Britta Schautz

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’.

Specific metabolic rates of major organs and tissues across adulthood: evaluation by mechanistic model of resting energy expenditure

BACKGROUND The specific resting metabolic rates (K(i); in kcal · kg(-1 )· d(-1)) of major organs and tissues in adults were suggested by Elia (in Energy metabolism: tissue determinants and cellular corollaries. New York, NY: Raven Press, 1992) to be as follows: 200 for liver, 240 for brain, 440 for heart and kidneys, 13 for skeletal muscle, 4.5 for adipose tissue, and 12 for residual organs and tissues. However, Elias K(i) values have never been fully evaluated. OBJECTIVES The objectives of the present study were to evaluate the applicability of Elias K(i) values across adulthood and to explore the potential influence of age on the K(i) values. DESIGN A new approach was developed to evaluate the K(i) values of major organs and tissues on the basis of a mechanistic model: REE = Σ(K(i) × T(i)), where REE is whole-body resting energy expenditure measured by indirect calorimetry, and T(i) is the mass of individual organs and tissues measured by magnetic resonance imaging. With measured REE and T(i), marginal 95% CIs for K(i) values were calculated by stepwise univariate regression analysis. An existing database of nonobese, healthy adults [n = 131; body mass index (in kg/m²) <30] was divided into 3 age groups: 21-30 y (young, n = 43), 31-50 y (middle-age, n = 51), and > 50 y (n = 37). RESULTS Elias K(i) values were within the range of 95% CIs in the young and middle-age groups. However, Elias K(i) values were outside the right boundaries of 95% CIs in the >50-y group, which indicated that Elias study overestimated K(i) values by 3% in this group. Age-adjusted K(i) values for adults aged >50 y were 194 for liver, 233 for brain, 426 for heart and kidneys, 12.6 for skeletal muscle, 4.4 for adipose tissue, and 11.6 for residuals. CONCLUSION The general applicability of Elias K(i) values was validated across adulthood, although age adjustment is appropriate for specific applications.

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/m2) 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 (R2: 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.

Impact of age on leptin and adiponectin independent of adiposity

Age-related changes in leptin and adiponectin levels remain controversial, being affected by inconsistent normalisation for adiposity and body fat distribution in the literature. In a cross-sectional study on 210 Caucasians (127 women, eighty-three men, 18-78 years, BMI 16.8-46.8 kg/m²), we investigated the effect of age on adipokine levels independent of fat mass (FM measured by densitometry), visceral and subcutaneous adipose tissue volumes (VAT and SAT assessed by whole-body MRI). Adiponectin levels increased with age in both sexes, whereas leptin levels decreased with age in women only. There was an age-related increase in VAT (as a percentage of total adipose tissue, VAT%TAT), associated with a decrease in SAT(legs)%TAT. Adiposity was the main predictor of leptin levels, with 75.1 % of the variance explained by %FM in women and 76.6 % in men. Independent of adiposity, age had a minor contribution to the variance in leptin levels (5.2 % in women only). The variance in adiponectin levels explained by age was 14.1 % in women and 5.1 % in men. In addition, independent and inverse contributions to the variance in adiponectin levels were found for truncal SAT (explaining additional 3.0 % in women and 9.1 % in men) and VAT%TAT (explaining additional 13.0 % in men). In conclusion, age-related changes in leptin and adiponectin levels are opposite to each other and partly independent of adiposity and body fat distribution. Normalisation for adiposity but not for body fat distribution is required for leptin. Adiponectin levels are adversely affected by subcutaneous and visceral trunk fat.

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.

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/m2) 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/m2 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/m2 in women; FMI>7.77 kg/m2 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.

Advances in the understanding of specific metabolic rates of major organs and tissues in humans.

Purpose of reviewTo present recent evidence on organ and tissue metabolic rates in humans to explain the variance in resting energy expenditure (REE). Recent findingsIn humans, present knowledge on specific metabolic activities (i.e. ki-values) refers to seven organs and tissues – brain, heart, liver, kidneys, skeletal muscle, adipose tissue and residual mass – with ki-values of 240, 440, 200, 440, 13, 4.5 and 12 kcal/kg/day, provided by Elia in 1992. Detailed body composition data, as derived from whole body MRI together with measurements of whole body REE, were used to validate ki-values in nonobese, healthy and middle-aged adults. There is no sex difference, but minor, that is 2 and 3% deviations are found for age above 55 years and obesity, respectively. By contrast, in adolescents, differences of about 100 kcal/day or 7.3% of measured REE were observed. There is first evidence for changes in ki-values with either weight loss or weight regain after weight loss. Altogether these data suggest that in adolescence and at age above 55 years, in the obese and with weight change, organ and tissue masses differ in cellularity and/or their specific metabolic rates. Presently, direct assessment of specific organ and tissue metabolic rates in humans by either NMR spectroscopy or PET, together with detailed body composition analysis, has not been performed systematically. SummaryWe need to become more skilled in methods and models used for detailed body composition analysis together with detailed assessment of energy expenditure in humans.

Impact of Intra‐ and Extra‐Osseous Soft Tissue Composition on Changes in Bone Mineral Density With Weight Loss and Regain

Recent studies report a significant gain in bone mineral density (BMD) after diet‐induced weight loss. This might be explained by a measurement artefact. We therefore investigated the impact of intra‐ and extra‐osseous soft tissue composition on bone measurements by dual X‐ray absorptiometry (DXA) in a longitudinal study of diet‐induced weight loss and regain in 55 women and 17 men (19–46 years, BMI 28.2–46.8 kg/m2). Total and regional BMD were measured before and after 12.7 ± 2.2 week diet‐induced weight loss and 6 months after significant weight regain (≥30%). Hydration of fat free mass (FFM) was assessed by a 3‐compartment model. Skeletal muscle (SM) mass, extra‐osseous adipose tissue, and bone marrow were measured by whole body magnetic resonance imaging (MRI). Mean weight loss was −9.2 ± 4.4 kg (P < 0.001) and was followed by weight regain in a subgroup of 24 subjects (+6.3 ± 2.9 kg; P < 0.001). With weight loss, bone marrow and extra‐osseous adipose tissue decreased whereas BMD increased at the total body, lumbar spine, and the legs (women only) but decreased at the pelvis (men only, all P < 0.05). The decrease in BMDpelvis correlated with the loss in visceral adipose tissue (VAT) (P < 0.05). Increases in BMDlegs were reversed after weight regain and inversely correlated with BMDlegs decreases. No other associations between changes in BMD and intra‐ or extra‐osseous soft tissue composition were found. In conclusion, changes in extra‐osseous soft tissue composition had a minor contribution to changes in BMD with weight loss and decreases in bone marrow adipose tissue (BMAT) were not related to changes in BMD.

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/m2 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.

Associations between breast adipose tissue, body fat distribution and cardiometabolic risk in women: cross-sectional data and weight-loss intervention

Background/Objectives:Recent studies have shown that a high breast volume predicts visceral adipose tissue (VAT) and risk for type 2 diabetes independently of body mass index (BMI) and waist circumference (WC). To investigate the relationships between breast adipose tissue (BrAT), body fat distribution and cardiometabolic risk factors.Subjects/Methods:In all, 97 healthy females (age 19–46 years, BMI 16.8–46.8 kg/m2) were examined cross-sectionally. A subgroup of 57 overweight and obese women (BMI 34.7±4.5 kg/m2) was investigated before and after diet-induced weight loss (−8.3±4 kg). Fat mass (FM) was measured by air-displacement plethysmography. Volumes of BrAT, VAT and subcutaneous adipose tissue (SAT) of the trunk and extremeties were assessed by whole-body magnetic resonance imaging (MRI). Cardiometabolic risk was assessed by lipid profile, fasting glucose, insulin, adiponectin and leptin levels.Results:A high proportion of BrAT was associated with higher truncal and lower leg SAT. Weight loss-induced decline in BrAT as a percentage of total adipose tissue was correlated with decreases in SATtrunk and inversely with SATlegs and VAT. No relationships were found between BrAT and cardiometabolic risk factors. By contrast, SATtrunk and VAT showed positive and SATlegs inverse associations with cardiometabolic risk factors in cross-sectional as well as longitudinal analysis. The association between BrAT and VAT was lost after adjusting for %FM and truncal SAT.Conclusions:Our results indicate that high BrAT reflects a phenotype with increased SATtrunk and low SATlegs. BrAT showed no independent relationships with VAT and cardiometabolic risk factors.