Human energy expenditure: advances in organ-tissue prediction models.

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.

Pathways and mechanisms linking dietary components to cardiometabolic disease: thinking beyond calories.

Calories from any food have the potential to increase risk for obesity and cardiometabolic disease because all calories can directly contribute to positive energy balance and fat gain. However, various dietary components or patterns may promote obesity and cardiometabolic disease by additional mechanisms that are not mediated solely by caloric content. Researchers explored this topic at the 2017 CrossFit Foundation Academic Conference 'Diet and Cardiometabolic Health - Beyond Calories', and this paper summarizes the presentations and follow-up discussions. Regarding the health effects of dietary fat, sugar and non-nutritive sweeteners, it is concluded that food-specific saturated fatty acids and sugar-sweetened beverages promote cardiometabolic diseases by mechanisms that are additional to their contribution of calories to positive energy balance and that aspartame does not promote weight gain. The challenges involved in conducting and interpreting clinical nutritional research, which preclude more extensive conclusions, are detailed. Emerging research is presented exploring the possibility that responses to certain dietary components/patterns are influenced by the metabolic status, developmental period or genotype of the individual; by the responsiveness of brain regions associated with reward to food cues; or by the microbiome. More research regarding these potential 'beyond calories' mechanisms may lead to new strategies for attenuating the obesity crisis.

Quantification of whole-body and segmental skeletal muscle mass using phase-sensitive 8-electrode medical bioelectrical impedance devices.

BACKGROUND/OBJECTIVES: Bioelectrical impedance analysis (BIA) provides noninvasive measures of skeletal muscle mass (SMM) and visceral adipose tissue (VAT). This study (i) analyzes the impact of conventional wrist-ankle vs segmental technology and standing vs supine position on BIA equations and (ii) compares BIA validation against magnetic resonance imaging (MRI) and dual X-ray absorptiometry (DXA). SUBJECTS/METHODS: One hundred and thirty-six healthy Caucasian adults (70 men, 66 women; age 40±12 years) were measured by a phase-sensitive multifrequency BIA (seca medical body composition analyzers 515 and 525). Multiple stepwise regression analysis was used to generate prediction equations. Accuracy was tested vs MRI or DXA in an independent multiethnic population. RESULTS: Variance explained by segmental BIA equations ranged between 97% for total SMMMRI, 91-94% for limb SMMMRI and 80-81% for VAT with no differences between supine and standing position. When compared with segmental measurements using conventional wrist-ankle technology. the relationship between measured and predicted SMM was slightly deteriorated (r=0.98 vs r=0.99, P<0.05). Although BIA results correctly identified ethnic differences in muscularity and visceral adiposity, the comparison of bias revealed some ethnical effects on the accuracy of BIA equations. The differences between LSTDXA and SMMMRI at the arms and legs were sizeable and increased with increasing body mass index. CONCLUSIONS: A high accuracy of phase-sensitive BIA was observed with no difference in goodness of fit between different positions but an improved prediction with segmental compared with conventional wrist-ankle measurement. A correction factor for certain ethnicities may be required. When compared with DXA MRI-based BIA equations are more accurate for predicting muscle mass.

Abdominal fat distribution differently affects muscle strength of the upper and lower extremities in women.

BACKGROUND/OBJECTIVES: In obese subjects, reduced muscle mass and strength are associated with impaired functional and metabolic capacity. We therefore investigated the association between abdominal fat distribution, fat mass index and maximum muscle strength of upper and lower extremities in obese patients. SUBJECTS/METHODS: One hundred and fifty five outpatients with a body mass index (BMI)⩾30 kg/m2 (105 women; 45.1±14.6 years; BMI 43.5±8.2 kg/m2) were included in the study. Grip and knee extension strength were measured with dynamometers. Using bioelectric impedance analysis, fat-free mass and fat mass indices (FFMI, FMI) were calculated. The influence of age, weight, FFMI, FMI and waist-to-hip ratio (WHR) on grip and knee extension strength was investigated in a risk factor-adjusted regression analysis. RESULTS: BMI did not correlate with hand grip or knee extension strength. The regression model confirmed a positive effect of higher weight on strength parameters; however, increased FMI was shown to have a negative effect on strength in both sexes (women, knee: ß: -1.495, confidence interval (CI): -2.075 to -0.0914, P<0.0001; women, hand: ß: -0.714, CI: -1.156 to -0.273, P=0.002; men, hand: ß:-1.448, CI: -2.618 to -0.278, P=0.016). Although increased WHR positively influenced knee extension strength (ß: 24.286, CI: 0.728-47.844, P<0.043), it did not affect grip strength in women. This association was not seen in men. CONCLUSIONS: Body fat distribution rather than BMI alone needs to be considered when evaluating strength parameters in obesity. As the relationship between obesity and strength of the upper versus lower extremities differs, grip strength cannot be considered an indicator of whole body strength in obese individuals.

Impact of dietary glycemic challenge on fuel partitioning.

The 'carbohydrate-insulin theory of obesity' is used to justify popular health claims stating that carbohydrates make you fat or a high glycemic load and consumption of sugar-sweetened beverages (SSBs) and breakfast skipping increase fat gain. According to this theory, the elevated postprandial insulin secretion to a high glycemic challenge is blamed as a causal mechanism by directing nutrients away from oxidation in muscle towards storage in adipose tissue. Scientific evidence is however largely disagreeing with an adverse effect of postprandial hyperinsulinemia on fuel partitioning. Possible reasons for this disagreement are differences in insulin sensitivity and energy balance. Diet-induced hyperinsulinemia may lead to a higher fat storage only at a positive energy balance. A shift in fuel partitioning towards fat storage requires improved or maintained insulin sensitivity in adipose tissue when compared with skeletal muscle. This may be the case during refeeding (after weight loss), physical inactivity or in metabolically healthy obese subjects (relative to insulin-resistant subjects). The adverse effect of a high-glycemic diet, SSBs consumption or breakfast skipping on body weight is likely due to increased energy consumption rather than to increased fat storage.

Effect of low-glycemic-sugar-sweetened beverages on glucose metabolism and macronutrient oxidation in healthy men.

BACKGROUND/OBJECTIVES: Sugar-sweetened-beverages (SSB) provide high amounts of rapidly absorbable sugar and have been shown to impair insulin sensitivity and promote weight gain. We hypothesized that when compared with high-glycemic index (GI) SSB low-GI SSB lead to lower insulin secretion and thus an improved preservation of insulin sensitivity and fat oxidation during an inactive phase. SUBJECTS/METHODS: In a controlled cross-over dietary intervention 13 healthy men (age: 23.7±2.2 years, body mass index: 23.6±1.9 kg m(-)(2)) consumed low-GI (isomaltulose) or high-GI (75% maltodextrin+25% sucrose, adapted for sweetness) SSBs providing 20% of energy requirement for 7 days. During this phase, participant's habitual high physical activity (11 375±3124 steps per day) was reduced (2363±900 steps per day). The provided ad libitum diet comprised 55% CHO, 30% fat and 15% protein. Glycemic and insulinemic responses were assessed: Day-long (7-day continuous interstitial glucose monitoring, 24-h-urinary c-peptide excretion), during meal test (37 g isomaltulose vs 28 g maltodextrin+9g sucrose) and measures of insulin sensitivity (basal: homeostasis model assessment of insulin resistance (HOMA-IR), postprandial: Matsuda-ISI). Macronutrient oxidation was assessed by non-protein respiratory quotient (npRQ) in the fasted state (npRQfasting) and postprandial as the area under the npRQ-curve during meal test (npRQtAUC-meal). RESULTS: Day-long glycemia was lower with low-GI compared with high-GI SSB (-5%, P<0.05). Low-GI SSB led to lower insulin secretion during meal test (-28%, P<0.01) and throughout the day (-31%, P<0.01), whereas postprandial glucose levels did not differ between low-GI and high-GI SSBs. Insulin sensitivity deteriorated on inactivity with both SSBs, but was better preserved with low-GI isomaltulose compared with high-GI maltodextrin-sucrose (ΔHOMA-IR: +0.37±0.52 vs +0.85±0.86; ΔMatsuda-ISI: -5.1±5.5 vs -9.6±5.1, both P<0.05). Both, fasting and postprandial fat oxidation declined on inactivity, with no difference between high-GI and low-GI SSBs. CONCLUSIONS: Compared with high-GI SSB, 7-day consumption of beverages sweetened with low-GI isomaltulose had beneficial effects on inactivity-induced impairment of glucose metabolism without effecting fuel selection.

Deep body composition phenotyping during weight cycling: relevance to metabolic efficiency and metabolic risk.

Weight cycling may lead to adverse effects on metabolic efficiency (i.e. adaptive thermogenesis or 'metabolic slowing') and metabolic risks (e.g. increased risk for insulin resistance and the metabolic syndrome). In order to investigate these topics, the partitioning of fat and lean mass (i.e. the change in the proportion of both compartments) needs to be extended to the organ and tissue level because metabolic risk differs between adipose tissue depots and lean mass is metabolically heterogeneous being composed of organs and tissues differing in metabolic rate. Contrary to data obtained with severe weight loss and regain in lean people, weight cycling most likely has no adverse effects on fat distribution and metabolic risk in obese patients. There is even evidence for an increased ability of fat storage in subcutaneous fat depots (at the trunk in men and at the limbs in women) with weight cycling that may provide a certain protection from ectopic lipid deposition and thus explain the preservation of a favourable metabolic profile despite weight regain. On the other hand, the mass-specific metabolic rate of lean mass may increase with weight gain and decrease with weight loss mainly because of an increase and respective decrease in the proportion (and/or activity) of metabolically active organ mass. Obese people could therefore have a higher slope of the regression line between resting energy expenditure (REE) and fat-free mass that leads to an overestimation of metabolic efficiency when applied to normalize REE data after weight loss. Furthermore, in addressing the impact of macronutrient composition of the diet on partitioning of lean and fat mass, and the old controversy about whether a calorie is a calorie, we discuss recent evidence in support of a low glycaemic weight maintenance diet in countering weight regain and challenge this concept for weight loss by proposing the opposite.

Identification of skeletal muscle mass depletion across age and BMI groups in health and disease--there is need for a unified definition.

Although reduced skeletal muscle mass is a major predictor of impaired physical function and survival, it remains inconsistently diagnosed to a lack of standardized diagnostic approaches that is reflected by the variable combination of body composition indices and cutoffs. In this review, we summarized basic determinants of a normal lean mass (age, gender, fat mass, body region) and demonstrate limitations of different lean mass parameters as indices for skeletal muscle mass. A unique definition of lean mass depletion should be based on an indirect or direct measure of skeletal muscle mass normalized for height (fat-free mass index (FFMI), appendicular or lumbal skeletal muscle index (SMI)) in combination with fat mass. Age-specific reference values for FFMI or SMI are more advantageous because defining lean mass depletion on the basis of total FFMI or appendicular SMI could be misleading in the case of advanced age due to an increased contribution of connective tissue to lean mass. Mathematical modeling of a normal lean mass based on age, gender, fat mass, ethnicity and height can be used in the absence of risk-defined cutoffs to identify skeletal muscle mass depletion. This definition can be applied to identify different clinical phenotypes like sarcopenia, sarcopenic obesity or cachexia.

Assessment and definition of lean body mass deficiency in the elderly.

Although the effect of age on body composition has been intensively discussed during the past 20 years, we do not have a uniform definition of sarcopenia. A suitable definition of low, lean body mass should be based on magnetic resonance imaging (MRI) estimates of muscle mass. Using recent MRI data of a population of 446 healthy free-living Caucasian volunteers (247 females, 199 males) age 18-78 years, a low skeletal muscle mass and sarcopenia were defined as a skeletal muscle mass >1 and >2 s.d. below the mean value obeserved in younger adults at age 18-39 years. The cutoffs for low muscle mass according to the skeletal muscle index (skeletal muscle mass/(height)(2)) or the appendicular skeletal muscle mass index (skeletal muscle mass of the limbs/(height)(2)) were 6.75 or 4.36 kg/m(2) for females and 8.67 or 5.54 kg/m(2) for males, respectively. On the basis of these cutoffs, prevalences of sarcopenia in the group of adults at >60 years are calculated to be 29% in females and 19.0% in males. Faced with different sarcopenic phenotypes (that is, sarcopenia related to frailty and osteopenia; sarcopenic obesity related to metabolic risks; cachexia related to wasting diseases), future definitions of sarcopenia should be extended to the relations between (i) muscle mass and adipose tissue and (ii) muscle mass and bone mass. Suitable cutoffs should be based on the associations between estimates of body compositions and metabolic risks (for axample, insulin resistance), inflammation and muscle function (that is, muscle strength).

Carbohydrate intake and glycemic index affect substrate oxidation during a controlled weight cycle in healthy men.

BACKGROUND/OBJECTIVES: Because both, glycemic index (GI) and carbohydrate content of the diet increase insulin levels and could thus impair fat oxidation, we hypothesized that refeeding a low GI, moderate-carbohydrate diet facilitates weight maintenance. SUBJECTS/METHODS: Healthy men (n=32, age 26.0±3.9 years; BMI 23.4±2.0 kg/m(2)) followed 1 week of controlled overfeeding, 3 weeks of caloric restriction and 2 weeks of hypercaloric refeeding (+50, -50 and +50% energy requirement) with low vs high GI (41 vs 74) and moderate vs high CHO intake (50% vs 65% energy). We measured adaptation of fasting macronutrient oxidation and the capacity to supress fat oxidation during an oral glucose tolerance test. Changes in fat mass were measured by quantitative magnetic resonance. RESULTS: During overfeeding, participants gained 1.9±1.2 kg body weight, followed by a weight loss of -6.3±0.6 kg and weight regain of 2.8±1.0 kg. Subjects with 65% CHO gained more body weight compared with 50% CHO diet (P<0.05) particularly with HGI meals (P<0.01). Refeeding a high-GI diet led to an impaired basal fat oxidation when compared with a low-GI diet (P<0.02), especially at 65% CHO intake. Postprandial metabolic flexibility was unaffected by refeeding at 50% CHO but clearly impaired by 65% CHO diet (P<0.05). Impairment in fasting fat oxidation was associated with regain in fat mass (r=0.43, P<0.05) and body weight (r=0.35; P=0.051). CONCLUSIONS: Both higher GI and higher carbohydrate content affect substrate oxidation and thus the regain in body weight in healthy men. These results argue in favor of a lower glycemic load diet for weight maintenance after weight loss.

Functional body composition and related aspects in research on obesity and cachexia: report on the 12th Stock Conference held on 6 and 7 September 2013 in Hamburg, Germany.

The 12th Stock Conference addressed body composition and related functions in two extreme situations, obesity and cancer cachexia. The concept of 'functional body composition' integrates body components into regulatory systems relating the mass of organs and tissues to corresponding in vivo functions and metabolic processes. This concept adds to an understanding of organ/tissue mass and function in the context of metabolic adaptations to weight change and disease. During weight gain and loss, there are associated changes in individual body components while the relationships between organ and tissue mass are fixed. Thus an understanding of body weight regulation involves an examination of the relationships between organs and tissues rather than individual organ and tissue masses only. The between organ/tissue mass relationships are associated with and explained by crosstalks between organs and tissues mediated by cytokines, hormones and metabolites that are coupled with changes in body weight, composition and function as observed in obesity and cancer cachexia. In addition to established roles in intermediary metabolism, cell function and inflammation, organ-tissue crosstalk mediators are determinants of body composition and its change with weight gain and loss. The 12th Stock Conference supported Michael Stocks' concept of gaining new insights by integrating research ideas from obesity and cancer cachexia. The conference presentations provide an in-depth understanding of body composition and metabolism.

Adiposity rebound is misclassified by BMI rebound.

BACKGROUND/OBJECTIVES: Adiposity rebound (AR) is defined as the nadir or the inflexion point of body mass index (BMI) percentiles between the age of 3 and 7 years. An early rebound is seen as a risk of obesity and, thus, AR is considered as a suitable time period for prevention. As BMI does not reflect body composition, we aimed to examine the rebounds of fat mass index (FMI) and fat-free mass index (FFMI) together with BMI. SUBJECTS/METHODS: Cross-sectional data of 19 264 children aged 3-11 years were pooled from three German studies (Kiel Obesity Prevention Study, the project 'Better diet. More exercise. KINDERLEICHT-REGIONS' and regular examinations of Jena children). Height and weight were measured. Fat mass (FM) and fat-free mass (FFM) were obtained from bioelectrical impedance analysis and analysed using a population-specific algorithm. Percentiles of BMI, FMI and FFMI were constructed by the LMS method. RESULTS: Both BMI and FMI percentiles showed a rebound, whereas FFMI percentiles steadily increased with age. On P90, FMI rebound was about 1.6-1.8 years later compared with that of BMI, that is, at ages 4.2 years (BMI) and 5.8 years (FMI) in boys and at 4.2 years (BMI) and 6.0 years (FMI) in girls. At AR, the slope of the BMI-P90 was explained by increases in FFMI rather than FMI. By contrast, at FMI rebound, the slope of BMI was strongly related to FMI. CONCLUSIONS: BMI rebound does not equal the rebound of FM. At AR, the slope in BMI is determined by the increase in FFMI. AR should be defined as FMI rebound rather than BMI rebound.

Impact of body-composition methodology on the composition of weight loss and weight gain.

BACKGROUND/OBJECTIVES: We intended to (i) to compare the composition of weight loss and weight gain using densitometry, deuterium dilution (D2O), dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI) and the four-compartment (4C) model and (ii) to compare regional changes in fat mass (FM), fat-free mass (FFM) and skeletal muscle as assessed by DXA and MRI. SUBJECTS/METHODS: Eighty-three study participants aged between 21 and 58 years with a body mass index range of 20.2-46.8 kg/m(2) had been assessed at two different occasions with a mean follow-up between 23.5 and 43.5 months. Body-weight changes within < 3% were considered as weight stable, a gain or a loss of >3% of initial weight was considered as a significant weight change. RESULTS: There was a considerable bias between the body-composition data obtained by the individual methods. When compared with the 4C model, mean bias of D2O and densitometry was explained by the erroneous assumption of a constant hydration of FFM, thus, changes in FM were underestimated by D2O but overestimated by densitometry. Because hydration does not normalize after weight loss, all two-component models have a systematic error in weight-reduced subjects. The bias between 4C model and DXA was mainly explained by FM% at baseline, whereas FFM hydration contributed to additional 5%. As to the regional changes in body composition, DXA data had a considerable bias and, thus, cannot replace MRI. CONCLUSIONS: To assess changes in body composition associated with weight changes, only the 4C model and MRI can be used with confidence.

Adaptive thermogenesis with weight loss in humans.

UNLABELLED: Adaptive thermogenesis (AT) with weight loss refers to underfeeding-associated fall in resting and non-resting energy expenditure (REE, non-REE); this is independent of body weight and body composition. In humans, the existence of AT was inconsistently shown and its clinical significance has been questioned. OBJECTIVES: Discrepant findings are mainly due to different definitions of AT, the use of various and nonstandardized study protocols, and the limits of accuracy of methods to assess energy expenditure. With controlled underfeeding, AT takes more than 2 wk to develop. AT accounts to an average of 0.5 MJ (or 120 kcal) with a considerable between subject variance. DESIGN AND METHODS: Low-sympathetic nervous system activity, 3,5,3'-tri-iodothyronine (T3) and leptin are likely to add to AT; however, the kinetic changes of their plasma levels with underfeeding differ from the time course of AT and controlled intervention studies substituting and titrating these hormones are rare in humans. AT in response to underfeeding is independent of thermogenesis in response to either diet or cold. Although fat-free mass (FFM) and, thus, liver, and skeletal muscle are considered as major sites of AT, cold-induced nonshivering thermogenesis relates to the metabolism of brown adipose tissue (BAT). In humans, diet-induced thermogenesis is related to postprandial substrate metabolism of FFM with a questionable role of BAT. Obviously, the REE component of AT differs from and its non-REE component with respect to organ contribution as well as mechanisms. Thus, AT cannot be considered as unique. CONCLUSIONS: AT should be characterized based on individual components of daily energy expenditure, detailed body composition analyses, and mathematical modeling. The biological basis of AT as well as the influences of age, sex, obesity, stress, and inflammation remain to be established in humans.

Effect of weight loss and regain on adipose tissue distribution, composition of lean mass and resting energy expenditure in young overweight and obese adults.

BACKGROUND: Although weight cycling is frequent in obese patients, the adverse consequences on body composition and an increased propensity to weight gain remain controversial. OBJECTIVE: We investigated the effect of intentional weight loss and spontaneous regain on fat distribution, the composition of lean mass and resting energy expenditure (REE). DESIGN: Weight regainers (≥ 30% of loss, n=27) and weight-stable subjects (within <± 20% of weight change, n=20) were selected from 103 overweight and obese subjects (body mass index 28-43 kg m(-2), 24-45 years) who passed a 13-week low-calorie diet intervention. REE and body composition (by densitometry and whole-body magnetic resonance imaging) were examined at baseline, after weight loss and at 6 months of follow-up. RESULTS: Mean weight loss was -12.3 ± 3.3 kg in weight-stable subjects and -9.0 ± 4.3 kg in weight regainers (P<0.01). Weight regain was incomplete, accounting for 83 and 42% of weight loss in women and men. Regain in total fat and different adipose tissue depots was in proportion to weight regain except for a higher regain in adipose tissue of the extremities in women and a lower regain in extremity and visceral adipose tissue in men. In both genders, regain in skeletal muscle of the trunk lagged behind skeletal muscle regain at the extremities. In contrast to weight-stable subjects, weight regainers showed a reduced REE adjusted for changes in organ and tissue masses after weight loss (P<0.001). CONCLUSION: Weight regain did not adversely affect body fat distribution. Weight loss-associated adaptations in REE may impair weight loss and contribute to weight regain.

What makes a BIA equation unique? Validity of eight-electrode multifrequency BIA to estimate body composition in a healthy adult population.

BACKGROUND/OBJECTIVES: The validity of bioelectrical impedance analysis (BIA) for body composition analysis is limited by assumptions relating to body shape. Improvement in BIA technology could overcome these limitations and reduce the population specificity of the BIA algorithm. SUBJECTS/METHODS: BIA equations for the prediction of fat-free mass (FFM), total body water (TBW) and extracellular water (ECW) were generated from data obtained on 124 Caucasians (body mass index 18.5-35 kg/m(2)) using a four-compartment model and dilution techniques as references. The algorithms were validated in an independent multiethnic population (n=130). The validity of BIA results was compared (i) between ethnic groups and (ii) with results from the four-compartment model and two-compartment methods (air-displacement plethysmography, dual-energy X-ray absorptiometry and deuterium dilution). RESULTS: Indices were developed from segmental R and Xc values to represent the relative contribution of trunk and limbs to total body conductivity. The coefficient of determination for all prediction equations was high (R(2): 0.94 for ECW, 0.98 for FFM and 0.98 for TBW) and root mean square error was low (1.9 kg for FFM, 0.8 l for ECW and 1.3 kg for TBW). The bias between BIA results and different reference methods was not statistically different between Afro-American, Hispanic, Asian or Caucasian populations and showed a similar difference (-0.2-0.2 kg FFM) when compared with the bias between different two-compartment reference methods (-0.2-0.3 kg FFM). CONCLUSIONS: An eight-electrode, segmental multifrequency BIA is a valid tool to estimate body composition in healthy euvolemic adults compared with the validity and precision of other two-compartment reference methods. Population specificity is of minor importance when compared with discrepancies between different reference methods.

Total and regional relationship between lean and fat mass with increasing adiposity--impact for the diagnosis of sarcopenic obesity.

BACKGROUND/OBJECTIVE: Besides the effect of age used to define sarcopenia, there is need to understand the impact of adiposity on the relationship between lean (fat-free mass, FFM) and fat mass (FM) in order to diagnose sarcopenic obese phenotypes. More importantly, the regional distribution of skeletal muscle (SM) to adipose tissue (AT) or the composition of FFM (that is, SM proportion of lean mass) may also depend on adiposity. SUBJECTS/METHODS: In a large database (n=1737) of healthy males and females (age 11-84 years, BMI 13.5-52.5 kg/m(2)) we investigated changes in the relationship between FFM and FM (normalized by height as fat-free mass index and fat mass index: FFMI and FMI, kg/m(2) assessed by densitometry) with increasing adiposity and age. In a subgroup (n=263) we analyzed the relationship between regional SM and (i) AT (by magnetic resonance imaging) or (ii) lean soft tissue (by dual X-ray absorptiometry) with increasing adiposity. RESULTS: The relationship between lean and FM was influenced by adiposity, age and gender. With increasing adiposity, SM/AT declined faster at the trunk in men and at the extremities in women. The contribution of appendicular SM to lean soft tissue of arms and legs tended to decrease at a higher adiposity in both genders (FMI >6.97 kg/m(2) in women; FMI>7.77 kg/m(2) in men). CONCLUSION: Besides age and gender, adiposity and body region should be considered when evaluating the normal relationship between lean and FM, SM/FFM and SM/AT.

Beyond the body mass index: tracking body composition in the pathogenesis of obesity and the metabolic syndrome.

Body composition is related to various physiological and pathological states. Characterization of individual body components adds to understand metabolic, endocrine and genetic data on obesity and obesity-related metabolic risks, e.g. insulin resistance. The obese phenotype is multifaceted and can be characterized by measures of body fat, leg fat, liver fat and skeletal muscle mass rather than by body mass index. The contribution of either whole body fat or fat distribution or individual fat depots to insulin resistance is moderate, but liver fat has a closer association with (hepatic) insulin resistance. Although liver fat is associated with visceral fat, its effect on insulin resistance is independent of visceral adipose tissue. In contrast to abdominal fat, appendicular or leg fat is inversely related to insulin resistance. The association between 'high fat mass + low muscle mass' (i.e. 'sarcopenic adiposity') and insulin resistance deserves further investigation and also attention in daily clinical practice. In addition to cross-sectional data, longitudinal assessment of body composition during controlled under- and overfeeding of normal-weight healthy young men shows that small decreases and increases in fat mass are associated with corresponding decreases and increases in insulin secretion as well as increases and decreases in insulin sensitivity. However, even under controlled conditions, there is a high intra- and inter-individual variance in the changes of (i) body composition; (ii) the 'body composition-glucose metabolism relationship' and (iii) glucose metabolism itself. Combining individual body components with their related functional aspects (e.g. the endocrine, metabolic and inflammatory profiles) will provide a suitable basis for future definitions of a 'metabolically healthy body composition'.

Evolving concepts on adjusting human resting energy expenditure measurements for body size.

Establishing if an adult's resting energy expenditure (REE) is high or low for their body size is a pervasive question in nutrition research. Early workers applied body mass and height as size measures and formulated the Surface Law and Kleiber's Law, although each has limitations when adjusting REE. Body composition methods introduced during the mid-20th century provided a new opportunity to identify metabolically homogeneous 'active' compartments. These compartments all show improved correlations with REE estimates over body mass-height approaches, but collectively share a common limitation: REE-body composition ratios are not 'constant' but vary across men and women and with race, age and body size. The now-accepted alternative to ratio-based norms is to adjust for predictors by applying regression models to calculate 'residuals' that establish if an REE is relatively high or low. The distinguishing feature of statistical REE-body composition models is a 'non-zero' intercept of unknown origin. The recent introduction of imaging methods has allowed development of physiological tissue-organ-based REE prediction models. Herein, we apply these imaging methods to provide a mechanistic explanation, supported by experimental data, for the non-zero intercept phenomenon and, in that context, propose future research directions for establishing between-subject differences in relative energy metabolism.