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Background: The normal-weight BMI range (18.5-24.9 kg/m2) includes adults with body shape and cardiometabolic disease risk features of excess adiposity, although a distinct phenotype developed on a large and diverse sample is lacking., Objective: To identify demographic, behavioral, body composition, and health-risk biomarker characteristics of people in the normal-weight BMI range who are at increased risk of developing cardiovascular and metabolic diseases based on body shape., Methods: Six nationally representative waist circumference index (WCI, weight/height0.5) prediction formulas, with BMI and age as covariates, were developed using data from 17,359 non-Hispanic (NH) white, NH black, and Mexican-American NHANES 1999-2006 participants. These equations were then used to predict WCI in 5594 NHANES participants whose BMI was within the normal weight range. Men and women in each race/Hispanic-origin group were then separated into high, medium, and low tertiles based on the difference (residual) between measured and predicted WCI. Characteristics were compared across tertiles; P values for significance were adjusted for multiple comparisons., Results: Men and women in the high WCI residual tertile, relative to their BMI and age-equivalent counterparts in the low tertile, had significantly lower activity levels; higher percent trunk and total body fat (e.g. NH white men, X ± SE, 25.3 ± 0.2% compared with 20.4 ± 0.2%); lower percent appendicular lean mass (skeletal muscle) and bone mineral content; and higher plasma insulin and triglycerides, higher homeostatic model assessment of insulin resistance (e.g. NH white men, 1.45 ± 0.07 compared with 1.08 ± 0.06), and lower plasma HDL cholesterol. Percent leg fat was also significantly higher in men but lower in women. Similar patterns of variable statistical significance were present within sex and race/ethnic groups., Conclusions: Cardiometabolic disease risk related to body shape in people who are normal weight according to BMI is characterized by a distinct phenotype that includes potentially modifiable behavioral health risk factors., (Copyright © The Author(s) on behalf of the American Society for Nutrition 2020.)

Background: 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 = αH β , 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 < 0.05) differences in powers from the general population. The coefficients of the adjusted terms ranged from -0.22 to 0.26 and therefore the scaling exponent does not deviate far from 2. Thresholds for BMI classification of overweight in the KNHANES database were BMI = 21 kg/m 2 (AUC = 0.89) for males 18 kg/m 2 (AUC = 0.97) for females. Obesity classification was calculated as BMI = 26 kg/m 2 (AUC = 0.81) and 23 kg/m 2 (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.

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.

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

Quantification of energy storage is essential in understanding energy balance and can be determined by bioelectrical impedance analysis (BIA). Here, we have developed a smartphone form factor multi-frequency BIA device that incorporates an analog front end for body composition measurements. The device was compared against a reference gel-electrode based BIA system in a clinical trial of 311 subjects for predicting BIA equations by calibrating the impedance index to body composition data from dual energy X-ray absorptiometry (DXA). Strong correlations were observed between DXA-based lean soft tissue and the impedance index generated at 50 KHz (R(2)=0.87; p<;0.001). A similar trend was also evident at higher frequencies which matched results from the reference gel-electrode BIA device. The findings support the role of our consumer-oriented mobile Health initiative for multi-frequency BIA assessments to aid weight management.

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

A dramatic rise in obesity has occurred among humans within the last several decades. Little is known about whether similar increases in obesity have occurred in animals inhabiting human-influenced environments. We examined samples collectively consisting of over 20 000 animals from 24 populations (12 divided separately into males and females) of animals representing eight species living with or around humans in industrialized societies. In all populations, the estimated coefficient for the trend of body weight over time was positive (i.e. increasing). The probability of all trends being in the same direction by chance is 1.2 × 10(-7). Surprisingly, we find that over the past several decades, average mid-life body weights have risen among primates and rodents living in research colonies, as well as among feral rodents and domestic dogs and cats. The consistency of these findings among animals living in varying environments, suggests the intriguing possibility that the aetiology of increasing body weight may involve several as-of-yet unidentified and/or poorly understood factors (e.g. viral pathogens, epigenetic factors). This finding may eventually enhance the discovery and fuller elucidation of other factors that have contributed to the recent rise in obesity rates.

Background: Body weight scales to height with a power of ≈2, thus forming the basis of body mass index (weight/height(2)). The corresponding scaling of body composition to height has not been established in a representative sample of US adults., Objective: The aim of the study was to examine the scaling of weight, fat, fat-free mass, and bone mineral content to height., Design: Adult non-Hispanic white (NHW), non-Hispanic black (NHB), and Mexican American National Health and Nutrition Examination Survey (NHANES) participants were included in allometric analyses if they had complete age, weight, height, and body-composition data as measured by dual-energy X-ray absorptiometry. Powers of height in allometric regression models were developed for each measure and adjusted for age., Results: The analyses included 13,183 subjects (6699 NHW, 3015 NHB, and 3469 Mexican American). The scaling of weight to height across sex-race groups provided powers (mean ± SE) ranging from 1.85 ± 0.12 in Mexican American women to 2.48 ± 0.17 in Mexican American men. Powers of height for body composition similarly ranged widely and were often outside the 95% CI for a power of 2. Of the 3 body-composition measures, the mean age-adjusted powers of height rounded to 2 as the nearest integer in 16 of 18 sex-race groups., Conclusions: Adult weight and body composition scale to height with variable age-adjusted powers that are sometimes outside the 95% CI for a power of 2 but frequently round to 2 as the nearest integer. These observations have implications for developing height-adjusted body-composition indexes.

Background: Body image distortions are a core feature of anorexia nervosa (AN). We, and others, previously reported abnormalities in adipose tissue distribution after acute weight restoration in adult women with AN compared with body mass index-matched healthy control women. Whether these abnormalities persist over time remains unknown., Objectives: We aimed to 1) replicate previous findings that showed preferential central accumulation of adipose tissue in recently weight-restored AN women compared with control subjects, 2) describe the change within patients with longer-term (1-y) weight maintenance, and 3) compare adipose tissue distribution after 1-y maintenance with that of control subjects., Design: Body composition and adipose tissue distribution were assessed by whole-body magnetic resonance imaging in women with AN shortly after weight normalization (n = 30) and again 1 y after hospital discharge (n = 16) and in 8 female control subjects at 2 time points., Results: With acute weight restoration, AN patients had significantly greater visceral and intermuscular adipose tissue compared with control women [visceral: 0.75 +/- 0.26 compared with 0.51 +/- 0.26 kg in AN patients and controls, respectively (P = 0.02); intermuscular: 0.46 +/- 0.17 compared with 0.29 +/- 0.13 kg in AN patients and controls, respectively (P = 0.01)]. With maintenance of normal weight for approximately 1 y, visceral adipose tissue distribution in AN patients was not different from that in healthy control subjects., Conclusions: In adult women with AN, normalization of weight in the short term is associated with a distribution of adipose tissue that is consistent with a central adiposity phenotype. This abnormal distribution appears to normalize within a 1-y period of weight maintenance. This research was registered at clinicaltrials.gov as NCT 00271921 and NCT 00368667.

Thermostats have "set-points" that engineers design with mathematical rigor. Work in this issue of Cell Metabolism (Tam et al., 2009) applies similar modeling strategies to explore the control of murine energy and body weight homeostasis by leptin.

Adult resting energy expenditure (REE) scales as height( approximately 1.5), whereas body weight (BW) scales as height( approximately 2). Mass-specific REE (i.e., REE/BW) is thus lower in tall subjects compared with their shorter counterparts, the mechanism of which is unknown. We evaluated the hypothesis that high-metabolic-rate brain mass scales to height with a power significantly less than that of BW, a theory that if valid would provide a potential mechanism for height-related REE effects. The hypothesis was tested by measuring brain mass on a large (n = 372) postmortem sample of Thai men. Since brain mass-body size relations may be influenced by age, the hypothesis was secondarily explored in Thai men age < or =45 yr (n = 299) and with brain magnetic resonance imaging (MRI) studies in Korean men (n = 30) age > or =20<30 yr. The scaling of large body compartments was examined in a third group of Asian men living in New York (NY, n = 28) with MRI and dual-energy X-ray absorptiometry. Brain mass scaled to height with a power (mean +/- SEE; 0.46 +/- 0.13) significantly smaller (P < 0.001) than that of BW scaled to height (2.36 +/- 0.19) in the whole group of Thai men; brain mass/BW scaled negatively to height (-1.94 +/- 0.20, P < 0.001). Similar results were observed in younger Thai men, and results for brain mass/BW vs. height were directionally the same (P = 0.09) in Korean men. Skeletal muscle and bone scaled to height with powers similar to that of BW (i.e., approximately 2-3) in the NY Asian men. Models developed using REE estimates in Thai men suggest that brain accounts for most of the REE/BW height dependency. Tall and short men thus differ in relative brain mass, but the proportions of BW as large compartments appear independent of height, observations that provide a potential mechanistic basis for related differences in REE and that have implications for the study of adult energy requirements.

Two observations favor the presence of a lower mass-specific resting energy expenditure (REE/weight) in taller adult humans: an earlier report of height (H)-related differences in relative body composition; and a combined model based on Quetelet and Kleiber's classic equations suggesting that REE/weight proportional, variantH(-0.5). This study tested the hypothesis stating that mass-specific REE scales negatively to height with a secondary aim exploration of related associations between height, weight (W), surface area (SA), and REE. Two independent data sets (n = 344 and 884) were evaluated, both with REE measured by indirect calorimetry and the smaller of the two including fat estimates by dual-energy X-ray absorptiometry. Results support Quetelet's equation (W proportional, variantH(2)), but Kleiber's equation approached the interspecific mammal form (REE proportional, variantW(0.75)) only after adding adiposity measures to weight and age as REE predictors. REE/weight scaled as H( approximately (-0.5)) in support of the hypothesis with P values ranging from 0.17 to <0.001. REE and SA both scaled as H( approximately 1.5), and REE/SA was nonsignificantly correlated with height in all groups. These observations suggest that adiposity needs to be considered when evaluating the intraspecific scaling of REE to weight; that relative to their weight, taller subjects require a lower energy intake for replacing resting heat losses than shorter subjects; that fasting endurance, approximated as fat mass/REE, increases as H(0.5); and that thermal balance is maintained independent of stature by evident stable associations between resting heat production and capacity of external heat release. These observations have implications for the modeling of adult human energy requirements and associate with anthropological concepts founded on body size.

Background: Although Quetelet first reported in 1835 that adult weight scales to the square of stature, limited or no information is available on how anatomical body compartments, including adipose tissue (AT), scale to height., Objective: We examined the critical underlying assumptions of adiposity-body mass index (BMI) relations and extended these analyses to major anatomical compartments: skeletal muscle (SM), bone, residual mass, weight (AT+SM+bone), AT-free mass, and organs (liver, brain)., Design: This was a cross-sectional analysis of 2 body-composition databases: one including magnetic resonance imaging and dual-energy X-ray absorptiometry (DXA) estimates of evaluated components in adults (total n=411; organs=76) and the other a larger DXA database (n=1346) that included related estimates of fat, fat-free mass, and bone mineral mass., Results: Weight, primary lean components (SM, residual mass, AT-free mass, and fat-free mass), and liver scaled to height with powers of approximately 2 (all P<0.001); bone and bone mineral mass scaled to height with powers >2 (2.31-2.48), and the fraction of weight as bone mineral mass was significantly (P<0.001) correlated with height in women. AT scaled weakly to height with powers of approximately 2, and adiposity was independent of height. Brain mass scaled to height with a power of 0.83 (P=0.04) in men and nonsignificantly in women; the fraction of weight as brain was inversely related to height in women (P=0.002)., Conclusions: These observations suggest that short and tall subjects with equivalent BMIs have similar but not identical body composition, provide new insights into earlier BMI-related observations and thus establish a foundation for height-normalized indexes, and create an analytic framework for future studies.

The proportion of fat-free mass (FFM) as body cell mass (BCM) is highly related to whole body resting energy expenditure. However, the magnitude of BCM/FFM may have been underestimated in previous studies. This is because Moore's equation [BCM (kg) = 0.00833 x total body potassium (in mmol)], which was used to predict BCM, underestimates BCM by approximately 11%. The aims of the present study were to develop a theoretical BCM/FFM model at the cellular level and to explore the influences of sex, age, and adiposity on the BCM/FFM. Subjects were 112 adults who had the following measurements: total body water by (2)H(2)O or (3)H(2)O dilution; extracellular water by NaBr dilution; total body nitrogen by in vivo neutron activation analysis; and bone mineral by dual-energy X-ray absorptiometry. FFM was calculated using a multicomponent model and BCM as the difference between FFM and the sum of extracellular fluid and solids. The developed theoretical model revealed that the proportion of BCM to FFM is mainly determined by water distribution (i.e., E/I, the ratio of extracellular to intracellular water). A significant correlation (r = 0.90, P < 0.001) was present between measured and model-predicted BCM/FFM for all subjects pooled. Measured BCM/FFM [mean (SD)] was 0.584 +/- 0.041 and 0.529 +/- 0.041 for adult men and women (P < 0.001), respectively. A multiple linear regression model showed that there are independent significant associations of sex, age, and fat mass with BCM/FFM.

Background: Body composition differs between African American (AA) and white women, and the resting metabolic rate (RMR) is likely to be lower in AA women than in white women., Objective: We tested 2 hypotheses: that AA women have a greater proportion of low-metabolic-rate skeletal muscle (SM) and bone than do white women and that between-race musculoskeletal differences are a function of body weight., Design: Hypothesis 1 was tested by comparing SM, bone, adipose tissue, and high-metabolic-rate residual mass across 22 pairs of matched AA and white women. Magnetic resonance imaging and dual-energy X-ray absorptiometry were used to partition weight into 4 components, and RMR was both calculated from tissue-organ mass and measured. Hypothesis 2 was evaluated by measuring SM, bone, fat, and residual mass in 521 AA and white women with the use of dual-energy X-ray absorptiometry alone., Results: Hypothesis 1: AA women had greater SM ( +/- SD group difference: 1.52 +/- 2.48 kg; P < 0.01) and musculoskeletal mass (1.72 +/- 2.66 kg; P < 0.01) than did white women. RMR calculated from body composition and measured RMR did not differ; RMR estimated by both approaches tended to be lower (approximately 160 kJ/d) in AA women than in white women. Hypothesis 2: SM was significantly correlated with weight, height, age, and race x weight interaction; greater SM in the AA women was a function of body weight., Conclusions: Lower RMRs in AA women than in white women are related to corresponding differences in the proportions of heat-producing tissues and organs, and these race-related body-composition differences increase as a function of body weight.

Artificial sweeteners provide lower calorie food products but have been thought to increase appetite and perhaps lead to increased body weight compared with high-sucrose foods. A recent trial demonstrated that subjects supplementing their diets with artificially sweetened foods lost weight, whereas those consuming high-sucrose foods gained weight. This review sheds some light on the controversy regarding the effects of artificial sweeteners in body weight control.

PurposeTotal and regional body composition are important indicators of health and mortality risk, but their measurement is usually restricted to controlled environments in clinical settings with expensive and specialized equipment. A method that approaches the accuracy of the current gold standard method, dual-energy x-ray absorptiometry (DXA), while only requiring input from widely available consumer grade equipment, would enable the measurement of these important biometrics in the wild, enabling data collection at a scale that would have previously been prohibitive in time and expense. We describe an algorithm for predicting three-dimensional (3D) body shape and composition from a single frontal 2-dimensional image acquired with a digital consumer camera.MethodsDuplicate 3D optical scans, two-dimensional (2D) optical images, and DXA whole-body scans were available for 183 men and 233 women from the Shape Up! Adults Study. A principal component analysis vector basis was fit to 3D point clouds of a training subset of 152 men and 194 women. The relationship between this vector space and DXA-derived body composition was modeled with linear regression. The principal component 3D shape was then fitted to match a silhouette extracted from a 2D photograph of a novel body. Body composition was predicted from the resulting 3D shape match using the linear mapping between the principal component parameters and the DXA metrics. Accuracy of body composition estimates from the silhouette method was evaluated against a simple model using height and weight as a baseline, and against DXA measurements as ground truth. Test-retest precision of the silhouette method was evaluated using the duplicate 2D optical images and compared against precision of the duplicate DXA scans. Paired t-tests were performed to detect significant differences between the sets.ResultsResults were reported on a held-out set. Body composition prediction achieved R2 s of 0.81 and 0.74 for percent fat prediction of males and females, respectively, on a held-out test set consisting of 31 males and 39 females. Precision estimates for fat mass were 2.31% and 2.06% for males and females, respectively, compared to 1.26% and 0.68% for DXA scans. The t-tests revealed no statistically significant differences between the silhouette method measurements and DXA measurements, or between retests.ConclusionTotal and regional body composition measures can be estimated from a single frontal photograph of a human body. Body composition prediction using consumer level photography can enable early screening and monitoring of possible physiological indicators of metabolic disease in regions where medical imagery or clinical assessment is inaccessible.

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

Objective: Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight‐loss interventions. Although such equations are known to introduce group‐ and individual‐level error into REE prediction, their validity has largely been assessed in weight‐stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12‐month weight‐loss intervention. Methods: Changes in predictive error of four models (Mifflin‐St‐Jeor, Harris‐Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1‐, 6‐, and 12‐month time points in adults (n = 66, 76% female, aged 18‐55 years, BMI = 27‐45 kg/m2) enrolled in a randomized clinical weight‐loss trial. Results: All equations experienced significant negative shifts in bias (measured − predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat‐free mass (p ≤ 0.01). Conclusions: Changes in body composition and mass during a 12‐month weight‐loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation. [ABSTRACT FROM AUTHOR]

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

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

Background/Objectives: Body mass index (BMI) is a proxy for obesity that is commonly used in spite of its limitation in estimating body fatness. Trained observers with repeated exposure to different body types can estimate body fat (BF) of individuals compared to criterion methods with reasonable accuracy. The purpose of this study was to develop and validate a computer algorithm to provide a valid estimate %BF using digital photographs. Subjects/Methods: Our sample included 97 children and 226 adults (age in years: 11.3±3.3; 38.1±11.6, respectively). Measured height and weight were used (BMI in kg/m2: 20.4±4.4; 28.7±6.6 for children and adults, respectively). Dual x-ray absorptiometry (DXA) was the criterion method. Body volume (BVPHOTO) and body shape (BSPHOTO) were derived from two digital images. Final support vector regression (SVR) models were trained using age, sex, race, BMI for % BFNOPHOTO, plus BVPHOTO and BSPHOTO for %BFPHOTO. Separate validation models were used to evaluate the learning algorithm in children and adults. The differences in correlations between %BFDXA, %BFNOPHOTO and %BFPHOTO were tested using the Fisher’s Z-score transformation. Results: Mean BFDXA and BFPHOTO were 27.0%±9.2 vs. 26.7%± 7.4 in children and 32.9± 10.4% vs. 32.8%±9.3 in adults. SVR models produced %BFPHOTO values strongly correlated with %BFDXA. Our final model produced correlations of rDP = 0.80 and rDP = 0.87 in children and adults, respectively for %BFPHOTO vs. %BFDXA. The correlation between %BFNOPHOTO and %BFDXA was moderate, yet statistically significant in both children rDB = 0.70; p <0.0001 and adults rDB = 0.86; p<0.0001. However, the correlations for rDP were statistically higher than rDB (%BFDXA vs. %BFNOPHOTO) in both children and adults (children: Z = 5.95, p<0.001; adults: Z = 3.27, p<0.0001). Conclusions: Our photographic method produced valid estimates of BF in both children and adults. Further research is needed to create norms for subgroups by sex, race/ethnicity, and mobility status. [ABSTRACT FROM AUTHOR]

Background: Experimentally establishing a group's body weight maintenance energy requirement is an important component of metabolism research. At present, the reference approach for measuring the metabolizable energy intake (MEI) from foods required for body weight maintenance in non-confined subjects is the doubly-labeled water (DLW)-total energy expenditure (TEE) method. In the current study, we evaluated an energy-intake weight balance method as an alternative to DLW that is more flexible and practical to apply in some settings. Methods: The hypothesis was tested that MEI from foods observed in a group of subjects maintaining a constant energy intake while keeping their weight within ±1 kg over 10 days is non-significantly different from DLW-measured TEE (TEEDLW). Six non-obese subjects evaluated as part of an earlier study completed the inpatient protocol that included a 3-day initial adjustment period. Results: The group body weight coefficient of variation (X ± SD) during the 10-day balance period was 0.38 ± 0.10% and the slope of the regression line for body weight versus protocol day was non-significant at 1.8 g/day (R², 0.002, p = 0.98). MEI from foods observed during the 10-day balance period (2390 ± 543 kcal/day) was non-significantly different (p = 0.96) from TEE measured by DLW (2373 ± 713 kcal/day); the MEI/TEEDLW ratio was 1.03 ± 0.15 (range 0.87-1.27) and the correlation between MEI from foods and TEEDLW was highly significant (R², 0.88, p = 0.005). Conclusions: A carefully managed 10-day protocol that includes a constant MEI level from foods with weight stability (±1 kg) will provide a group's body weight maintenance energy requirement similar to that obtained with DLW. This approach opens the possibility of conducting affordable weight balance studies, shorter in duration than those previously reported, that are needed to answer a wide range of questions in clinical nutrition. [ABSTRACT FROM AUTHOR]

Background: The phase angle (PA) has been used as a prognostic marker in several clinical situations. Nevertheless, its biological meaning is not completely understood. Objective: We verified how body-composition components could explain the PA. Design: The trial was a cross-sectional study involving 1442 participants (women: 58.5%; Caucasian: 40.2%) from body-composition studies. Labeled tritium dilution and total-body potassium were used to estimate total-body water (TBW) and intracellular water (ICW), respectively. Extracellular water (ECW) and the ECW:ICW ratio were estimated from the difference and the ratio of these values. Fat-free mass (FFM) and fat mass (FM) were estimated with the use of dual-energy X-ray absorptiometry, underwater weighing (UWW), and TBW. The PA was estimated with the use of a single-frequency bioelectrical impedance analysis system. Correlations between the PA and all body-composition variables were evaluated. A multivariate linear regression analysis was performed to adjust for the effects of body-composition variables on the PA variability. All analyses were performed separately by sex. Results: Compared with men, women exhibited significantly larger ECW:ICW ratios and FM. The highest positive correlation was shown between the PA and FFM obtained with the use of UWW (both sexes). The highest negative correlation was shown between the PA and ECW: ICW ratios for both sexes. Age, race, height, ECW:ICW, and FFM from UWW were significant PA determinants in a multivariate linear regression model. Even after adjustment for all significant covariates, the explained PA variance was low (adjusted R² = 0.539 and 0.421 in men and women, respectively). The greatest impact on the total PA prediction in both men and women were age, FFM, and height. Conclusions: Age is the most significant PA predictor in men and women followed by FFM and height. The ECW:ICW contribution may explain the association of the PA observed in the clinical setting and in people who are obese. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background/objectives: The body adiposity index (BAI) estimates the amount of body fat (BF) in humans. In Mexican-American and African-American populations, BAI has performed better than body mass index (BMI). The aim of this study was to evaluate the performance of BAI in estimating percentage (BF%) in severely obese Brazilian patients, with air displacement plethysmography (ADP) used as the reference method.Subjects/methods: Estimation of BF% by ADP, anthropometric measurements (height, abdominal and hip circumferences, body weight, and BMI) and BAI calculation were performed in 72 obese subjects (BMI ≥ 30 kg/m(2)) aged 30-55 years.Results: The mean BF% estimates ± standard deviation were 52.1 ± 5.7 % for ADP and 47.7 ± 7.4% for BAI, with a positive Pearson correlation (rp = 0.66) and a positive Lin's concordance correlation (rc = 0.479) observed between these methods. The 95% limits of individual agreement between BAI and ADP ranged from -5.769% to 16.036%, with BAI exhibiting an average positive bias of 5.13% compared to the reference method. For each studied variable, BAI exhibited a systematic bias, as evidenced by a tendency for low BF% values to be overestimated.Conclusion: For Brazilian patients with severe obesity, BAI does not provide an accurate estimate of BF%. [ABSTRACT FROM AUTHOR]

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 R2 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: R2 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: R2 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: (R2 0·70−0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R2 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. [ABSTRACT FROM PUBLISHER]

The article offers information about the move of the American Heart Association (AHA) to define ideal cardiovascular health in its 2020 Impact Goals for cardiovascular health promotion as well as disease prevention by including a body mass index (BMI) which the AHA recognizes as an ideal health behavior. Information about the complexity of obesity, which has been treated as a single disease process with universal prevention and treatment strategies, is presented.

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. The POUNDS Lost study was registered at clinicaltrials.gov as NCT00072995. [ABSTRACT FROM AUTHOR]

Background: Muscle mass reflects and influences health status. Its reliable estimation would be of value for epidemiology. Objective: The aim of the study was to derive and validate anthropometric prediction equations to quantify whole-body skeletal muscle mass (SM) in adults. Design: The derivation sample included 423 subjects (227 women) aged 18-81 y with a body mass index (BMI; in kg/m²) of 15.9-40.8. The validation sample included 197 subjects (105 women) aged 19-83 y with a BMI of 15.7-36.4. Both samples were of mixed ethnic/racial groups. All underwent whole-body magnetic resonance imaging to quantify SM (dependent variable for multiple regressions) and anthropometric variables (independent variables). Results: Two prediction equations with high practicality and optimal derivation correlations with SM were further investigated to assess agreement and bias by using Bland-Altman plots and validated in separate data sets. Including race as a variable increased R² by only 0.1% in men and by 8% in women. For men: SM (kg) = 39.5 + 0.665 body weight (BW; kg) - 0.185 waist circumference (cm) - 0.418 hip circumference (cm) - 0.08 age (y) (derivation: R² = 0.76, SEE = 2.7 kg; validation: R² = 0.79, SEE = 2.7 kg). Bland-Altman plots showed moderate agreement in both derivation and validation analyses. For women: SM (kg) = 2.89 + 0.255 BW (kg) - 0.175 hip circumference (cm) - 0.038 age (y) + 0.118 height (cm) (derivation: R² = 0.58, SEE = 2.2 kg; validation: R² = 0.59, SEE = 2.1 kg). Bland-Altman plots had a negative slope, indicating a tendency to overestimate SM among women with smaller muscle mass and to underestimate SM among those with larger muscle mass. Conclusions: Anthropometry predicts SM better in men than in women. Equations that include hip circumference showed agreement between methods, with predictive power similar to that of BMI to predict fat mass, with the potential for applications in groups, as well as epidemiology and survey settings. [ABSTRACT FROM AUTHOR]

Background: To date, there is no consensus regarding adult cutoffs of percentage of body fat or estimated cutoffs on the basis of nationally representative samples with rigorous body-composition measurements. Objective: We developed cutoffs of percentage of body fat on the basis of the relation between dual-energy x-ray absorptiometry- measured fat mass and BMI (in kg/m2) stratified by sex, age, and race-ethnicity by using 1999-2004 NHANES data. Design: A simple regression (percentage of body fat = β0 + β1 x 1÷ BMI) was fit for each combination of sex (men and women), 3 age groups (18-29, 30-49, and 50-84 y of age), and 3 race-ethnicity groups (non-Hispanic whites, non-Hispanic blacks, and Mexican Americans). Model fitting included a consideration of complex survey design and multiple imputations. Cutoffs of percentage of body fat were computed that corresponded to BMI cutoffs of 18.5, 25, 30, 35, and 40 on the basis of estimated prediction equations. Results: R2 ranged from 0.54 to 0.72 for men (n = 6544) and 0.58 to 0.79 for women (n = 6362). In men, the percentage of body fat that corresponded to a BMI of 18.5, 25, 30, 35, and 40 across age and racial-ethnic groups ranged from 12.2% to 19.0%, 22.6% to 28.0%, 27.5%'to 32.3%, 31.0% to 35.3%, and 33.6% to 37.6%, respectively; the corresponding ranges in women were from 24.6% to 32.3%, 35.0% to 40.2%, 39.9% to 44.1%, 43.4% to 47.1%, and 46.1 % to 49.4%, respectively. The oldest age group had the highest cutoffs of percentage of body fat. Non-Hispanic blacks had the lowest cutoffs of percentage of body fat. Cutoffs of percentage of body fat were higher in women than in men. Conclusions: Cutoffs of percentage of body fat that correspond to the current US BMI cutoffs are a function of sex, age, and race-ethnicity. These factors should be taken into account when considering the appropriateness of levels of percentage of body fat. [ABSTRACT FROM AUTHOR]

Background: The association between waist circumference (WC) and mortality is particularly strong and direct when adjusted for body mass index (BMI). One conceivable explanation for this association is that WC adjusted for BMI is a better predictor of the presumably most harmful intra-abdominal fat mass (IAFM) than WC alone. We studied the prediction of abdominal subcutaneous fat mass (ASFM) and IAFM by WC alone and by addition of BMI as an explanatory factor. Methodology/Principal Findings: WC, BMI and magnetic resonance imaging data from 742 men and women who participated in clinical studies in Canada and Finland were pooled. Total adjusted squared multiple correlation coefficients (R2) of ASFM and IAFM were calculated from multiple linear regression models with WC and BMI as explanatory variables. Mean BMI and WC of the participants in the pooled sample were 30 kg/m2 and 102 cm, respectively. WC explained 29% of the variance in ASFM and 51% of the variance in IAFM. Addition of BMI to WC added 28% to the variance explained in ASFM, but only 1% to the variance explained in IAFM. Results in subgroups stratified by study center, sex, age, obesity level and type 2 diabetes status were not systematically different. Conclusion/Significance: The prediction of IAFM by WC is not improved by addition of BMI. [ABSTRACT FROM AUTHOR]

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 &y& Elsevier]

Kleiber's law describes the quantitative association between whole-body resting energy expenditure (REE, in kcal/d) and body mass (M, in kg) across mature mammals as REE = 70.0 × M0.75. The basis of this empirical function is uncertain. Objectives. The study objective was to establish an organ-tissue level REE model across mammals and to explore the body composition and physiologic basis of Kleiber's law. Design. We evaluated the hypothesis that REE in mature mammals can be predicted by a combination of two variables: the mass of individual organs/tissues and their corresponding specific resting metabolic rates. Data on the mass of organs with highmetabolic rate (i.e., liver, brain, heart, and kidneys) for 111 species ranging in bodymass from0.0075 (shrew) to 6650 kg (elephant) were obtained froma literature review. Results. REEp predicted by the organtissue levelmodel was correlated with bodymass (correlation r = 0.9975) and resulted in the function REEp = 66.33×M0.754, with a coefficient and scaling exponent, respectively, close to 70.0 and 0.75 (P > 0.05) as observed by Kleiber. There were no differences between REEp and REEk calculated by Kleiber's law; REEp was correlated (r = 0.9994) with REEk. The mass-specific REEp, that is, (REE/M)p, was correlated with body mass (r = 0.9779) with a scaling exponent -0.246, close to -0.25 as observed with Kleiber's law. Conclusion. Our findings provide new insights into the organ/tissue energetic components of Kleiber's law. The observed large rise in REE and lowering of REE/M from shrew to elephant can be explained by corresponding changes in organ/tissue mass and associated specific metabolic rate. [ABSTRACT FROM AUTHOR]

Resting energy expenditure (REE)-power relationships result from multiple underlying factors including weight and height. In addition, detailed body composition, including fat free mass (FFM) and its components, skeletal muscle mass and internal organs with high metabolic rates (i.e. brain, heart, liver, kidneys), are major determinants of REE. Since the mass of individual organs scales to height as well as to weight (and, thus, to constitution), the variance in these associations may also add to the variance in REE. Here we address body composition (measured by magnetic resonance imaging) and REE (assessed by indirect calorimetry) in a group of 330 healthy volunteers differing with respect to age (17-78 years), sex (61% female) and BMI (15.9-47.8 kg/m2). Using three dimensional data interpolation we found that the inter-individual variance related to scaling of organ mass to height and weight and, thus, the constitution-related variances in either FFM (model 1) or kidneys, muscle, brain and liver (model 2) explained up to 43% of the inter-individual variance in REE. These data are the first evidence that constitution adds to the complexity of REE. Since organs scale differently as weight as well as height the ''fit'' of organ masses within constitution should be considered as a further trait. [ABSTRACT FROM AUTHOR]

The article presents a study on the negative scale of mass-specific total energy expenditure (TEE) to height and weight in adults less than 50 years old with non-negative activity energy expenditure (AEE). It says that linear regression analysis and stratification of subjects into height quintiles are used to explore the allometric-based observations with different analytic strategies. Result shows that weight increase in adults as REE and TEE also increases in heights and mass-specific REE.

Background: To compare the relationship of skeletal muscle mass with bone mineral content in an ethnically diverse group of 6 to 18 year old boys and girls. Methods: 175 healthy children (103 boys; 72 girls) had assessments of body mass, height, and Tanner stage. Whole body bone mineral content, non-bone lean body mass (nbLBM), skeletal muscle mass, and fat mass were assessed using dual-energy X-ray absorptiometry (DXA). Muscle mass was estimated from an equation using appendicular lean soft tissue measured by DXA, weight and height. Estimates of skeletal muscle mass and adipose tissue were also assessed by whole body multi-slice magnetic resonance imaging (MRI). Linear regression was used to determine whether skeletal muscle mass assessed by DXA or by MRI were better predictors of bone mineral content compared with nbLBM after adjusting for sex, age, race or ethnicity, and Tanner stage. Results: Greater skeletal muscle mass was associated with greater bone mineral content (p < 0.001). The skeletal muscle mass assessed by MRI provided a better fitting regression model (determined by R2 statistic) compared with assessment by DXA for predicting bone mineral content. The proportion of skeletal muscle mass in nbLBM was significantly associated with greater bone mineral content adjusted for total nbLBM. Conclusions: This study is among the first to describe and compare the relationship of skeletal muscle to bone using both MRI and DXA estimates. The results demonstrate that the use of MRI provides a modestly better fitting model for the relationship of skeletal muscle to bone compared with DXA. Skeletal muscle had an impact on bone mineral content independent of total non-bone lean body mass. In addition, Hispanics had greater bone mineral content compared to other race and ethnic groups after adjusting for sex, age, adipose tissue, skeletal muscle mass, and height. [ABSTRACT FROM AUTHOR]

Background: The Forbes equation relating fat-free mass (FFM) to fat mass (FM) has been used to predict longitudinal changes in FFM during weight change but has important limitations when paired with a one dimensional energy balance differential equation. Direct use of the Forbes model within a one dimensional energy balance differential equation requires calibration of a translate parameter for the specific population under study. Comparison of translates to a representative sample of the US population indicate that this parameter is a reflection of age, height, race and gender effects. Results: We developed a class of fourth order polynomial equations relating FFM to FM that consider age, height, race and gender as covariates eliminating the need to calibrate a parameter to baseline subject data while providing meaningful individual estimates of FFM. Moreover, the intercepts of these polynomial equations are nonnegative and are consistent with observations of very low FM measured during a severe Somali famine. The models preserve the predictive power of the Forbes model for changes in body composition when compared to results from several longitudinal weight change studies. Conclusions: The newly developed FFM-FM models provide new opportunities to compare individuals undergoing weight change to subjects in energy balance, analyze body composition for individual parameters, and predict body composition during weight change when pairing with energy balance differential equations. [ABSTRACT FROM AUTHOR]

The article compares bioimpedance (BIA) research methods for measuring total body water (TBW) and resting energy expenditure (REE) using the Tanita TBF-310 body composition analyzer. It cites that research procedures such as deuterium (D²O) dilution and metabolic carts are posing a problem in the clinical setting. It stresses the need for accurate and precise measures of weight and body composition to test the efficacy of new strategies to stop an increase in body size.

The article discusses various issues relating to the association between waist circumference (WC) and cardiometabolic risk. It provides some techniques for measuring WC at all anatomic sites. It highlights the biological mechanisms attributed to the association between WC and cardiometabolic risk. It cites the role of WC in predicting cardiometabolic risk factors and adverse outcomes based on epidemiological studies.

Protein is an important body component for monitoring growth, development, and nutritional status. We previously developed a total body potassium (TBK, in mmol) and bone mineral (Mo, in kg) model for predicting total body protein (TBPro, in kg) in adults (TBPro = 0.00252 × TBK + 0.732 × Mo). However, the applicability of the TBK-Mo model for children is unknown. The study aims were to develop a TBK-independent 6-component (6-C) TBPro approach as the criterion, and then to validate the TBK-Mo model in children. The following measurements were made in adolescents and children (n = 62,38 boys and 24 girls, aged 5–;17 y): body weight (BW, in kg), body volume (By, in liters) by air displacement plethysmography, total body water (TBW, in kg) by 2H2O dilution, Mo by dual-energy X-ray absorptiometry, and TBK by whole-body counting. A 6-C model was derived as TBPro = 2.922 × BW - 0.301 × TBW - 2.039 × Mo - 2.632 × BV. The TBPro estimates did not differ between the 6-C and TBK-Mo models (mean ± SD, 0.20 ± 0.86 kg). There was a significant correlation between TBPro by the 6-C and TBK-Mo models (r = 0.94, P < 0.001). Bland-Altman analysis indicated that the differences between TBPro by 6-C and TBK-Mo models were not significantly correlated with the mean TBPro estimates by the 2 models (r= 0.032, P> 0.05). The TBK-Mo model can thus be used to estimate TBPro in healthy adults, adolescents, and children >5 y old. [ABSTRACT FROM AUTHOR]

Aging is associated with the onset of chronic diseases that lead to pathological expansion of the extracellular water (ECW) compartment. Healthy aging, in the absence of disease, is also reportedly accompanied by a relative expansion of the ECW compartment, although the studies on which this observation is based are few in number, applied different ECW measurement methods, included small ethnically homogeneous subject samples, and failed to adjust ECW for non-age-related influencing factors. The aim of the current study was to examine, in a large (n = 1,538) ethnically diverse [African American (AA), Asian, Caucasian, Hispanic] subject group the cross-sectional relationships between ECW and age after controlling first for other potential factors that may influence fluid distribution. ECW and intracellular water (ICW) were derived from measured total body water (isotope dilution) and potassium (40K whole body counting). The cross-sectional relationships between ECW, ICW, and ECW/ICW (E/I), and age were developed using multiple regression modelling methods. Body weight, weight squared, height, age, sex, race, and interactions were all significant ECW predictors. The slope of the observed race × age interaction was significantly greater in AA (β = 0.0005, P = 0.005) than in the three other race groups. Race, sex, and age differences in fluid distribution persisted after adjusting for body composition in a subgroup (n = 994) with dual-energy X-ray absorptiometry lean soft tissue and fat measurements. A relative ECW expansion (i.e., E/I) was present with greater age in most sex-race groups, although the effect was not significantly larger in AA males (P > 0.05) compared with the other race groups, except Asians (P < 0.05). For females, a larger E/I-age effect was found in AA compared with the other race groups, but only the comparison against Hispanics was significant (P < 0.05). The ECW compartment and E/I are thus variably larger, according to race, in healthy older subjects independent of sex, lean soft tissue, and fat mass. [ABSTRACT FROM AUTHOR]

Abstract: We compared bone mineral density (BMD) and body fat percentage (%fat) between two pencil-beam (Lunar DPX and DPX-L) and two fan-beam (Lunar Prodigy, Hologic Delphi A) dual-energy X-ray absorptiometry (DXA) systems. We examined these values in the total-body, spine, femur, and forearm scans in 78 healthy adults across these four DXA systems. BMD and %fat values were highly correlated among the four instruments. DPX-L gave the lowest mean %fat and Prodigy gave the highest mean %fat for both sexes. The means were system dependent for %fat estimates across the four DXA machines. There was a significant difference detected in BMD estimates across manufacturers, with the Delphi A providing systematically lower values than the Lunar systems in the whole body, spine, and femur sites but higher values than the Lunar systems in the forearm. The present study results show that both %fat and bone mineral estimates between pencil-beam and fan-beam systems are highly correlated, but vary by system. Significant differences exist between the instruments, especially between different manufacturers, and most of the comparisons are sex dependent. We conclude that longitudinal studies should always be evaluated on the same system when possible, and translation models should be used to assess cross-instrument differences. [Copyright &y& Elsevier]

Presents the study "Metabolic Syndrome in Normal-Weight Americans." Concept of the metabolically obese, normal weight individual; Factors that lead to the metabolic syndrome; Aim of the study; Results of the study.

Examines a potential approach for developing percentage body fat ranges. Linkage of health body mass index (BMI) guidelines established by the United States National Institutes of Health and the World Health Organization with predicted percentage body fat; Development of sex-specific formulas for estimating relative body fatness from BMI.