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.

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.

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.

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'.

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.

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 SAT(trunk) and inversely with SAT(legs) and VAT. No relationships were found between BrAT and cardiometabolic risk factors. By contrast, SAT(trunk) and VAT showed positive and SAT(legs) 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 SAT(trunk) and low SAT(legs). BrAT showed no independent relationships with VAT and cardiometabolic risk factors.

Mechanistic model of mass-specific basal metabolic rate: evaluation in healthy young adults.

Mass-specific basal metabolic rate (mass-specific BMR), defined as the resting energy expenditure per unit body mass per day, is an important parameter in energy metabolism research. However, a mechanistic explanation for magnitude of mass-specific BMR remains lacking. The objective of the present study was to validate the applicability of a proposed mass-specific BMR model in healthy adults. A mechanistic model was developed at the organ-tissue level, mass-specific BMR = Σ(Ki × Fi), where Fi is the fraction of body mass as individual organs and tissues, and Ki is the specific resting metabolic rate of major organs and tissues. The Fi values were measured by multiple MRI scans and the Ki values were suggested by Elia in 1992. A database of healthy non-elderly non-obese adults (age 20 - 49 yrs, BMI <30 kg/m2) included 49 men and 57 women. Measured and predicted mass-specific BMR of all subjects was 21.6 ± 1.9 (mean ± SD) and 21.7 ± 1.6 kcal/kg per day, respectively. The measured mass-specific BMR was correlated with the predicted mass-specific BMR (r = 0.82, P <0.001). A Bland-Altman plot showed no significant trend (r = 0.022, P = 0.50) between the measured and predicted mass-specific BMR, versus the average of measured and predicted mass-specific BMR. In conclusion, the proposed mechanistic model was validated in non-elderly non-obese adults and can help to understand the inherent relationship between mass-specific BMR and body composition.