Heritability of Body Composition Measured by DXA in the Diabetes Heart Study

Objective: The purpose of this study was to investigate the heritability of body composition measured by DXA in the Diabetes Heart Study (DHS). Research Methods and Procedures: Participants were 292 women and 262 men (age, 38 to 86 years; BMI, 17 to 57 kg/m²) from 244 families. There were 492 white and 49 African‐American sibling pairs. DXA measurements of percentage fat mass (FM), whole body FM, and lean mass (LM), as well as regional measurements of trunk fat mass (TFM) and appendicular lean mass (ALM), were obtained. Heritability of FM, LM, and BMI were estimated using Sequential Oligogenic Linkage Analysis Routines. Results: After adjusting for age, gender, ethnicity, and height, the heritability estimates of various compositional attributes were %FM = 0.64, whole body FM = 0.71, TFM = 0.63, whole body LM = 0.60, ALM = 0.66, and BMI = 0.64 (all p < 0.0001). Additional adjustment for diabetes status, smoking, dietary intake, and physical activity resulted in only minor changes in the heritability estimates (?² = 0.63 to 0.72, all p < 0.0001). Furthermore, heritability of TFM after additional adjustment for whole body FM was significant (?² = 0.55, p < 0.0001), and heritability of ALM after additional adjustment for whole body LM was also significant (?² = 0.51, p < 0.0001). Discussion: These data suggest that FM and LM measured by DXA are highly heritable and can be effectively used in designing linkage studies to locate genes governing body composition. In addition, regional distribution of FM and LM may be genetically determined.     

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.     

Accuracy of Specific BIVA for the Assessment of Body Composition in the United States Population

Background

Bioelectrical impedance vector analysis (BIVA) is a technique for the assessment of hydration and nutritional status, used in the clinical practice. Specific BIVA is an analytical variant, recently proposed for the Italian elderly population, that adjusts bioelectrical values for body geometry.

Objective

Evaluating the accuracy of specific BIVA in the adult U.S. population, compared to the ‘classic’ BIVA procedure, using DXA as the reference technique, in order to obtain an interpretative model of body composition.

Design

A cross-sectional sample of 1590 adult individuals (836 men and 754 women, 21–49 years old) derived from the NHANES 2003–2004 was considered. Classic and specific BIVA were applied. The sensitivity and specificity in recognizing individuals below the 5^th and above the 95^th percentiles of percent fat (FM_DXA%) and extracellular/intracellular water (ECW/ICW) ratio were evaluated by receiver operating characteristic (ROC) curves. Classic and specific BIVA results were compared by a probit multiple-regression.

Results

Specific BIVA was significantly more accurate than classic BIVA in evaluating FM_DXA% (ROC areas: 0.84–0.92 and 0.49–0.61 respectively; p = 0.002). The evaluation of ECW/ICW was accurate (ROC areas between 0.83 and 0.96) and similarly performed by the two procedures (p = 0.829). The accuracy of specific BIVA was similar in the two sexes (p = 0.144) and in FM_DXA% and ECW/ICW (p = 0.869).

Conclusions

Specific BIVA showed to be an accurate technique. The tolerance ellipses of specific BIVA can be used for evaluating FM% and ECW/ICW in the U.S. adult population.     

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.     

The Use of Anthropometric and Dual-Energy X-ray Absorptiometry (DXA) Measures to Estimate Total Abdominal and Abdominal Visceral Fat in Men and Women

CLASEY, JODY L., CLAUDE BOUCHARD, C. DAVID TEATES, JILL E. RIBLETT, MICHAEL O. THORNER, MARK L. HARTMAN, AND ARTHUR WELTMAN. the use of anthropometric and dual-energy X-ray absorptiometry (DXA) measures to estimate total abdominal and abdominal visceral fat in men and women. Obes Res. Objective: A single-slice computed tomography (CT) scan provides a criterion measure of total abdominal fat (TAF) and abdominal visceral fat (AVF), but this procedure is often prohibitive due to radiation exposure, cost, and accessibility. In the present study, the utility of anthropometric measures and estimates of trunk and abdominal fat mass by dual-energy X-ray absorptiometry (DXA) to predict CT measures of TAF and AVF (cross-sectional area, cm²) was assessed. Research Methods and Procedures: CT measures of abdominal fat (at the level of the L4-L5 inter-vertebral space), DXA scans, and anthropometric measures were obtained in 76 Caucasian adults ages 20–80 years. Results: Results demonstrated that abdominal sagittal diameter measured by anthropometry is an excellent predictor of sagittal diameter measured from a CT image (r = 0. 88 and 0. 94; Total Error [TE]=4. 1 and 3. 1 cm, for men and women, respectively). In both men and women, waist circumference and abdominal sagittal diameter were the anthropometric measures most strongly associated with TAF (r = 0. 87 to 0. 93; Standard Error of Estimate (SEE) = 60. 7 to 75. 4 cm²) and AVF (r = 0. 84 to 0. 93; SEE = 0. 7 to 30. 0 cm²). The least predictive anthropometric measure of TAF or AVF was the commonly used waist-to-hip ratio (WHR). DXA estimates of trunk and abdominal fat mass were strongly associated with TAF (r =. 94 to 0. 97; SEE = 36. 9 to 50. 9 cm²) and AVF (r = 0. 86 to 0. 90; SEE = 4. 9 to 27. 7 cm²). Discussion: The present results suggest that waist circumference and/or abdominal sagittal diameter are better predictors of TAF and AVF than the more commonly used WHR. DXA trunk fat and abdominal fat appear to be slightly better predictors of TAF but not AVF compared to these anthropometric measures. Thus DXA does not offer a significant advantage over anthropometry for estimation of AVF.     

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.     

Relationship of Leptin Concentration to Gender,Menopause, Age,Diabetes, and Fat Mass in African Americans

This investigation was designed to determine the relationship of leptin concentration to gender, sex hormones, menopause, age, diabetes, and fat mass in African Americans. Participants included 101 African Americans, 38 men (mean age, 34. 2 ± 7. 4 years), 29 age-matched premenopausal women (mean age, 32. 6 ± 3. 7 years), and 36 postmenopausal women (mean age, 57. 8 ± 5. 9 years). The women were not taking exogenous sex hormones, and 12 subjects were diabetic. Percent body fat was calculated with the Siri formula, fat mass (FM) was calculated as weight x percent body fat, and Fat-free mass (FFM) was calculated as weight minus FM. Fasting plasma was assayed for leptin, estradiol, free testosterone, glucose, and insulin concentrations. The nondiabetics had an oral glucose tolerance test (OGTT). The diabetics compared with the non-diabetics had a higher central fat index (^P=0. 04) but otherwise were similar to nondiabetics in all parameters measured. Body mass index, percent body fat, and FM were greater in women than men (p<0. 001). Leptin concentrations in men, premenopausal, and postmenopausal women were: 7. 51 ± 8. 5, 33. 9 ± 17. 3, 31. 4 ± 22. 3 ng/mL. Leptin/FM x 100 in the three groups were: 28. 9 ± 16. 1, 98. 65 ± 44. 9, 77. 1 ± 44. 5 ng/mL/kg. The gender difference in leptin concentration and leptin/FM was significant (p<0. 001), but the difference between premenopausal and postmenopausal women was not. In each group, weight, percent body fat, and FM were highly correlated with leptin concentration. Multiple regression analyses with leptin concentration as the dependent variable and age, diabetic status, percent body fat, weight, FM, FFM, estradiol, and free testosterone concentrations as independent variables demonstrated that the determinants of leptin concentration in men was weight only (R=0. 83,p<0. 001), in premenopausal women it was FM only (R=0. 57,P<0. 001), and in postmenopausal women it was weight only (R=0. 67, p<0. 001). With diabetics excluded, the multiple regression analysis was repeated with fasting insulin concentration and the area under the insulin curve during the OGTT included as independent variables. The results for this multiple regression analyses were the same as the first. Therefore, leptin concentration in African Americans is determined by gender and fat mass. Menopause, age, and diabetes do not affect leptin concentration.     

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.     

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.     

Visceral Adipose Tissue in Women: Longitudinal Study of the Effects of Fat Gain,Time, and Race

Objective: To determine the effects of fat gain, time, and race on the accumulation of visceral adipose tissue (VAT) in a group of normal‐weight premenopausal women. Research Methods and Procedures: Sixty‐five women participated in the study (32 African American and 33 white). The mean age of subjects was 34 ± 6 years (range, 22 to 47 years). Eligible subjects were women who had body mass indices <25 kg/m² at baseline and who had completed evaluations at baseline and at follow‐up year 1, without intervention. A subset of subjects was reevaluated annually for up to 4 years. Body composition was assessed by DXA, and VAT was determined from a single computed tomography scan. A linear mixed model was used to examine changes in VAT over time, with total body fat as a covariate Results: Total fat mass was not significantly different between races at baseline and increased significantly in both groups over time (p < 0.001). Time‐related increases in total body fat were greater in African‐American women (p < 0.01). VAT was significantly higher in white women at baseline (p < 0.01) and increased significantly over time in both races (p < 0.01), but remained higher in white women (p < 0.001). Increases in VAT, relative to total body fat, were greater than the increases in total body fat over time, independent of age and race (p < 0.001). Discussion: Gaining total body‐fat mass results in a higher increase in VAT, relative to total body fat, regardless of race and age, although African‐American women maintain a lower VAT levels across time.     

The Body Adiposity Index (Hip Circumference ÷ Height1.5) Is Not a More Accurate Measure of Adiposity Than Is BMI,Waist Circumference,or Hip Circumference

Based on cross‐sectional analyses, it was suggested that hip circumference divided by height^1.5 ?18 (the body adiposity index (BAI)), could directly estimate percent body fat without the need for further correction for sex or age. We compared the prediction of percent body fat, as assessed by dual‐energy X‐ray absorptiometry (PBF_DXA), by BAI, BMI, and circumference (waist and hip) measurements among 1,151 adults who had a total body scan by DXA and circumference measurements from 1993 through 2005. After accounting for sex, we found that PBF_DXA was related similarly to BAI, BMI, waist circumference, and hip circumference. In general, BAI underestimated PBF_DXA among men (2.5%) and overestimated PBF_DXA among women (4%), but the magnitudes of these biases varied with the level of body fatness. The addition of covariates and quadratic terms for the body size measures in regression models substantially improved the prediction of PBF_DXA, but none of the models based on BAI could more accurately predict PBF_DXA than could those based on BMI or circumferences. We conclude that the use of BAI as an indicator of adiposity is likely to produce biased estimates of percent body fat, with the errors varying by sex and level of body fatness. Although regression models that account for the nonlinear association, as well as the influence of sex, age, and race, can yield more accurate estimates of PBF_DXA, estimates based on BAI are not more accurate than those based on BMI, waist circumference, or hip circumference.     

Fat Mass Measured by DXA Varies with Scan Velocity

Objective: To study the influence of scan velocities of DXA on the measured size of fat mass, lean body mass, bone mineral content and density, and total body weight. Research Methods and Procedures: The subjects were 71 healthy white adults, 38 women and 33 men. The mean age was 41.7 ± 13.5 years and body mass index was 28.6 ± 5.6 kg/m². The subjects were scanned consecutively in slow, medium, and fast scan mode by a Lunar DPX-IQ DXA scanner. Results: Throughout the body mass index and sagittal height ranges, scanned lean body mass significantly decreased with higher scan velocity and lean body mass was 2.7% lower in fast than in medium mode (p < 0.0001). In contrast, fat mass, percentage of body fat, and bone mineral contents were higher with increasing scan velocity. Areas not analyzed by the scanner, so called blue spots, increased with scan velocity and sagittal height, and their presence significantly enhanced the error. Body weight estimated by DXA in slow mode was −0.8% lower than scale weight in the women (p < 0.001) and −0.2% in men (not significant), and the difference was greater with increasing scan velocity. Discussion: Scan velocity significantly influences the measured fat mass size, lean body mass, bone mineral content, and body weight. To obtain the most accurate results, slow mode is preferable and fast scans should be avoided. Future studies should report and take scan velocity into consideration.     

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.     

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.     

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.     

Improved Prediction of Body Fat by Measuring Skinfold Thickness,Circumferences, and Bone Breadths

Objective: To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. Research Methods and Procedures: 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross‐validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. Results: The final best predictive sex‐specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM_New (kg) for men = ?40.750 + [(0.397 × waist circumference) + [6.568 × (log triceps SF + log subscapular SF + log abdominal SF)]] and BFM_New (kg) for women = ?75.231 + [(0.512 × hip circumference) + [8.889 × (log chin SF + log triceps SF + log subscapular SF)] + (1.905 × knee breadth)]. The estimates of BFM from both validation and cross‐validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two‐compartment (Durnin and Womersley) or a four‐compartment (Peterson) model as the reference method. Discussion: Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background.     

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.     

Prediction of Percentage Body Fat in Rural Thai Population Using Simple Anthropometric Measurements

Objective: To develop and validate sex‐specific equations for predicting percentage body fat (%BF) in rural Thai population, based on BMI and anthropometric measurements. Research Methods and Procedures: %BF (DXA; GE Lunar Corp., Madison, WI) was measured in 181 men and 255 women who were healthy and between 20 and 84 years old. Anthropometric measures such as weight (kilograms), height (centimeters), BMI (kilograms per meter squared), waist circumference (centimeters), hip circumference (centimeters), thickness at triceps skinfold (millimeters), biceps skinfold (millimeters), subscapular skinfold (millimeters), and suprailiac skinfold (millimeters) were also measured. The sample was randomly divided into a development group (98 men and 125 women) and a validation group (83 men and 130 women). Regression equations of %BF derived from the development group were then evaluated for accuracy in the validation group. Results: The equation for estimating %BF in men was: %BF(men) = 0.42 × subscapular skinfold + 0.62 × BMI ? 0.28 × biceps skinfold + 0.17 × waist circumference ? 18.47, and in women: %BF(women) = 0.42 × hip circumference + 0.17 × suprailiac skinfold + 0.46 × BMI ? 23.75. The coefficient of determination (R²) for both equations was 0.68. Without anthropometric variables, the predictive equation using BMI, age, and sex was: %BF = 1.65 × BMI + 0.06 × age ? 15.3 × sex ? 10.67 (where sex = 1 for men and sex = 0 for women), with R² = 0.83. When these equations were applied to the validation sample, the difference between measured and predicted %BF ranged between ±9%, and the positive predictive values were above 0.9. Discussion: These results suggest that simple, noninvasive, and inexpensive anthropometric variables may provide an accurate estimate of %BF and could potentially aid the diagnosis of obesity in rural Thais.     

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.     

Usefulness of Anthropometry and DXA in Predicting Intra‐abdominal Fat in Obese Men and Women

Objective: To investigate the usefulness of anthropometry and DXA in predicting intra‐abdominal fat (IAF) in obese men and women. Research Methods and Procedures: Observational, cross sectional study of 22 women and 18 men with a body mass index of 30 or above. IAF from 20 cm above and 10 cm below the L4 to L5 intervertebral disc was measured by magnetic resonance imaging (MRI) as a reference method. Central abdominal fat was measured from the upper border of L2 to the lower border of L4 by DXA. Waist and hip circumferences were also measured. Results: In obese women DXA, waist circumference and waist‐hip ratio were equally well correlated with IAF (r = 0.74, 0.75, and 0.70, respectively). In obese men DXA was moderately correlated with IAF measured by MRI (r = 0.46), whereas waist circumference and waist‐hip ratio were not significantly correlated with IAF. Discussion: The prediction of IAF in obese subjects was highly dependent on sex more than in non‐obese persons. Anthropometry and DXA were equally useful in obese women, whereas anthropometry had no predictive power and DXA was the only acceptable predictor of IAF in obese men.