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It remains unknown why adiponectin levels are associated with poor physical functioning, skeletal muscle mass and increased mortality in older populations. In 190 healthy adults (59–86 years, BMI 17–37 kg/m2, 56.8% female), whole body skeletal muscle mass (normalized by height, SMI, kg/m2), muscle and liver fat were determined by magnetic resonance imaging. Bone mineral content (BMC) and density (BMD) were assessed by dual X‐ray absorptiometry (n= 135). Levels of insulin‐like growth factor 1 (IGF‐1), insulin, inflammation markers, leptin and fibroblast growth factor 21 were measured as potential determinants of the relationship between adiponectin and body composition. Higher adiponectin levels were associated with a lower SMI (r= −0.23, P< 0.01), BMC (r= −0.17, P< 0.05) and liver fat (r= −0.20, P< 0.05) in the total population and with higher muscle fat in women (r= 0.27, P< 0.01). By contrast, IGF‐1 showed positive correlations with SMI (r= 0.33), BMD (r= 0.37) and BMC (r= 0.33) (all P< 0.01) and a negative correlation with muscle fat (r= −0.17, P< 0.05). IGF‐1 was negatively associated with age (r= −0.21, P< 0.01) and with adiponectin (r= −0.15, P< 0.05). Stepwise regression analyses revealed that IGF‐1, insulin and leptin explained 18% of the variance in SMI, and IGF‐1, leptin and age explained 16% of the variance in BMC, whereas adiponectin did not contribute to these models. Associations between higher adiponectin levels and lower muscle or bone mass in healthy older adults may be explained by a decrease in IGF‐1 with increasing adiponectin levels.

Background: Bioelectrical impedance analysis (BIA) is a technique widely used for estimating body composition and health-related parameters. The technology is relatively simple, quick, and non-invasive, and is currently used globally in diverse settings, including private clinicians’ offices, sports and health clubs, and hospitals, and across a spectrum of age, body weight, and disease states. BIA parameters can be used to estimate body composition (fat, fat-free mass, total-body water and its compartments). Moreover, raw measurements including resistance, reactance, phase angle, and impedance vector length can also be used to track health-related markers, including hydration and malnutrition, and disease-prognostic, athletic and general health status. Body composition shows profound variability in association with age, sex, race and ethnicity, geographic ancestry, lifestyle, and health status. To advance understanding of this variability, we propose to develop a large and diverse multi-country dataset of BIA raw measures and derived body components. The aim of this paper is to describe the ‘BIA International Database’ project and encourage researchers to join the consortium. Methods: The Exercise and Health Laboratory of the Faculty of Human Kinetics, University of Lisbon has agreed to host the database using an online portal. At present, the database contains 277,922 measures from individuals ranging from 11 months to 102 years, along with additional data on these participants. Conclusion: The BIA International Database represents a key resource for research on body composition.

Body mass is the primary metabolic compartment related to a vast number of clinical indices and predictions. The extent to which skeletal muscle (SM), a major body mass component, varies between people of the same sex, weight, height, and age is largely unknown. The current study aimed to explore the magnitude of muscularity variation present in adults and to examine if variation in muscularity associates with other body composition and metabolic measures. Muscularity was defined as the difference (residual) between a person's actual and model‐predicted SM mass after controlling for their weight, height, and age. SM prediction models were developed using data from a convenience sample of 492 healthy non‐Hispanic (NH) White adults (ages 18–80 years) who had total body SM and SM surrogate, appendicular lean soft tissue (ALST), measured with magnetic resonance imaging and dual‐energy X‐ray absorptiometry, respectively; residual SM (SMR) and ALST were expressed in kilograms and kilograms per square meter. ALST mass was also evaluated in a population sample of 8623 NH‐White adults in the 1999–2006 National Health and Nutrition Examination Survey. Associations between muscularity and variation in the residual mass of other major organs and tissues and resting energy expenditure were evaluated in the convenience sample. The SM, on average, constituted the largest fraction of body weight in men and women up to respective BMIs of 35 and 25 kg/m2. SM in the convenience sample varied widely with a median of 31.2 kg and an SMRinter‐quartile range/min/max of 3.35 kg/−10.1 kg/9.0 kg in men and 21.1 kg and 2.59 kg/−7.2 kg/7.5 kg in women; per cent of body weight as SM at 25th and 75th percentiles for men were 33.1% and 39.6%; corresponding values in women were 24.2% and 30.8%; results were similar for SMRindices and for ALST measures in the convenience and population samples. Greater muscularity in the convenience sample was accompanied by a smaller waist circumference (men/women: P< 0.001/=0.085) and visceral adipose tissue (P= 0.014/0.599), larger liver (P= 0.065/<0.001), kidneys (P= 0.051/<0.009), and bone mineral (P< 0.001/<0.001), and larger magnitude resting energy expenditure (P< 0.001/<0.001) than predicted for the same sex, age, weight, and height. Muscle mass is the largest body compartment in most adults without obesity and is widely variable in mass across people of similar body size and age; and high muscularity is accompanied by distinct body composition and metabolic characteristics. This previously unrecognized heterogeneity in muscularity in the general population has important clinical and research implications.

Background: In humans, it is unclear how different estimates of energy balance (EB) compare with each other and whether the resulting changes in body weight (bw) and body composition (BC) are predictable and reproducible. Methods: This is a secondary data analysis of effects of sequential 7d over- (OF), 21d under- (UF) and 14d refeeding (RF) on EB. Energy intake (EI) was controlled at +/− 50% of energy needs in a 32 normal weight men (see Am J Clin Nutr. 2015; 102:807–819). EB was calculated (i) directly from the difference between EI and energy expenditure (EE) and (ii) indirectly from changes in BC. Changes in fat mass (FM) were compared with predicted changes according to Hall et al. (Lancet2011; 378:826–37). Finally, within-subject reproducibility of changes in bw and BC was tested in a subgroup. Results: There were interindividual and day-by-day variabilities in changes in bw and BC. During OF and RF, the two estimates of EB were similar while with UF the indirect approach underestimated the direct estimate by 10593 ± 7506 kcal/21d (p&lt; 0.001). Considerable differences became evident between measured and predicted changes in FM. Adjusting measured for predicted values did not reduce their interindividual variance. During UF, changes in bw and BC were reproducible, while corresponding changes during OF were not. Conclusion: During hypercaloric nutrition the direct estimate of EB corresponded to the indirect estimate whereas this was not true during UF. Changes in bw and BC in response to OF were not reproducible while they were during UF.

Weight control is hypothesized to be improved when physical activity and energy intake are both high [high energy turnover (ET)].

Aim: The objective of this study is to generate metabolic phenotypes based on structure–function relationships. Methods: In 459 healthy adults (54% females, 18 and 40 years old), we analyzed body composition by air-displacement densitometry (to assess fat mass, (FM) and fat-free mass (FFM)) and whole-body magnetic resonance imaging (to assess skeletal muscle mass (SMM) and masses of brain, heart, liver, kidneys, and subcutaneous (SAT) and visceral adipose tissue (VAT)), resting energy expenditure (REE) by indirect calorimetry, and plasma concentrations of insulin (Ins) and leptin (Lep). Results: Three “functional body composition-derived phenotypes” (FBCPs) were derived: (1) REE on FFM-FBCP, (2) Lep on FM-FBCP, and (3) Ins on VAT-FBCP. Assuming that being within the ±?5% range of the respective regression lines reflects a “normal” structure–function relationship, three “normal” FBCPs were generated with prevalences of 9.0%, 5.1%, and 6.8%, respectively, of the study population. The three “FBCPs” did not overlap and were independent from each other. When compared with the two other FBCPs, the “Lep on FM-FBCP” was leanest, whereas the “REE on FFM-FBCP” had the highest BMI and SAT. Taking into account FFM composition, a hierarchical multi-level model is proposed with brain at level 1, the liver at level 2, and SMM and FM at level 3 with insulin coordinating the interplay between level 1 and 2, whereas variance in plasma insulin levels impacts energy and substrate metabolism in SMM and AT. Conclusion: Structure–function relationships can be used to generate FBCPs. Different FBCPs reflect different dimensions of normality (or health). This is evidence for the idea that there is no across the board “normal” state.

Common obesity-associated hepatic steatosis (nonalcoholic fatty liver disease (NAFLD)) and insulin resistance are mainly caused by dysfunctional adipose tissue. This adipose tissue dysfunction leads to increased delivery of NEFA and glycerol to the liver that (i) drives hepatic gluconeogenesis and (ii) facilitates the accumulation of lipids and insulin signaling inhibiting lipid intermediates. Dysfunctional adipose tissue can be caused by impaired lipid storage (overflow hypothesis, characterized by large visceral adipocytes) or increased lipolysis (due to impaired postprandial suppression of lipolysis in inflamed, insulin-resistant adipocytes). In line with the adipose tissue expandability hypothesis the amount and distribution of adipose tissue correlate with its dysfunction and thus with liver fat. This relationship is however modified by endocrine effects on lipid storage and lipolysis as well as dietary effects on hepatic lipogenesis and lipid oxidation. The association between body composition characteristics like visceral obesity or fat cell size and ectopic liver fat is modified by these influences. Phenotyping obesity according to metabolic risk should integrate body composition characteristics, endocrine parameters and information on diet.

Body mass in humans and animals is strongly associated with the rate of heat production as defined by resting energy expenditure (REE). Beginning with the ancient Greeks up to the present time, philosophers and scientists have endeavored to understand the nature and sources of bodily 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 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.

We investigated whether fat mass (FM) and total adipose tissue (TAT) can be used interchangeably and FM per TAT adds to metabolic risk assessment. Cross-sectional data were assessed in 377 adults (aged 18–60 years; 51.2% women). FM was measured by either 4-compartment (4C) model or quantitative magnetic resonance (QMR); total-, subcutaneous- and visceral adipose tissue (TAT, SAT, VAT), and liver fat by whole-body MRI; leptin, insulin, homeostasis model assessment of insulin resistance (HOMA-IR), C-reactive protein (CRP), and triglycerides; resting energy expenditure and respiratory quotient by indirect calorimetry were determined. Correlations and stepwise multivariate regression analyses were performed. FM4Cand FMQMRwere associated with TAT (r4C= 0.96, rQMR= 0.99) with a mean FM per TAT of 0.85 and 1.01, respectively. Regardless of adiposity, there was a considerable inter-individual variance of FM/TAT-ratio (FM4C/TAT-ratio: 0.77–0.94; FMQMR/TAT-ratio: 0.89–1.10). Both, FM4Cand TAT were associated with metabolic risks. Further, FM4C/TAT-ratio was positively related to leptin but inversely with CRP. There was no association between FM4C/TAT-ratio and VAT/SAT or liver fat. FM4C/TAT-ratio added to the variance of leptin and CRP. Independent of FM or TAT, FM4C/TAT-ratio adds to metabolic risk assessment. Therefore, the interchangeable use of FM and TAT to assess metabolic risks is questionable as both parameters may complement each other.

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.

It seems reasonable that overweight and obesity should be defined based on body composition rather than indirect indices like BMI or waist circumference. The use of conventional parameters like fat mass or visceral fat is however of similar and limited value for disease risk prediction at the population level and does not contribute much beyond the use of simple BMI or waist circumference. This conundrum may be partly explained by using complex phenotypes (e.g., Metabolic Syndrome or whole body insulin resistance) rather than more disease-specific outcomes like liver- and muscle insulin resistance. In addition, there are multifactorial causes of similar body composition phenotypes that may add to explain the variance in metabolic consequences of these phenotypes. An intriguing hypothesis is that fat mass represents the metabolic load that interacts with fat-free mass that stands for metabolic capacity to determine disease risk. This concept has important implications for assessment of healthy growth and development and when it is challenged with weight gain in adults. Integration of body composition information at the whole body, organ-tissue and cellular level is not required to improve the diagnosis of obesity but facilitates a better understanding of the etiology of obesity-associated metabolic complications.

Objective: Overfeeding with a high-fat and/or high-carbohydrate (CHO) diet is known to increase plasma concentrations of endotoxin (lipopolysaccharide [LPS]) that may lead to metabolic disturbances like insulin resistance. The impact of CHO quality (i.e., the glycemic index [GI]) independent of fat intake on metabolic endotoxemia remains unclear. In the present study, the effects of changes in energy balance and GI on plasma endotoxin were studied.Methods: Fifteen healthy young men overconsumed diets containing 65% CHO and 20% fat for 1 week (OF; +50% of energy requirement) followed by 3 weeks of caloric restriction (CR; −50% of energy requirement) and were then randomized to 2 weeks hypercaloric refeeding (RF, +50% of energy requirement) with either a low- or high-GI (40 vs 74) diet.Results: During OF, subjects gained 1.9 ± 0.7 kg body weight (+0.6 ± 0.8% fat mass) followed by a weight loss of 6.1 ± 0.8 kg (−2.0 ± 0.6% fat mass) and weight regain of 4.0 ± 0.6 kg (0.9 ± 0.8% fat mass). Fasting insulin and homeostasis model assessment–insulin resistance (HOMAIR) increased with OF and RF and decreased with CR, MatsudaISIdecreased by 37% after RF (all p< 0.05). Endotoxin significantly increased by 30.8% with OF and by 24.7% with RF (both p< 0.05), whereas CR normalized endotoxin levels. No difference in endotoxin levels was observed between refeeding a hypercaloric high- or low-GI diet. Changes in endotoxin levels with RF were not related to changes in insulin sensitivity.Conclusion: A hypercaloric diet (OF and RF) increased plasma endotoxin irrespective of GI, whereas a negative energy balance did not reduce endotoxemia. Impaired insulin sensitivity with hypercaloric refeeding on a high-GI diet was not explained by metabolic endotoxemia.

Research on obesity treatment has shifted its focus from weight loss to weight-loss maintenance strategies. The conventional approach of a low-fat diet is challenged by insights from glycemic effects of carbohydrates on body weight regulation.

The impact of weight cycling on body composition and metabolic risk remains controversial. Very few studies, however, meet the methodological requirements to analyze and normalize changes in body composition with weight loss and regain.

To present recent evidence on organ and tissue metabolic rates in humans to explain the variance in resting energy expenditure (REE).

The assessment of extra-, intracellular and total body water (ECW, ICW, TBW) is important in many clinical situations. Bioimpedance spectroscopy (BIS) has advantages over dilution methods in terms of usability and reproducibility, but a careful analysis reveals systematic deviations in extremes of body composition and morbid states. Recent publications stress the need to set up and validate BIS equations in a wide variety of healthy subjects and patients with fluid imbalance. This paper presents two new equations for determination of ECW and ICW (referred to as body composition spectroscopy, BCS) based on Hanai mixture theory but corrected for body mass index (BMI). The equations were set up by means of cross validation using data of 152 subjects (120 healthy subjects, 32 dialysis patients) from three different centers. Validation was performed against bromide/deuterium dilution (NaBr, D2O) for ECW/TBW and total body potassium (TBK) for ICW. Agreement between BCS and the references (all subjects) was ?0.4 ± 1.4 L (mean ± SD) for ECW, 0.2 ± 2.0 L for ICW and ?0.2 ± 2.3 L for TBW. The ECW agreement between three independent reference methods (NaBr versus D2O?TBK) was ?0.1 ± 1.8 L for 74 subjects from two centers. Comparing the new BCS equations with the standard Hanai approach revealed an improvement in SEE for ICW and TBW by 0.6 L (24%) for all subjects, and by 1.2 L (48%) for 24 subjects with extreme BMIs (<20 and >30). BCS may be an appropriate method for body fluid volume determination over a wide range of body compositions in different states of health and disease.

Objective: We aimed to define the effect of l-3,5,3′-tri-iodothyronine (T3) on metabolic adaptation in underweight patients with anorexia nervosa (AN) as well as during weight gain.Methods: This involved clinical investigation of 28 underweight patients with AN, who were compared with 49 normal-weight controls. A subgroup of 17 patients was followed during weight gain. Resting energy expenditure was measured by indirect calorimetry. Body composition was measured by anthropometry as well as bioelectrical impedance analysis. Energy intake (EI) was assessed by a 3-day dietary record. Plasma concentrations of thyroid hormones (thyroxine (T4), T3and thyrotropin (TSH)) were analyzed by enzyme immunoassays.Results: When compared with normal-weight women, underweight patients with AN had reduced fat mass (FM) (−71.3%), fat-free mass (FFM) (−13.1%), resting energy expenditure (REE) (−21.8%), T3- (−33.4%) and T4-concentrations (−19.8%) at unchanged TSH. REE remained reduced after adjustment for FFM (−24.6%). T3showed a close association with REE. This association remained after adjustment of REE for FFM. Treatment of underweight AN patients resulted in a mean weight gain of 8.3 kg. This was mainly explained by an increase in FM with small or no changes in FFM. REE and T3also increased (+9.3% and +33.3% respectively) at unchanged TSH and T4. There was a highly significant association between weight gain-induced changes in T3and changes in adjusted REE (r= 0.78, P< 0.001, based on Pearson’s correlation). An increase in plasma T3concentrations of 1.8 pmol/l could explain an increase in REE of 0.6 MJ/day (that is, a 32% increase in T3was associated with a 13% increase in REE).Conclusions: Our data provide evidence that the low T3concentrations add to metabolic adaptation in underweight patients with AN. During weight gain, increases in T3are associated with increases in REE, which is independent of FFM. Both results are evidence for a physiologic role of T3in modulation of energy expenditure in humans.

This is a review on recent studies regarding methodological aspects of assessment of energy expenditure in children and adolescents.