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.     

Low Plasma Leptin Concentration and Low Rates of Fat Oxidation in Weight‐Stable Post‐Obese Subjects

Objective: A low resting metabolic rate for a given body size and composition, a low rate of fat oxidation, low levels of physical activity, and low plasma leptin concentrations are all risk factors for body weight gain. The aim of the present investigation was to compare resting metabolic rate (RMR), respiratory quotient (RQ), levels of physical activity, and plasma leptin concentrations in eight post‐obese adults (2 males and 6 females; 48.9 ± 12.2 years; body mass index [BMI]: 24.5 ± 1.0 kg/m²; body fat 33 ± 5%; mean ± SD) who lost 27.1 ± 21.3 kg (16 to 79 kg) and had maintained this weight loss for ≥2 months (2 to 9 months) to eight age‐ and BMI‐matched control never‐obese subjects (1 male and 7 females; 49.1 ± 5.2 years; BMI 24.4 ± 1.0 kg/m²; body fat 33 ± 7%). Research Methods and Procedures: Following 3 days of weight maintenance diet (50% carbohydrate and 30% fat), RMR and RQ were measured after a 10‐hour fast using indirect calorimetry and plasma leptin concentrations were measured using radioimmunoassay. Levels of physical activity were estimated using an accelerometer over a 48‐hour period in free living conditions. Results: After adjustment for fat mass and fat‐free mass, post‐obese subjects had, compared with controls, similar levels of physical activity (4185 ± 205 vs. 4295 ± 204 counts) and similar RMR (1383 ± 268 vs. 1430 ± 104 kcal/day) but higher RQ (0.86 ± 0.04 vs. 0.81 ± 0.03, p < 0.05). Leptin concentration correlated positively with percent body fat (r = 0.57, p < 0.05) and, after adjusting for fat mass and fat‐free mass, was lower in post‐obese than in control subjects (4.5 ± 2.1 vs. 11.6 ± 7.9 ng/mL, p < 0.05). Discussion: The low fat oxidation and low plasma leptin concentrations observed in post‐obese individuals may, in part, explain their propensity to relapse.     

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.     

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.     

Four‐Compartment Cellular Level Body Composition Model: Comparison of Two Approaches**

Objective: The aim of this study was to develop and compare two DXA‐based four‐compartment [body weight = body cell mass (BCM) + extracellular fluid (ECF) + extracellular solids (ECS) + fat] cellular level models. Research Methods and Procedures: Total body potassium (TBK) model: BCM from TBK by whole‐body counting—ECF_TBK = LST ? [BCM_TBK + 0.73 × osseous mineral (Mo)]. Bromide model: ECF from sodium bromide dilution—BCM_BROMIDE = LST ? (ECF_BROMIDE + 0.73 × Mo); Mo and LST measurements came from DXA. The two approaches were evaluated in 99 healthy men and 118 women. Results: BCM estimates were highly correlated (r = 0.97, p < 0.001), as were ECF estimates (r = 0.87, p < 0.001); a small statistically significant mean difference was present (mean ± SD; BCM_TBK model, 30.4 ± 8.9 kg; BCM_BROMIDE, 31.4 ± 9.3 kg; Δ = 1.0 ± 2.8 kg; p < 0.001; ECF_TBK, 18.5 ± 4.2 kg; ECF_BROMIDE, 17.5 ± 3.6 kg; Δ = 1.0 ± 2.8 kg; p < 0.001). A high correlation (r = 0.97, p < 0.001) and good agreement (38.9 ± 9.5 vs. 38.9 ± 9.5 kg; Δ = 0.0 ± 2.4 kg; p = 0.39) were present between TBW, derived as the sum of intracellular water from TBK and ECW from bromide, and measured TBW by ²H₂O dilution. Discussion: Two developed four‐compartment cellular level DXA models, one of which is appropriate for use in most clinical and research settings, provide comparable results and are applicable for BCM and ECF estimation of subject groups with hydration disturbances.     

Body fat responses to a 1‐year combined exercise training program in male coronary artery disease patients

Objective:

To analyze the body fat (BF) content and distribution modifications in coronary artery disease (CAD) patients in response to a 1‐year combined aerobic and resistance exercise training (CET) program.

Design and Methods:

We followed two groups of CAD male patients for 12 months. One group consisted of 17 subjects (57 ± 12 years) who engaged in a CET program (CET group) and the other was a age‐matched control group of 10 subjects (58 ± 11 years). BF content and distribution were measured through dual energy X‐ray absorptiometry (DXA) at baseline and follow‐up.

Results:

We found no differences on body mass and BMI between baseline and end of follow‐up in both groups but, in CET group, we found significant reductions in all analyzed BF depots, including total BF (21.60 ± 6.00 vs. 20.32 ± 5.89 kg, P < 0.01), % total BF (27.8 ± 5.5 vs. 26.4 ± 5.4%, P < 0.05), trunk fat (12.54 ± 3.99 vs. 11.77 ± 4.01 kg, P < 0.05), % trunk fat (31.1 ± 6.9 and 29.2 ± 7.1%, P < 0.05), appendicular fat (8.22 ± 2.08 vs. 7.72 ± 2.037 kg, P < 0.01), % appendicular fat (25.7 ± 4.9 and 24.5 ± 4.9%, P < 0.05), and abdominal fat (2.95 ± 1.06 vs. 2.75 ± 1.10 kg, P < 0.05). Control group showed significant increase in appendicular fat (7.63 ± 1.92 vs. 8.10 ± 2.12 kg, P < 0.05).

Conclusions:

These results confirm the positive effect of CET on body composition of CAD patients, despite no changes in body mass or BMI. In this study, we observed no alterations on BF distribution meaning similar rate of fat loss in all analyzed BF depots. These results also alert for the limitations of BMI for tracking body composition changes.     

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.     

Total and Regional Fat and Serum Cardiovascular Disease Risk Factors in Lean and Obese Children and Adolescents

Objective: This study was conducted to evaluate the association of total and central adiposity with serum cardiovascular disease (CVD) risk factors in lean and obese Portuguese children and adolescents. Research Methods and Procedures: A total of 87 girls (13.2 ± 1.6 years old, 29.9 ± 6.4% body fat [mean ± SD]) and 72 boys (13.2 ± 1.6 years old, 20.8 ± 9.9% body fat) volunteered for the study. Whole‐body composition and fat distribution, from DXA and anthropometry, and serum lipids, lipoproteins, and apolipoproteins were evaluated. Results: The sum of three trunk skinfolds (STS) was highly correlated with total trunk fat mass measured by DXA (p < 0.001). Body mass index, DXA‐measured percentage of body fat, trunk fat mass, STS, and the waist‐to‐height ratio were generally found to be associated with triacylglycerol, the ratio of total cholesterol (TC) to high density lipoprotein‐cholesterol (HDL‐C), low density lipoprotein‐cholesterol (LDL‐C), and apolipoprotein B levels, (significant age‐adjusted r between 0.16 and 0.27, p < 0.05). Body mass index, STS, and the waist circumference were also associated with HDL‐C (p < 0.05), whereas no body composition variable significantly correlated with TC or apolipoproteins A‐I. The STS was significantly correlated with HDL‐C (p < 0.01), TC/HDL‐C (p < 0.05), and apolipoproteins A‐I (p < 0.05) independently of whole‐body fatness. Obese subjects (n = 73) had higher TC, LDL‐C, TC/HDL‐C, and apolipoprotein B than did non‐obese subjects (n = 86), and significant associations between central adiposity and some lipid variables (triacylglycerol and HDL‐C) were found in obese children and adolescents that were not present in leaner individuals. Discussion: DXA‐ and anthropometry‐based whole‐body and central fat measures are associated with serum CVD risk factors in Portuguese boys and girls. Obese children and adolescents have a poorer lipid profile than do their leaner counterparts. Trunk skinfolds, which are easy to obtain even in large samples, predict CVD risk factors to the same extent as DXA‐based variables, in some cases, independently of total fatness.     

Body composition of obese subjects by air displacement plethysmography: The influence of hydration

Objective: We investigated whether air displacement plethysmography (ADP) could detect small changes in body composition of obese subjects with alterations in hydration. Research Methods and Procedures: Ten obese subjects (mean BMI, 39.3 ± 2.8 kg/m²) entered the ADP chamber without and with oil (1, 2, or 4 liters), water (1, 2, or 4 liters), or mixed (1 liter oil + 1 liter water or 2 liters oil + 2 liters water) loads. Real and measured changes in body composition were compared by regression analysis and Bland‐Altman procedures. Results: The ADP‐measured changes in volume did not differ from the real values and were strongly correlated with them (r = 0.98). In all cases, loads of differing composition and similar volume led to different values of fat, fat‐free mass, and percentage fat. Water was detected as increased fat‐free mass only with loads of ≥2 liters, most of the water being falsely detected as increased fat mass. The observed changes were correlated with the real ones for fat mass (r = 0.68; p < 0.0001), fat‐free mass (r = 0.66; p < 0.0001), and percentage fat (r = 0.61; p < 0.0001), but fat mass changes were overestimated by ～1 kg, and fat‐free mass changes were underestimated by ～1 kg. This underestimation increased with the highest water loads, as shown by the Bland‐Altman plot (r = ?0.27; p < 0.05). Percentage fat changes were overestimated by 0.8% (p < 0.001); the magnitude of the error was correlated with the weight of the water load (r = 0.62; p < 0.0001). Discussion: ADP accurately measures changes in body volume, discriminating small changes in body composition. It overestimates changes in adiposity, as most of the increased hydration is detected as an enlarged fat mass.     

A Weight Reduction Program Preserves Fat‐Free Mass but Not Metabolic Rate in Obese Adolescents

Objective: To determine the effects of a multidisciplinary weight reduction program on body composition and energy expenditure (EE) in severely obese adolescents. Research Methods and Procedures: Twenty‐six severely obese adolescents, 12 to 16 years old [mean BMI: 33.9 kg/m²; 41.5% fat mass (FM)] followed a 9‐month weight reduction program including moderate energy restriction and progressive endurance and resistance training. Body composition was assessed by DXA, basal metabolic rate by indirect calorimetry, and EE by whole‐body indirect calorimetry with the same activity program over 36‐hour periods before starting and 9 months after the weight reduction period. Results: Adolescents gained (least‐square mean ± SE) 2.9 ± 0.2 cm in height, lost 16.9 ± 1.3 kg body weight (BW), 15.2 ± 0.9 kg FM, and 1.8 ± 0.5 kg fat‐free mass (FFM) (p < 0.001). Basal metabolic rate, sleeping, sedentary, and daily EE were 8% to 14% lower 9 months after starting (p < 0.001) and still 6% to 12% lower after adjustment for FFM (p < 0.05). Energy cost of walking decreased by 22% (p < 0.001). The reduction in heart rate during sleep and sedentary activities (?10 to ?13 beats/min), and walking (?20 to ?25 beats/min) (p < 0.001) resulted from both the decrease in BW and physical training. Discussion: A weight reduction program combining moderate energy restriction and physical training in severely obese adolescents resulted in great BW and FM losses and improvement of cardiovascular fitness but did not prevent the decline in EE even after adjustment for FFM.     

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.     

Reproducibility of Dual-Energy X-ray Absorptiometry Measurements in Prepubertal Girls

Objective : Regulation of growth and development, clinical assessment, and obesity are among the areas of nutritionrelated research, wherein accurate assessment of body composition is important. We want to test the hypothesis that dual-energy X-ray absorptiometry (DXA) measurements are reproducible in healthy girls. Research Methods and Procedures : We determined total body composition measurements in healthy prepubertal girls using DXA twice, 6 weeks apart. Results : We studied 61 healthy, normal-weight, prepubertal girls, aged 4.8 years to 10.3 years. The girls' DXA-derived mean weight between visits 1 and 2 significantly increased (27.14 kg vs. 27.80 kg, P<0.0001). The increased weight was due to significant increases in total body fat-free mass (FFM) (19.53 kg vs. 19.89 kg,P<O.001), total body bone mass (1.05 kg vs. 1.07 kg, P<0.0001), and total body fat mass (7.61 kg vs. 7.91 kg,P<0.03). The girls' DXA-derived mean total trunk mass between visits 1 and 2 significantly increased (11.23 kg vs. 11.63 kg, p<<0.0001), as did total leg mass (9.33 kg vs. 9.53 kg, p<<0.001), although no significant differences were observed in total arm mass (2.52 kg vs. 2.54 kg, p< = 0.37). The Pearson coefficient of correlation (r) and total coefficient of variation (CV) for intraindividual measurements by DXA were: weight—r = 0.99, CV= 1.97%; total body FFM—r = 0.96, CV = 2.30%; total body bone mass—r = 0.99, CV = 2.08%; total body fat mass—r = 0.96, CV = 6.55%; percentage total body fat— r = 0.91, CV = 5.69%; total trunk mass—r = 0.96, CV = 3.59%; total arm mass—r = 0.95, CV = 4.09%; and total leg mass—r = 0.99, CV = 2.75%. Discussion : Total body FFM, total body bone mass, total body fat mass, percentage of total body fat mass, as well as regional mass determinations by DXA, were highly reproducible in healthy, normal-weight, prepubertal girls. We highly recommend the use of DXA for total body composition studies in girls aged 5 years to 10 years.     

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.     

In Utero Gender Dimorphism of Adiponectin Reflects Insulin Sensitivity and Adiposity of the Fetus

Circulating adiponectin reflects the degree of energy homeostasis and insulin sensitivity of adult individuals. Low abundance of the high molecular weight (HMW) multimers, the most active forms mediating the insulin‐sensitizing effects of adiponectin, is indicative of impaired metabolic status. The increase in fetal adiponectin HMW compared with adults is a distinctive features of human neonates. To further understand the functional properties of adiponectin during fetal life, we have evaluated the associations of adiponectin with insulin sensitivity, body composition, and gender. Umbilical cord adiponectin, adiponectin complexes, and metabolic parameters were measured at term by elective cesarean delivery. The associations between adiponectin, measures of body composition, and insulin sensitivity were evaluated in relation to fetal gender in 121 singleton neonates. Higher total adiponectin concentrations in female compared with male fetuses (34.3 ± 9.5 vs. 24.9 ± 8.6, P < 0.001) were associated with a 3.2‐fold greater abundance in circulating HMW complexes (0.20 ± 0.03 vs. 0.08 ± 0.03, P < 0.001, n = 9). Adiponectin was positively correlated with neonatal fat mass (r = 0.27, P < 0.04) and percent body fat in female fetuses (r = 0.28, P < 0.03) and with lean mass in males (r = 0.28, P < 0.03). There was no significant correlation between cord adiponectin and fasting insulin concentrations or fetal insulin sensitivity as estimated by homeostasis model assessment of insulin resistance (HOMA‐IR). The gender dimorphism for plasma adiponectin concentration and complex distribution first appears in utero. In sharp contrast to the inverse correlation found in adults, the positive relationship between adiponectin and body fat is a specific feature of the fetus.     

Effect of Protein Intake on Bone Mineralization during Weight Loss: A 6‐Month Trial

Objective: The long‐term effect of dietary protein on bone mineralization is not well understood. Research Methods and Procedures: Sixty‐five overweight (body mass index, 25 to 29.9 kg/m²) or obese (≥30 kg/m²) subjects were enrolled in a randomized, placebo‐controlled, 6‐month dietary‐intervention study comparing two controlled ad libitum diets with matched fat contents: high protein (HP) or low protein (LP). Body composition was assessed by DXA. Results: In the HP group, dietary‐protein intake increased from 91.4 g/d to a 6‐month intervention mean of 107.8 g/d (p < 0.05) and decreased in the LP group from 91.1 g/d to 70.4 g/d (p < 0.05). Total weight loss after 6 months was 8.9 kg in the HP group, 5.1 kg in the LP group, and none in the control group. After 6 months, bone mineral content (BMC) had declined by 111 ± 13 g (4%) in the HP group and by 85 ± 13 g (3%) in the LP group (not significant). Loss of BMC was more positively correlated with loss of body fat mass (r = 0.83; p < 0.0001) than with loss of body weight. Six‐month BMC loss, adjusted for differences in fat loss, was greater in the LP group than in the HP group [difference in LP vs. HP, 44.8 g (95% confidence interval, 16 to 73.8 g); p < 0.05]. Independent of change in body weight and composition during the intervention, highprotein intake was associated with a diminished loss of BMC (p < 0.01). Discussion: Body‐fat loss was the major determinant of loss of BMC, and we found no adverse effects of 6 months of high‐protein intake on BMC.     

Association Between the 4 bp Proinsulin Gene Insertion Polymorphism (IVS‐69) and Body Composition in Black South African Women

The objective of the study was to examine the association between a functional 4 bp proinsulin gene insertion polymorphism (IVS‐69), fasting insulin concentrations, and body composition in black South African women. Body composition, body fat distribution, fasting glucose and insulin concentrations, and IVS‐69 genotype were measured in 115 normal‐weight (BMI <25 kg/m²) and 138 obese (BMI ≥30 kg/m²) premenopausal women. The frequency of the insertion allele was significantly higher in the class 2 obese (BMI ≥35kg/m²) compared with the normal‐weight group (P = 0.029). Obese subjects with the insertion allele had greater fat mass (42.3 ± 0.9 vs. 38.9 ± 0.9 kg, P = 0.034) and fat‐free soft tissue mass (47.4 ± 0.6 vs. 45.1 ± 0.6 kg, P = 0.014), and more abdominal subcutaneous adipose tissue (SAT, 595 ± 17 vs. 531 ± 17 cm², P = 0.025) but not visceral fat (P = 0.739), than obese homozygotes for the wild‐type allele. Only SAT was greater in normal‐weight subjects with the insertion allele (P = 0.048). There were no differences in fasting insulin or glucose levels between subjects with the insertion allele or homozygotes for the wild‐type allele in the normal‐weight or obese groups. In conclusion, the 4 bp proinsulin gene insertion allele is associated with extreme obesity, reflected by greater fat‐free soft tissue mass and fat mass, particularly SAT, in obese black South African women.     

Glucose Metabolism and Diet Predict Changes in Adiposity and Fat Distribution in Weight‐reduced Women

Among obesity‐prone individuals, metabolic state may interact with diet in determining body composition. We tested the hypotheses that, among 103 weight‐reduced women over 1 year, (i) insulin sensitivity would be positively associated with change in %fat; (ii) this association would be modulated by dietary glycemic load (GL); and (iii) changes in fat distribution would be related to indexes of glucose metabolism. Insulin sensitivity, glucose effectiveness, fasting and postchallenge insulin and glucose, and glucose tolerance were assessed during intravenous glucose tolerance test (IVGTT). Changes in %fat and fat distribution were examined using dual‐energy X‐ray absorptiometry and computed tomography. Dietary GL was assessed on 67 women using food records. On average, women showed a +5.3 ± 3.0% change in %fat over 1 year, with the magnitude of this change being greater in relatively insulin sensitive women (+6.0 ± 0.4%, mean ± s.e.m.) than in relatively insulin resistant women (+4.4 ± 0.4 kg; P < 0.05). Women who were relatively insulin sensitive and who consumed a higher GL diet showed a +6.8 ± 0.7% change in %fat, which was greater than those who were less insulin sensitive, regardless of diet (P < 0.05), but did not differ from women who were relatively insulin sensitive and who consumed a lower GL diet (P = 0.105). Changes in intra‐abdominal and deep subcutaneous abdominal fat were inversely associated with the postchallenge decline in serum glucose. In conclusion, greater insulin sensitivity may predispose to adiposity among weight reduced women, an effect that may be ameliorated by a lower GL diet. The potential association between indexes of glucose disposal and changes in fat distribution warrants further study.     

Regional Body Composition: Cross‐calibration of DXA Scanners—QDR4500W and Discovery Wi

Differences exist in body composition assessed by dual‐energy X‐ray absorptiometers (DXAs) between devices produced by different manufacturers and different models from the same manufacturer. Cross‐calibration is needed to allow body composition results to be compared in multicenter trials or when scanners are replaced. The aim was to determine reproducibility and extent of agreement between two fan‐beam DXA scanners (QDR4500W, Discovery Wi) for body composition of regional sites. The sample was: 39 women 50.6 ± 9.6 years old with BMI 26.8 ± 5.5 kg/m², body fat 33 ± 7%. Four whole body scans (two on each device) were performed over 3 weeks. Major variables were fat mass, nonosseous lean mass, and bone mineral content (BMC) for the truncal and appendicular regions. Extent of agreement was assessed using Bland and Altman plots. Both devices demonstrated good precision with mean test–retest differences close to zero for fat mass, nonosseous lean mass, and BMC of the truncal and appendicular regions. Evaluation of interdevice agreement revealed significant differences for truncal and appendicular BMC, nonosseous lean mass, and fat mass. The greatest interdevice difference was for truncal fat mass (0.69 ± 0.60 kg). Differences in truncal and appendicular fat mass increased in magnitude at higher mean values. Furthermore, differences in truncal and appendicular fat mass were strongly related to BMI (R = ?0.61, R = ?0.55, respectively). In conclusion, in vivo cross‐calibration is important to ensure comparability of regional body composition data between scanners, especially for truncal fat mass and for subjects with higher BMI.     

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.