Determinants of bone mass in older adults with normal- and overweight derived from the crosstalk with muscle and adipose tissue.

Lower bone mass in older adults may be mediated by the endocrine crosstalk between muscle, adipose tissue and bone. In 150 community-dwelling adults (59-86 years, BMI 17-37 kg/m2; 58.7% female), skeletal muscle mass index, adipose tissue and fat mass index (FMI) were determined. Levels of myokines, adipokines, osteokines, inflammation markers and insulin were measured as potential determinants of bone mineral content (BMC) and density (BMD). FMI was negatively associated with BMC and BMD after adjustment for mechanical loading effects of body weight (r-values between -0.37 and -0.71, all p < 0.05). Higher FMI was associated with higher leptin levels in both sexes, with higher hsCRP in women and with lower adiponectin levels in men. In addition to weight and FMI, sclerostin, osteocalcin, leptin × sex and adiponectin were independent predictors of BMC in a stepwise multiple regression analysis. Muscle mass, but not myokines, showed positive correlations with bone parameters that were weakened after adjusting for body weight (r-values between 0.27 and 0.58, all p < 0.01). Whereas the anabolic effect of muscle mass on bone in older adults may be partly explained by mechanical loading, the adverse effect of obesity on bone is possibly mediated by low-grade inflammation, higher leptin and lower adiponectin levels.

Analysis of the adiponectin paradox in healthy older people.

BACKGROUND: It remains unknown why adiponectin levels are associated with poor physical functioning, skeletal muscle mass and increased mortality in older populations. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Issues related to the assessment of energy balance during short-term over-, under- and refeeding in normal weight men.

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. (Lancet 2011; 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 < 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.

What Is a 2021 Reference Body?

The historical 1975 Reference Man is a 'model' that had been used as a basis for the calculation of radiation doses, metabolism, pharmacokinetics, sizes for organ transplantation and ergonomic optimizations in the industry, e.g., to plan dimensions of seats and other formats. The 1975 Reference Man was not an average individual of a population; it was based on the multiple characteristics of body compositions that at that time were available, i.e., mainly from autopsy data. Faced with recent technological advances, new mathematical models and socio-demographic changes within populations characterized by an increase in elderly and overweight subjects a timely 'state-of-the-art' 2021 Reference Body are needed. To perform this, in vivo human body composition data bases in Kiel, Baton Rouge, San Francisco and Honolulu were analyzed and detailed 2021 Reference Bodies, and they were built for both sexes and two age groups (≤40 yrs and >40 yrs) at BMIs of 20, 25, 30 and 40 kg/m2. We have taken an integrative approach to address 'structure−structure' and 'structure−function' relationships at the whole-body level using in depth body composition analyses as assessed by gold standard methods, i.e., whole body Magnetic Resonance Imaging (MRI) and the 4-compartment (4C-) model (based on deuterium dilution, dual-energy X-ray absorptiometry and body densitometry). In addition, data obtained by a three-dimensional optical scanner were used to assess body shape. The future applications of the 2021 Reference Body relate to mathematical modeling to address complex metabolic processes and pharmacokinetics using a multi-level/multi-scale approach defining health within the contexts of neurohumoral and metabolic control.

Configuration of bioelectrical impedance measurements affects results for phase angle.

OBJECTIVE: Phase angle (PhA) obtained by bioelectrical impedance analysis is a well-established predictor of malnutrition that reflects the amount and quality of soft tissue. However, PhA results may depend on configurations of the measurement that differ between devices. The aim was to analyze differences between devices for supine and standing measurements. APPROACH: In a cross-sectional study, differences in PhA were analyzed comparing supine vs. standing positions, metal vs. adhesive electrodes and the right vs. left side of the body in 302 multi-ethnic adults (18-65y) and 1298 Mexican children and adolescents (4-20y). MAIN RESULTS: PhA was higher in supine than in standing position (from 0.71°±0.22° in children to 0.97°±0.25° in adults; all p < 0.001) with approximately fifty percent of observed differences explained by electrode placement. PhA differences increased with increasing PhA (r = 0.419) and decreased with age (r = -0.346) in adults, but increased with PhA (r = 0.677), age (r = 0.752) and height (r = 0.737) in children (all p <0.001). In adults, PhA was higher on the right side of the body (standing 0.18°±0.17°; supine 0.36°±0.33°; p <0.001). SIGNIFICANCE: Phase angle results are influenced by posture and electrode placement. Measurement configuration must be considered when phase angle values are compared between different devices or with literature values.

Reference Values for Skeletal Muscle Mass - Current Concepts and Methodological Considerations.

Assessment of a low skeletal muscle mass (SM) is important for diagnosis of ageing and disease-associated sarcopenia and is hindered by heterogeneous methods and terminologies that lead to differences in diagnostic criteria among studies and even among consensus definitions. The aim of this review was to analyze and summarize previously published cut-offs for SM applied in clinical and research settings and to facilitate comparison of results between studies. Multiple published reference values for discrepant parameters of SM were identified from 64 studies and the underlying methodological assumptions and limitations are compared including different concepts for normalization of SM for body size and fat mass (FM). Single computed tomography or magnetic resonance imaging images and appendicular lean soft tissue by dual X-ray absorptiometry (DXA) or bioelectrical impedance analysis (BIA) are taken as a valid substitute of total SM because they show a high correlation with results from whole body imaging in cross-sectional and longitudinal analyses. However, the random error of these methods limits the applicability of these substitutes in the assessment of individual cases and together with the systematic error limits the accurate detection of changes in SM. Adverse effects of obesity on muscle quality and function may lead to an underestimation of sarcopenia in obesity and may justify normalization of SM for FM. In conclusion, results for SM can only be compared with reference values using the same method, BIA- or DXA-device and an appropriate reference population. Limitations of proxies for total SM as well as normalization of SM for FM are important content-related issues that need to be considered in longitudinal studies, populations with obesity or older subjects.

Limitations of Fat-Free Mass for the Assessment of Muscle Mass in Obesity.

BACKGROUND: A high amount of adipose tissue limits the accuracy of methods for body composition analysis in obesity. OBJECTIVES: The aim was to quantify and explain differences in fat-free mass (FFM) (as an index of skeletal muscle mass, SMM) measured with bioelectrical impedance analysis (BIA), dual energy X-ray absorptiometry (DXA), air displacement plethysmography (ADP), and deuterium dilution in comparison to multicompartment models, and to improve the results of BIA for obese subjects. METHODS: In 175 healthy subjects (87 men and 88 women, BMI 20-43.3 kg/m2, 18-65 years), FFM measured by these methods was compared with results from a 3- (3C) and a 4-compartment (4C) model. FFM4C was compared with SMM measured by magnetic resonance imaging. RESULTS: BIA and DXA overestimated and ADP underestimated FFM in comparison to 3C and 4C models with increasing BMI (all p < 0.001). -Differences were largest for DXA. In obesity, BIA results were improved: valuecorrected = -valueuncorrected - a(BMI - 30 kg/m2), a = 0.256 for FFM and a = 0.298 for SMM. SMM accounts for 45% of FFM in women and 49% in men. CONCLUSIONS: In obesity, the use of FFM is limited by a systematic error of reference methods. In addition, SMM accounts for about 50% of FFM only. Corrected measurement of SMM by BIA can overcome these drawbacks.

Association between fat mass, adipose tissue, fat fraction per adipose tissue, and metabolic risks: a cross-sectional study in normal, overweight, and obese adults.

BACKGROUND/OBJECTIVES: We investigated whether fat mass (FM) and total adipose tissue (TAT) can be used interchangeably and FM per TAT adds to metabolic risk assessment. SUBJECTS/METHODS: 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. RESULTS: FM4C and FMQMR were 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, FM4C and 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. CONCLUSIONS: 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.

Determinants of ectopic liver fat in metabolic disease.

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 composition and cardiometabolic health: the need for novel concepts.

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.

Normalizing resting energy expenditure across the life course in humans: challenges and hopes.

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.

The prediction of total skeletal muscle mass in a Caucasian population - comparison of Magnetic resonance imaging (MRI) and Dual-energy X-ray absorptiometry (DXA).

Dual-energy X-ray (DXA) is an alternative to magnetic resonance imaging (MRI) to measure skeletal muscle mass. DXA assesses lean body mass (LBM), and MRI measures skeletal muscle mass (SMM). Kim et al. (Am J Clin Nutr 2002; 76: 378; J Appl Physiol (1985) 2004; 97: 655) developed MRI-based algorithms to estimate whole-body SMM by DXA. These algorithms were based on an ethnically mixed study population (Kim et al., Am J Clin Nutr 2002; 76: 378; J Appl Physiol (1985) 2004; 97: 655). It is unclear whether Kim's algorithms are accurate in an exclusive Caucasian population. The aim of our study was to validate Kim's equation in a Caucasian population of 346 subjects. SMMMRI was assessed using MRI, and LBM and BMCDXA were measured by DXA and fat mass (FMADP ) by air-displacement plethysmographie (ADP). SMMMRI and predicted SMM were highly correlated (r = 0·944; P<0·05). The standard error of estimate of the regression equation was 2·4 kg. However, Bland-Altman plots showed a significant (P<0·001) systematic bias between SMMMRI (median 25·1 kg; IQ 20·2-31·1 kg) and predicted SMM (median 26·3 kg; IQ 22·6-33·0 kg), overestimating SMM by 9·8%. Multiple regression analyses showed that weight explained 4·4% of the variance in the differences between SMMMRI and predicted SMM with the major part unexplained. Kim's algorithm has a systematic unexplained bias and is not recommended in Caucasians.

Estimation of Skeletal Muscle Mass and Visceral Adipose Tissue Volume by a Single Magnetic Resonance Imaging Slice in Healthy Elderly Adults.

BACKGROUND: Assessing skeletal muscle (SM) and visceral adipose tissue (VAT) by a single MRI slice at lumbar vertebra (L) 3 can replace whole-body MRI in young and middle-aged adults. However, this technique has not been proven in older adults. OBJECTIVE: The aim of this analysis was to reinvestigate the best estimate for SM and VAT in an independent population of healthy elderly people. METHODS: SM and VAT were assessed by whole-body MRI in 84 subjects ≥60 y [45 men; mean ± SD age: 68.4 ± 5.4 y, mean ± SD body mass index (in kg/m2): 25.5 ± 3.5]. SM and VAT areas of 9 slices at the lumbar spine were analyzed. The best estimate was investigated by Pearson correlations. Total volumes (in liters) were predicted by the area at lumbar vertebra 3 (AL3). Besides Bland-Altman analysis, linear regressions were performed to explain the variance of the bias by age, height, and percentage of fat mass (%FM). In a mixed population (healthy elderly plus reference population), linear regression with total SM and VAT volume as dependent variables and AL3, age, and height as independent variables was applied. RESULTS: When comparing the correlation coefficients between the tissue areas and total volumes, L3 was identified as the best estimate (r range: 0.71-0.94; all P < 0.05). However, Bland-Altman analysis showed a positive SM bias in men (mean ± SD: -1.0% ± 9.0%; P < 0.05) and a negative SM bias in women (mean ± SD: 3.7% ± 9.6%; P < 0.05). Contrary to SM, no significant bias was observed for VAT. In the elderly, stepwise linear regression showed height as a predictor for SM bias (R2 = 0.21, SEE = 2.07 L; P < 0.05) and %FM and age as predictors of the nonsignificant VAT bias (R2 = 0.26, SEE = 0.22L, P < 0.05), in men only. In the mixed population, AL3 and height were predictors for total SM, and AL3 for total VAT, independent of sex. CONCLUSIONS: AL3 was confirmed as the best estimate for SM and VAT volumes in healthy elderly adults. Contrary to VAT, there is a bias for SM, and height has to be added to the algorithm.

Beyond BMI: Conceptual Issues Related to Overweight and Obese Patients.

BMI is widely used as a measure of weight status and disease risks; it defines overweight and obesity based on statistical criteria. BMI is a score; neither is it biologically sound nor does it reflect a suitable phenotype worthwhile to study. Because of its limited value, BMI cannot provide profound insight into obesity biology and its co-morbidity. Alternative assessments of weight status include detailed phenotyping by body composition analysis (BCA). However, predicting disease risks, fat mass, and fat-free mass as assessed by validated techniques (i.e., densitometry, dual energy X ray absorptiometry, and bioelectrical impedance analysis) does not exceed the value of BMI. Going beyond BMI and descriptive BCA, the concept of functional body composition (FBC) integrates body components into regulatory systems. FBC refers to the masses of body components, organs, and tissues as well as to their inter-relationships within the context of endocrine, metabolic and immune functions. FBC can be used to define specific phenotypes of obesity, e.g. the sarcopenic-obese patient. Well-characterized obesity phenotypes are a precondition for targeted research (e.g., on the genomics of obesity) and patient-centered care (e.g., adequate treatment of individual obese phenotypes such as the sarcopenic-obese patient). FBC contributes to a future definition of overweight and obesity based on physiological criteria rather than on body weight alone.

Age-Dependent Changes in Resting Energy Expenditure (REE): Insights from Detailed Body Composition Analysis in Normal and Overweight Healthy Caucasians.

Age-related changes in organ and tissue masses may add to changes in the relationship between resting energy expenditure (REE) and fat free mass (FFM) in normal and overweight healthy Caucasians. Secondary analysis using cross-sectional data of 714 healthy normal and overweight Caucasian subjects (age 18-83 years) with comprehensive information on FFM, organ and tissue masses (as assessed by magnetic resonance imaging (MRI)), body density (as assessed by Air Displacement Plethysmography (ADP)) and hydration (as assessed by deuterium dilution (D2O)) and REE (as assessed by indirect calorimetry). High metabolic rate organs (HMR) summarized brain, heart, liver and kidney masses. Ratios of HMR organs and muscle mass (MM) in relation to FFM were considered. REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. REE, FFM, specific metabolic rates, the REE-FFM relationship, HOMA, CRP, and thyroid hormone levels change with age. The age-related decrease in FFM explained 59.7% of decreases in REE. Mean residuals of the REE-FFM association were positive in young adults but became negative in older subjects. When compared to young adults, proportions of MM to FFM decreased with age, whereas contributions of liver and heart did not differ between age groups. HOMA, TSH and inflammation (plasma CRP-levels) explained 4.2%, 2.0% and 1.4% of the variance in the REE-FFM residuals, but age and plasma T3-levels had no effects. HMR to FFM and MM to FFM ratios together added 11.8% on to the variance of REE-FFM residuals. Differences between REE and REEc increased with age, suggesting age-related changes in specific metabolic rates of organs and tissues. This bias was partly explained by plasmaT3-levels. Age-related changes in REE are explained by (i) decreases in fat free mass; (ii) a decrease in the contributions of organ and muscle masses to FFM; and (iii) decreases in specific organ and tissue metabolic rates. Age-dependent changes in the REE-FFMassociation are explained by composition of FFM, inflammation and thyroid hormones.

Application of standards and models in body composition analysis.

The aim of this review is to extend present concepts of body composition and to integrate it into physiology. In vivo body composition analysis (BCA) has a sound theoretical and methodological basis. Present methods used for BCA are reliable and valid. Individual data on body components, organs and tissues are included into different models, e.g. a 2-, 3-, 4- or multi-component model. Today the so-called 4-compartment model as well as whole body MRI (or computed tomography) scans are considered as gold standards of BCA. In practice the use of the appropriate method depends on the question of interest and the accuracy needed to address it. Body composition data are descriptive and used for normative analyses (e.g. generating normal values, centiles and cut offs). Advanced models of BCA go beyond description and normative approaches. The concept of functional body composition (FBC) takes into account the relationships between individual body components, organs and tissues and related metabolic and physical functions. FBC can be further extended to the model of healthy body composition (HBC) based on horizontal (i.e. structural) and vertical (e.g. metabolism and its neuroendocrine control) relationships between individual components as well as between component and body functions using mathematical modelling with a hierarchical multi-level multi-scale approach at the software level. HBC integrates into whole body systems of cardiovascular, respiratory, hepatic and renal functions. To conclude BCA is a prerequisite for detailed phenotyping of individuals providing a sound basis for in depth biomedical research and clinical decision making.

Gender-Specific Associations in Age-Related Changes in Resting Energy Expenditure (REE) and MRI Measured Body Composition in Healthy Caucasians.

BACKGROUND: The effect of gender as well as gender-specific changes of fat free mass (FFM) and its metabolic active components (muscle mass and organ masses [OMs]) and fat mass (FM) on age-related changes in resting energy expenditure (REE) are not well defined. We hypothesized that there are gender differences in (1) the age-specific onset of changes in detailed body composition (2); the onset of changes in body composition-REE associations with age. METHODS: Using a cross-sectional magnetic resonance imaging database of 448 Caucasian participants (females and males) with comprehensive data on skeletal muscle (SM) mass, adipose tissue (AT), OMs, and REE. RESULTS: We observed gender-specific differences in the onset of age-related changes in metabolic active components and REE. Declines in body composition and REE started earlier in females than in males for SM (29.4 vs 39.6 years), AT (38.2 vs 49.9 years), OM (34.7 vs 45.7 years), and REE (31.9 vs 36.8 years). The age-related decrease of AT was significantly higher in females than in males (-5.69kg/decade vs -0.59kg/decade). In females adjusted REEmFFM&FM (resting energy expenditure measured adjusted for FFM and FM) and REEmSM/OM/AT (resting energy expenditure measured adjusted for skeletal muscle and organ mass and adipose tissue) decreased by -145 kJ/d/decade and -604.8 kJ/d/ decade after the age of 35.2 respectively 34.3 years. SM was main determinant of REEm in females (R (2) = .67) and males (R (2) = .66) with remaining variance mainly explained by kidney mass (R (2) = .07) in females and liver mass (R (2) = .09) in males. CONCLUSION: We concluded that gender affects the age-related changes in body composition as well as changes in body composition-REE relationship. This trial was registered at linicaltrials.gov as NCT01737034.

Metabolic adaptation to caloric restriction and subsequent refeeding: the Minnesota Starvation Experiment revisited.

BACKGROUND: Adaptive thermogenesis (AT) is the fat-free mass (FFM)-independent reduction of resting energy expenditure (REE) to caloric restriction (CR). AT attenuates weight loss and favors weight regain. Its variance, dynamics, and control remain obscure. OBJECTIVES: Our aims were to address the variance and kinetics of AT, its associations with body composition in the context of endocrine determinants, and its effect on weight regain. DESIGN: Thirty-two nonobese men underwent sequential overfeeding (1 wk at +50% of energy needs), CR (3 wk at -50% of energy needs), and refeeding (2 wk at +50% of energy needs). AT and its determinants were measured together with body composition as assessed with the use of quantitative magnetic resonance, whole-body MRI, isotope dilution, and nitrogen and fluid balances. RESULTS: Changes in body weight were +1.8 kg (overfeeding), -6.0 kg (CR), and +3.5 kg (refeeding). CR reduced fat mass and FFM by 114 and 159 g/d, respectively. Within FFM, skeletal muscle (-5%), liver (-13%), and kidneys (-8%) decreased. CR also led to reductions in REE (-266 kcal/d), respiratory quotient (-15%), heart rate (-14%), blood pressure (-7%), creatinine clearance (-12%), energy cost of walking (-22%), activity of the sympathetic nervous system (SNS) (-38%), and plasma leptin (-44%), insulin (-54%), adiponectin (-49%), 3,5,3'-tri-iodo-thyronine (T3) (-39%), and testosterone (-11%). AT was 108 kcal/d or 48% of the decrease in REE. Changes in FFM composition explained 36 kcal, which left 72 kcal/d for true AT. The decrease in AT became significant at ≤3 d of CR and was related to decreases in insulin secretion (r = 0.92, P < 0.001), heart rate (r = 0.60, P < 0.05), creatinine clearance (r = 0.79, P < 0.05), negative fluid balance (r = 0.51, P < 0.01), and the free water clearance rate (r = -0.90, P < 0.002). SNS activity and plasma leptin, ghrelin, and T3 and their changes with CR were not related to AT. CONCLUSION: During early weight loss, AT is associated with a fall in insulin secretion and body fluid balance. This trial was registered at clinicaltrials.gov as NCT01737034.

What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults?

BACKGROUND: Whole-body magnetic resonance imaging (MRI) is the gold standard for the assessment of skeletal muscle (SM) and adipose tissue volumes. It is unclear whether single-slice estimates can replace whole-body data. OBJECTIVE: We evaluated the accuracy of the best single lumbar and midthigh MRI slice to assess whole-body SM, visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT). DESIGN: Whole-body MRI was performed in 142 healthy adults aged 19-65 y [mean ± SD age: 37.0 ± 11.8 y; BMI (in kg/m(2)): 25.3 ± 5.9]. Single slices were taken at lumbar vertebrae L1-L5 plus intervertebral discs and the thigh (midthigh, 10 cm distally from the midthigh, and 10 cm proximally from the midthigh). The value of single-slice areas was also tested in a longitudinal study on 48 healthy volunteers during weight loss (8.2 ± 5.2 kg). RESULTS: Cross-sectionally, all SM and adipose tissue single-slice areas correlated with total tissue volumes (P < 0.01). Because of the close associations between L3 areas and corresponding tissue volumes (r = 0.832-0.986, P < 0.01), this location was identified as the reference to estimate SM and adipose tissue in both sexes. SM, SAT, and VAT areas at L3 explained most of the variance of total tissue volumes (69-97%, with SEs of estimation of 1.96 and 2.03 L for SM, 0.23 and 0.61 L for VAT, and 4.44 and 2.47 L for SAT for men and women, respectively. There was no major effect on the explained variance compared with that for optimal slices. For SM, the optimal slice area was shown at midthigh. With weight-loss changes in total SM, VAT, and SAT, volumes were significantly different from those at baseline (SM changes: -2.8 ± 2.9 L; VAT changes: -0.7 ± 1.0 L; SAT changes: -5.1 ± 6.0 L). The area at L3 reflected changes in total VAT and SAT. To assess changes in total SM volumes, areas at midthigh showed the best evidence. CONCLUSION: In both sexes, a single MRI scan at the level of L3 is the best compromise site to assess total tissue volumes of SM, VAT, and SAT. By contrast, L3 does not predict changes in tissue components. This trial was registered at clinicaltrials.gov as NCT01737034.

Impact of body composition during weight change on resting energy expenditure and homeostasis model assessment index in overweight nonsmoking adults.

BACKGROUND: Weight change affects resting energy expenditure (REE) and metabolic risk factors. The impact of changes in individual body components on metabolism is unclear. OBJECTIVE: We investigated changes in detailed body composition to assess their impacts on REE and insulin resistance. DESIGN: Eighty-three healthy subjects [body mass index (BMI; in kg/m²) range: 20.2-46.8; 50% obese] were investigated at 2 occasions with weight changes between -11.2 and +6.5 kg (follow-up periods between 23.5 and 43.5 mo). Detailed body composition was measured by using the 4-component model and whole-body magnetic resonance imaging. REE, plasma thyroid hormone concentrations, and insulin resistance were measured by using standard methods. RESULTS: Weight loss was associated with decreases in fat mass (FM) and fat-free mass (FFM) by 72.0% and 28.0%, respectively. A total of 87.9% of weight gain was attributed to FM. With weight loss, sizes of skeletal muscle, kidneys, heart, and all fat depots decreased. With weight gain, skeletal muscle, liver, kidney masses, and several adipose tissue depots increased except for visceral adipose tissue (VAT). After adjustments for FM and FFM, REE decreased with weight loss (by 0.22 MJ/d) and increased with weight gain (by 0.11 MJ/d). In a multiple stepwise regression analysis, changes in skeletal muscle, plasma triiodothyronine, and kidney masses explained 34.9%, 5.3%, and 4.5%, respectively, of the variance in changes in REE. A reduction in subcutaneous adipose tissue rather than VAT was associated with the improvement of insulin sensitivity with weight loss. Weight gain had no effect on insulin resistance. CONCLUSION: Beyond a 2-compartment model, detailed changes in organ and tissue masses further add to explain changes in REE and insulin resistance.