Measurement site for waist circumference affects its accuracy as an index of visceral and abdominal subcutaneous fat in a Caucasian population.

Following experts' consensus, waist circumference (WC) is the best anthropometric obesity index. However, different anatomic sites are used, and currently there is no universally accepted protocol for measurement of WC. In this study, we compare the associations between WC measured at different sites with total visceral adipose tissue (VAT) volume and cardiometabolic risk. Cross-sectional data were obtained from 294 adults and 234 children and adolescents. In addition, longitudinal data were provided in 75 overweight adults before and after dietary-induced weight loss. WC was measured below the lowest rib (WC(rib)), above the iliac crest (WC(iliac crest)), and midway between both sites (WC(middle)). Volumes of VAT and abdominal subcutaneous adipose tissue (SAT) were obtained using MRI. Cardiometabolic risk included blood pressure, plasma lipids, glucose, and homeostasis model (HOMA index). WC differed according to measurement site as WC(rib) < WC(middle) < WC(iliac crest) (P < 0.001) in children and women, and WC(rib) < WC(middle), WC(iliac crest) (P < 0.001) in men. Elevated WC differed by 10-20% in females and 6-10% in males, dependent on measurement site. In men and children, all WC had similar relations with VAT, SAT, and cardiometabolic risk factors. In women, WC(rib) correlated with weight loss-induced decreases in VAT (r = 0.35; P < 0.05). By contrast, WC(iliac crest) had the lowest associations with VAT and cardiometabolic risk factors in women. Each WC had a stronger correlation with SAT than with VAT, suggesting that WC is predominantly an index of abdominal subcutaneous fat. There is need for a unified measurement protocol.

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/m(2)). 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 BMD(pelvis) correlated with the loss in visceral adipose tissue (VAT) (P < 0.05). Increases in BMD(legs) were reversed after weight regain and inversely correlated with BMD(legs) 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.

Why doesn't the brain lose weight, when obese people diet?

OBJECTIVE: As has been shown recently, obesity is associated with brain volume deficits. We here used an interventional study design to investigate whether the brain shrinks after caloric restriction in obesity. To elucidate mechanisms of neuroprotection we assessed brain-pull competence, i.e. the brain's ability to properly demand energy from the body. METHODS: In 52 normal-weight and 42 obese women (before and after ≈10% weight loss) organ masses of brain, liver and kidneys (magnetic resonance imaging), fat (air displacement plethysmography) and muscle mass (dual-energy X-ray absorptiometry) were assessed. Body metabolism was measured by indirect calorimetry. To investigate how energy is allocated between brain and body, we used reference data obtained in the field of comparative biology. We calculated the distance between each woman and a reference mammal of comparable size in a brain-body plot and named the distance 'encephalic measure'. To elucidate how the brain protects its mass, we measured fasting insulin, since 'cerebral insulin suppression' has been shown to function as a brain-pull mechanism. RESULTS: Brain mass was equal in normal-weight and obese women (1,441.8 ± 14.6 vs. 1,479.2 ± 12.8 g; n.s.) and was unaffected by weight loss (1,483.8 ± 12.7 g; n.s.). In contrast, masses of muscle, fat, liver and kidneys decreased by 3-18% after weight loss (all p < 0.05). The encephalic measure was lower in obese than normal-weight women (5.8 ± 0.1 vs. 7.4 ± 0.1; p < 0.001). Weight loss increased the encephalic measure to 6.3 ± 0.1 (p < 0.001). Insulin concentrations were inversely related to the encephalic measure (r = -0.382; p < 0.001). CONCLUSION: Brain mass is normal in obese women and is protected during caloric restriction. Our data suggest that neuroprotection during caloric restriction is mediated by a competent brain-pull exerting cerebral insulin suppression.

Association of pericardial fat with liver fat and insulin sensitivity after diet-induced weight loss in overweight women.

Pericardial adipose tissue (PAT) is positively associated with fatty liver and obesity-related insulin resistance. Because PAT is a well-known marker of visceral adiposity, we investigated the impact of weight loss on PAT and its relationship with liver fat and insulin sensitivity independently of body fat distribution. Thirty overweight nondiabetic women (BMI 28.2-46.8 kg/m(2), 22-41 years) followed a 14.2 ± 4-weeks low-calorie diet. PAT, abdominal subcutaneous (SAT), and visceral fat volumes (VAT) were measured by magnetic resonance imaging (MRI), total fat mass, trunk, and leg fat by dual-energy X-ray absorptiometry and intrahepatocellular lipids (IHCL) by ((1))H-magnetic resonance spectroscopy. Euglycemic hyperinsulinemic clamp (M) and homeostasis model assessment of insulin resistance (HOMA(IR)) were used to assess insulin sensitivity or insulin resistance. At baseline, PAT correlated with VAT (r = 0.82; P < 0.001), IHCL (r = 0.46), HOMA(IR) (r = 0.46), and M value (r = -0.40; all P < 0.05). During intervention, body weight decreased by -8.5%, accompanied by decreases of -12% PAT, -13% VAT, -44% IHCL, -10% HOMA2-%B, and +24% as well as +15% increases in HOMA2-%S and M, respectively. Decreases in PAT were only correlated with baseline PAT and the loss in VAT (r = -0.56; P < 0.01; r = 0.42; P < 0.05) but no associations with liver fat or indexes of insulin sensitivity were observed. Improvements in HOMA(IR) and HOMA2-%B were only related to the decrease in IHCL (r = 0.62, P < 0.01; r = 0.65, P = 0.002) and decreases in IHCL only correlated with the decrease in VAT (r = 0.61, P = 0.004). In conclusion, cross-sectionally PAT is correlated with VAT, liver fat, and insulin resistance. Longitudinally, the association between PAT and insulin resistance was lost suggesting no causal relationship between the two.

Influence of changes in body composition and adaptive thermogenesis on the difference between measured and predicted weight loss in obese women.

BACKGROUND: There is a difference between measured and predicted weight loss in obese patients. This might be explained by the composition of weight loss, adaptive thermogenesis, or poor compliance. PATIENTS AND METHODS: 48 overweight and obese female patients (31.5 +/- 6.1 years; BMI 35.4 +/- 4.4 kg/m(2)) were investigated before and 13.9 +/- 2.4 weeks after dietary treatment (1,000 kcal/day). Body composition was measured by air-displacement plethysmography and resting energy expenditure (REE) by indirect calorimetry. Physical activity was assessed using electronic pedometers in order to calculate total energy expenditure from REE and physical activity level (PAL). Fat mass (FM) and fat-free mass (FFM) were converted into caloric equivalents using 9.45 kcal/g FM and 1.13 kcal/g FFM. Predicted weight loss was calculated by Wishnofsky's '7,700 kcal/kg rule'. RESULTS: Weight (-8.4 +/- 3.9 kg; p < 0.001), FM (-7.8 +/- 3.6 kg; p < 0.001), and FFM (-0.6 +/- 2.0 kg; p < 0.05) decreased with caloric restriction. Measured weight loss was only 44% of the predicted value. Since FM contributed to 87% of weight loss, the energy deficit/kg weight loss was considerably higher (9,098 +/- 2,349 kcal/kg) than the assumed 7,700 kcal/kg. Adaptive thermogenesis after weight loss was significant in 26 of 48 women (-3.2 +/- 1.2 kcal per kg FFM; p < 0.001). CONCLUSION: 14% of the difference between measured and predicted weight loss was explained by the higher proportion of FM in weight loss and 38% by adaptive thermogenesis (in 54% of the women). Thus, poor compliance was responsible for about 50% of the difference between measured and predicted weight loss only.

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.