Wiebke Later

Phase Angle From Bioelectrical Impedance Analysis: Population Reference Values by Age, Sex, and Body Mass Index

BACKGROUND The use of bioelectrical impedance phase angle has been recommended as a prognostic tool in the clinical setting, but published reference data bases are discrepant and incomplete (eg, they do not consider body mass index [BMI], and data are lacking for children). METHODS Phase angle reference values stratified by age, sex, and BMI were generated in a large German data base of 15,605 children and adolescents and 214,732 adults, and the determinants of phase angle values were assessed. The reference values were applied to 3 groups of patients and compared with previously published reference values from the United States and Switzerland. RESULTS Gender and age were the main determinants of phase angle in adults, with men and younger subjects having higher phase angles. In children and adolescents, age and BMI were the main determinants of phase angle. In normal and overweight adults, phase angle increased with increasing BMI, but there was an inverse association at a BMI >40 kg/m2. In cirrhosis, the prevalence of a low phase angle increased with the state of disease, whereas it was not different between patients with the metabolic syndrome and controls. There are considerable differences between phase angle reference values from different populations. These differences are not explained by age or BMI and may be due to differences between impedance analyzers. CONCLUSION The determinants of phase angle differ between adults and children. In adults, the influence of BMI on phase angle depended on the BMI range. The prognostic value of phase angle may differ in different clinical settings. The use of population-specific and probably impedance-analyzer-specific reference values for phase angle is recommended.

Influence of Partial Sleep Deprivation on Energy Balance and Insulin Sensitivity in Healthy Women

Background: Voluntary sleep restriction is a lifestyle feature of modern societies that may contribute to obesity and diabetes. The aim of the study was to investigate the impact of partial sleep deprivation on the regulation of energy balance and insulin sensitivity. Subjects and Methods: In a controlled intervention, 14 healthy women (age 23–38 years, BMI 20.0–36.6 kg/m2) were investigated after 2 nights of >8 h sleep/night (T0), after 4 nights of consecutively increasing sleep curtailment (7 h sleep/ night, 6 h sleep/night, 6 h sleep/night and 4 h sleep/night; T1) and after 2 nights of sleep recovery (>8 h sleep/night; T2). Resting and total energy expenditure (REE, TEE), glucose-induced thermogenesis (GIT), physical activity, energy intake, glucose tolerance and endocrine parameters were assessed. Results: After a decrease in sleep du-ration, energy intake (+20%), body weight (+0.4 kg), leptin / fat mass (+29%), free triiodothyronine (+19%), free thyroxine (+10%) and GIT (+34%) significantly increased (all p < 0.05). Mean REE, physical activity, TEE, oral glucose tolerance, and ghrelin levels remained unchanged at T1. The effect of sleep loss on GIT, fT3 and fT4 levels was inversely related to fat mass. Conclusion: Short-term sleep deprivation increased energy intake and led to a net weight gain in women. The effect of sleep restriction on energy expenditure needs to be specifically addressed in future studies using reference methods for total energy expenditure.

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.

Contribution of individual organ mass loss to weight loss–associated decline in resting energy expenditure

BACKGROUND Weight loss leads to reduced resting energy expenditure (REE) independent of fat-free mass (FFM) and fat mass (FM) loss, but the effect of changes in FFM composition is unclear. OBJECTIVE We hypothesized that a decrease in REE adjusted for FFM with weight loss would be partly explained by a disproportionate loss in the high metabolic activity component of FFM. DESIGN Forty-five overweight and obese women [body mass index (in kg/m(2)): 28.7-46.8] aged 22-46 y followed a low-calorie diet for 12.7 +/- 2.2 wk. Body composition was measured by magnetic resonance imaging, dual-energy X-ray absorptiometry, and a 4-compartment model. REE measured by indirect calorimetry (REEm) was compared with REE calculated from detailed body-composition analysis (REEc) by using specific organ metabolic rates (ie, organ REE/mass). RESULTS Weight loss was 9.5 +/- 3.4 kg (8.0 +/- 2.9 kg FM and 1.5 +/- 3.1 kg FFM). Decreases in REE (-8%), free triiodothyronine concentrations (-8%), muscle (-3%), heart (-5%), liver (-4%), and kidney mass (-6%) were observed (all P < 0.05). Relative loss in organ mass was significantly higher (P < 0.01) than was the change in low metabolically active FFM components (muscle, bone, and residual mass). After weight loss, REEm - REEc decreased from 0.24 +/- 0.58 to 0.01 +/- 0.44 MJ/d (P = 0.01) and correlated with the decrease in free triiodothyronine concentrations (r = 0.33, P < 0.05). Women with high adaptive thermogenesis (defined as REEm - REEc < -0.17 MJ/d) had less weight loss and conserved FFM, liver, and kidney mass. CONCLUSIONS After weight loss, almost 50% of the decrease in REEm was explained by losses in FFM and FM. The variability in REEm explained by body composition increased to 60% by also considering the weight of individual organs.

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.

Functional body composition: insights into the regulation of energy metabolism and some clinical applications

The application of advanced methods and techniques and their continuous development enable detailed body composition analyses (BCAs) and modeling of body composition at different levels (e.g., at atomic, molecular, organ-tissue and whole body level). Functional body composition integrates body components into regulatory systems (e.g., on energy balance). Regulation of body weight is closely linked to the mass and function of individual body components. Fat mass is part of the energy intake regulatory feedback system. In addition, fat-free mass (FFM) and fat mass are both determinants of resting energy expenditure (REE). Up to 80% of the variance in energy intake and energy expenditure is explained by body composition. A deviation from normal associations between body components and function suggests a metabolic disequilibrium (e.g., in the REE–FFM relationship or in the plasma leptin–fat mass association) that may occur in response to weight changes and diseases. The concept of functional body composition adds to a more sophisticated view on nutritional status and diseases, as well as to a characterization of biomedical traits that will provide functional evidence relating genetic variants.

Effect of constitution on mass of individual organs and their association with metabolic rate in humans--a detailed view on allometric scaling.

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

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.

Evaluation of Specific Metabolic Rates of Major Organs and Tissues: Comparison Between Nonobese and Obese Women

Elia (1992) identified the specific resting metabolic rates (Ki) of major organs and tissues in young adults with normal weight: 200 for liver, 240 for brain, 440 for heart and kidneys, 13 for skeletal muscle, 4.5 for adipose tissue and 12 for residual mass (all units in kcal/kg per day). The aim of the present study was to assess the applicability of Elias Ki values for obese adults. A sample of young women (n = 80) was divided into two groups, nonobese (BMI <29.9 kg/m2) and obese (BMI 30.0–43.2 kg/m2). This study was based on the mechanistic model: REE = σ (Ki × Ti), where REE is whole‐body resting energy expenditure measured by indirect calorimetry and Ti is the mass of individual organs and tissues measured by magnetic resonance imaging. For each organ/tissue, the corresponding Elias Ki value was analyzed respectively for nonobese and obese groups by using stepwise univariate regression analysis. Elias Ki values were within the range of 95% confidence intervals (CIs) in the nonobese group. However, Elias Ki values were outside the right boundaries of 95% CIs in the obese group and a corresponding obesity‐adjusted coefficient was calculated as 0.98, indicating that Elias values overestimate Ki by 2.0% in obese adults. Obesity‐adjusted Ki values were 196 for liver, 235 for brain, 431 for heart and kidneys, 12.7 for skeletal muscle, 4.4 for adipose tissue, and 11.8 for residual mass. In conclusion, although Elias Ki values were validated in nonobese women, obesity‐adjustments are appropriate for application in obese women.

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.