Impact of glycaemic index and dietary fibre on insulin sensitivity during the refeeding phase of a weight cycle in young healthy men.

Previous studies suggest that a low-glycaemic index (LGI) diet may improve insulin sensitivity (IS). As IS has been shown to decrease during refeeding, we hypothesised that an LGI- v. high-GI (HGI) diet might have favourable effects during this phase. In a controlled nutritional intervention study, sixteen healthy men (aged 26·8 (SD 4·1) years, BMI 23·0 (SD 1·7) kg/m2) followed 1 week of overfeeding, 3 weeks of energy restriction and of 2 weeks refeeding at ^50% energy requirement (50% carbohydrates, 35% fat and 15% protein). During refeeding, subjects were divided into two matched groups receiving either high-fibre LGI or lower-fibre HGI foods (GI 40 v. 74, fibre intake 65 (SD 6) v. 27 (SD 4) g/d). Body weight was equally regained in both groups with refeeding (mean regain 70·5 (SD 28·0)% of loss). IS was improved by energy restriction and decreased with refeeding. The decreases in IS were greater in the HGI than in the LGIgroup (group £ time interactions for insulin, homeostasis model assessment of insulin resistance (HOMAIR), Matsuda IS index (MatsudaISI);all P,0·05). Mean interstitial glucose profiles during the day were also higher in the HGI group (DAUCHGI-LGI of continuous interstitial glucose monitoring: 6·6 mmol/l per 14 h, P»0·04). At the end of refeeding, parameters of IS did not differ from baseline values in either diet group (adiponectin, insulin, HOMAIR, Matsuda ISI, M-value; all P.0·05). In conclusion, nutritional stress imposed by dietary restriction and refeeding reveals a GI/fibre effect in healthy non-obese subjects. LGI foods rich in fibre may improve glucose metabolism during the vulnerable refeeding phase of a weight cycle.

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 (K(i)) 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 Elia's K(i) values for obese adults. A sample of young women (n = 80) was divided into two groups, nonobese (BMI <29.9 kg/m(2)) and obese (BMI 30.0-43.2 kg/m(2)). This study was based on the 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. For each organ/tissue, the corresponding Elia's K(i) value was analyzed respectively for nonobese and obese groups by using stepwise univariate regression analysis. Elia's K(i) values were within the range of 95% confidence intervals (CIs) in the nonobese group. However, Elia's K(i) 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 Elia's values overestimate K(i) by 2.0% in obese adults. Obesity-adjusted K(i) 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 Elia's K(i) values were validated in nonobese women, obesity-adjustments are appropriate for application in obese women.

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/m(2)). 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.

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.

Evaluation of specific metabolic rates of major organs and tissues: comparison between men and women.

OBJECTIVES: The specific resting metabolic rates (K(i) , in kcal/kg per day) of major organs and tissues in the Reference Man were suggested in 1992 by Elia: 200 for liver, 240 for brain, 440 for heart and kidneys, 13 for skeletal muscle, 4.5 for adipose tissue and 12 for the residual mass. However, it is unknown whether gender influences the K(i) values. The aim of the present study was to compare the K(i) values observed in nonelderly nonobese men to the corresponding values in women. METHODS: Elia's K(i) values were evaluated based on 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 major organs and tissues measured by magnetic resonance imaging. Marginal 95% confidence intervals (CIs) for the model-estimated K(i) values were calculated by stepwise univariate regression analysis. Subjects were nonelderly (age 20-49 years) nonobese (BMI 18.5-29.9 kg/m(2) ) men (n = 49) and women (n = 57). RESULTS: The measured REE (REEm) and the mass of major organs and skeletal muscle were all greater in the men than in women. The predicted REE by Elia's K(i) values were correlated with REEm in men (r = 0.87) and women (r = 0.86, both P < 0.001). Elia's K(i) values were within the range of 95% CIs for both men and women groups, revealing that gender adjustment is not necessary. CONCLUSIONS: Elia's proposed adult K(i) values are valid in both nonelderly nonobese men and women. Further studies are needed to explore the potential influences of age and obesity on K(i) values 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/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.

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, Elia's K(i) values have never been fully evaluated. OBJECTIVES: The objectives of the present study were to evaluate the applicability of Elia's 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: Elia's K(i) values were within the range of 95% CIs in the young and middle-age groups. However, Elia's K(i) values were outside the right boundaries of 95% CIs in the >50-y group, which indicated that Elia's 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 Elia's K(i) values was validated across adulthood, although age adjustment is appropriate for specific applications.

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.

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.

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.

No evidence of mass dependency of specific organ metabolic rate in healthy humans.

BACKGROUND: In humans, resting energy expenditure (REE) can be calculated from organ and tissue masses using constant specific organ metabolic rates. However, interspecies data suggest allometric relations between body mass and organ metabolic rate with higher specific metabolic rates in mammals with a smaller body mass. OBJECTIVE: The objective was to compare the accuracy of REE prediction with the use of either constant or body mass-dependent specific organ metabolic rates. DESIGN: Healthy subjects (79 women, 75 men) within the normal range of fat mass (FM) expected for a healthy body mass index and aged 18-78 y were stratified into tertiles of body mass. Fifty subjects were grouped as tertile 1 (<66.3 kg), 52 as tertile 2 (> or =66.3 to < or =77.2 kg), and 52 as tertile 3 (>77.2 kg). Magnetic resonance imaging was used to assess the volume of 4 internal organs (brain, heart, liver, and kidneys). REE was measured by indirect calorimetry (REE(m)) and compared with REE calculated from previously published constant (REE(c1)) and body mass-dependent organ metabolic rates (REE(c2)). RESULTS: REE(m) increased significantly with weight tertile (tertile 1: 5536 +/- 529 kJ/d; tertile 2: 6389 +/- 672 kJ/d; tertile 3: 7467 +/- 745 kJ/d; P < 0.01). The deviation REE(m)-REE(c1) did not differ between weight tertiles (tertile 1: 66 +/- 382 kJ/d; tertile 2: 167 +/- 507 kJ/d; tertile 3: 86 +/- 480 kJ/d; NS) and showed no relation with body mass (r = -0.05, NS). By contrast, REE(m)-REE(c2) increased with increasing weight tertile (tertile 1: -45 +/- 369 kJ/d; tertile 2: 150 +/- 503 kJ/d; tertile 3: 193 +/- 482 kJ/d; P < 0.05) and correlated significantly with body mass (r = 0.16, P < 0.05). CONCLUSION: Our data do not support a lower specific organ metabolic rate in humans with a larger body mass than in those with a smaller body mass.

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/m(2)) 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.

Accuracy of bioelectrical impedance consumer devices for measurement of body composition in comparison to whole body magnetic resonance imaging and dual X-ray absorptiometry.

OBJECTIVE: To compare body composition determined by bioelectrical impedance (BIA) consumer devices against criterion estimates determined by whole body magnetic resonance imaging (MRI) and dual energy X-ray absorptiometry (DXA) in healthy normal weight, overweight and obese adults. METHODS: In 106 adults (54 females, 52 males, age 54.2 +/- 16.1 years, BMI 25.8 +/- 4.4 kg/m(2)) fat mass (FM), skeletal muscle mass (SM), total body bone-free lean mass (TBBLM), and level of visceral fat mass (VF) were estimated by 3 single-frequency bipedal (foot-to-foot) and one tretrapolar BIA device, and compared to body composition measured by MRI and DXA. Bland-Altman and simple linear regression analyses were used to determine agreement between methods. RESULTS: %FMDXA, SMMRI or TBBLMDXA showed good relative and absolute agreement with two bipolar and one tetrapolar instrument (r(2) = 0.92-0.96; all p < 0.001; mean bias <1.5 %FM and <1 kg SM or TBBLM) and less relative and absolute agreement for another bipolar device (r(2) = 0.82 and 0.84, mean bias approximately 3 %FM and approximately 3 kg SM). The 95% limits of agreement (bias +/- 2 SD) were narrowest for the tetrapolar device (-6.59 to 4.61 %FM and -4.62 to 4.74 kg SM) and widest for bipolar instruments (up to -14.54 to 8.58 %FM and -9.52 to 3.92 kg SM). Systematic biases for %FM were found for all bipedal devices, but not for the tetrapolar instrument. CONCLUSION: Because of the lower agreement between foot-to-foot BIA and DXA or MRI for the assessment of body composition in individuals, tetrapolar electrode arrangement should be preferred for individual or public use. Bipolar devices provide accurate results for field studies with group estimation.

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.