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.     

Is Body Mass Index a Measure of Adiposity in Elderly Women?

Objective: To evaluate the accuracy of body mass index (BMI) as a predictor of body fat in elderly women. Research Methods and Procedures: A total of 1423 women aged 67 ± 5 (mean ± SD, range: 60 to 88) years were consecutively enrolled into the study. Fat mass (FM) was measured using DXA. Results: BMI explained 72.9% of FM variance (p < 0.0001), with a root mean square error of estimate (RMSE) of 3.5 kg. After standardization of RMSE on the dependent variable as RMSE%, the prediction error equaled 15%. BMI explained 54.8% of FM% variance (p < 0.0001), with an RMSE of 4.1%, corresponding to an RMSE% of 11%. Discussion: The relatively high RMSE% of the FM and FM%‐BMI associations caution against the use of BMI as an adiposity index in individual elderly women. However, an error corresponding to 11% of FM% may be accepted for population studies of body fat in elderly women.     

Aged-Related Changes in Body Composition and Association between Body Composition with Bone Mass Density by Body Mass Index in Chinese Han Men over 50-year-old

Objectives

Aging, body composition, and body mass index (BMI) are important factors in bone mineral density (BMD). Although several studies have investigated the various parameters and factors that differentially influence BMD, the results have been inconsistent. Thus, the primary goal of the present study was to further characterize the relationships of aging, body composition parameters, and BMI with BMD in Chinese Han males older than 50 years.

Methods

The present study was a retrospective analysis of the body composition, BMI, and BMD of 358 Chinese male outpatients between 50 and 89 years of age that were recruited from our hospital between 2009 and 2011. Qualified subjects were stratified according to age and BMI as follows: 50–59 (n = 35), 60–69 (n = 123), 70–79 (n = 93), and 80–89 (n = 107) years of age and low weight (BMI: < 20 kg/m²; n = 21), medium weight (20 ≤ BMI < 24 kg/m²; n = 118), overweight (24 ≤ BMI < 28 kg/m²; n = 178), and obese (BMI ≥ 28 kg/m²; n = 41). Dual-energy X-ray absorptiometry (DEXA) was used to assess bone mineral content (BMC), lean mass (LM), fat mass (FM), fat-free mass (FFM), lumbar spine (L1-L4) BMD, femoral neck BMD, and total hip BMD. Additionally, the FM index (FMI; FM/height²), LM index (LMI; LM/height²), FFM index (FFMI; [BMC+LM]/height²), percentage of BMC (%BMC; BMC/[BMC+FM+LM] × 100%), percentage of FM (%FM; FM/[BMC+FM+LM] × 100%), and percentage of LM (%LM; LM/(BMC+FM+LM) × 100%) were calculated. Osteopenia or osteoporosis was identified using the criteria and T-score of the World Health Organization.

Results

Although there were no significant differences in BMI among the age groups, there was a significant decline in height and weight according to age (p < 0.0001 and p = 0.0002, respectively). The LMI and FFMI also declined with age (both p < 0.0001) whereas the FMI exhibited a significant increase that peaked in the 80-89-years group (p = 0.0145). Although the absolute values of BMC and LM declined with age (p = 0.0031 and p < 0.0001, respectively), there was no significant difference in FM. In terms of body composition, there were no significant differences in %BMC but there was an increase in %FM (p < 0.0001) and a decrease in %LM (p < 0.0001) with age. The femoral neck and total hip BMD significantly declined with age (p < 0.0001 and p = 0.0027, respectively) but there were no differences in L1-L4. BMD increased at all sites (all p < 0.01) as BMI increased but there were declines in the detection rates of osteoporosis and osteopenia (both p < 0.001). A logistic regression revealed that when the medium weight group was given a BMI value of 1, a decline in BMI was an independent risk factor of osteoporosis or osteopenia, while an increase in BMI was a protective factor for BMD. At the same time, BMD in L1-L4 exhibited a significant positive association with FMI (p = 0.0003) and the femoral neck and total hip BMDs had significant positive associations with FFMI and LMI, respectively (both p < 0.0001).

Conclusions

These data indicate that LMI and FFMI exhibited significant negative associations with aging in Chinese Han males older than 50 years, whereas FMI had a positive association. BMD in the femoral neck and total hip declined with age but an increased BMI was protective for BMD. LMI and FFMI were protective for BMD in the femoral neck and total hip.     

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.     

Body Composition and Energy Metabolism Following Roux‐en‐Y Gastric Bypass Surgery

Roux‐en‐Y gastric bypass (RYGB) surgery has become an accepted treatment for excessive obesity. We conducted a longitudinal study to assess regional body composition, muscle proteolysis, and energy expenditure before RYGB, and 6 and 12 months after RYGB. Whole‐body and regional fat mass (FM) and lean mass (LM) were assessed via dual energy X‐ray absorptiometry (DXA), and myofibrillar protein degradation was estimated by urinary 3‐methylhistidine (3‐MeH) in 29 subjects. Energy expenditure and substrate oxidation were also determined using a whole‐room, indirect calorimeter in 12 of these subjects. LM loss constituted 27.8 ± 10.2% of total weight loss achieved 12 months postoperatively, with the majority of LM loss (18 ± 6% of initial LM) occurring in the first 6 months following RYGB. During this period, the trunk region contributed 66% of whole‐body LM loss. LM loss occurred in the first 6 months after RYGB despite decreased muscle protein breakdown, as indicated by a decrease in 3‐MeH concentrations and muscle fractional breakdown rates. Sleep energy expenditure (SEE) decreased from 2,092 ± 342 kcal/d at baseline to 1,495 ± 190 kcal/day at 6 months after RYGB (P < 0.0001). Changes in both LM and FM had an effect on the reduction in SEE (P < 0.001 and P = 0.005, respectively). These studies suggest that loss of LM after RYGB is significant and strategies to maintain LM after surgery should be explored.     

Anthropometrics Do Not Influence Dual X-ray Absorptiometry (DXA) Measurement of Fat in Normal to Obese Adults: A Comparison with In Vivo Neutron Activation Analysis (IVNA)

Dual-energy X-ray absorptiometry (DXA) is now a commonly used method for the determination of bone mineral status and body composition in humans. The purposes of this study were to compare fat mass by in vivo neutron activation analysis (FM_IVNA) with that by DXA (FM_DXA) in an anthropometrically heterogeneous sample of healthy adult men (n=33) and women (n=36) (19=≤BMI≤39), and to determine whether differences in fat mass estimates between the two methods (ΔFM) were attributable to subject anthropometry as defined by several circumference (waist, iliac crest, thigh) and skinfold thickness (umbilical, suprailiac, abdominal) measurements. No significant differences between FM_DXA and FM_IVNA were observed in men (p=0.46) or women (p=0.09). The two methods were very highly correlated in both sexes (women r²=0.97, p<0.001, men r²=0.91, p<0.001), although the regression line for men was significantly different from the line of identity (p=0.043). These results suggest modest trends toward underestimation of FM_DXA in men when FM_IVNA<18 kg, and overestimation in men when FM_IVNA>18 kg. ΔFM (IVNA-DXA) was not significantly related to any combination of skinfold thicknesses and circumferences in either gender. Age explained 27% of the variance in ΔFM for the men (p=0.008). Furthermore, ΔFM was not significantly related to inter-method disparity in total-body bone mineral measurements in men or women (p<0.05). The present study demonstrates strong correlation in fat measurements between IVNA and DXA in men and women ranging from normal to markedly obese. Correction for subject anthropometry does not significantly improve this relationship.     

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.     

Similarity in Percent Body Fat Between White and Vietnamese Women: Implication for a Universal Definition of Obesity

It has been widely assumed that for a given BMI, Asians have higher percent body fat (PBF) than whites, and that the BMI threshold for defining obesity in Asians should be lower than the threshold for whites. This study sought to test this assumption by comparing the PBF between US white and Vietnamese women. The study was designed as a comparative cross‐sectional investigation. In the first study, 210 Vietnamese women ages between 50 and 85 were randomly selected from various districts in Ho Chi Minh City (Vietnam). In the second study, 419 women of the same age range were randomly selected from the Rancho Bernardo Study (San Diego, CA). In both studies, lean mass (LM) and fat mass (FM) were measured by dual‐energy X‐ray absorptiometry (DXA) (QDR 4500; Hologic). PBF was derived as FM over body weight. Compared with Vietnamese women, white women had much more FM (24.8 ± 8.1 kg vs. 18.8 ± 4.9 kg; P < 0.0001) and greater PBF (36.4 ± 6.5% vs. 35.0 ± 6.2%; P = 0.012). However, there was no significant difference in PBF between the two groups after matching for BMI (35.1 ± 6.2% vs. 35.0 ± 5.7%; P = 0.87) or for age and BMI (35.6 ± 5.1% vs. 35.8 ± 5.9%; P = 0.79). Using the criteria of BMI ≥30, 19% of US white women and 5% of Vietnamese women were classified as obese. Approximately 54% of US white women and 53% of Vietnamese women had their PBF >35% (P = 0.80). Although white women had greater BMI, body weight, and FM than Vietnamese women, their PBF was virtually identical. Further research is required to derive a more appropriate BMI threshold for defining obesity for Asian women.     

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.     

Validation of a dual energy X-ray absorptiometer: measurement of bone mass and soft tissue composition

We investigated the reproducibility of total and regional body composition measurements performed on a dual energy X-ray absorptiometer (DXA). A group of 38 women aged 21–81 (mean 52. 4) years was scanned twice with repositioning to determine intra-observer reproducibility of measurements of bone mineral density (BMD, g · cm⁻²), bone mineral content (BMC, g), lean mass (LM, kg) and fat mass (FM, kg) of the total body and of the major subregions of the body. In addition, the ability of the DXA machine to detect changes in LM and FM (simulated by placing 11.1 and 22.3 kg porcine lard on the body of 11 subjects) was examined. Coefficients of variations calculated from the root mean square averages of individual standard deviations were as follows (BMD, BMC, FM, LM): 1.4%, 1.1%, 1.4%, 1.7% (total body), 2.2%, 2.1%,-,- (head), 2.8%, 2.8%, 2.0%, 2.2% (trunk), 3.6%, 3.9%, 4.0%, 4.9% (arms), 2.7%, 1.3%, 2.6%, 2.8% (legs). Percentage fat (%fat) of exogenous lard was 81.3 (SD 3.5)% as assessed by the absorptiometer which corresponded well with the result of chemical analysis (82.8%). Estimated %fat of exogenous lard was not influenced by initial body mass or percentage body fat. Percentages of expected mean values with 11.1 kg lard placed on the body were 99.9 (SD 0.3) for body mass, 100.5 (SD 2.1) for LM, and 99.5 (SD 3.5) for FM. BMD was overestimated by 3.2% (P < 0.005) with 11.1 kg lard on the body. BMD as well as BMC increased significantly with 22.3␣kg lard on the body (P < 0.005). The results showed that BMD, BMC, LM, and FM of the total body were precisely estimated by the DXA machine used. Regional measurements were less precise. Changes in total body soft tissue composition were precisely and accurately estimated. The lard placed on the body falsely affected BMD and BMC measurements. Changes in body mass could have a similar effect. Accepted: 6 January 1997     

Relationships between Insulin Sensitivity and Measures of Body Fat in Asymptomatic Men and Women

Objective: To assess whether measures of body fat by DXA scanning can improve prediction of insulin sensitivity (S_I) beyond what is possible with traditional measures, such as BMI, waist circumference, and waist‐to‐hip ratio (WHR). Research Methods and Procedures: Frequently sampled intravenous glucose tolerance tests were performed in 256 asymptomatic non‐Hispanic white subjects from Rochester, MN (age 19‐60 years; 123 men and 133 women) to determine the S_I index by Bergman's minimal model technique. Height, weight, and waist and hip circumferences were measured for calculation of BMI and WHR; DXA was used to determine fat in the head, upper body, abdomen, and lower body. Linear regression was used to assess their relationships with S_I after sex stratification and adjustment for age. Results: After controlling for age, increases in traditional and DXA measures of fat were consistently associated with smaller declines in S_I among women than among men. In men, after controlling for age, all of the predictive information of S_I was provided by waist circumference (additional R² = 0.39, p < 0.001); none of the DXA measures improved the ability to predict S_I. In women, after adjustment for age, BMI, and WHR, the only DXA measure that improved the prediction of S_I was percentage head fat (additional R² = 0.03, p < 0.001). Discussion: Equivalent increases in most measures of body fat had lesser impact on S_I in women than in men. In both sexes, the predictive information provided by DXA measures is approximately equal to, but not additive to, that provided by simpler, traditional measures.     

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.     

Regional Subcutaneous‐Fat Loss Induced by Caloric Restriction in Obese Women

Objective: With anthropometric models using skinfolds and circumferences, we studied changes in the percentage of subcutaneous fat in the total cross‐sectional area (SF%) at four body sites in obese women, before and after weight loss induced by 6 months of caloric restriction. Research Methods and Procedures: In 61 obese women [31 African Americans and 30 whites; ages, 24 to 68 years; body mass index (BMI), ≥28kg/m²], we measured SF% at the midpoint of the upper arm and thigh and the waistline and hipline, and we measured the percentage of total body fat by DXA before (Obs#1) and after (Obs#2) a 6‐month nonintervention control period and then after 6 months on a 1200 kcal/d diet (Obs#3). Results: The mean body weight and BMI increased (1.8 kg and 0.61 kg/m²; p = 0.0001), but there were no significant changes in any other body composition measurements from Obs#1 to Obs#2. The means of Obs#3 for weight and BMI decreased by 11%, and the percentage of total body fat decreased by 13% of Obs#2 mean values (p = 0.0001). The mean SF% at each site decreased 7.6% to 18.0% of the Obs#2 mean values (p < 0.001). The SF% decreases were greater at mid‐arm and mid‐thigh than in the cross‐sectional regions at the waistline and hipline (p = 0.05). There was no interaction between age or ethnicity (p > 0.2). Conclusions: In obese women, caloric restriction alone reduces anthropometrically measured subcutaneous fat proportionally more in peripheral than in central regions.     

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.     

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.     

Leptin Levels Are Associated with Fat Oxidation and Dietary‐Induced Weight Loss in Obesity

Objective: To examine the relationship between fasting plasma leptin and 24‐hour energy expenditure (EE), substrate oxidation, and spontaneous physical activity (SPA) in obese subjects before and after a major weight reduction compared with normal weight controls. To test fasting plasma leptin, substrate oxidations, and SPA as predictive markers of success during a standardized weight loss intervention. Research Methods and Procedures: Twenty‐one nondiabetic obese (body mass index: 33.9 to 43.8 kg/m²) and 13 lean (body mass index: 20.4 to 24.7 kg/m²) men matched for age and height were included in the study. All obese subjects were reexamined after a mean weight loss of 19.2 kg (95% confidence interval: 15.1–23.4 kg) achieved by 16 weeks of dietary intervention followed by 8 weeks of weight stability. Twenty‐four‐hour EE and substrate oxidations were measured by whole‐body indirect calorimetry. SPA was assessed by microwave radar. Results: In lean subjects, leptin adjusted for fat mass (FM) was correlated to 24‐hour EE before (r = ?0.56, p < 0.05) but not after adjustment for fat free mass. In obese subjects, leptin correlated inversely with 24‐hour and resting nonprotein respiratory quotient (r = ?0.47, p < 0.05 and r = ?0.50, p < 0.05) both before and after adjustments for energy balance. Baseline plasma leptin concentration, adjusted for differences in FM, was inversely related to the size of weight loss after 8 weeks (r = ?0.41, p = 0.07), 16 weeks (r = ?0.51, p < 0.05), and 24 weeks (r = ?0.50, p < 0.05). Discussion: The present study suggests that leptin may have a stimulating effect on fat oxidation in obese subjects. A low leptin level for a given FM was associated with a greater weight loss, suggesting that obese subjects with greater leptin sensitivities are more successful in reducing weight.     

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.     

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.     

Effect of sarcopenia on cardiovascular disease risk factors in obese postmenopausal women

Objective: To compare sarcopenic‐obese and obese postmenopausal women for risk factors predisposing to cardiovascular disease (CVD) and determine whether there may be a relationship between muscle mass and metabolic risk in obese postmenopausal women. Research Methods and Procedures: In this cross‐sectional study, 22 healthy obese postmenopausal women (mean age, 66 ± 5 years; mean BMI, 27 ± 3 kg/m²) were divided into two groups matched for age (±2 years) and fat mass (FM) (±2%). Sarcopenia was defined as a muscle mass index of <14.30 kg fat‐free mass (FFM)/m² (which corresponds to 1 standard deviation below the values of a young reference population), and obesity was defined as an FM of >35% (which corresponds to the World Health Organization guidelines). FM, FFM (measured by DXA), daily energy expenditure (accelerometry), dietary intake (3‐day dietary record), and blood biochemical analyses (lipid profile, insulin, glucose, and C‐reactive protein) were obtained. Visceral fat mass (VFM) was calculated by the equation of Bertin, which estimates VFM from DXA measurements. Results: Obese women had more FFM (p = 0.006), abdominal FM (p = 0.047), and VFM (p = 0.041) and a worse lipid profile [p = 0.040 for triglycerides; p = 0.004 for high‐density lipoprotein (HDL); p = 0.026 for total cholesterol/HDL] than sarcopenic‐obese postmenopausal women. Obese women also ingested significantly more animal (p = 0.001) and less vegetal proteins (p = 0.013), although both groups had a similar total protein intake (p = 0.967). Discussion: Sarcopenia seems to be associated with lower risk factors predisposing to CVD in obese postmenopausal women. With the increase in the number of aging people, the health implications of being sarcopenic‐obese merit more attention.     

Racial Differences in Metabolic Predictors of Obesity among Postmenopausal Women

NICKLAS, BARBARA S., DORA M. BERMAN, DAWN C. DAVIS, C. LYNNE DOBROVOLNY, AND KAREN E. DENNIS. Racial differences in metabolic predictors of obesity among postmenopausal women. Ober Res. Objective: This study determined whether there are racial differences in resting metabolic rate (RMR), fat oxidation, and maximal oxygen consumption (VO,max) in obese [body mass index (BMI = 34±2 kg/m²)], postmenopausal (58±2 years) women. Research Methods and Procedures: Twenty black and 20 white women were matched for fat mass and lean mass (LM), as determined by dual energy X-ray absorptiometry. RMR and fat oxidation were measured by indirect calorimetry in the early morning after a 12-hour fast using the ventilated hood technique. VO₂max was measured on a treadmill during a progressive exercise test to voluntary exhaustion. Results: RMR, adjusted for differences in LM, was 5% higher in white than black women (1566±27 and 1490±26 kcal/day, respectively; p<0. 05); and fat oxidation rate was 17% higher in white than black women (87±4 and 72±3 g/day, respectively; p<0. 01). VO₂max (L/minute) was 150 mL per minute (8%) higher (p<0. 05) in white than black women. VO₂max correlated with LM in black (r = 0. 44, p = 0. 05) and white (r=0. 53, p<0. 05) women, but the intercept of the regression line was higher in white than black women (p<0. 05), with no significant difference in slopes. In a multiple regression model including race, body weight, LM, and age, LM was the only independent predictor of RMR (r² = 0. 46, p<0. 0001), whereas race was the only independent predictor of fat oxidation (r² = 0. 18,p<0. 05). The best predictors of VO,max were LM (r² = 0. 22, p<0. 05) and race (cumulative r² = 0. 30, p<0. 05). Discussion: These results show there are racial differences in metabolic predictors of obesity. Determination of whether these ethnic differences lead to, or are an effect of, obesity status or other lifestyle factors requires further study.