International journal of body composition research最新文献

During the past two decades, a major outgrowth of efforts by our research group at St. Luke's-Roosevelt Hospital is the development of body composition models that include cellular level models, models based on body component ratios, total body potassium models, multi-component models, and resting energy expenditure-body composition models. This review summarizes these models with emphasis on component ratios that we believe are fundamental to understanding human body composition during growth and development and in response to disease and treatments. In-vivo measurements reveal that in healthy adults some component ratios show minimal variability and are relatively 'stable', for example total body water/fat-free mass and fat-free mass density. These ratios can be effectively applied for developing body composition methods. In contrast, other ratios, such as total body potassium/fat-free mass, are highly variable in vivo and therefore are less useful for developing body composition models. In order to understand the mechanisms governing the variability of these component ratios, we have developed eight cellular level ratio models and from them we derived simplified models that share as a major determining factor the ratio of extracellular to intracellular water ratio (E/I). The E/I value varies widely among adults. Model analysis reveals that the magnitude and variability of each body component ratio can be predicted by correlating the cellular level model with the E/I value. Our approach thus provides new insights into and improved understanding of body composition ratios in adults.

OBJECTIVE AND METHODS: Given the profound weight loss after gastric banding and bypass we compared fat compartmentalization by whole body magnetic resonance imaging in women and men after these procedures to two groups of non-surgical controls who were either matched for age, weight and height or were of lower body mass index (BMI). RESULT: In women post-surgery (n=17; BMI 31.7 kg/m(2)) there was lower visceral adipose tissue (VAT) (1.4 vs 2.5 kg; P<0.01) compared with matched controls (n=59; BMI 32.1 kg/m(2)). In contrast, VAT (5.3 vs 5.4 kg) was nearly identical in men post-surgery (n=10; BMI 34.1 kg/m(2)) compared with matched controls (n=10; BMI 32.1 kg/m(2)) even though the degree of weight reduction was not significantly different from women (27.4 vs 32.6%). Furthermore, VAT when adjusted for total adipose tissue (TAT) was 43% less in women post-surgery (1.2 vs 2.1 kg; P=0.03) than in controls with lower BMI (25.1 kg/m(2)). After adjustment for TAT, subcutaneous adipose tissue in women post-surgery was significantly greater than matched controls (35.1 vs 34.2 kg; P=0.03). There was a significant negative correlation of VAT and the degree of weight loss in women (r=-0.57; P=0.018) but this relationship was not significant in men (r=-0.39; P=0.27). Skeletal muscle was lower in both sexes compared with matched controls (women, 21.8 vs 23.1 kg; men, 32.5 vs 35.5 kg). CONCLUSION: Prospective studies are necessary to confirm if there is a sexual dimorphism in the effects of bariatric surgery on body composition.

OBJECTIVE: The purpose of this study was to examine the validity of two anthropometric and four bioelectric impedance (BIA) equations to estimate body composition from dual-energy x-ray absorptiometry (DXA) in adolescent girls of various ethnicities. The rationale for this study was to develop a prediction equation for percent body fat in a multi-ethnic, representative sample of sixth to eighth grade girls. DESIGN: One-hundred and sixty-six girls (51 African-American, 45 non-Black Hispanic, 55 non-Hispanic Caucasian, 15 multi-ethnic) participated. Estimates of percent fat and fat-free mass (FFM) from six published BIA and anthropometric equations and the equation developed from this study were compared to body composition determined from DXA. An RJL Systems analyzer was used to measure BIA. Anthropometry included body weight, height, and triceps and calf skinfolds. RESULTS: Average (± SD) age, size and body composition was as follows: age, 12.1±1.2 yrs, body mass 52.7±15.9 kg, height, 154.6±8.1 cm; DXA percent fat, 27.9±10.4; fat mass (FM), 15.6±10.2 kg; and fat free mass (FFM) 35.7±6.8 kg. No ethnic differences were found in the relationships between estimated and DXA measured body composition, with the exception of the skinfold equation. The six equations explained on average 82% of the variance in percent fat, 94% of the variance in fat mass, and 88% in fat free mass. Bland-Altman analysis indicated that none of the equations performed satisfactorily in our sample. CONCLUSIONS: The BIA and anthropometric equations were significantly related to DXA body composition parameters, however none met the criteria for cross-validation.

OBJECTIVE: Given the increasing concerns about the levels of obesity being reached throughout the world, this paper analyses the relationship between the most common index of obesity, the BMI, and levels of body fat. RESEARCH METHODS AND PROCEDURES: The statistical relationship, in terms of functional form, between body fat and BMI is analysed using a large data set which can be categorized by race, sex and age. RESULTS: Irrespective of race, body fat and BMI are linearly related for males, with age entering logarithmically and with a positive effect on body fat. Caucasian males have higher body fat irrespective of age, but African American males' body fat increases with age faster than that of Asians and Hispanics. Age is not a significant predictor of body fat for females, where the relationship between body fat and BMI is nonlinear except for Asians. Caucasian females have higher predicted body fat than other races, except at low BMIs, where Asian females are predicted to have the highest body fat. DISCUSSION: Using BMIs to make predictions about body fat should be done with caution, as such predictions will depend upon race, sex and age and can be relatively imprecise. The results are of practical importance for informing the current debate on whether standard BMI cut-off values for overweight and obesity should apply to all sex and racial groups given that these BMI values are shown to correspond to different levels of adiposity in different groups.

AIM: Equations for estimating % fat mass (%BF) and fat-free mass (FFM) from bioelectrical impedance analysis (BIA) that work in adolescent girls from different racial/ethnic backgrounds are not available. We investigated whether race/ethnicity influences estimation of body composition in adolescent girls. PRINCIPAL PROCEDURES: Prediction equations were developed for estimating FFM and %BF from BIA in 166 girls, 10-15 years old, consisting of 51 Black (B), 45 non-Black Hispanic (H), 55 non-Hispanic White (W) and 15 mixed (M) race/ethnicity girls, using dual energy x-ray absorptiometry (DXA) as the criterion method. FINDINGS: Black girls had similar %BF compared to other groups, yet were heavier per unit of height according to body mass index (BMI: kg.m(-2)) due to significantly greater FFM. BIA resistance index, age, weight and race/ethnicity were all significant predictors of FFM (R(2) = 0.92, SEE = 1.81 kg). Standardized regression coefficients showed resistance index (0.63) and weight (0.34) were the most important predictors of FFM. Errors in %BF (~2%) and FFM (~1.0 kg) were greater when race/ethnicity was not included in the equation, particularly in Black girls. We conclude the BIA-composition relationship in adolescent girls is influenced by race, and consequently have developed new BIA equations for adolescent girls for predicting FFM and %BF.

The purpose of this study was to compare fat-free mass (FFM) and percent body fat determined by two bio-electrical impedance analysis (BIA) instruments against criterion estimates determined by dual-energy x-ray absorptiometry (DXA) in a multi-racial/ethnic sample of adolescent girls. BIA was assessed in 151 girls (n=51 African-American; n=45 Hispanic; n=55 Caucasian; age 12.2 +/- 1.2 yr) using the RJL Quantum II and the American Weights and Measures Body-Comp Scale (BCS). Percent body fat determined by BIA was significantly related to that determined by DXA (R(2)=0.87, SEE=2.8% for RJL vs DXA, P<0.0001; R(2)=0.71, SEE=4.4% for BCS vs DXA, P<0.0001). The agreement between DXA and BIA for FFM was also significant (R(2)=0.91, SEE=0.03 kg for RJL, P <0.0001; R(2)=0.79, SEE=0.04 kg for BCS, P <0.0001). The BCS overestimated FFM by 2.7 kg (P<0.0001) and underestimated percent body fat by over 4% (P<0.001). There were no differences in percent body fat between DXA and the RJL, and although the RJL significantly overestimated FFM, the absolute difference was <1 kg. Within each ethnic group, the RJL instrument more closely estimated FFM and percent body fat than did the BCS. Although both BIA instruments compared favorably with DXA, the RJL had better stability and accuracy than the BCS, for both the total sample and for the three ethnic groups. Considering its relatively low cost and minimal time required for technical training, BIA is a useful and appropriate technique for assessing body composition in adolescent girls.

The purpose of this study was to assess the agreement of the Lunar DPX-L with the newer Prodigy dual-energy X-ray absorptiometer (DXA) for determining total-body and regional (arms, legs, trunk) bone mineral density (BMD), bone mineral content (BMC), fat mass (FM), lean tissue mass (LTM), total body mass (BM) and percent fat. A total of 106 apparently healthy males (n=34) and females (n=72) between the ages of 8-72 years were scanned consecutively on the DPX-L (software version 1.35) and Prodigy DXA (enCORE v. 3.6 software). Paired t-tests indicated significantly higher measures by Prodigy for BM (percent difference= 1.1%) and total-body BMD (2.2%), BMC (2.9%), FM (3.5%), and percent fat (2.8%; P<0.001), but not LTM (-0.2%). Regional estimates of FM and bone tended to be overestimated by Prodigy relative to DPX-L. The percent difference was most pronounced for FM in the arms (14.2%) and trunk (8.5%), BMD in the legs (4.9%), LTM in arms (5.6%), and BMC in the trunk (5.9%); but all total-body and regional measures were strongly and significantly correlated (P<0.001). The method of Bland and Altman indicated that the Prodigy overestimated DPX-L for BM (r=0.343; P<0.001), and total-body measures of BMD (r=0.460; P<0.001), and BMC (r=0.321; P<0.001) at higher values, as indicated by the significant, positive association between difference (Prodigy-DPX-L) versus mean ((Prodigy+DPX-L)/2). Regionally, Prodigy overestimated DPX-L for BMD in the legs, BMC in the legs and trunk, and FM in the arms at higher values (P<0.001). In contrast, FM in the legs was underestimated by Prodigy relative to DPX-L at higher values (P<0.001), and no regional bias was observed for LTM. In conclusion, we recommend that correction equations be used for comparing BM, total-body BMD and BMC, and regionally for BMD in the legs, BMC in the legs and trunk, and FM in the arms and legs. The use of correction equations for other estimates is not required for making direct comparisons.

We have previously validated the use of dual-energy X-ray absorptiometry (DXA) for measuring body composition of mice using the GE-Lunar PIXImus and software version 1.42 [1]. Since that report, newer versions of the software have been released. The purpose of the present study was to compare results from our original study with results analyzed using two newer versions of software (versions 1.44 and 1.45). Body composition data (lean tissue mass [LTM], fat mass [FM], bone mineral content [BMC], and bone mineral density [BMD]) were obtained from DXA scans of twenty-five, anesthetized male C57Bl/6J mice (6-11 weeks old; 19 to 29g). Relative to version 1.42, versions 1.44 and 1.45 significantly (P<0.001) overestimated LTM and BMD and underestimated FM and BMC. However, compared to carcass analysis, versions 1.44 and 1.45 significantly overestimated both FM and LTM and underestimated BMC. Results from 1.44 and 1.45 were highly correlated with carcass values for all body composition parameters. Prediction equations were developed for the two new software versions. Applying the prediction equation from 1.42, to the data obtained from 1.44 and 1.45 resulted in FM and LTM that were worse than if no equation was used. However, using their own developed equations resulted in data that were not significantly different than that from carcass analysis. These data suggest that software-specific equations are necessary for comparing DXA-derived data to that of chemical analysis.

Peripheral quantitative computed tomography (pQCT) was used to determine percent body fat in mice, and relative liver fat in lemmings fasted for 0, 6, 12 or 18 hours to induce a wide range of liver fat content. Accuracy of the pQCT was determined by comparing pQCT-derived fat to that from chemical extraction using 30 male mice (whole body) and 26 female lemmings (liver only). To determine whether pQCT could measure changes in liver fat (%) in live animals, two groups of lemmings were scanned on 4 consecutive days under anesthesia. Controls (n = 3) had ad libitum access to food, whereas the fasted group (n = 5) was deprived of food for 18 hr before being measured on day 2 and then refed. The coefficient of variation (CV) for determining percent body fat in mice using the pQCT was 3.9% (±1.8 SD). Percent body fat determined by pQCT significantly overestimated percent fat as measured by chemical extraction (14.5 ± 3.2 vs 12.3 ± 2.9% respectively, P < 0.01, mean ± SD). However, percent body fat by pQCT was highly related to chemical extraction percent fat (r = 0.95, P < 0.001). The liver attenuation values from pQCT were highly related to percent liver fat (r=0.98, P<0.001) in lemmings. The technique showed excellent precision with a CV of 0.3 ± 0.1%. The two groups (control vs fasted) did not differ in their percent liver fat on day 1 (5.4% vs 5.8%). On day 2 the fasted group had a significantly higher percent liver fat than controls (5.9% vs 17.3%; p<0.05). Following refeeding, there were no significant group differences in percent liver fat on days 3 and 4. Our data indicate that pQCT has good accuracy and precision for determining percent body fat, and liver fat in small animals and can be used to track changes in liver fat over time.

We describe the results of a proof-of-principle to measure the potassium content in the human brain using the natural radioisotope (40)K that is in equilibrium with the stable isotopes of potassium, (39)K and (41)K. A fixed relationship exists between radioactive potassium and the total potassium in the brain, which in turn reflects the brain's cell mass and intracellular water compartment. Accordingly, we explored whether measurements of brain potassium could serve as possible indicators of intracellular cerebral edema. We designed, built, and then calibrated our system using a spherical phantom containing KCl salt dissolved in water at levels comparable to those in the human brain. Emitted radiation was detected using sodium iodide (Nal) and high-purity germanium (HP-Ge) detectors. Our results with phantoms and with five volunteers demonstrate the feasibility of measuring potassium at the levels normally present in human brain tissue. We plan to extend the system to detect the onset of brain edema in patients with multiple sclerosis.