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.     

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.     

Percentage Fat in Overweight and Obese Children: Comparison of DXA and Air Displacement Plethysmography**

Objective: To compare percentage body fat (percentage fat) estimates from DXA and air displacement plethysmography (ADP) in overweight and obese children. Research Methods and Procedures: Sixty‐nine children (49 boys and 20 girls) 14.0 ± 1.65 years of age, with a BMI of 31.3 ± 5.6 kg/m² and a percentage fat (DXA) of 42.5 ± 8.4%, participated in the study. ADP body fat content was estimated from body density (D_b) using equations devised by Siri (ADP_Siri) and Lohman (ADP_Loh). Results: ADP estimates of percentage fat were highly correlated with those of DXA in both male and female subjects (r = 0.90 to 0.93, all p < 0.001; standard error of estimate = 2.50% to 3.39%). Compared with DXA estimates, ADP_Siri and ADP_Loh produced significantly (p < 0.01) lower estimates of mean body fat content in boys (?2.85% and ?4.64%, respectively) and girls (?2.95% and ?5.15%, respectively). Agreement between ADP and DXA methods was further examined using the total error and methods of Bland and Altman. Total error ranged from 4.46% to 6.38% in both male and female subjects. The 95% limits of agreement were relatively similar for all percentage fat estimates, ranging from ±6.73% to ±7.94%. Discussion: In this study, conversion of D_b using the Siri equation led to mean percentage fat estimates that agreed better with those determined by DXA compared with the Lohman equations. However, relatively high limits of agreement using either equation resulted in percentage fat estimates that were not interchangeable with percentage fat determined by DXA.     

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).     

Precision and Accuracy of Dual‐Energy X‐ray Absorptiometry for Determining in Vivo Body Composition of Mice

Objective: To evaluate the precision and accuracy of dual‐energy X‐ray absorptiometry (DXA) for the measurement of total‐bone mineral density (TBMD), total‐body bone mineral (TBBM), fat mass (FM), and bone‐free lean tissue mass (LTM) in mice. Research Methods and Procedures: Twenty‐five male C57BL/6J mice (6 to 11 weeks old; 19 to 29 g) were anesthetized and scanned three times (with repositioning between scans) using a peripheral densitometer (Lunar PIXImus). Gravimetric and chemical extraction techniques (Soxhlet) were used as the criterion method for the determination of body composition; ash content was determined by burning at 600°C for 8 hours. Results: The mean intraindividual coefficients of variation (CV) for the repeated DXA analyses were: TBMD, 0.84%; TBBM, 1.60%; FM, 2.20%; and LTM, 0.86%. Accuracy was determined by comparing the DXA‐derived data from the first scan with the chemical carcass analysis data. DXA accurately measured bone ash content (p = 0.942), underestimated LTM (0.59 ± 0.05g, p < 0.001), and overestimated FM (2.19 ± 0.06g, p < 0.001). Thus, DXA estimated 100% of bone ash content, 97% of carcass LTM, and 209% of carcass FM. DXA‐derived values were then used to predict chemical values of FM and LTM. Chemically extracted FM was best predicted by DXA FM and DXA LTM [FM = ?0.50 + 1.09(DXA FM) ? 0.11(DXA LTM), model r² = 0.86, root mean square error (RMSE) = 0.233 g] and chemically determined LTM by DXA LTM [LTM = ?0.14 + 1.04(DXA LTM), r² = 0.99, RMSE = 0.238 g]. Discussion: These data show that the precision of DXA for measuring TBMD, TBBM, FM, and LTM in mice ranges from a low of 0.84% to a high of 2.20% (CV). DXA accurately measured bone ash content but overestimated carcass FM and underestimated LTM. However, because of the close relationship between DXA‐derived data and chemical carcass analysis for FM and LTM, prediction equations can be derived to more accurately predict body composition.     

Effect of high contents of dietary animal-derived protein or carbohydrates on canine faecal microbiota

Background

Few equations have been developed in veterinary medicine compared to human medicine to predict body composition. The present study was done to evaluate the influence of weight loss on biometry (BIO), bioimpedance analysis (BIA) and ultrasonography (US) in cats, proposing equations to estimate fat (FM) and lean (LM) body mass, as compared to dual energy x-ray absorptiometry (DXA) as the referenced method. For this were used 16 gonadectomized obese cats (8 males and 8 females) in a weight loss program. DXA, BIO, BIA and US were performed in the obese state (T0; obese animals), after 10% of weight loss (T1) and after 20% of weight loss (T2). Stepwise regression was used to analyze the relationship between the dependent variables (FM, LM) determined by DXA and the independent variables obtained by BIO, BIA and US. The better models chosen were evaluated by a simple regression analysis and means predicted vs. determined by DXA were compared to verify the accuracy of the equations.

Results

The independent variables determined by BIO, BIA and US that best correlated (p?<?0.005) with the dependent variables (FM and LM) were BW (body weight), TC (thoracic circumference), PC (pelvic circumference), R (resistance) and SFLT (subcutaneous fat layer thickness). Using Mallows??Cp statistics, p value and r ² , 19 equations were selected (12 for FM, 7 for LM); however, only 7 equations accurately predicted FM and one LM of cats.

Conclusions

The equations with two variables are better to use because they are effective and will be an alternative method to estimate body composition in the clinical routine. For estimated lean mass the equations using body weight associated with biometrics measures can be proposed. For estimated fat mass the equations using body weight associated with bioimpedance analysis can be proposed.     

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.     

Body Composition at 6 months of Life: Comparison Of Air Displacement Plethysmography and Dual‐Energy X‐Ray Absorptiometry

Body composition assessment during infancy is important because it is a critical period for obesity risk development, thus valid tools are needed to accurately, precisely, and quickly determine both fat and fat‐free mass. The purpose of this study was to compare body composition estimates using dual‐energy x‐ray absorptiometry (DXA) and air displacement plethysmography (ADP) at 6 months old. We assessed the agreement between whole body composition using DXA and ADP in 84 full‐term average‐for‐gestational‐age boys and girls using DXA (Lunar iDXA v11–30.062; Infant whole body analysis enCore 2007 software, GE, Fairfield, CT) and ADP (Infant Body Composition System v3.1.0, COSMED USA, Concord, CA). Although the correlations between DXA and ADP for %fat (r = 0.925), absolute fat mass (r = 0.969), and absolute fat‐free mass (r = 0.945) were all significant, body composition estimates by DXA were greater for both %fat (31.1 ± 3.6% vs. 26.7 ± 4.7%; P < 0.001) and absolute fat mass (2,284 ± 449 vs. 1,921 ± 492 g; P < 0.001), and lower for fat‐free mass (5,022 ± 532 vs. 5,188 ± 508 g; P < 0.001) vs. ADP. Inter‐method differences in %fat decreased with increasing adiposity and differences in fat‐free mass decreased with increasing infant age. Estimates of body composition determined by DXA and ADP at 6 months of age were highly correlated, but did differ significantly. Additional work is required to identify the technical basis for these rather large inter‐method differences in infant body composition.     

Leptin Responses to Weight Loss in Postmenopausal Women: Relationship to Sex‐Hormone Binding Globulin and Visceral Obesity

Objective: Leptin concentrations increase with obesity and tend to decrease with weight loss. However, there is large variation in the response of serum leptin levels to decreases in body weight. This study examines which endocrine and body composition factors are related to changes in leptin concentrations following weight loss in obese, postmenopausal women. Research Methods and Procedures: Body composition (DXA), visceral obesity (computed tomography), leptin, cortisol, insulin, and sex hormone‐binding globulin (SHBG) concentrations were measured in 54 obese (body mass index [BMI] = 32.0 ± 4.5 kg/m²; mean ± SD), women (60 ± 6 years) before and after a 6‐month hypocaloric diet (250 to 350 kcal/day deficit). Results: Body weight decreased by 5.8 ± 3.4 kg (7.1%) and leptin levels decreased by 6.6 ± 11.9 ng/mL (14.5%) after the 6‐month treatment. Insulin levels decreased 10% (p < 0.05), but mean SHBG and cortisol levels did not change significantly. Relative changes in leptin with weight loss correlated positively with relative changes in body weight (r = 0.50, p < 0.0001), fat mass (r = 0.38, p < 0.01), subcutaneous fat area (r = 0.52, p < 0.0001), and with baseline values of SHBG (r = 0.38, p < 0.01) and baseline intra‐abdominal fat area (r = ?0.27, p < 0.06). Stepwise multiple regression analysis showed that baseline SHBG levels (r² = 0.24, p < 0.01), relative changes in body weight (cumulative r² = 0.40, p < 0.05), and baseline intra‐abdominal fat area (cumulative r² = 0.48, p < 0.05) were the only independent predictors of the relative change in leptin, accounting for 48% of the variance. Discussion: These results suggest that obese, postmenopausal women with a lower initial SHBG and more visceral obesity have a greater decrease in leptin with weight loss, independent of the amount of weight lost.     

Birth weight is inversely associated with central adipose tissue in healthy children and adolescents

Objective: Previous studies have explored the association between birth weight and excess childhood body fat, but few have used precise measures of body composition, leading to equivocal and sometimes contradictory results. Research Methods and Procedures: Subjects included 101 children who underwent DXA measurements between 1995 and 2000. Birth weight and gestational age were assessed using maternal recall. Multiple linear regression analysis was used to determine the relationship between birth weight and the following four outcome variables: total fat mass (FM), truncal fat mass (TrFM), percentage body fat (%BF), and TrFM adjusted for FM (TrFMadj), controlling for current weight and Tanner stage. Results: The mean age of the children studied was 12.9 ± 2.4 years, and the mean birth weight reported by subjects’ mothers was 3.3 ± 0.5 kg. The FM and TrFM were 12.8 ± 8.7 kg and 5.1 ± 4.1 kg, respectively, and the mean %BF was 22.9 ± 10.3%. Birth weight was a significant predictor of FM (p = 0.02) and %BF (p = 0.038). However, birth weight adjusted for gestational age (BWTadj) was a significant (p = 0.03) negative predictor of TrFMadj, independently of race, sex, Tanner stage, and current weight. Discussion: These results provide evidence that, even in childhood and adolescence, a higher birth weight is associated with higher FM and %BF, while a low birth weight is associated with TrFM, adjusted for FM.     

Changes in body composition with weight loss: obese subjects randomized to surgical and medical programs

Objective: To assess changes in body composition with weight loss in obese subjects randomized to a laparoscopic adjustable gastric band surgical program or a medical program using a very‐low‐energy diet and orlistat. Research Methods and Procedures: Using body composition measurements by DXA, neutron activation for total body nitrogen, and whole body γ counting for total body potassium, we studied changes in fat mass, fat distribution, fat‐free mass, total bone mineral content, total body protein, and body cell mass at 6 (n = 61 paired) and 24 months (n = 53 paired) after randomization. Results: At 24 months, the surgical group had lost significantly more weight (surgical, 20.3 ± 6.5 kg; medical, 5.9 ± 8.0 kg). There was favorable fat‐free mass to fat mass loss ratios for both groups (surgical, 1:5.5; medical, 1:5.9). Changes in total body nitrogen or potassium were favorable in each group. A small reduction in mean bone mineral content occurred throughout the study but was not associated with extent of weight loss or treatment group. At 6 months, weight loss for both groups was similar (surgical, 14.1 ± 4.5 kg; medical, 13.3 ± 7.3 kg). The medical program subjects lost less fat‐free mass and skeletal muscle and had increased total body protein. The proportion of body fat to limb fat remained remarkably constant throughout the study. Discussion: Weight loss programs used in this study induced fat loss without significant deleterious effects on the components of fat‐free mass.     

Comparison of DXA and CT in the Assessment of Body Composition in Premenopausal Women With Obesity and Anorexia Nervosa

Accurate methods for assessing body composition in subjects with obesity and anorexia nervosa (AN) are important for determination of metabolic and cardiovascular risk factors and to monitor therapeutic interventions. The purpose of our study was to assess the accuracy of dual‐energy X‐ray absorptiometry (DXA) for measuring abdominal and thigh fat, and thigh muscle mass in premenopausal women with obesity, AN, and normal weight compared to computed tomography (CT). In addition, we wanted to assess the impact of hydration on DXA‐derived measures of body composition by using bioelectrical impedance analysis (BIA). We studied a total of 91 premenopausal women (34 obese, 39 with AN, and 18 lean controls). Our results demonstrate strong correlations between DXA‐ and CT‐derived body composition measurements in AN, obese, and lean controls (r = 0.77–0.95, P < 0.0001). After controlling for total body water (TBW), the correlation coefficients were comparable. DXA trunk fat correlated with CT visceral fat (r = 0.51–0.70, P < 0.0001). DXA underestimated trunk and thigh fat and overestimated thigh muscle mass and this error increased with increasing weight. Our study showed that DXA is a useful method for assessing body composition in premenopausal women within the phenotypic spectrum ranging from obesity to AN. However, it is important to recognize that DXA may not accurately assess body composition in markedly obese women. The level of hydration does not significantly affect most DXA body composition measurements, with the exceptions of thigh fat.     

Dual‐energy X‐ray Absorptiometry and Anthropometric Estimates of Visceral Fat in Black and White South African Women

Visceral adipose tissue (VAT) is associated with increased risk for cardiovascular disease, and therefore, accurate methods to estimate VAT have been investigated. Computerized tomography (CT) is the gold standard measure of VAT, but its use is limited. We therefore compared waist measures and two dual‐energy X‐ray absorptiometry (DXA) methods (Ley and Lunar) that quantify abdominal regions of interest (ROIs) to CT‐derived VAT in 166 black and 143 white South African women. Anthropometry, DXA ROI, and VAT (CT at L4–L5) were measured. Black women were younger (P < 0.001), shorter (P < 0.001), and had higher body fat (P < 0.05) than white women. There were no ethnic differences in waist (89.7 ± 18.2 cm vs. 90.1 ± 15.6 cm), waist:height ratio (WHtR, 0.56 ± 0.12 vs. 0.54 ± 0.09), or DXA ROI (Ley: 2.2 ± 1.5 vs. 2.1 ± 1.4; Lunar: 2.3 ± 1.4 vs. 2.3 ± 1.5), but black women had less VAT, after adjusting for age, height, weight, and fat mass (76 ± 34 cm² vs. 98 ± 35 cm²; P < 0.001). Ley ROI and Lunar ROI were correlated in black (r = 0.983) and white (r = 0.988) women. VAT correlated with DXA ROI (Ley: r = 0.729 and r = 0.838, P < 0.01; Lunar: r = 0.739 and r = 0.847, P < 0.01) in black and white women, but with increasing ROI android fatness, black women had less VAT. Similarly, VAT was associated with waist (r = 0.732 and r = 0.836, P < 0.01) and WHtR (r = 0.721 and r = 0.824, P < 0.01) in black and white women. In conclusion, although DXA‐derived ROIs correlate well with VAT as measured by CT, they are no better than waist or WHtR. Neither DXA nor anthropometric measures are able to accurately distinguish between high and low levels of VAT between population groups.     

Relationship of Body Composition to Body‐Fatness Estimation in Japanese University Students

Objective: To examine the relationship between self‐estimated whole body size and fatness and whole body and regional composition, and the relationship between self‐estimated whole body fatness and self‐estimated regional fatness in Japanese university students. Research Methods and Procedures: This was a cross‐sectional study using Japanese university students (110 men and 79 women). The percentage of body fat, fat mass (FM), and fat‐free mass (FFM) were measured by underwater weighing and used as body composition variables. Subcutaneous fat thicknesses were determined at seven sites by ultrasonography to estimate regional body composition, and six circumferences and four breadths to estimate regional size. Relative body size and fatness were self‐estimated using a questionnaire. Results: Only women tended to estimate themselves as being fatter than they actually were. Self‐estimated body fatness moderately correlated with the percentage of body fat (men, r = 0.41; women, r = 0.40) FM (men, r = 0.50; women, r = 0.51), and body mass index (r = 0.56 for men and 0.56 for women). After adjusting for the percentages of body fat and FM, self‐estimated fatness correlated with body mass index (r = 0.31 for men and r = 0.37 for women). Among self‐estimated regional fatness, self‐estimated abdominal fatness had the strongest correlation with self‐estimated whole body fatness in both genders. Discussion: The low correlation between estimated and actual body fatness in both genders indicates that Japanese university students, especially women, inaccurately estimate their percentage of body fat. In fact, both men and women primarily estimate their whole body fatness by body weight relative to height.     

PIXImus DXA with Different Software Needs Individual Calibration to Accurately Predict Fat Mass

Objective: To validate GE PIXImus2 DXA fat mass (FM) estimates by chemical analysis, to compare previously published correction equations with an equation from our machine, and to determine intermachine variation. Research Methods and Procedures: C57BL/6J (n = 16) and Aston (n = 14) mice (including ob/ob), Siberian hamsters (Phodopus sungorus) (n = 15), and bank voles (Clethrionomys glareolus) (n = 37) were DXA scanned postmortem, dried, then fat extracted using a Soxhlet apparatus. We compared extracted FM with DXA‐predicted FM corrected using an equation designed using wild‐type animals from split‐sample validation and multiple regression and two previously published equations. Sixteen animals were scanned on both a GE PIXImus2 DXA in France and a second machine in the United Kingdom. Results: DXA underestimated FM of obese C57BL/6J by 1.4 ± 0.19 grams but overestimated FM for wild‐type C57BL/6J (2.0 ± 0.11 grams), bank voles (1.1 ± 0.09 grams), and hamsters (1.1 ± 0.13 grams). DXA‐predicted FM corrected using our equation accurately predicted extracted FM (accuracy 0.02 grams), but the other equations did not (accuracy, ?1.3 and ?1.8 grams; paired Student's t test, p < 0.001). Two similar DXA instruments gave the same FM for obese mutant but not lean wild‐type animals. Discussion: DXA using the same software could use the same correction equation to accurately predict FM for obese mutant but not lean wild‐type animals. PIXImus machines purchased with new software need validating to accurately predict FM.     

Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women

Objective: To compare estimates of total and truncal fatness from eight‐electrode bioelectrical impedance analysis equipment (BIA₈) with those from DXA in centrally obese women. The secondary aim was to examine BMI and waist circumference (WC) as proxy measures for percentage total body fat (%TBF) and truncal body fat percentage (tr%BF). Research Methods and Procedures: This was a cross‐sectional study of 136 women (age, 48.1 ± 7.7 years; BMI, 30.4 ± 2.9 kg/m²; %TBF_DXA, 46.0 ± 3.7%; WC, 104 ± 8 cm). Fatness was measured by DXA and Tanita BC‐418 equipment (Tanita Corp., Tokyo, Japan). Agreement among methods was assessed by Bland‐Altman plots, and regression analysis was used to evaluate anthropometric measures as proxies for total and abdominal fatness. Results: The percentage of overweight subjects was 41.9%, whereas 55.9% of the subjects were obese, as defined by BMI, and all subjects had a WC exceeding the World Health Organization cut‐off point for abdominal obesity. Compared with DXA, the BIA₈ equipment significantly underestimated total %BF (?5.0; ?3.6 to ?8.5 [mean; 95% confidence interval]), fat mass (?3.6; ?3.9 to ?3.2), and tr%BF (?8.5; ?9.1 to ?7.9). The discrepancies between the methods increased with increasing adiposity for both %TBF and tr%BF (both p < 0.001). Variation in BMI explained 28% of the variation in %TBF_DXA and 51% of %TBF_BIA8. Using WC as a proxy for truncal adiposity, it explained only 18% of tr%BF_DXA variance and 27% of tr%BF_BIA8 variance. The corresponding figures for truncal fat mass were 49% and 35%, respectively. No significant age effects were observed in any of the regressions. Discussion: BIA₈ underestimated both total and truncal fatness, compared with DXA, with higher dispersion for tr%BF than %TBF. The discrepancies increased with degree of adiposity, suggesting that the accuracy of BIA is negatively affected by obesity.     

Comparison of body adiposity index (BAI) and bmi with estimations of % body fat in clinically severe obese women

Objective:

Body adiposity index (BAI), a new surrogate measure of body fat (hip circumference/(height^1.5 – 18)), has been proposed as an alternative to body mass index (BMI). We compared BAI with BMI, and each of them with laboratory measures of body fat‐derived from bioimpedance analysis (BIA), air displacement plethysmography (ADP), and dual‐energy X‐ray absorptiometry (DXA) in clinically severe obese (CSO) participants.

Design and Methods:

Nineteen prebariatric surgery CSO, nondiabetic women were recruited (age = 32.6 ± 7.7 SD; BMI = 46.5 ± 9.0 kg/m²). Anthropometrics and body fat percentage (% fat) were determined from BIA, ADP, and DXA. Scatter plots with lines of equality and Bland–Altman plots were used to compare BAI and BMI with % fat derived from BIA, ADP, and DXA. BAI and BMI correlated highly with each other (r = 0.90, P < 0.001).

Results:

Both BAI and BMI correlated significantly with % fat from BIA and ADP. BAI, however, did not correlate significantly with % fat from DXA (r = 0.42, P = 0.08) whereas BMI did (r = 0.65, P = 0.003). BMI was also the single best predictor of % fat from both BIA (r² = 0.80, P < 0.001) and ADP (r² = 0.65, P < 0.001). The regression analysis showed that the standard error of the estimate (SEE), or residual error around the regression lines, was greater for BAI comparisons than for BMI comparisons with BIA, ADP, and DXA. Consistent with this, the Bland and Altman plots indicated wider 95% confidence intervals for BAI difference comparisons than for BMI difference comparisons for their respective means for BIA, ADP, and DXA.

Conclusions:

Thus, BAI does not appear to be an appropriate proxy for BMI in CSO women.     

Dynamics of Change in Total and Regional Body Composition After Gastric Bypass in Obese Patients

Little is known on patterns of change over time in body composition, especially lean body mass (LBM), during massive weight loss after Roux‐en‐Y gastric bypass (RYGB) in obese patients. We performed sequential measurements of total and regional body composition in patients after RYGB, and we compared a subsample of patients after surgery to a nonsurgical control group of similar age and body fatness. We used dual‐energy X‐ray absorptiometry (DXA) before and at 3, 6, and 12 months after RYGB in 42 obese women (before surgery: age 39.5 ± 11.6 years; BMI 44.6 ± 6.1 kg/m²; mean ± s.d.) and in 48 control obese women referred for nonsurgical weight management, before weight loss. During 1‐year follow‐up after RYGB, there was a continuous decrease in body weight (?36.0 ± 12.5 kg at 1 year), total fat mass (FM) (?26.0 ± 9.1 kg), as well as in trunk and appendicular FM. In contrast, the decrease in total LBM (?9.8 ± 4.8 kg at 1 year), as well as in trunk and appendicular LBM, plateaued after 3–6 months. Rates of loss in weight, FM, and LBM were highest during the first 3‐month period after RYGB (6.4 ± 1.8, 4.1 ± 1.7, and 2.3 ± 1.2 kg/month, respectively), then decreased continuously for FM but plateaued for LBM. There was no evidence of a decrease in total, trunk, or appendicular LBM in weight‐reduced subjects compared to the control group. In conclusion, follow‐up of these obese women revealed a differential pattern of change in FM and LBM after RYGB. Despite an important loss in LBM, especially during the 3–6 months of initial period, LBM appears to be spared thereafter.     

Predictors of Long‐term Weight Maintenance

Objective: The purpose of this study was to evaluate available variables of a long‐term weight maintenance study to investigate possible factors predisposing to weight regain after a period of weight loss. Research Methods and Procedures: The Maastricht Weight Maintenance Study is an ongoing longitudinal study of healthy men and women (29 men and 62 women; 18 to 65 years of age; BMI = 30.2 ± 3.1 kg/m²). A variety of parameters were measured before and after a very‐low‐energy diet and after a follow‐up of at least 2 years. Results: Mean weight loss was 7.9 ± 3.6 kg, and percent weight regain was 113.8 ± 98.1%. Percent BMI regain was negatively associated with an increase in dietary restraint (r = ?0.47, p < 0.05). Percent weight regain was negatively correlated with baseline resting metabolic rate (r = ?0.38, p = 0.01) and baseline fat mass (r = ?0.24, p = 0.05) and positively correlated with the magnitude of change in body weight (BW) expressed as maximum amplitude of BW (r = 0.21, p < 0.05). In addition, amplitude of BW was positively correlated with the frequency of dieting (r = 0.57, p < 0.01). Discussion: The best predictors for weight maintenance after weight loss were an increase in dietary restraint during weight loss, a high baseline resting metabolic rate, a relatively high baseline fat mass favoring a fat‐free mass–sparing effect during weight loss, a rather stable BW, and a low frequency of dieting. Therefore, BW maintenance after BW loss seems to be a multifactorial issue, including mechanisms that regulate an individuals’ energy expenditure, body composition, and eating behavior in such a way that energy homeostasis is maintained.     

Bioimpedance Analysis Parameters and Epicardial Adipose Tissue Assessed by Cardiac Magnetic Resonance Imaging in Patients With Heart Failure

There is increasing evidence that body composition should be considered as a systemic marker of disease severity in congestive heart failure (CHF). Prior studies established bioelectrical impedance analysis (BIA) as an objective indicator of body composition. Epicardial adipose tissue (EAT) quantified by cardiac magnetic resonance (CMR) is the visceral fat around the heart secreting various bioactive molecules. Our purpose was to investigate the association between BIA parameters and EAT assessed by CMR in patients with CHF. BIA and CMR analysis were performed in 41 patients with CHF and in 16 healthy controls. Patients with CHF showed a decreased indexed EAT (22 ± 5 vs. 34 ± 4 g/m², P < 0.001) and phase angle (PA) (5.5° vs. 6.4°, P < 0.02) compared to healthy controls. Linear regression analysis showed a significant correlation of CMR indexed EAT with left ventricular ejection fraction (LV‐EF) (r = 0.56, P < 0.001), PA (r = 0.31, P = 0.01), total body muscle mass (TBMM) (r = 0.41, P = 0.001), fat‐free mass (FFM) (r = 0.30, P = 0.02), and intracellular water (ICW) (0.47, P = 0.0003). Multivariable analysis demonstrated that LV‐EF was the only independent determinant of indexed EAT (P < 0.0001). Receiver operating characteristic curve analysis indicated good predictive performance of PA and EAT (area under the curve (AUC) = 0.86 and 0.82, respectively) with respect to cardiac death. After a follow‐up period of 5 years, 8/41 (19.5%) patients suffered from cardiac death. Only indexed EAT <22 g/m² revealed a statistically significant higher risk of cardiac death (P = 0.02). EAT assessed by CMR correlated with the BIA‐derived PA in patients with CHF. EAT and BIA‐derived PA might serve as additional prognostic indicators for survival in these patients. However, further clinical studies are needed to elucidate the prognostic relevance of these new findings.