Search

BACKGROUND: When a lifestyle intervention combines caloric restriction and increased physical activity energy expenditure (PAEE), there are two components of energy balance, energy intake (EI) and physical activity energy expenditure (PAEE), that are routinely misreported and expensive to measure. Energy balance models have successfully predicted EI if PAEE is known. Estimating EI from an energy balance model when PAEE is not known remains an open question. OBJECTIVE: The objective was to evaluate the performance of an energy balance differential equation model to predict EI in an intervention that includes both calorie restriction and increases in PAEE. DESIGN: The Antonetti energy balance model that predicts body weight trajectories during weight loss was solved and inverted to estimate EI during weight loss. Using data from a calorie restriction study that included interventions with and without prescribed PAEE, we tested the validity of the Antonetti weight predictions against measured weight and the Antonetti EI model against measured EI using the intake-balance method at 168 days. We then evaluated the predicted EI from the model against measured EI in a study that prescribed both calorie restriction and increased PAEE. RESULTS: Compared with measured body weight at 168 days, the mean (±SD) model error was 1.30 ± 3.58 kg. Compared with measured EI at 168 days, the mean EI (±SD) model error in the intervention that prescribed calorie restriction and did not prescribe increased PAEE, was −84.9 ± 227.4 kcal/d. In the intervention that prescribed calorie restriction combined with increased PAEE, the mean (±SD) EI model error was −155.70±205.70 kcal/d. CONCLUSION: The validity of the newly developed EI model was supported by experimental observations and can be used to determine EI during weight loss.

Objective To identify relationships between body shape, body composition, sex and performance on the new US Army Combat Fitness Test (ACFT). Methods Two hundred and thirty-nine United States Military Academy cadets took the ACFT between February and April of 2021. The cadets were imaged with a Styku 3D scanner that measured circumferences at 20 locations on the body. A correlation analysis was conducted between body site measurements and ACFT event performance and evaluated using Pearson correlation coefficients and p-values. A k-means cluster analysis was performed over the circumference data and ACFT performance were evaluated between clusters using t-tests with a Holm-Bonferroni correction. Results The cluster analysis resulted in 5 groups: 1. “V” shaped males, 2. larger males, 3. inverted “V” shaped males and females, 4. “V” shaped smaller males and females, and 5. smallest males and females. ACFT performance was the highest in Clusters 1 and 2 on all events except the 2-mile run. Clusters 3 and 4 had no statistically significant differences in performance but both clusters performed better than Cluster 5. Conclusions The association between ACFT performance and body shape is more detailed and informative than considering performance solely by sex (males and females). These associations may provide novel ways to design training programs from baseline shape measurements.

This study evaluated feasibility and acceptability of adding energy balance modeling displayed on weight graphs combined with a wrist-worn bite counting sensor against a traditional online behavioral weight loss program. Adults with a BMI of 27–45 kg/m2 (83.3% women) were randomized to receive a 12-week online behavioral weight loss program with 12 weeks of continued contact (n = 9; base program), the base program plus a graph of their actual and predicted weight change based on individualized physiological parameters (n = 7), or the base program, graph, and a Bite Counter device for monitoring and limiting eating (n = 8). Participants attended weekly clinic weigh-ins plus baseline, midway (12 weeks), and study culmination (24 weeks) assessments of feasibility, acceptability, weight, and behavioral outcomes. In terms of feasibility, participants completed online lessons (M = 7.04 of 12 possible lessons, SD = 4.02) and attended weigh-ins (M = 16.81 visits, SD = 7.24). Six-month retention appears highest among nomogram participants, and weigh-in attendance and lesson completion appear highest in Bite Counter participants. Acceptability was sufficient across groups. Bite Counter use (days with ≥ 2 eating episodes) was moderate (47.8%) and comparable to other studies. Participants lost 4.6% ± 4.5 of their initial body weight at 12 weeks and 4.5% ± 5.8 at 24 weeks. All conditions increased their total physical activity minutes and use of weight control strategies (behavioral outcomes). Although all groups lost weight and the study procedures were feasible, acceptability can be improved with advances in the technology. Participants were satisfied with the online program and nomograms, and future research on engagement, adherence, and integration with other owned devices is needed. ClinicalTrials.gov Identifier: NCT02857595

BACKGROUND Exercise is recommended for weight management, yet exercise produces less weight loss than expected, which is called weight compensation. The mechanisms for weight compensation are unclear. OBJECTIVE The aim of this study was to identify the mechanisms responsible for compensation. METHODS In a randomized controlled trial conducted at an academic research center, adults (n = 198) with overweight or obesity were randomized for 24 wk to a no-exercise control group or 1 of 2 supervised exercise groups: 8 kcal/kg of body weight/wk (KKW) or 20 KKW. Outcome assessment occurred at weeks 0 and 24. Energy intake, activity, and resting metabolic rate (RMR) were measured with doubly labeled water (DLW; with and without adjustments for change in RMR), armband accelerometers, and indirect calorimetry, respectively. Appetite and compensatory health beliefs were measured by self-report. RESULTS A per-protocol analysis included 171 participants (72.5% women; mean ± SD baseline body mass index: 31.5 ± 4.7 kg/m2). Significant (P

BACKGROUND The Administrative Procedure Act of 1946 guarantees the public an opportunity to view and comment on the 2020 Dietary Guidelines as part of the policymaking process. In the past, public comments were submitted by postal mail or public hearings. The convenience of public comment through the Internet has generated increased comment volume, making manual analysis challenging. OBJECTIVES To apply natural language processing (NLP NLP is natural language processing.) to identify sentiment, emotion, and themes in the 2020 Dietary Guidelines public comments. METHODS Written comments to the Scientific Report of the 2020 Dietary Guidelines Advisory Committee that were uploaded and visible at https://beta.regulations.gov/docket/FNS-2020-0015 were extracted using a computer program and retained for analysis. All comments were filtered, and duplicates were removed. A 2-round latent Dirichlet analysis (LDA) was used to identify 3 overarching topics as well as subtopics addressed in the comments. Sentiment analysis was applied to categorize emotion and overall positive and negative sentiment within each topic. RESULTS Three different topics were identified by LDA. The first topic involved negative sentiment surrounding removing dairy from the guidelines because the commenters felt dairy is unnecessary. The second topic focused on positive sentiment involved in restricting added sugars. The third topic was too diverse to characterize under 1 theme. A second LDA within the third topic had 3 subtopics containing positive sentiment. The first subtopic valued the inclusion of dairy in the recommendations, the second involved the health benefits of consuming beef, and the third indicated that the recommendations lead to overall good health outcomes. CONCLUSIONS Public comments were diverse, held conflicting viewpoints, and often did not base comments on personal anecdotes or opinions without citing scientific evidence. Because the volume of public comments has grown dramatically, NLP has promise to assist in objective analysis of public comment input.

Obesity and nutrition interventions often provide patients with a focused lifestyle change strategy such as altering energy consumed or physical activity. As obesity prevalence rates jumped in the early 1970s, funding agencies were less inclined to support small sample size experiments, and the obesity and nutrition research community moved away from direct monitoring of energy intake and energy expenditure. Sedentary behavior and obesity-related comorbidities are highly correlated with mortality. For studies involving biochemical analysis of consumption and exertion, the use of isotopically labeled biomolecules allows for the participants to engage in routine daily lives outside the laboratory over the duration of the experiment. Developing interventions for obesity treatment requires precise and reliable knowledge of two components of energy balance: energy intake and energy expenditure. For decades, investigators and clinicians relied on self-report surveys such as 24-hour recall, food frequency questionnaires, and physical activity questionnaires.

Being able to draw accurate conclusions from childhood obesity trials is important to make advances in reversing the obesity epidemic. However, obesity research sometimes is not conducted or reported to appropriate scientific standards. To constructively draw attention to this issue, we present 10 errors that are commonly committed, illustrate each error with examples from the childhood obesity literature, and follow with suggestions on how to avoid these errors. These errors are as follows: using self-reported outcomes and teaching to the test; foregoing control groups and risking regression to the mean creating differences over time; changing the goal posts; ignoring clustering in studies that randomize groups of children; following the forking paths, subsetting, p-hacking, and data dredging; basing conclusions on tests for significant differences from baseline; equating "no statistically significant difference" with "equally effective"; ignoring intervention study results in favor of observational analyses; using one-sided testing for statistical significance; and stating that effects are clinically significant even though they are not statistically significant. We hope that compiling these errors in one article will serve as the beginning of a checklist to support fidelity in conducting, analyzing, and reporting childhood obesity research.

The basis of heat generated by the human body has been a source of speculation and research for more than 2,000 years. Basal heat production, now usually referred to as resting energy expenditure (REE), is currently recognized as deriving from biochemical reactions at subcellular and cellular levels that are expressed in the energy expended by the body's 78 organs and tissues. These organs and tissues, and the 11 systems to which they belong, influence body size and shape. Connecting these subcellular-/cellular-level reactions to organs and tissues, and then on to body size and shape, provides a comprehensive understanding of individual differences in REE, a contemporary topic of interest in obesity research and clinical practice. This review critically examines these linkages, their association with widely used statistical and physiological REE prediction formulas, and often-unappreciated aspects of measuring basal heat production in humans.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2 or COVID‐19) disease has led to a wide‐spread global pandemic (1). COVID‐19 symptoms and mortality are disproportionately more severe in people with obesity and obesity related comorbidities (2, 3). This is of concern for the United States, where ~42% have obesity and of these, 85% have type 2 diabetes.

IntroductionAthletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity.MethodsUS Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.ResultsThe ANN model outperformed two other models, (ANN, AUC = 0.70, [0.68,0.72], logistic regression AUC = 0.67, [0.62,0.72], random forest AUC = 0.65, [0.61,0.70]).ConclusionsBody shape profiles generated from a three-dimensional body scanning imaging in military personnel predicted dischargeable physical injury. The ANN model can be programmed into the scanner to deliver instantaneous predictions of risk, which may provide an opportunity to intervene to prevent injury.

A long-standing question in nutrition and obesity research involves quantifying the relationship between body fat and anthropometry. To date, the mathematical formulation of these relationships has relied on pairing easily obtained anthropometric measurements such as the body mass index (BMI), waist circumference or hip circumference to body fat. Recent advances in 3D body shape imaging technology provides a new opportunity for quickly and accurately obtaining hundreds of anthropometric measurements within seconds, however, there does not yet exist a large diverse database that pairs these measurements to body fat. Herein, we leverage 3D scanned anthropometry obtained from a population of United States Army basic training recruits to derive four sub-populations of homogenous body shape archetypes using a combined principle components and cluster analysis. While the Army database was large and diverse, it did not have body composition measurements. Therefore, these body shape archetypes were paired to an alternate smaller sample of participants from the Pennington Biomedical Research Center in Baton Rouge, LA that were not only similarly imaged by the same 3D scanning machine, but also had concomitant measures of body composition by dual energy X-ray absorptiometry body composition . With this enhanced ability to obtain anthropometry through 3D scanning quickly of large populations, our machine learning approach for pairing body shapes from large datasets to smaller datasets that also contain state of the art body composition measurements can be extended to pair other health outcomes to 3D body shape anthropometry.

Body mass in humans and animals is strongly associated with the rate of heat production as defined by resting energy expenditure (REE). Starting with the ancient Greeks up to the present time philosophers and scientists have endeavored to understand the nature and sources of body heat. Today we recognize that body mass consists of organs and tissues, each of which produces a specified amount of heat at rest. An individual organ’s REE can now be estimated in vivo as the product of its assumed mass-specific metabolic rate and its imaging-derived mass; whole-body REE reflects the sum of organ and tissue metabolic rates. The sizes of organs present and total body mass in adults are governed by two main factors, a person’s stature or height, and their level of adiposity. With greater body size, as represented by adult height independent of adiposity, organs remain stable or increase in mass according to distinct “scaling” patterns. Similarly, with greater relative adiposity organs adaptively accommodate to the increase in imposed mechanical and metabolic loading conditions. Through a detailed analysis of these stature and adiposity effects we show how classical statistical REE prediction models can be mechanistically understood at the anatomic body composition level.

The objective for percent body fat standards in the United States Army Body Composition Program (ABCP) is to ensure soldiers maintain optimal well-being and performance under all conditions. However, conducting large-scale experiments within the United States Army to evaluate the efficacy of the thresholds is challenging. A receiver operating characteristic (ROC) analysis with corresponding area under the curve (AUC) was performed on body mass index (BMI) and waist circumference to determine optimal gender-specific age cohort thresholds that meet ABCP percent body fat standards in the National Health and Nutrition Examination Survey (NHANES) III. A second dataset consisting of a cohort of basic training recruits (N = 20,896 soldiers, 28% female) with BMI and waist circumference measured using a 3D body image scanner was applied to calculate what percent of basic training recruits meet the ABCP percent body fat standards. Regression models to determine the contribution of different circumference sites to the predictions of percent body fat were developed using a database compiled at the New York Obesity Research Center (N = 500). Optimal BMI thresholds ranged from 23.65 kg/m2 (17–21-year-old cohort) to 26.55 kg/m2 (40 and over age cohort) for males and 21.75 to 24.85 kg/m2 for females. The AUC values were between 0.86 and 0.92. The waist circumference thresholds ranged 81.35 to 97.55 cm for males and 77.05 to 89.35 cm for females with AUC values between 0.90 and 0.91. These BMI thresholds were exceeded by 65% of male and 74% of female basic training recruits and waist circumference thresholds were exceeded by 73% of male and 85% of female recruits. The single circumference that contributed most to prediction of body fat was waist circumference in males and mid-thigh circumference in females. The ABCP percent body fat thresholds yield BMI thresholds that are below the United States Army BMI standards, especially in females which suggests the ABCP percent body fat standards may be too restrictive. The United States Army percent body fat standards could instead be matched to existing national health guidelines.

Humans expend energy at rest (REE), and this major energy exchange component is now usually estimated using statistical equations that include weight and other predictor variables. While these formulas are useful in evaluating an individual's or group's REE, an important gap remains: available statistical models are inadequate for explaining underlying organ-specific and tissue-specific mechanisms accounting for resting heat production. The lack of such systems level REE prediction models leaves many research questions unanswered. A potential approach that can fill this gap began with investigators who first showed in animals and later in humans that REE reflects the summated heat production rates of individual organs and tissues. Today, using advanced imaging technologies, REE can be accurately estimated from the measured in vivo mass of 10 organ-tissue mass components combined with their respective mass-specific metabolic rates. This review examines the next frontier of energy expenditure models and discusses how organ-tissue models have the potential not only to better predict REE but also to provide insights into how perturbations in organ mass lead to structure-function changes across other interacting organ systems. The introductory ideas advanced in this review provide a framework for future human energy expenditure modelling research.

Objective Little is known about long-term metabolic (energy expenditure) adaptation after bariatric surgery. Methods Resting metabolic rate under basal conditions (RMR), total daily energy expenditure (TDEE), and body composition were measured in 25 participants in the Longitudinal Assessment of Bariatric Surgery-2. Results Six months after surgery, BMI (±SD) decreased (47 ± 6 kg/m2 to 37 ± 5 kg/m2 ), body fat went from 48% ± 6% to 40% ± 6% fat, and fat-free mass went from 67 ± 9 kg to 60 ± 9 kg. In absolute terms, RMR and TDEE both decreased significantly (1,730 ± 278 kcal/d vs. 1,430 ± 200 kcal/d and 2,879 ± 544 kcal/d vs. 2,369 ± 304 kcal/d), and the achieved energy balance was -1,293 ± 355 kcal/d. Sixteen of these participants underwent repeated measures at ∼24 months; TDEE decreased 6 months postoperatively (2,957 ± 540 kcal/d to 2,423 ± 324 kcal/d; P = 0.0003), but at ∼24 months, TDEE (2,602 ± 471 kcal/d) was not significantly different compared with month 6. The average negative energy balance from baseline to month 24 was -379 ± 131 kcal/d. Conclusions RMR and TDEE fall precipitously in the first 6 months after bariatric surgery, but these adaptive changes were no longer significant after 2 years.

The Office on Women's Health funded five pilot healthy weight intervention studies for lesbian and bisexual (LB) women, which included a program called Strong. Healthy. Energized (SHE). SHE was a 12-session program, targeted toward LB women age 60 and older, which focused on exercise, including a pedometer to track steps; nutrition; stress management; and group discussions. The program enrolled 39 participants. Waist circumference decreased by 3.7% across the group (p .01). Participants with the lowest one-third baseline step count saw a marked step increase. This intervention was effective in improving health behaviors and short-term health outcomes for older LB women.

Maximizing fat mass (FM) loss while preserving or increasing fat-free mass (FFM) is a central goal for athletic performance but the composition of body weight (BW) changes over time with training are largely unknown.We aimed to analyze FM and FFM contributions to BW changes and to test if these contributions follow established rules and predictions over one athletic season.Seventy athletes (42 men; handball, volleyball, basketball, triathlon, and swimming) were evaluated from the beginning to the competitive stage of the season and were empirically divided into those who lost (n = 20) or gained1.5% BW (n = 50). FM and FFM were evaluated with a four-compartment model. Energy densities (ED) of 1.0 kcal·g for FFM and 9.5 kcal·g for FM were used to calculate ED/per kilogram BW change.Athletes that lost1.5% BW decreased FM by 1.7 ± 1.6 kg (P0.05), whereas FFM loss was nonsignificant (-0.7 ± 2.1 kg). Those who gained1.5% BW increased FFM by 2.3 ± 2.1 kg (P0.05) with nonsignificant FM gains (0.4 ± 2.2 kg). The proportion of BW change as FM for those who lost or gained BW was 90% (ED: 8678 ± 2147 kcal·kg) and 5% (ED: 1449 ± 1525 kcal·kg), respectively (P0.001). FFM changes from Forbes Curve were inversely related to observed changes (r = -0.64; r = -0.81, respectively for those who lost or gained BW).Athletes that lost BW used 90% of the energy from FM while in those gaining BW, 95% was directed to FFM. When BW is lost, dynamic changes in its composition do not follow established rules and predictions used for lean or overweight/obese nonathletic populations.

Fetal trajectories characterizing growth ratesin uterohave relied primarily on goodness of fit rather than mechanistic properties exhibitedin utero. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key eventsin utero.

Objectives Recent reports on body regional mass scalings to height have advanced understanding differences in adult heights. These studies resulted in conjectures on how regional lengths and circumferences may scale to height. We provide evidence for these conjectures by analyzing a large sample of regional limb, trunk, chest, and head lengths and circumferences in a large sample of US Army basic training recruits. Methods Participants consisted of 10 271 males and 2760 females ages 17 to 21 years old who reported for basic training at Fort Jackson, SC. Participants were imaged by a three-dimensional (3D) body scanner for uniform sizing which yielded 159 body measurements of total mass, lengths and circumferences at regional sites of arms, legs, trunk, chest, and head. The allometric model, Body Measur e i = α i H β i was applied to derive scaling exponents which were applied to estimate regional mass scalings. Results Body mass scaled to height with powers of ∼2.0 (mean β ± SE, 1.98 ± 0.04, 1.93 ± 0.06). Arm and leg lengths scaled to exponents larger than 1.0 and head height and circumferences at regional sites scaled to exponents smaller than 1.0. The leg, arm, and trunk mass scaling exponents were all above 2.0. Head mass scaled to powers smaller than 2.0. Conclusions The 3D scanner allowed hundreds of anthropometric measurements to be obtained within seconds. The ensuing analysis revealed that greater height yielded disproportional increases in limb lengths, limb mass and trunk mass. These analyses provide evidence that could not be previously measured that further both biomechanical and metabolic conjectures.

The dietary supplement industry has estimated sales of over $30 billion in the US and over $100 billion globally. Many consumers believe that dietary supplements are safer and possibly more effective than drugs to treat diabetes. The sheer volume of the literature in this space makes compiling them into one review challenging, so much so that primarily narrative reviews currently exist. By applying the interactive database supplied by the Office of Dietary Supplements at the National Institutes of Health, we identified the top 100 ingredients that appeared most often in dietary supplement products. One-hundred different keyword searches using the ingredient name and the word diabetes were performed using a program developed to automatically scrape PubMed. Each search was retained in a separate Excel spreadsheet, which was then reviewed for inclusion or exclusion. The studies that met the inclusion criteria were evaluated for effect of reducing and controlling diabetes. The PubMed scrape resulted in 6217 studies. For each keyword search only the most recent 100 were retained, which refined the total to 1823 studies. Of these 425 met the screening criteria. The ingredients, fiber, selenium and zinc had the most studies associated with improvement in diabetes. Several popular supplement ingredients (phosphorus, pantothenic acid, calcium, magnesium, glutamine, isoleucine, tyrosine, choline, and creatine monohydrate) did not result in any studies meeting our screening criteria. Our study demonstrates how to automate reviews to filter and collapse literature in content areas that have an enormous volume of studies. The aggregated set of studies suggest there is little clinical evidence for the use of dietary supplements to reduce or control diabetes.

BACKGROUND: Regression to the mean (RTM) is a statistical phenomenon where initial measurements of a variable in a nonrandom sample at the extreme ends of a distribution tend to be closer to the mean upon a second measurement. Unfortunately, failing to account for the effects of RTM can lead to incorrect conclusions on the observed mean difference between the 2 repeated measurements in a nonrandom sample that is preferentially selected for deviating from the population mean of the measured variable in a particular direction. Study designs that are susceptible to misattributing RTM as intervention effects have been prevalent in nutrition and obesity research. This field often conducts secondary analyses of existing intervention data or evaluates intervention effects in those most at risk (i.e., those with observations at the extreme ends of a distribution). OBJECTIVES: To provide best practices to avoid unsubstantiated conclusions as a result of ignoring RTM in nutrition and obesity research. METHODS: We outlined best practices for identifying whether RTM is likely to be leading to biased inferences, using a flowchart that is available as a web-based app at https://dustyturner.shinyapps.io/DecisionTreeMeanRegression/. We also provided multiple methods to quantify the degree of RTM. RESULTS: Investigators can adjust analyses to include the RTM effect, thereby plausibly removing its biasing influence on estimating the true intervention effect. CONCLUSIONS: The identification of RTM and implementation of proper statistical practices will help advance the field by improving scientific rigor and the accuracy of conclusions. This trial was registered at clinicaltrials.gov as NCT00427193.

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/height2 ), 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/height0.5 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/height0.5 , when combined with BMI, can play an important role when phenotyping adults for excess adiposity and associated health risks in research and clinical settings.

Validated thermodynamic energy balance models that predict weight change are ever more in use today. Delivery of model predictions using web-based applets and/or smart phones has transformed these models into viable clinical tools. Here, we provide the general framework for thermodynamic energy balance model derivation and highlight differences between thermodynamic energy balance models using four representatives. Energy balance models have been used to successfully improve dietary adherence, estimate the magnitude of food waste, and predict dropout from clinical weight loss trials. They are also being used to generate hypotheses in nutrition experiments. Applications of thermodynamic energy balance weight change prediction models range from clinical applications to modify behavior to deriving epidemiological conclusions. Novel future applications involve using these models to design experiments and provide support for treatment recommendations.

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 29.9 kg/m2 ); 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.

PurposeTo provide empirically-supported thresholds for step-based intensity (i.e., peak 30-min cadence; average of the top 30 steps/min in a day) and steps/day in relation to cardiometabolic health outcomes.MethodsReceiver operating characteristic curve analysis was applied to the National Health and Nutrition Examination Survey (NHANES) 2005-2006 accelerometer-derived step data to determine steps/day and peak 30-min cadence as risk screening values (i.e., thresholds) for fasting glucose, body mass index, waist circumference, high blood pressure, triglycerides, and HDL cholesterol. Thresholds for peak 30-min cadence and steps/day were derived that, when exceeded, classify the absence of each cardiometabolic risk factor. Additionally, logistic regression models that included the influence of age and smoking were developed using the sample weights, primary sampling units (PSUs), and stratification variables provided by the NHANES survey. Finally, a decision tree analysis was performed to delineate criteria for at-risk versus healthy populations using cadence bands.ResultsPeak 30-min cadence thresholds across cardiometabolic outcomes ranged from 66-72 steps/min. Steps/day thresholds ranged from 4325-6192 steps/day. Higher thresholds were observed in men compared to women. In men, higher steps/day thresholds were observed in age ranges of 30-39, while in women, higher thresholds were observed in the age-range 50-59 years. Decision trees for classifying being at low risk for metabolic syndrome contained one risk-free leaf at higher cadence bands, specifically for any time accumulated at ≥120 steps/min.ConclusionsMinimum thresholds representing absence of cardiometabolic risk range from 4325-6192 steps/day and 66-72 steps/min for peak 30-min cadence. Any time accumulated at ≥120 steps/min was associated with an absence of cardiometabolic risk. Although based on cross-sectional data, these thresholds represent potentially important and clinically interpretable daily physical activity goals.

Background Body mass index (BMI) represents a normalization of weight to height and is used to classify adiposity. While the capacity of BMI as an adiposity index has been experimentally validated in Caucasians, but there has been little testing Asian populations. Methods To determine whether weight scales to height squared in Asian Indians across the general population and in Asian Indian tribes an allometric analysis on the power law model, W = αHβ, where W is weight (kg) and H is height (m) was performed on cross-sectional weight and height data from India (N = 43,880) collected through the Anthropological Survey of India. The database contained males 18–84 years of age spanning 161 districts of 14 states and including 33 different tribes (N = 5,549). Models were developed that were unadjusted and adjusted for tribe membership. The Korean National Health and Nutrition Examination Survey (KNHANES) was used to compare to height–weight data from the Anthropological Survey of India and to calculate BMI thresholds for obesity status using a receiver operating characteristic. Results The unadjusted power was β = 2.08 (s = 0.02). The power for the general population (non-tribal) was β = 2.11 (s = 0.02). Powers when adjusted for tribe ranged from 1.87 to 2.35 with 24 of the 33 tribes resulting in statistically significant (p 2 (AUC = 0.89) for males 18 kg/m2 (AUC = 0.97) for females. Obesity classification was calculated as BMI = 26 kg/m2 (AUC = 0.81) and 23 kg/m2 (AUC = 0.83) for females. Conclusions Our study confirms that weight scales to height squared in Asian Indian males even after adjusting for tribe membership. We also demonstrate that optimal BMI thresholds are lower in a Korean population in comparison to currently used BMI thresholds. These results support the application of BMI in Asian populations with potentially lower thresholds.

It is generally agreed that placental pathology accounts for the majority of perinatal morbidity and mortality. If a placental prodrome could be diagnosed in vivo, risk for maternal or fetal complications could be estimated and acted upon before clinical symptoms are apparent. This is especially relevant in early diagnoses of gestational diabetes mellitus, which can be controlled through carefully monitored diet and activity changes. To meet this important need, there have been increased efforts to identify early gestation biomarkers of placental dysfunction using innovative imaging technologies. Here we outline innovative quantitative markers of placental shape and their relationship to placental function, clinical implications of these quantifiers, and the most recent mathematical models that utilize placental images to delineate at risk from normal pregnancies. We propose that novel contexts of readily available placental measures and routine collection of in vivo placental images in all pregnancies may be all that are needed to advance the identification of early risk determination of complicated pregnancies from placental images.

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.

Summary Objective Providing effective dietary counselling so that pregnancy weight gain remains within the 2009 Institute of Medicine (IOM) guidelines requires accurate maternal energy intake measures. Current practice is based on self-reported intake that has been demonstrated unreliable. This study applies an objective calculation of energy intake from a validated mathematical model to identify characteristics of individuals more likely to misreport during pregnancy. Methods A validated maternal energy balance equation was used to calculate energy intake from gestational weight gain in 1,368 subjects. The difference between self-reported and model-predicted energy intake was tested for demographics, economic status, education level and maternal health status. Results A weight gain of 15.2 kg resulted in model-predicted intake during pregnancy of 2,882.97 ± 135.71 kcal day−1, which differed from self-reported intake of 2,180.5 ± 856.0 kcal day−1. The achieved weight gain exceeded the IOM guidelines; however, the model predicted weight gain from self-reported energy intake was below IOM guidelines. Higher income (p = 0.004), education (p = 0.003), birth weight (p = 0.017), gestational diabetes (p = 0.008) and pre-existing diabetes (p

Summary Background Low levels of energy expenditure (TEE) may contribute to excess weight during childhood, but limited longitudinal data exist. Objectives This is to test whether low TEE during the first 6 years of life could predict excess weight status at 8 years. Methods Total energy expenditure from doubly labelled water, weight, stature, waist circumference and fat mass and fat-free mass (FFM) in children at 0.25, 2, 4 and 6 years of age. This cohort includes individuals at high (n = 27) and low risk (n = 26) for childhood obesity, based upon whether pre-pregnant maternal obesity. A linear mixed effects model was fit to TEE. Individual variation was accounted for as a random effect. Residual TEE was calculated for age and individually averaged across time. Results Fat-free mass (kg) was highly correlated (R2 = 0.91) with TEE (kcal/day), and waist circumference and sex were also significant predictors of TEE. TEE residual tracked within individuals. TEE residuals did not correlate with either BMI or %fat at age 8 years. Conclusion Using the residual TEE approach to identify high and low TEE during the first 6 years of life did not explain excess weight at 8 years of life in this cohort of children at high and low risk of obesity based upon maternal obesity status.

Body mass index (BMI) is now the most widely used measure of adiposity on a global scale. Nevertheless, intense discussion centers on the appropriateness of BMI as a phenotypic marker of adiposity across populations differing in race and ethnicity. BMI-adiposity relations appear to vary significantly across race/ethnic groups, but a collective critical analysis of these effects establishing their magnitude and underlying body shape/composition basis is lacking. Accordingly, we systematically review the magnitude of these race-ethnic differences across non-Hispanic (NH) white, NH black and Mexican American adults, their anatomic body composition basis and potential biologically linked mechanisms, using both earlier publications and new analyses from the US National Health and Nutrition Examination Survey. Our collective observations provide a new framework for critically evaluating the quantitative relations between BMI and adiposity across groups differing in race and ethnicity; reveal new insights into BMI as a measure of adiposity across the adult age-span; identify knowledge gaps that can form the basis of future research and create a quantitative foundation for developing BMI-related public health recommendations.

BACKGROUND. Exercise interventions result in modest weight loss, yet exercise is frequently prescribed for weight loss. PURPOSE. To identify individuals who become discouraged when exercise fails to achieve weight loss. METHODS. Representative samples of U.S. adults were recruited using Google Consumer Surveys in August–October 2014. Respondents were asked about beliefs and potential discouragement regarding the role of exercise and weight loss. An analysis of variance was performed to predict individuals that become discouraged if exercise does not lead to weight loss. RESULTS. The belief that exercise is a very effective way to lose weight was common (71% of respondents). Stronger belief that exercise is an effective way to lose weight (p < 0.001) in individuals with higher weight status (p = 0.04) positively predicted discouragement with exercise. Higher weight status combined with the belief that exercise reduces weight was a significant positive predictor of discouragement (p = 0.01). CONCLUSIONS. Individuals with higher weight status that believe that exercise is an effective way to lose weight are more likely to become discouraged when exercise does not lead to weight loss. Prescribing exercise for weight loss might contribute to discouragement. Future studies should evaluate ways to encourage exercise without promoting the belief that exercise will yield weight loss.

This study uses 3D body scanner measurements of US Air Force recruits to compare ideal body proportions represented by Leonardo da Vinci’s Vitruvian Man with contemporary body proportions in young adult men and women.