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

Purpose: The aim of this study was (1) to assess AT through 13 different mathematical approaches and to compare their results; and (2) to understand if AT occurs after moderate WL., Methods: Ninety-four participants [mean (SD); BMI, 31.1 (4.3) kg/m 2 ; age, 43.0 (9.4) years; 34% females] underwent a 1-year lifestyle intervention (clinicaltrials.gov ID: NCT03031951) and were randomized to intervention (IG, n = 49) or control groups (CG, n = 45), and all measurements were made at baseline and after 4 months. Fat mass (FM) and fat-free mass (FFM) were measured by dual-energy X-ray absorptiometry and REE by indirect calorimetry. AT was assessed through 13 different approaches, varying in how REE was predicted and/or how AT was assessed., Results: IG underwent a mean negative energy balance (EB) of 270 (289) kcal/day, p < 0.001), resulting in a WL of - 4.8 (4.9)% and an FM loss of - 11.3 (10.8)%. Regardless of approach, AT occurred in the IG, ranging from ~ - 65 to ~ - 230 kcal/day and three approaches showed significant AT in the CG., Conclusions: Regardless of approach, AT occurred after moderate WL in the IG. AT assessment should be standardized and comparisons among studies with different methodologies to assess AT must be avoided., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

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.)

Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

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

Importance: The efficacy and safety of time-restricted eating have not been explored in large randomized clinical trials., Objective: To determine the effect of 16:8-hour time-restricted eating on weight loss and metabolic risk markers., Interventions: Participants were randomized such that the consistent meal timing (CMT) group was instructed to eat 3 structured meals per day, and the time-restricted eating (TRE) group was instructed to eat ad libitum from 12:00 pm until 8:00 pm and completely abstain from caloric intake from 8:00 pm until 12:00 pm the following day., Design, Setting, and Participants: This 12-week randomized clinical trial including men and women aged 18 to 64 years with a body mass index (BMI, calculated as weight in kilograms divided by height in meters squared) of 27 to 43 was conducted on a custom mobile study application. Participants received a Bluetooth scale. Participants lived anywhere in the United States, with a subset of 50 participants living near San Francisco, California, who underwent in-person testing., Main Outcomes and Measures: The primary outcome was weight loss. Secondary outcomes from the in-person cohort included changes in weight, fat mass, lean mass, fasting insulin, fasting glucose, hemoglobin A1c levels, estimated energy intake, total energy expenditure, and resting energy expenditure., Results: Overall, 116 participants (mean [SD] age, 46.5 [10.5] years; 70 [60.3%] men) were included in the study. There was a significant decrease in weight in the TRE (-0.94 kg; 95% CI, -1.68 to -0.20; P = .01), but no significant change in the CMT group (-0.68 kg; 95% CI, -1.41 to 0.05, P = .07) or between groups (-0.26 kg; 95% CI, -1.30 to 0.78; P = .63). In the in-person cohort (n = 25 TRE, n = 25 CMT), there was a significant within-group decrease in weight in the TRE group (-1.70 kg; 95% CI, -2.56 to -0.83; P < .001). There was also a significant difference in appendicular lean mass index between groups (-0.16 kg/m2; 95% CI, -0.27 to -0.05; P = .005). There were no significant changes in any of the other secondary outcomes within or between groups. There were no differences in estimated energy intake between groups., Conclusions and Relevance: Time-restricted eating, in the absence of other interventions, is not more effective in weight loss than eating throughout the day., Trial Registration: ClinicalTrials.gov Identifiers: NCT03393195 and NCT03637855.

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.)

Background: Current consensus is to combine a functional measure with muscle quantity to assess/confirm sarcopenia. However, the proper body size adjustment for muscle quantity is debated and sarcopenia in obesity is not well described. Further, functional measures are not muscle-specific or sensitive to etiology, and can be confounded by, for example, fitness/pain. For effective detection/treatment/follow-up, muscle-specific biomarkers linked to function are needed., Methods: Nine thousand six hundred and fifteen participants were included and current sarcopenia thresholds (EWGSOP2: DXA, hand grip strength) applied to investigate prevalence. Fat-tissue free muscle volume (FFMV) and muscle fat infiltration (MFI) were quantified through magnetic resonance imaging (MRI) and sex-and-body mass index (BMI)-matched virtual control groups (VCGs) were used to extract each participant's FFMV/height2 z-score (FFMVVCG). The value of combining FFMVVCG and MFI was investigated through hospital nights, hand grip strength, stair climbing, walking pace, and falls., Results: Current thresholds showed decreased sarcopenia prevalence with increased BMI (underweight 8.5%/normal weight 4.3%/overweight 1.1%/obesity 0.1%). Contrary, the prevalence of low function increased with increasing BMI. Previously proposed body size adjustments (division by height2/weight/BMI) introduced body size correlations of larger/similar magnitude than before. VCG adjustment achieved normalization and strengthened associations with hospitalization/function. Hospital nights, low hand grip strength, slow walking pace, and no stair climbing were positively associated with MFI (p < .05) and negatively associated with FFMVVCG (p < .01). Only MFI was associated with falls (p < .01). FFMVVCG and MFI combined resulted in highest diagnostic performance detecting low function., Conclusions: VCG-adjusted FFMV enables proper sarcopenia assessment across BMI classes and strengthened the link to function. MFI and FFMV combined provides a more complete, muscle-specific description linked to function enabling objective sarcopenia detection., (© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America.)

Objective: This study aimed to evaluate ertugliflozin in patients with overweight and obesity with type 2 diabetes mellitus., Methods: Data from three placebo-controlled, randomized, Phase 3 studies were pooled. Patients with baseline BMI ≥ 25 (1,377/1,544; 89%) were assessed with a stratification by BMI subgroup., Results: At week 26, reductions from baseline in glycated hemoglobin A1c (HbA1c), fasting plasma glucose, body weight (BW), and systolic blood pressure (SBP) were greater with ertugliflozin versus placebo. For placebo, ertugliflozin 5 mg, and ertugliflozin 15 mg, respectively, least squares mean change was 0.1%, -0.8%, and -0.9% for HbA1c and -1.2 kg, -3.1 kg, and -3.2 kg for BW. HbA1c reductions were consistent across BMI subgroups. For ertugliflozin 5 mg and 15 mg, least squares mean change (placebo adjusted) in absolute BW was -1.4 kg and -1.2 kg for BMI 25 to < 30, -1.8 kg and -1.9 kg for BMI 30 to < 35, and -2.5 kg and -2.9 kg for BMI ≥ 35. Percent BW changes were similar across BMI subgroups. Incidence of adverse events was 52.5%, 44.6%, and 50.1% with placebo, ertugliflozin 5 mg, and ertugliflozin 15 mg, respectively., Conclusions: Meaningful reductions in HbA1c, fasting plasma glucose, BW, and SBP were observed with ertugliflozin in patients with overweight and obesity with type 2 diabetes mellitus. Ertugliflozin improved HbA1c and SBP and reduced BW across BMI subgroups. Ertugliflozin was generally well tolerated., (© 2020 The Authors. Obesity published by Wiley Periodicals, Inc. on behalf of The Obesity Society (TOS).)

Phenotyping adults for excess adiposity and related health risks usually include three body size measurements: height, weight and waist circumference (WC). Height and weight are now widely used as components of the body shape measure, body mass index (BMI, weight/height 2 ), with the height power referred to as the scaling factor, α. At present, WC is usually not adjusted for height or is expressed as WC/height in which α = 1. Although other α values have been proposed, a critical review of these shape measures is lacking. Here, we examine classical pathways by which the scaling exponent for height used in BMI was developed and then apply this strategy to identify the optimum WC index characteristic of adult shape. Our analyses explored anthropometric, body composition and clinically-relevant data from US and Korean National Health and Nutrition Surveys. Our findings provide further support for the WC index of WC/height 0.5 as having the strongest associations with adiposity while having the weakest correlations with height across non-Hispanic white and black, Mexican American and Korean men and women. The WC index, defined as WC/height 0.5 , when combined with BMI, can play an important role when phenotyping adults for excess adiposity and associated health risks in research and clinical settings., (© 2019 World Obesity Federation.)

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

The emerging obesity epidemic and accompanying health consequences led The Obesity Society (TOS) in 2008 to publish a position paper defining obesity as a disease. Since then, new information has emerged on the underlying mechanisms leading to excess adiposity and the associated structural, cardiometabolic, and functional disturbances. This report presents the updated TOS 2018 position statement on obesity as a noncommunicable chronic disease., (© 2018 The Obesity Society.)

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

Obesity continues to be among the top health concerns across the globe. Despite our failure to contain the high prevalence of obesity, we now have a better understanding of its pathophysiology, and how excess adiposity leads to type 2 diabetes, hypertension, and cardiovascular disease. Lifestyle modification is recommended as the cornerstone of obesity management, but many patients do not achieve long-lasting benefits due to difficulty with adherence as well as physiological and neurohormonal adaptation of the body in response to weight loss. Fortunately, 5 drug therapies-orlistat, lorcaserin, liraglutide, phentermine/topiramate, and naltrexone/bupropion-are available for long-term weight management. Additionally, several medical devices are available for short-term and long-term use. Bariatric surgery yields substantial and sustained weight loss with resolution of type 2 diabetes, although due to the high cost and a small risk of serious complications, it is generally recommended for patients with severe obesity. Benefit-to-risk balance should guide treatment decisions., (Copyright © 2018 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objective: To evaluate the percentage of body fat (%BF)-BMI relationship, identify %BF levels corresponding to adult BMI cut points, and examine %BF-BMI agreement in a diverse Hispanic/Latino population., Methods: %BF by bioelectrical impedance analysis was corrected against %BF by (18) O dilution in 434 participants of the ancillary Hispanic Community Health Study/Study of Latinos. Corrected %BF was regressed against 1/BMI in the parent study (n = 15,261), fitting models for each age group, by sex, and Hispanic/Latino background; predicted %BF was then computed for each BMI cut point., Results: Bioelectrical impedance analysis underestimated %BF by 8.7 ± 0.3% in women and 4.6 ± 0.3% in men (P < 0.0001). The %BF-BMI relationship was nonlinear and linear for 1/BMI. Sex- and age-specific regression parameters between %BF and 1/BMI were consistent across Hispanic/Latino backgrounds (P > 0.05). The precision of the %BF-1/BMI association weakened with increasing age in men but not women. The proportion of participants classified as nonobese by BMI but as having obesity by %BF was generally higher among women and older adults (16.4% in women vs. 12.0% in men aged 50-74 years)., Conclusions: %BF was linearly related to 1/BMI with consistent relationship across Hispanic/Latino backgrounds. BMI cut points consistently underestimated the proportion of Hispanics/Latinos with excess adiposity., (© 2016 The Obesity Society.)

Objective: To examine change in county-level adult obesity prevalence between 2004 and 2009 and identify associated community characteristics., Methods: Change in county-level adult (≥20 years) obesity prevalence was calculated for a 5-year period (2004-2009). Community measures of economic, healthcare, recreational, food environment, population structure, and education contexts were also calculated. Regression analysis was used to assess community characteristics associated (p<0.01) with change in adult obesity prevalence., Results: Mean±SD change in obesity prevalence was 5.1±2.4%. Obesity prevalence decreased in 1.4% (n = 44) and increased in 98% (n = 3,060) of counties from 2004-2009. Results showed that both baseline levels and increases in physically inactive adults were associated with greater increases in obesity prevalence, while baseline levels of and increases in physician density and grocery store/supercenter density were related to smaller increases in obesity rates. Baseline levels of the Hispanic population share were negatively linked to changing obesity levels, while places with greater Hispanic population growth saw greater increases in obesity., Conclusions: Most counties in the U.S. experienced increases in adult obesity prevalence from 2004 to 2009. Findings suggest that community-based interventions targeting adult obesity need to incorporate a range of community factors, such as levels of physical inactivity, access to physicians, availability of food outlets, and ethnic/racial population composition.

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R (2) and Bland-Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg)=0·198 weight (kg)+0·478 waist (cm)-0·147 height (cm)-12·8 (validation: R 2 0·79, CV=20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg)+0·0786 age (years)-0·342 height (cm)+24·5 (validation: R (2) 0·84, CV=13 %, SEE=3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R (2) 0·70-0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R (2) 0·70-0·80), with mean bias of 2·5-4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness., Competing Interests: There are no conflicts of interest to declare.

Non-Communicable diseases (NCDs), including obesity, are emerging as the major health concern of the 21st century. Excess adiposity and related NCD metabolic disturbances have stimulated development of new lipid compartment measurement technologies to help us to understand cellular energy exchange, to refine phenotypes, and to develop predictive markers of adverse clinical outcomes. Recent advances now allow quantification of multiple intracellular lipid and adipose tissue compartments that can be evaluated across the human lifespan. With magnetic resonance methods leading the way, newer approaches will give molecular structural and metabolic information beyond the laboratory in real-world settings. The union between these new technologies and the growing NCD population is creating an exciting interface in advancing our understanding of chronic disease mechanisms., (Copyright © 2015 Elsevier Ltd. All rights reserved.)

Purpose of Review: The obesity paradox is a highly controversial concept that may be attributed to methodological limitations related to its identification. One of the primary concerns is the use of BMI to define obesity. This index does not differentiate lean versus adipose tissue compartments (i.e. body composition) confounding health consequences for morbidity and mortality, especially in clinical populations. This review will describe the past year's evidence on the obesity paradox phenomenon, primarily focusing on the role of abnormal body composition phenotypes in explaining the controversies observed in the literature., Recent Findings: In spite of the substantial number of articles investigating the obesity paradox phenomenon, less than 10% used a direct measure of body composition and when included, it was not fully explored (only adipose tissue compartment evaluated). When lean tissue or muscle mass is taken into account, the general finding is that a high BMI has no protective effect in the presence of low muscle mass and that it is the latter that associates with poor prognosis., Summary: In view of the body composition variability of patients with identical BMI, it is unreasonable to rely solely on this index to identify obesity. The consequences of a potential insubstantial obesity paradox are mixed messages related to patient-related prognostication.

Current bioelectric impedance analysis (BIA) systems are often large, cumbersome devices which require strict electrode placement on the user, thus inhibiting mobile capabilities. In this work, we developed a handheld BIA device that measures impedance from multiple frequencies (5 kHz~200 kHz) with four contact electrodes and evaluated the BIA device against standard body composition analysis systems: a dual-energy X-ray absorptiometry (DXA) system (GE Lunar Prodigy, GE Healthcare, Buckinghamshire, UK) and a whole-body BIA system (InBody S10, InBody, Co. Ltd, Seoul, Korea). In the study, 568 healthy participants, varying widely in body mass index, age, and gender, were recruited at two research centers: the Samsung Medical Center (SMC) in South Korea and the Pennington Biomedical Research Center (PBRC) in the United States. From the measured impedance data, we analyzed individual body fat and skeletal muscle mass by applying linear regression analysis against target reference data. Results indicated strong correlations of impedance measurements between the prototype pathways and corresponding InBody S10 electrical pathways (R = 0.93, p < 0.0001). Additionally, body fat estimates from DXA did not yield significant differences (p > 0.728 (paired t-test), DXA mean body fat 29.45 ± 10.77 kg, estimated body fat 29.52 ± 12.53 kg). Thus, this portable BIA system shows a promising ability to estimate an individual's body composition that is comparable to large stationary BIA systems.

Objective: To investigate ethnic difference in the associations of BMI with comorbidity, mortality, and body composition between mainland Chinese and U.S. whites., Methods: Ethnic-comparison study using data from China Health and Nutrition Survey, U.S. National Health and Nutrition Examination Survey, and data from Zhejiang University (China) and Columbia University (U.S.)., Results: Chinese people experienced greater odds of comorbidities than whites for a given BMI after standardizing for age and sex: 43% for diabetes, 30% for dyslipidemia, 28% for hypertension, 38% for metabolic syndrome, and 48% for hyperuricemia. Comparisons of BMI-mortality associations found that the U-shaped BMI-mortality curve shifted 1-2 kg m(-2) to the left in Chinese compared to whites. Compared to whites at BMIs of 25 and 30 kg m(-2), corresponding cutoffs in Chinese were 22.5 and 25.9 kg m(-2) in men, and 22.8 and 26.6 kg m(-2) in women after both fat and fat distribution were taken into account., Conclusions: Comorbidity, mortality, and body composition data consistently support the use of lower BMI cutoffs in Chinese than those in whites., (© 2015 The Obesity Society.)

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.)

Objective: Significant clusters of high- and low-obesity counties have been demonstrated across the United States (US). This study examined regional disparities in obesity prevalence and differences in the related structural characteristics across regions of the US., Methods: Drawing on model-based estimates from the Centers for Disease Control and Prevention, regional differences in county-level adult obesity prevalence (percent of the adult population [≥ 20 years] that was obese [BMI ≥ 30 kg/m(2) ] within a county, 2009) were assessed with a LISA (Local Indicators of Spatial Association) analysis to identify geographic concentrations of high and low obesity levels. Regional regime analysis was utilized to identify factors that were differentially associated with obesity prevalence between regions of the US., Results: High- and low-obesity county clusters and the effect of a number of county-level characteristics on obesity prevalence differed significantly by region. These included the positive effect of African American populations in the South, the negative effect of Hispanic populations in the Northeast, and the positive effect of unemployed workers in the Midwest and West., Conclusions: Our findings suggest the need for public health policies and interventions that account for different regional characteristics underlying obesity prevalence variation across the US., (© 2014 The Obesity Society.)

Body composition refers to the amount of fat and lean tissues in our body; it is a science that looks beyond a unit of body weight, accounting for the proportion of different tissues and its relationship to health. Although body weight and body mass index are well-known indexes of health status, most researchers agree that they are rather inaccurate measures, especially for elderly individuals and those patients with specific clinical conditions. The emerging use of imaging techniques such as dual energy x-ray absorptiometry, computerized tomography, magnetic resonance imaging, and ultrasound imaging in the clinical setting have highlighted the importance of lean soft tissue (LST) as an independent predictor of morbidity and mortality. It is clear from emerging studies that body composition health will be vital in treatment decisions, prognostic outcomes, and quality of life in several nonclinical and clinical states. This review explores the methodologies and the emerging value of imaging techniques in the assessment of body composition, focusing on the value of LST to predict nutrition status., (© 2014 American Society for Parenteral and Enteral 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.

Background: Obesity, defined by body mass index (BMI), appears to have a paradoxical protective effect in several chronic diseases., Objective: We investigated the obesity paradox in cancer patients by using body composition., Design: The study was an observational study of 175 cancer patients assessed before chemotherapy. Obesity was defined as BMI (in kg/m(2)) ≥30 or fat mass index (FMI; fat mass divided by the square of height) ≥5.2 (men) and ≥8.2 (women) measured by using a bioelectrical impedance analysis. Low muscle mass (sarcopenia) was defined as fat-free mass index (fat-free mass divided by the square of height) <17.5 (men) and <15.1 (women)., Results: Most patients were women (65.7%) and had a mean (±SD) age of 56.9 ± 12.8 y. According to BMI criteria, 60% of patients were overweight or obese. The median survival time for overweight (2.64 y; range: 0.23-3.16 y) and obese (2.61 y; range: 0.26-3.20 y) patients was significantly higher than for patients with a normal (2.04 y; range: 0.06-3.05 y) or low (0.52 y; range: 0.19-0.98 y) BMI (P < 0.001). Sarcopenic patients had shorter survival, regardless of their FMI. Obesity predicted higher survival rates only when sarcopenia was absent. In a multivariate Cox regression model, sarcopenia was an independent predictor of higher mortality (HR: 5.19; 95% CI: 2.58, 10.43) after we controlled for BMI, age, and tumor stage., Conclusions: The obesity paradox is present in cancer patients only when obesity is defined by BMI. Patients with sarcopenic obesity had the poorest prognosis. Cancer patients with high mortality risk can be identified by a body-composition assessment.

Objective: This study used spatial statistical methods to test the hypotheses that county-level adult obesity prevalence in the United States is (1) regionally concentrated at significant levels, and (2) linked to local-level factors, after controlling for state-level effects., Methods: Data were obtained from the Centers for Disease Control and Prevention and other secondary sources. The units of analysis were counties. The dependent variable was the age-adjusted percentage of adults who were obese in 2009 (body mass index >30 kg/m2)., Results: The prevalence of county-level obesity varied from 13.5% to 47.9% with a mean of 30.3%. Obesity prevalence across counties was not spatially random: 15.8% belonged to high-obesity regions and 13.5% belonged to low-obesity regions. Obesity was positively associated with unemployment, outpatient healthcare visits, physical inactivity, female-headed families, black populations, and less education. Obesity was negatively correlated with physician numbers, natural amenities, percent ≥65 years, Hispanic populations, and larger population size. A number of variables were notable for not reaching significance after controlling for other factors, including poverty and food environment measures., Conclusions: The findings demonstrate the importance of local-level factors in explaining geographic variation in obesity prevalence, and thus hold implications for geographically targeted interventions to combat the obesity epidemic., (Copyright © 2013 The Obesity Society.)

Objective: Obesity prevalence in the United States appears to be leveling, but the reasons behind the plateau remain unknown. Mechanistic insights can be provided from a mathematical model. The objective of this study is to model known multiple population parameters associated with changes in body mass index (BMI) classes and to establish conditions under which obesity prevalence will plateau., Design and Methods: A differential equation system was developed that predicts population-wide obesity prevalence trends. The model considers both social and nonsocial influences on weight gain, incorporates other known parameters affecting obesity trends, and allows for country specific population growth., Results: The dynamic model predicts that: obesity prevalence is a function of birthrate and the probability of being born in an obesogenic environment; obesity prevalence will plateau independent of current prevention strategies; and the US prevalence of overweight, obesity, and extreme obesity will plateau by about 2030 at 28%, 32%, and 9% respectively., Conclusions: The US prevalence of obesity is stabilizing and will plateau, independent of current preventative strategies. This trend has important implications in accurately evaluating the impact of various anti-obesity strategies aimed at reducing obesity prevalence., (Copyright © 2013 The Obesity Society.)

Objective: The purpose of this study was to determine the association between anthropometric measures of obesity and all-cause mortality in white and African American men and women., Design and Methods: The sample included 14,343 adults 18-89 years of age. Height, weight, and waist and hip circumferences were measured, and the BMI (kg m(-2) ), body adiposity index (BAI = ([hip circumference in centimeters]/[height in meters])(1.5) - 18), waist-to-height ratio (WHtR) and waist-to-hip ratio (WHR) were computed. Vital status of the participants was determined from linkage with the National Death Index through 2009. Cox regression was used to assess the association between anthropometry and all-cause mortality, adjusting for age, sex, year of baseline examination, study code, smoking status, alcohol consumption and physical activity. Hazard ratios (HR) are expressed per standard deviation of each variable., Results: A total of 438 deaths occurred during 120,637 person-years of follow-up. All anthropometric markers demonstrated significant associations with all-cause mortality in white subjects. In multivariable-adjusted models, BMI (HR 1.34; 95% CI: 1.19-1.50), waist circumference (1.41; 1.25-1.60), BAI (1.34; 1.17-1.53), WHtR (1.46; 1.28-1.65), and WHR (1.40; 1.23-1.61) all demonstrated significant relationships with mortality in white participants, but not in African Americans. In categorical analyses, there was a significant association between BMI status and mortality in whites but not African Americans. However, the risk associated with elevated waist circumference was similar in whites (1.49; 1.15-1.94) and African Americans (1.60; 1.06-2.40)., Conclusion: In summary, this study has demonstrated race differences in the association between anthropometry and all-cause mortality., (Copyright © 2012 The Obesity Society.)

Background: Many beliefs about obesity persist in the absence of supporting scientific evidence (presumptions); some persist despite contradicting evidence (myths). The promulgation of unsupported beliefs may yield poorly informed policy decisions, inaccurate clinical and public health recommendations, and an unproductive allocation of research resources and may divert attention away from useful, evidence-based information., Methods: Using Internet searches of popular media and scientific literature, we identified, reviewed, and classified obesity-related myths and presumptions. We also examined facts that are well supported by evidence, with an emphasis on those that have practical implications for public health, policy, or clinical recommendations., Results: We identified seven obesity-related myths concerning the effects of small sustained increases in energy intake or expenditure, establishment of realistic goals for weight loss, rapid weight loss, weight-loss readiness, physical-education classes, breast-feeding, and energy expended during sexual activity. We also identified six presumptions about the purported effects of regularly eating breakfast, early childhood experiences, eating fruits and vegetables, weight cycling, snacking, and the built (i.e., human-made) environment. Finally, we identified nine evidence-supported facts that are relevant for the formulation of sound public health, policy, or clinical recommendations., Conclusions: False and scientifically unsupported beliefs about obesity are pervasive in both scientific literature and the popular press. (Funded by the National Institutes of Health.).

Based on cross-sectional analyses, it was suggested that hip circumference divided by height(1.5) -18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual-energy X-ray absorptiometry (PBF(DXA)), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBF(DXA) was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBF(DXA) among men (2.5%) and overestimated PBF(DXA) among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBF(DXA), but none of the models based on BAI could more accurately predict PBF(DXA) than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBF(DXA), estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference.

Earlier cross-sectional studies found that a single magnetic resonance imaging (MRI) slice predicts total visceral and subcutaneous adipose tissue (VAT and SAT) volumes well. We sought to investigate the accuracy of trunk single slice imaging in estimating changes of total VAT and SAT volume in 123 overweight and obese subjects who were enrolled in a 24-week CB-1R inverse agonist clinical trial (weight change, -7.7 ± 5.3 kg; SAT change, -5.4 ± 4.9 l, VAT change, -0.8 ± 1.0 l). VAT and SAT volumes at baseline and 24 weeks were derived from whole-body MRI images. The VAT area 5-10 cm above L(4)-L(5) (A(+5-10)) (R(2) = 0.59-0.70, P < 0.001) best predicted changes in VAT volume but the strength of these correlations was significantly lower than those at baseline (R(2) = 0.85-0.90, P < 0.001). Furthermore, the L(4)-L(5) slice poorly predicted VAT volume changes (R(2) = 0.24-0.29, P < 0.001). Studies will require 44-69% more subjects if (A(+5-10)) is used and 243-320% more subjects if the L(4)-L(5) slice is used for equivalent power of multislice total volume measurements of VAT changes. Similarly, single slice imaging predicts SAT loss less well than cross-sectional SAT (R(2) = 0.31-0.49 vs. R(2) = 0.52-0.68, P < 0.05). Results were the same when examined in men and women separately. A single MRI slice 5-10 cm above L(4)-L(5) is more powerful than the traditionally used L(4)-L(5) slice in detecting VAT changes, but in general single slice imaging poorly predicts VAT and SAT changes during weight loss. For certain study designs, multislice imaging may be more cost-effective than single slice imaging in detecting changes for VAT and SAT.

Objective: To investigate associations between anthropometric measurements and total body fat, abdominal adipose tissue, and cardiovascular disease risk factors in a large biracial sample., Patients and Methods: This study is limited to cross-sectional analyses of data from participants attending a baseline visit between January 26, 1996, and February 1, 2011. The sample included 2037 individuals aged 18 to 69 years: 488 African American women (24%), 686 white women (34%), 196 African American men (9%), and 667 white men (33%). Anthropometry included weight; hip circumference; waist circumference; waist-hip, waist-height, and weight-height ratios; body adiposity index; and body mass index. Body fat and percentage of fat were measured by dual-energy x-ray absorptiometry, and abdominal visceral and subcutaneous adipose tissue were measured by computed tomography. Bivariate correlations, logistic regression models, and receiver operator characteristic curves were used, and analyses were stratified by sex and race., Results: In each sex-by-race group, all anthropometric measures were highly correlated with percentage of fat, fat mass, and subcutaneous adipose tissue and moderately correlated with visceral adipose tissue, with the exception of the waist-hip ratio. The odds of having an elevated cardiometabolic risk were increased more than 2-fold per SD increase for most anthropometric variables, and the areas under the curve for each anthropometric measure were significantly greater than 0.5., Conclusion: Several common anthropometric measures were moderately to highly correlated with total body fat, abdominal fat, and cardiovascular disease risk factors in a biracial sample of women and men. This comprehensive analysis provides evidence of the linkage between simple anthropometric measurements and the purported pathways between adiposity and health., (Copyright © 2012 Mayo Foundation for Medical Education and Research. Published by Elsevier Inc. All rights reserved.)

Pharmacologic inhibition of the cannabinoid-1 receptor (CB1R) in rodent models leads to weight loss and time-dependent changes in energy balance. This study evaluated the effects of CB1R inhibition on weight loss, energy expenditure (EE), and food intake (FI) in an obese canine model following 4 weeks of treatment. Eighteen maintenance-fed obese beagles were evenly and randomly allocated to a CB1R inverse agonist (AM251) (2 mg/kg), a 70% food-restricted (FR) diet, or a control group (C). Evaluations included body weight and composition (dual-energy x-ray absorptiometry scan), EE (doubly labeled water), and FI. Change in body mass at week 4 was significantly greater (P < .050) in the AM251 (-1476.7 g) and FR groups (-1100.0 g) than in the C group (-228.3 g). Food intake was decreased from week 2 onward in the FR and AM251 groups (P < .05). Absolute and lean mass-adjusted EEs were decreased only in the FR group (P < .01); EE in the AM251 group was greater (P < .05) than that in the FR group. Pharmacologic inhibition of CB1R in a canine model led to sustained effects on FI and EE. Weight loss was greater with AM251 than could be accounted for by food restriction (∼25%), an effect likely mediated by the EE response to CB1R inhibition., (Copyright © 2012 Elsevier Inc. All rights reserved.)

The widespread use of the inadequately defined term "leptin resistance" led the National Institutes of Health to convene a workshop aimed at developing a quantitative definition of this term that would facilitate mechanistic research into leptin's actions in human health and disease. Although leptin-responsive conditions are recognized, the field is limited by a lack of robust, easily quantifiable behavioral or metabolic biomarkers of the hormone's action. Further advances require biomarkers that can be used to identify patients who may benefit from leptin therapy and that are useful for understanding the determinants of clinical leptin responsiveness., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Background: Although dietary weight-loss counseling usually employs a 500 to 1,000 kcal/day energy deficit to induce weight loss of 0.5 to 1 kg/week, this rate of weight loss is rarely achieved in research settings. Biological factors, such as changes in metabolic rate, are partly responsible, but would only account for a small deviation from expected weight loss. There must be other factors, behavioral or related to study design and implementation, that affect the rate of weight loss observed., Objective: To examine factors that influence the rate of weight loss obtained in clinical studies., Design: Thirty-five weight-loss studies published between 1995 and 2009 were identified that used dietary counseling to induce weight loss in healthy subjects. Studies were included if they had a duration of at least 6 weeks, used a strategy to counsel subjects to reduce free-living energy intakes, and reported weight-loss data based on a completers analysis. Variables that were associated with the rate of weight loss among age, sex (percent female subjects), initial body weight, frequency of dietary counseling, placebo use, exercise level, study length, and prescribed energy deficit were examined using linear regression analysis., Results: Study length was negatively related to the rate of weight loss (P<0.0001), whereas subject age (P<0.002), subject age squared (P=0.0073), initial body weight (P=0.0003), frequency of dietary counseling (P=0.0197), and prescribed energy deficit (P<0.0001) were positively related to the rate of weight loss observed in clinical studies., Conclusions: These findings provide a tool for investigators and clinical dietitians to predict the rate of weight loss that can be expected within a population given the age, initial body weight, frequency of dietary counseling, and energy deficit prescription. These data from clinical studies suggest that the rate of weight loss is greater in older and heavier subjects and with higher contact frequency and caloric restriction., (Copyright © 2012 Academy of Nutrition and Dietetics. Published by Elsevier Inc. All rights reserved.)

Elia (1992) identified the specific resting metabolic rates (K(i)) of major organs and tissues in young adults with normal weight: 200 for liver, 240 for brain, 440 for heart and kidneys, 13 for skeletal muscle, 4.5 for adipose tissue and 12 for residual mass (all units in kcal/kg per day). The aim of the present study was to assess the applicability of Elia's K(i) values for obese adults. A sample of young women (n = 80) was divided into two groups, nonobese (BMI <29.9 kg/m(2)) and obese (BMI 30.0-43.2 kg/m(2)). This study was based on the mechanistic model: REE = σ (K(i) × T(i)), where REE is whole-body resting energy expenditure measured by indirect calorimetry and T(i) is the mass of individual organs and tissues measured by magnetic resonance imaging. For each organ/tissue, the corresponding Elia's K(i) value was analyzed respectively for nonobese and obese groups by using stepwise univariate regression analysis. Elia's K(i) values were within the range of 95% confidence intervals (CIs) in the nonobese group. However, Elia's K(i) values were outside the right boundaries of 95% CIs in the obese group and a corresponding obesity-adjusted coefficient was calculated as 0.98, indicating that Elia's values overestimate K(i) by 2.0% in obese adults. Obesity-adjusted K(i) values were 196 for liver, 235 for brain, 431 for heart and kidneys, 12.7 for skeletal muscle, 4.4 for adipose tissue, and 11.8 for residual mass. In conclusion, although Elia's K(i) values were validated in nonobese women, obesity-adjustments are appropriate for application in obese women.