Wiebke Braun

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.

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 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.

Carbohydrate Quality and Quantity Affect Glucose and Lipid Metabolism during Weight Regain in Healthy Men

In this controlled, parallel-group feeding trial, we examined the impact of carbohydrate (CHO) intake and glycemic index (GI) on glucose and lipid metabolism during refeeding after weight loss. Healthy men (n = 32 total, age: 25.5 ± 3.9 y, BMI: 23.5 ± 2.0 kg/m2) overconsumed diets containing either 50% or 65% CHO for 1 wk (+50% of energy requirements) and then underwent 3 wk of calorie restriction (CR; -50%) followed by 2 wk of overconsuming (refeeding, +50%) the same diet but with either a low or high GI (40 vs.70 during CR, 41 vs.74 during refeeding) so that glycemic load (GL; dietary CHO content x GI) differed between groups during all phases. Glucose profiles were assessed by continuous interstitial glucose monitoring, insulin sensitivity (IS) by fasting blood sampling, oral glucose tolerance test (OGTT) and hyperinsulinemic-euglycemic clamp, and liver fat by MRI. Daytime area under the curve-glucose during refeeding was higher with high compared with low GI (P = 0.01) and 65% compared with 50% CHO intake (P = 0.05) and correlated with dietary GL (r = 0.71; P < 0.001). IS increased with CR and decreased again with refeeding in all groups. The decrease in OGTT-derived IS was greater with high- than with low-GI diets (-41 vs. -15%; P-interaction = 0.01) and correlated with dietary GL during refeeding (r = -0.51; P < 0.01). Serum triglycerides (TGs) and liver fat also improved with CR (-17 ± 38 mg/dL and -1.1 ± 1.3%; P < 0.05 and <0.001) and increased again with refeeding (+48 ± 48 mg/dL and +2.2 ± 1.6%; P < 0.001). After refeeding, serum TGs and liver fat were elevated above baseline values with 65% CHO intake only (+59.9 ± 37.5 mg/dL and +1.1 ± 1.7%, P-interaction <0.001 and <0.05). In conclusion, a diet low in GI and moderate in CHO content (i.e., low GL) may have health benefits by positively affecting daylong glycemia, IS, and liver fat.

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.

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/m2) 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.

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.

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.

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.

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.