mental math or a calculator without the need for an app, which makes it a useful tool for both health practitioners and the general public., (© 2016 American Society for Nutrition.)

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

Background: Currently, early weight-loss predictions of long-term weight-loss success rely on fixed percent-weight-loss thresholds., Objective: The objective was to develop thresholds during the first 3 mo of intervention that include the influence of age, sex, baseline weight, percent weight loss, and deviations from expected weight to predict whether a participant is likely to lose 5% or more body weight by year 1., Design: Data consisting of month 1, 2, 3, and 12 treatment weights were obtained from the 2-y Preventing Obesity Using Novel Dietary Strategies (POUNDS Lost) intervention. Logistic regression models that included covariates of age, height, sex, baseline weight, target energy intake, percent weight loss, and deviation of actual weight from expected were developed for months 1, 2, and 3 that predicted the probability of losing <5% of body weight in 1 y. Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds were calculated for each model. The AUC statistic quantified the ROC curve's capacity to classify participants likely to lose <5% of their body weight at the end of 1 y. The models yielding the highest AUC were retained as optimal. For comparison with current practice, ROC curves relying solely on percent weight loss were also calculated., Results: Optimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75 (95% CI: 0.71, 0.81), and 0.79 (95% CI: 0.74, 0.84), respectively. Percent weight loss alone was not better at identifying true positives than random chance (AUC ≤0.50)., Conclusions: The newly derived models provide a personalized prediction of long-term success from early weight-loss variables. The predictions improve on existing fixed percent-weight-loss thresholds. Future research is needed to explore model application for informing treatment approaches during early intervention., (© 2015 American Society for Nutrition.)

Background: Body mass index (BMI) is formulated on the assumption that body weight (BW) scales to height with a power of 2 (BW∝height(2)), independent of sex and race-ethnicity. Powers differing from 2 are observed in studies of selected samples, thus raising the question if BMI is a generalizable metric that makes BW independent of height across populations., Objectives: The objectives were to test the hypothesis that adult BW scales to height with a power of 2 independent of sex and race-ethnicity and to advance an understanding of BMI as a measure of shape by extending allometric analyses to waist circumference (WC)., Design: We conducted cross-sectional subject evaluations, including body composition, from the NHANES and the Korean NHANES (KNHANES). Variations of the allometric model (Y = αX(β)) were used to establish height scaling powers (β ± SE) across non-Hispanic white and black, Mexican American, and Korean men and women., Results: Exploratory analyses in population samples established age and adiposity as important independent determinants of height scaling powers (i.e., β). After age and adiposity in the next series of analyses were controlled for, BW scaling powers were nonsignificantly different between race/ethnic groups within each sex group; WC findings were similar in women, whereas small but significant between-race differences were observed in the men. Sex differences in β values were nonsignificant except for BW in non-Hispanic blacks and WC in Koreans (P < 0.05). Nationally representative powers for BW were (NHANES/KNHANES) 2.12 ± 0.05/2.11 ± 0.06 for men and 2.02 ± 0.04/1.99 ± 0.06 for women and for WC were 0.66 ± 0.03/0.67 ± 0.05 for men and 0.61 ± 0.04/0.56 ± 0.05 for women., Conclusions: Adult BW scales to height with a power of ∼2 across the 8 sex and race/ethnic groups, an observation that makes BMI a generalizable height-independent measure of shape across most populations. WC also follows generalizable scaling rules, a finding that has implications for defining body shape in populations who differ in stature., (© 2014 American Society for Nutrition.)

Background: Muscle mass reflects and influences health status. Its reliable estimation would be of value for epidemiology., Objective: The aim of the study was to derive and validate anthropometric prediction equations to quantify whole-body skeletal muscle mass (SM) in adults., Design: The derivation sample included 423 subjects (227 women) aged 18-81 y with a body mass index (BMI; in kg/m(2)) of 15.9-40.8. The validation sample included 197 subjects (105 women) aged 19-83 y with a BMI of 15.7-36.4. Both samples were of mixed ethnic/racial groups. All underwent whole-body magnetic resonance imaging to quantify SM (dependent variable for multiple regressions) and anthropometric variables (independent variables)., Results: Two prediction equations with high practicality and optimal derivation correlations with SM were further investigated to assess agreement and bias by using Bland-Altman plots and validated in separate data sets. Including race as a variable increased R(2) by only 0.1% in men and by 8% in women. For men: SM (kg) = 39.5 + 0.665 body weight (BW; kg) - 0.185 waist circumference (cm) - 0.418 hip circumference (cm) - 0.08 age (y) (derivation: R(2) = 0.76, SEE = 2.7 kg; validation: R(2) = 0.79, SEE = 2.7 kg). Bland-Altman plots showed moderate agreement in both derivation and validation analyses. For women: SM (kg) = 2.89 + 0.255 BW (kg) - 0.175 hip circumference (cm) - 0.038 age (y) + 0.118 height (cm) (derivation: R(2) = 0.58, SEE = 2.2 kg; validation: R(2) = 0.59, SEE = 2.1 kg). Bland-Altman plots had a negative slope, indicating a tendency to overestimate SM among women with smaller muscle mass and to underestimate SM among those with larger muscle mass., Conclusions: Anthropometry predicts SM better in men than in women. Equations that include hip circumference showed agreement between methods, with predictive power similar to that of BMI to predict fat mass, with the potential for applications in groups, as well as epidemiology and survey settings., (© 2014 American Society for Nutrition.)

Background: Clinical weight loss in individuals typically stabilizes at 6 mo. However, validated models of dynamic energy balance have consistently shown weight plateaus between 1 and 2 y. The cause for this discrepancy is unclear., Objective: We developed 2 mathematical models on the basis of the first law of thermodynamics to investigate plausible explanations for reaching an early weight plateau at 6 mo., Design: The first model was an energy-expenditure adaptation model and was applied to determine the degree of metabolic adaptation required to generate this plateau. The second model was an intermittent lack-of-adherence model formulated by using a randomly fluctuating energy intake term accounting for intermittent noncompliance in dietary intake to reach this plateau. To set model variables, validate models, and compare free-living weight-loss patterns to in-residence supervised programs, we applied the following 4 different studies: The US NHANES 1999-2004, Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) weight-loss study, the Bouchard Twin overfeeding study, and the Minnesota Starvation Experiment., Results: The metabolic adaptation model increased final weight but did not affect the predicted plateau time point. The intermittent lack-of-adherence model generated oscillating weight graphs that have been frequently observed in weight-loss studies. The model showed that a 6-mo weight-loss plateau can be attained despite what can be considered as high diet adherence. The model was programmed as a downloadable application., Conclusions: An intermittent lack of diet adherence, not metabolic adaptation, is a major contributor to the frequently observed early weight-loss plateau. The new weight-loss prediction software, which incorporates an intermittent lack of adherence, can be used to guide and inform patients on realistic levels of adherence on the basis of patient lifestyle., (© 2014 American Society for Nutrition.)

There is limited insight into the mechanisms, progression, and related comorbidities of obesity through simple modeling tools such as linear regression. Keeping in mind the words of the late George E. P. Box that “all models are wrong, some are useful,” this symposium presented 4 useful mathematical models or methodologic refinements. Presenters placed specific emphasis on how these novel models and methodologies can be applied to further our knowledge of the etiology of obesity.