Relationship Between Basal Metabolic Rate,Gender, Age,and Body Composition in 8,780 White Obese Subjects

The objective of the present study was to explore the relationship between basal metabolic rate (BMR), gender, age, anthropometric characteristics, and body composition in severely obese white subjects. In total, 1,412 obese white children and adolescents (BMI > 97° percentile for gender and age) and 7,368 obese adults (BMI > 30 kg/m²) from 7 to 74 years were enrolled in this study. BMR was measured using an indirect calorimeter equipped with a canopy and fat free mass (FFM) were obtained using tetrapolar bioelectrical impedance analysis (BIA). Using analysis of covariance, we tested the effect of gender on the relationship between BMR, age, anthropometry, and body composition. In children and adolescents, the predictor × gender interaction was significant in all cases except for FFM × gender. In adults, all predictor × gender interactions were significant. A prediction equation based on body weight (BW), age, and gender had virtually the same accuracy of the one based on FFM, age, and gender to predict BMR in both children and adults (R²_adj = 0.59 and 0.60, respectively). In conclusion, gender was a significant determinant of BMR in children and adolescents but not in adults. Our results support the hypothesis that the age‐related decline in BMR is due to a reduction in FFM. Finally, anthropometric predictors of BMR are as accurate as body composition estimated by BIA.     

Serial Measurements of Body Composition in Obese Subjects During a Very-Low-Energy Diet (VLED) Comparing Bioelectrical Impedance with Hydrodensitometry

Bioelectrical impedance analysis (BIA) is a convenient, inexpensive, and noninvasive technique for measuring body composition. BIA has been strongly correlated with total body water (TBW) and also has been validated against hydrodensitometry (HD). The accuracy and clinical utility of BIA and HD during periods of substantial weight loss remain controversial. We measured body composition in moderately and severely obese patients serially using both methods during a very-low-energy diet (VLED). Mean initial weight in these patients was 116 (± 30) kg (range, 74–196 kg). Mean weight loss was 24 (± 13) kg with a decrease in fat mass (FM) by HD of 20 kg (p<0.001) and a decrease in fat-free mass (FFM) of 3.6 kg (p<0.05). Loss of FFM is best predicted by the rate (kg/wk) of weight loss (r² = 0.86, p<0.0001). FFM, as predicted from BIA equations, was highly correlated with FFM as estimated by HD during all testing sessions (r=0.92-0.98). Although highly correlated, BIA overestimated FFM relative to HD and this difference appeared to be more pronounced for taller patients with greater truncal obesity. Although the discrepancy was no greater during weight-loss treatment, the level of disagreement was considerable. Therefore, the two methods cannot be used interchangeably to monitor relative changes in body composition in patients with obesity during treatment with VLED. The discrepancy between BIA and HD may be caused by body mass distribution considerations and by perturbations in TBW which affect the hydration quotient for FFM (BIA) and/or which affect the density constants for FFM and FM (HD).     

Assessing Body Composition among 3‐ to 8‐Year‐Old Children: Anthropometry,BIA, and DXA

Objective: To examine the inter‐relationships of body composition variables derived from simple anthropometry [BMI and skinfolds (SFs)], bioelectrical impedance analysis (BIA), and dual energy x‐ray (DXA) in young children. Research Methods and Procedures: Seventy‐five children (41 girls, 34 boys) 3 to 8 years of age were assessed for body composition by the following methods: BMI, SF thickness, BIA, and DXA. DXA served as the criterion measure. Predicted percentage body fat (%BF), fat‐free mass (FFM; kilograms), and fat mass (FM; kilograms) were derived from SF equations [Slaughter (SL)1 and SL2, Deurenberg (D) and Dezenberg] and BIA. Indices of truncal fatness were also determined from anthropometry. Results: Repeated measures ANOVA showed significant differences among the methods for %BF, FFM, and FM. All methods, except the D equation (p = 0.08), significantly underestimated measured %BF (p < 0.05). In general, correlations between the BMI and estimated %BF were moderate (r = 0.61 to 0.75). Estimated %BF from the SL2 also showed a high correlation with DXA %BF (r = 0.82). In contrast, estimated %BF derived from SFs showed a low correlation with estimated %BF derived from BIA (r = 0.38); likewise, the correlation between DXA %BF and BIA %BF was low (r = 0.30). Correlations among indicators of truncal fatness ranged from 0.43 to 0.98. Discussion: The results suggest that BIA has limited utility in estimating body composition, whereas BMI and SFs seem to be more useful in estimating body composition during the adiposity rebound. However, all methods significantly underestimated body fatness as determined by DXA, and, overall, the various methods and prediction equations are not interchangeable.     

Kinetics of stable isotope and body composition in olive baboons (Papio anubis) estimated by deuterium dilution space: a pilot study

Morphometrics and isotope-labelled water were used to determine body composition [total body water, total body fat and fat-free mass (FFM)] of three captive female olive baboons (Papio anubis). Mean mass was 16.5 kg, comparable with other captive settings but heavier than wild olive baboons. Average water content was 66%; FFM averaged 90.5%. Baboon females have less body fat than human counterparts. Compared with captive or wild baboons, these females were adequately nourished for their energy expenditure. A positive association between total mass and FFM existed, but due to the small sample no general relationship was observed for body fat or FFM and condition or size measures. The kinetics of deuterium equilibration in body fluids for baboons was determined as 3-4 hours after injection, similar to that for humans. Deuterium dilution technique appears to be an appropriate method for studying body composition in baboons, although a larger sample is needed for relationships between morphometric indices and body composition.     

Metabolically active components of fat free mass and resting energy expenditure in nonobese adults

Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFM(BIA) (r = 0.92), muscle mass(DEXA) (r = 0.89), and sum of internal organs(MRI) (r = 0.90). In a multiple stepwise regression analysis, FFM(BIA) alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organs(MRI) into the model increased the r(2) to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscle(DEXA) and liver mass(MRI) significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

Use of bioelectrical impedance to assess body composition changes at high altitude.

This study determined the feasibility of using bioelectrical impedance analysis (BIA) to assess body composition alterations associated with body weight (BW) loss at high altitude. The BIA method was also evaluated relative to anthropometric assessments. Height, BW, BIA, skinfold (SF, 6 sites), and circumference (CIR, 5 sites) measurements were obtained from 16 males (23-35 yr) before, during, and after 16 days of residence at 3,700-4,300 m. Hydrostatic weighings (HW) were performed pre- and postaltitude. Results of 13 previously derived prediction equations using various combinations of height, BW, age, BIA, SF, or CIR measurements as independent variables to predict fat-free mass (FFM), fat mass (FM), and percent body fat (%Fat) were compared with HW. Mean BW decreased from 84.74 to 78.84 kg (P less than 0.01). As determined by HW, FFM decreased by 2.44 kg (P less than 0.01), FM by 3.46 kg (P less than 0.01), and %Fat by 3.02% (P less than 0.01). The BIA and SF methods overestimated the loss in FFM and underestimated the losses in FM and %Fat (P less than 0.01). Only the equations utilizing the CIR measurements did not differ from HW values for changes in FFM, FM, and %Fat. It was concluded that the BIA and SF methods were not acceptable for assessing body composition changes at altitude.     

Are Ethnic and Gender Specific Equations Needed to Derive Fat Free Mass from Bioelectrical Impedance in Children of South Asian,Black African-Caribbean and White European Origin? Results of the Assessment of Body Composition in Children Study

Background

Bioelectrical impedance analysis (BIA) is a potentially valuable method for assessing lean mass and body fat levels in children from different ethnic groups. We examined the need for ethnic- and gender-specific equations for estimating fat free mass (FFM) from BIA in children from different ethnic groups and examined their effects on the assessment of ethnic differences in body fat.

Methods

Cross-sectional study of children aged 8–10 years in London Primary schools including 325 South Asians, 250 black African-Caribbeans and 289 white Europeans with measurements of height, weight and arm-leg impedance (Z; Bodystat 1500). Total body water was estimated from deuterium dilution and converted to FFM. Multilevel models were used to derive three types of equation {A: FFM = linear combination(height+weight+Z); B: FFM = linear combination(height²/Z); C: FFM = linear combination(height²/Z+weight)}.

Results

Ethnicity and gender were important predictors of FFM and improved model fit in all equations. The models of best fit were ethnicity and gender specific versions of equation A, followed by equation C; these provided accurate assessments of ethnic differences in FFM and FM. In contrast, the use of generic equations led to underestimation of both the negative South Asian-white European FFM difference and the positive black African-Caribbean-white European FFM difference (by 0.53 kg and by 0.73 kg respectively for equation A). The use of generic equations underestimated the positive South Asian-white European difference in fat mass (FM) and overestimated the positive black African-Caribbean-white European difference in FM (by 4.7% and 10.1% respectively for equation A). Consistent results were observed when the equations were applied to a large external data set.

Conclusions

Ethnic- and gender-specific equations for predicting FFM from BIA provide better estimates of ethnic differences in FFM and FM in children, while generic equations can misrepresent these ethnic differences.     

Determinants of insulin-stimulated glucose disposal in middle-aged, premenopausal women

Controversy exists regarding the relative importance of adiposity, physical fitness, and physical activity in the regulation of insulin-stimulated glucose disposal. To address this issue, we measured insulin-stimulated glucose disposal [mg. kg fat-free mass (FFM)(-1). min(-1); oxidative and nonoxidative components] in 45 nondiabetic, nonobese, premenopausal women (mean +/- SD; 47 +/- 3 yr) by use of hyperinsulinemic euglycemic clamp (40 mU. m(-2). min(-1)) and [6,6-2H2]glucose dilution techniques. We also measured body composition, abdominal fat distribution, thigh muscle fat content, maximal oxygen consumption (VO2 max), and physical activity energy expenditure ((2)H(2)(18)O kinetics) as possible correlates of glucose disposal. VO2 max was the strongest correlate of glucose disposal (r = 0.63, P < 0.01), whereas whole body and abdominal adiposity showed modest associations (range of r values from -0.32 to -0.46, P < 0.05 to P < 0.01). A similar pattern of correlations was observed for nonoxidative glucose disposal. None of the variables measured correlated with oxidative glucose disposal. The relationship of VO2 max to glucose disposal persisted after statistical control for FFM, percent body fat, and intra-abdominal fat (r = 0.40, P < 0.01). In contrast, correlations of total and regional adiposity measures to insulin sensitivity were no longer significant after statistical adjustment for VO2 max. VO2 max was the only variable to enter stepwise regression models as a significant predictor of total and nonoxidative glucose disposal. Our results highlight the importance of VO2 max as a determinant of glucose disposal and suggest that it may be a stronger determinant of variation in glucose disposal than total and regional adiposity in nonobese, nondiabetic, premenopausal women.     

Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

Bioimpedance for severe obesity: comparing research methods for total body water and resting energy expenditure

Objective: As the acceptance of surgical procedures for weight loss in morbid obesity is increasing, clinically useful baseline and follow‐up measures of total body water (TBW) and resting energy expenditure (REE) are important. Research methods such as deuterium (D²O) dilution and metabolic carts are problematic in the clinical setting. We compared bioimpedance analysis (BIA) predicted (Tanita TBF‐310) and measured TBW and REE. Methods and Procedures: Forty‐two paired presurgery studies were completed using BIA and D²O in patients with BMI (mean ± s.d.) 50.2 ± 8.8 kg/m² for TBW, and 30 patients with BMI 51.0 ± 13 kg/m² completed paired determinations of REE with metabolic carts and the Tanita balance with weight, height, sex, and age modifiers. Regression analysis and Bland‐Altman plots were applied. Results: When regression analysis was completed for TBW, regression line was consistent with the identity line “y = x.” The intercept was not different from 0 (95% confidence interval ?2.5 ± 7.0). The slope of the line was not different from 1.0 ± 0.1. The measured TBW 51.2 ± 10.1 l had a correlation with the predicted 49.5 ± 11.27 l of 0.92. There also was no significant difference (P = 0.33) between predicted (2,316 ± 559 kcal/day) and measured REE (2,383 ± 576 kcal/day);δ 66.7 ± 273 kcal/day. The two measures were highly correlated (r = 0.88) with no bias detected. Discussion: These observations support the use of the BIA system calibration in subjects with severe obesity. Without the use of complex, costly equipment and invasive procedures, BIA measurements can easily be obtained in clinical practice to monitor patient responses to treatment.     

Comparison of bioelectrical impedance and BMI in predicting obesity-related medical conditions

Objective : To determine the relative validity of specific bioelectrical impedance analysis (BIA) prediction equations and BMI as predictors of physiologically relevant general adiposity. Research Methods and Procedures : Subjects were >12, 000 men and women from the Third National Health and Nutrition Examination Survey population. We examined the correlations between BMI and percentage body fat based on 51 different predictive equations, blood pressure, and blood levels of glucose, high‐density lipoprotein cholesterol, and triglycerides, which are known to reflect adiposity, while controlling for other determinants of these physiological measures. Results : BMI consistently had one of the highest correlations across biological markers, and no BIA‐based measure was superior. Percent body fat estimated from BIA was minimally predictive of the physiological markers independent of BMI. Discussion : These results suggest that BIA is not superior to BMI as a predictor of overall adiposity in a general population.     

Segmental body composition assessment for obese Japanese adults by single-frequency bioelectrical impedance analysis with 8-point contact electrodes

This study aimed to determine the accuracy of segmental body composition variables estimated by single-frequency BIA with 8-point contact electrodes (SF-BIA8), compared with dual-energy X-ray absorptiometry (DXA). Subjects were 72 obese Japanese adults (43 males and 29 females) aged 30 to 66 years. Segmental body composition variables (fat free mass: FFM, fat mass: FM, and percent fat mass: %FAT) were measured by these techniques. The correlations between impedance values and FFM measured by DXA were calculated. To examine the consistency in predicted values (SF-BIA8) with the reference (DXA), significant mean differences were tested by t-test and the degree of the difference was assessed by effect size. Correlations between the reference and predicted values were calculated. Additionally, the standard error of estimation (SEE) when estimating the reference from the predictor and the relative value of the SEE to the mean value of the DXA measurement (%SEE) were calculated. Systematic error was examined by Bland-Altman plots. High correlations were found between impedance and FFM measured by SF-BIA8. FFM in the extremities showed high correlations with the reference values, but systematic error was found. SF-BIA8 tended to overestimate FFM in the trunk. The consistencies in %FAT and FM with the reference value are inferior to those for FFM, and SEE values in %FAT and FM were greater than those for FFM. The accuracy of the estimated values in the trunk (FFM, %FAT, and FM) are inferior to those of the total body and extremities.     

Reliability and validity of body composition measures in female athletes.

The purpose of this investigation was to determine the reliability and validity of bioelectrical impedance (BIA) and near-infrared interactance (NIR) for estimating body composition in female athletes. Dual-energy X-ray absorptiometry was used as the criterion measure for fat-free mass (FFM). Studies were performed in 132 athletes [age = 20.4 +/- 1.5 (SD) yr]. Intraclass reliabilities (repeat and single trial) were 0.987-0.997 for BIA (resistance and reactance) and 0.957-0.980 for NIR (optical densities). Validity of BIA and NIR was assessed by double cross-validation. Because correlations were high (r = 0.969-0.983) and prediction errors low, a single equation was developed by using all 132 subjects for both BIA and NIR. Also, an equation was developed for all subjects by using height and weight only. Results from dual-energy X-ray absorptiometry analysis showed FFM = 49.5 +/- 6.0 kg, which corresponded to %body fat (%BF) of 20.4 +/- 3.1%. BIA predicted FFM at 49.4 +/- 5.9 kg (r = 0.981, SEE = 1.1), and NIR prediction was 49. 5 +/- 5.8 kg (r = 0.975, SEE = 1.2). Height and weight alone predicted FFM at 49.4 +/- 5.7 kg (r = 0.961, SEE = 1.6). When converted to %BF, prediction errors were approximately 1.8% for BIA and NIR and 2.9% for height and weight. Results showed BIA and NIR to be extremely reliable and valid techniques for estimating body composition in college-age female athletes.     

Physical Activity Levels Are Low in Free‐Living Adults with Chronic Paraplegia

Objectives: To compare physical activity levels (PALs) of free‐living adults with chronic paraplegia with World Health Organization recommendations and to compare energy expenditure between persons with complete vs. incomplete paraplegia. Research Methods and Procedures: Twenty‐seven euthyroid adults (17 men and 10 women) with paraplegia (12.5 ± 9.5 years since onset; 17 with complete lesions and 10 with incomplete lesions) participated in this cross‐sectional study. Resting metabolic rate was measured by indirect calorimetry and total daily energy expenditure (TDEE) by heart rate monitoring. PAL was calculated as TDEE/resting metabolic rate. Total body water was measured by deuterium dilution and fat‐free mass (FFM) and fat mass (FM) by calculation (FFM = total body water/0.732; FM = weight ? FFM). Obesity was defined using the following percentage FM cutoffs: men 18 to 40 years >22% and 41 to 60 years >25%; and women 18 to 40 years >35% and 41 to 60 years >38%. Results: Nineteen subjects (70.4%; 13 men and six women) were obese. Fifteen subjects (56%) engaged in structured physical activity 1.46 ± 0.85 times during the observation period for a mean of 49.4 ± 31.0 minutes per session. Despite this, mean PAL of the group was 1.56 ± 0.34, indicative of limited physical activity. TDEE was 24.6% lower in subjects with complete paraplegia (2072 ± 505 vs. 2582 ± 852 kcal/d, p = 0.0372). Discussion: PAL of the group was low, indicating that persons with paraplegia need to engage in increased frequency, intensity, and/or duration of structured physical activity to achieve a PAL ≥1.75 and, thereby, to offset sedentary activities of daily living.     

Muscularity and the density of the fat-free mass in athletes.

The purpose of this study was to use estimates of body composition from a four-component model to determine whether the density of the fat-free mass (D(FFM)) is affected by muscularity or musculoskeletal development in a heterogenous group of athletes and nonathletes. Measures of body density by hydrostatic weighing, body water by deuterium dilution, bone mineral by whole body dual-energy X-ray absorptiometry (DXA), total body skeletal muscle estimated from DXA, and musculoskeletal development as measured by the mesomorphy rating from the Heath-Carter anthropometric somatotype were obtained in 111 collegiate athletes (67 men and 44 women) and 61 nonathletes (24 men and 37 women). In the entire group, D(FFM) varied from 1.075 to 1.127 g/cm3 and was strongly related to the water and protein fractions of the fat-free mass (FFM; r = -0.96 and 0.89) and moderately related to the mineral fraction of the FFM (r = 0.65). Skeletal muscle (%FFM) varied from 40 to 68%, and mesomorphy varied from 1.6 to 9.6, but neither was significantly related to D(FFM) (r = 0.11 and -0.14) or to the difference between percent fat estimated from the four-component model and from densitometry (r = 0.09 and -0.16). We conclude that, in a heterogeneous group of young adult athletes and nonathletes, D(FFM) and the accuracy of estimates of body composition from body density using the Siri equation are not related to muscularity or musculoskeletal development. Athletes in selected sports may have systematic deviations in D(FFM) from the value of 1.1 g/cm3 assumed in the Siri equation, resulting in group mean errors in estimation of percent fat from densitometry of 2-5% body mass, but the cause of these deviations is complex and not simply a reflection of differences in muscularity or musculoskeletal development.     

Free carnitine and acetyl carnitine plasma levels and their relationship with body muscular mass in athletes

Summary The purpose of the present study was to investigate the relationship between plasma carnitine concentration and body composition variation in relation to muscular and fat masses since there is no experimentally proved correlation between plasma carnitine and body masses. We used bioelectric impedance analysis (BIA), to determine body composition and to have a complete physical fitness evaluation. The post-absorptive plasma free carnitine and acetyl carnitine plasma levels, body composition as Fat-Free Mass (FFM) and Fat Mass (FM) in kg, as well as in percent of body mass, were analysed in 33 healthy subjects. A significant negative correlation was found between plasma acetyl carnitine and FFM in weight (kg) as well as in percent of body mass (respectively p < 0.0001; p < 0.01); a significant positive correlation was found only between FM in percent and plasma acetyl carnitine (p < 0.01). The observed negative correlation between plasma acetyl carnitine and muscular mass variation might reflect an oxidative metabolic muscle improvement in relation to muscular fat free mass increment and might be evidence that muscle metabolism change is in relation to plasma acetyl carnitine concentration.     

Effect of race and resistance training status on the density of fat-free mass and percent fat estimates.

The impact of race and resistance training status on the assumed density of the fat-free mass (D(FFM)) and estimates of body fatness via hydrodensitometry (%Fat(D)) vs. a four-component model (density, water, mineral; %Fat(D,W,M)) were determined in 45 men: white controls (W; n = 15), black controls (B; n = 15), and resistance-trained blacks (B-RT; n = 15). Body density by hydrostatic weighing, body water by deuterium dilution, and bone mineral by dual-energy X-ray absorptiometry were used to estimate %Fat(D,W,M). D(FFM) was not different between B and W (or 1.1 g/ml); however, D(FFM) in B-RT was significantly lower (1.091 +/- 0.012 g/ml; P < 0.05). Therefore, %Fat(D) using the Siri equation was not different from %Fat(D,W,M) in W (17.5 +/- 5.0 vs. 18.3 +/- 5.4%) or B (14.9 +/- 5.6 vs. 15.7 +/- 5.7%) but significantly overestimated %Fat(D,W,M) in B-RT (14.0 +/- 5.9 vs. 10.4 +/- 6.0%; P < 0.05). The use of a race-specific equation (assuming D(FFM) = 1.113 g/ml) did not improve the agreement between %Fat(D) and %Fat(D,W,M), resulting in a significantly greater mean (+/-SD) discrepancy for B (1.7 +/- 1.8% fat) and B-RT (6.2 +/- 4.3% fat). Thus race per se does not affect D(FFM) or estimates of %Fat(D); however, B-RT have a D(FFM) lower than 1.1 g/ml, leading to an overestimation of %Fat(D).     

Relationships of resting energy expenditure with body fat distribution and abdominal fatness in Japanese population

Body fat distribution and abdominal fatness are indicators of risks for coronary heart disease. However, the relationships between resting energy expenditure (REE) and the body fat distribution or the abdominal fatness are unclear. We examined the relationships of REE with whole-body fat distribution (waist, hip and waist-to-hip ratio: WHR) and abdominal fatness (intra-abdominal fat: IF and subcutaneous fat: SF) after adjustment for body composition. 451 men and 471 women were subdivided into two groups, 40-59 years: middle-aged group and 60-79 years: elderly group. REE was measured by an indirect calorimetry system. Percentage of fat mass (%FM), fat mass (FM) and fat-free mass (FFM) were assessed by a dual-energy x-ray absorptiometry method. The IF area (IFA) and SF area (SFA) at the level of the umbilicus were measured using computed tomography. Circumference of waist and hip were measured in a standing position. The WHR, waist circumference and SFA did not significantly (p>0.05) associate with the REE after adjusting for FM, FFM and age in any of the groups. The adjusted REE was significantly and inversely correlated with hip (r=-0.159, p<0.05) and IFA (r=-0.131, p<0.05) in the elderly men. These results suggest that lower REE may contribute to greater hip and IFA rather than WHR and waist in elderly men.     

Bioelectrical impedance vs. four-compartment model to assess body fat change in overweight adults

Objective: The Tanita TBF‐305 body fat analyzer is marketed for home and clinical use and is based on the principles of leg‐to‐leg bioelectrical impedance analysis (BIA). Few studies have investigated the ability of leg‐to‐leg BIA to detect change in percentage fat mass (%FM) over time. Our objective was to determine the ability of leg‐to‐leg BIA vs. the four‐compartment (4C) model to detect small changes in %FM in overweight adults. Research Methods and Procedures: Thirty‐eight overweight adults (BMI, 25.0 to 29.9 kg/m²; age, 18 to 44 years; 31 women) participated in a 6‐month, randomized, double‐blind, placebo‐controlled study of a nutritional supplement. Body composition was measured at 0 and 6 months using the Tanita TBF‐305 body fat analyzer [using equations derived by the manufacturer (%FM_T‐Man) and by Jebb et al. (%FM_T‐Jebb)] and the 4C model (%FM_4C). Results: Subjects in the experimental group lost 0.9%FM_4C (p = 0.03), a loss that did not reach significance using leg‐to‐leg BIA (0.6%FM_T‐Man, p = 0.151; 0.6%FM_T‐Jebb, p = 0.144). We observed large standard deviations (SDs) in the mean difference in %FM between the 4C model and the Tanita_Manufacturer (2.5%) and Tanita_Jebb (2.2%). Ten subjects fell outside ±1 SD of the mean differences at 0 and 6 months; those individuals were younger and shorter than those within ±1 SD. Discussion: Leg‐to‐leg BIA performed reasonably well in predicting decreases in %FM in this group of overweight adults but resulted in wide SDs vs. %FM_4C in individuals. Cross‐sectional determinations of %FM of overweight individuals using leg‐to‐leg BIA should be interpreted with caution.