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.     

No apparent progress in bioelectrical impedance accuracy: validation against metabolic risk and DXA

Bioelectrical impedance (BIA) is quick, easy, and safe when quantifying fat and lean tissue. New BIA models (Tanita BC-418 MA, abbreviated BIA(8)) can perform segmental body composition analysis, e.g., estimate %trunkal fatness (%TF). It is not known, however, whether new BIA models can detect metabolic risk factors (MRFs) better than older models (Tanita TBF-300, abbreviated BIA(4)). We therefore tested the correlation between MRF and percentage whole-body fat (%BF) from BIA(4) and BIA(8) and compared these with the correlation between MRF and dual-energy X-ray absorptiometry (DXA, used as gold standard), BMI and waist circumference (WC). The sample consisted of 136 abdominally obese (WC >or= 88 cm), middle-aged (30-60 years) women. MRF included fasting blood glucose and insulin; high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides; high sensitive C-reactive protein, plasminogen activator inhibitor-1 (PAI-1), and fibrinogen; and alanine transaminase (ALT) liver enzyme. We found that similar to DXA, but in contrast to BMI, neither %BF BIA(4) nor %BF BIA(8) correlated with blood lipids or ALT. In the segmental analysis of %TF, BIA(8) only correlated with inflammatory markers, but not insulin, blood lipids, or ALT liver enzyme (in contrast to WC and %TF DXA). %TF DXA was associated with homeostatic model assessment insulin resistance (HOMA-IR) independently of WC (P = 0.03), whereas %TF BIA(8) was not (P = 0.53). Receiver-operating characteristic (ROC) curves confirmed that %TF BIA(8) did not differ from chance in the detection of insulin resistance (P = 0.26). BIA estimates of fatness were, at best, weakly correlated with obesity-related risk factors in abdominally obese women, even the new eight-electrode model. Our data support the continued use of WC and BMI.     

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.     

Prediction of Fatness by Standing 8‐Electrode Bioimpedance: A Multiethnic Adolescent Population

The objective of this study was to validate an 8‐electrode bioimpedance analysis (BIA₈) device (BC‐418; Tanita, Tokyo, Japan) for use in populations of European, Maori, Pacific Island, and Asian adolescents. Healthy adolescents (215 M, 216 F; 129 Pacific Island, 120 Asian, 91 Maori, and 91 European; age range 12–19 years) were recruited by purposive sampling of high schools in Auckland, New Zealand. Weight, height, sitting height, leg length, waist circumference, and whole‐body impedance were measured. Fat mass (FM) and fat‐free mass (FFM) derived from the BIA₈ manufacturer's equations were compared with measurements by dual‐energy X‐ray absorptiometry (DXA). DXA‐measured FFM was used as the reference to develop prediction equations based on impedance. A double cross‐validation technique was applied. BIA₈ underestimated FM by 2.06 kg (P < 0.0001) and percent body fat (%BF) by 2.84% (P < 0.0001), on average. However, BIA₈ tended to overestimate FM and %BF in lean and underestimate FM and %BF in fat individuals. Sex‐specific equations developed showed acceptable accuracy on cross‐validation. In the total sample, the best prediction equations were, for boys: FFM (kg) = 0.607 height (cm)²/impedance (Ω) + 1.542 age (y) + 0.220 height (cm) + 0.096 weight (kg) + 1.836 ethnicity (0 = European or Asian, 1 = Maori or Pacific) ? 47.547, R² = 0.93, standard error of estimate (SEE) = 3.09 kg; and, for girls: FFM (kg) = 0.531 height (cm)²/impedance (Ω) + 0.182 height (cm) + 0.096 weight (kg) + 1.562 ethnicity (0 = non‐Pacific, 1 = Pacific) ? 15.782, R² = 0.91, SEE = 2.19 kg. In conclusion, equations for fatness estimation using BIA₈ developed for our sample perform better than reliance on the manufacturer's estimates. The relationship between BIA and body composition in adolescents is ethnicity dependent.     

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.     

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.     

Waist circumference correlates with metabolic syndrome indicators better than percentage fat

Objective : Percent fat is often considered the reference for establishing the magnitude of adipose tissue accumulation and the risk of excess adiposity. However, the increasing recognition of a strong link between central adiposity and metabolic disturbances led us to test whether waist circumference (WC) is more highly correlated with metabolic syndrome components than percent fat and other related anthropometric measures such as BMI. Research Methods and Procedures : BMI, WC, and percent fat, measured by DXA, were evaluated in 1010 healthy white and African‐American men and women [age, 48.3 ± 17.2 (standard deviation) years; BMI, 27.0 ± 5.3 kg/m²]. The associations of BMI, WC, and percent fat with age and laboratory‐adjusted health risk indicators (i.e., serum glucose, insulin, triglycerides, high‐density lipoprotein cholesterol, blood pressure) in each sex and ethnicity group were examined. Results : For 18 of 24 comparisons, the age‐ and laboratory‐adjusted correlations were lowest for percent fat and in 16 of 24 comparisons were highest for WC. Fifteen of the between‐method differences reached statistical significance. With health risk indicator as the dependent variable and anthropometric measures as the independent variable, the contribution of percent fat to the WC regression model was not statistically significant; in contrast, adding WC to the percent fat regression model did make a significant independent contribution for most health risk indicators. Discussion : WC had the strongest associations with health risk indicators, followed by BMI. Although percent fat is a useful measure of overall adiposity, health risks are best represented by the simply measured WC.     

Comparison of Methods to Assess Body Composition Changes during a Period of Weight Loss

Objective: To assess the accuracy of body composition measurements by air displacement plethysmography and bioelectrical impedance analysis (BIA) compared with DXA during weight loss. Research Methods and Procedures: Fifty‐six healthy but overweight participants, 34 women and 22 men (age, 52 ± 8.6 years; weight, 92.2 ± 11.6 kg; BMI, 33.3 ± 2.9 kg/m²) were studied in an outpatient setting before and after 6 months of weight loss (weight loss, 5.6 ± 5.5 kg). Subjects were excluded if they had initiated a new drug therapy within 30 days of randomization, were in a weight loss program, or took a weight loss drug within 90 days of randomization. Subjects were randomly assigned either to a self‐help program, consisting of two 20‐minute sessions with a nutritionist and provision of printed materials and other self‐help resources, or to attendance at meetings of a commercial program (Weight Watchers). Body composition was examined by each of the methods before and after weight loss. Results: BIA (42.4 ± 5.8%) underestimated percentage fat, whereas the BodPod (Siri = 51.7 ± 6.9%; Brozek = 48.5 ± 6.5%) overestimated percentage fat compared with DXA (46.1 ± 7.9%) before weight loss. Correlation coefficients for detecting changes in body composition between DXA and the other methods were relatively high, with Brozek Δfat mass (FM; r² = 0.63), Siri FM (r² = 0.65), tetrapolar BIA percentage fat (r² = 0.57), and Tanita FM (r² = 0.61) being the highest. Discussion: In conclusion, all of the methods were relatively accurate for assessing body composition compared with DXA, although there were biases. Furthermore, each of the methods was sensitive enough to detect changes with weight loss.     

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.     

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

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

Racial/ethnic differences in body fatness among children and adolescents

Background: Although the BMI is widely used as a measure of adiposity, it is a measure of excess weight, and its association with body fatness may differ across racial or ethnic groups. Objective: To determine whether differences in body fatness between white, black, Hispanic, and Asian children vary by BMI‐for‐age, and whether the accuracy of overweight (BMI‐for‐age ≥ Centers for Disease Control and Prevention (CDC) 95th percentile) as an indicator of excess adiposity varies by race/ethnicity. Methods and Procedures: Total body dual‐energy X‐ray absorptiometry (DXA) provided estimates of %body fat among 1,104 healthy 5‐ to 18‐year‐olds. Results: At equivalent levels of BMI‐for‐age, black children had less (mean, 3%) body fatness than white children, and Asian girls had slightly higher (1%) levels of %body fat than white girls. These differences, however, varied by BMI‐for‐age, with the excess body fatness of Asians evident only among relatively thin children. The ability of overweight to identify girls with excess body fatness also varied by race/ethnicity. Of the girls with excess body fatness, 89% (24/27) of black girls, but only 50% (8/16) of Asian girls, were overweight (P = 0.03). Furthermore, the proportion of overweight girls who had excess body fatness varied from 62% (8/13) among Asians to 100% (13/13) among whites. Discussion: There are racial or ethnic differences in body fatness among children, but these differences vary by BMI‐for‐age. If race/ethnicity differences in body fatness among adults also vary by BMI, it may be difficult to develop race‐specific BMI cut points to identify equivalent levels of %body fat.     

Percentage of Body Fat Assessment Using Bioelectrical Impedance Analysis and Dual-Energy X-ray Absorptiometry in a Weight Loss Program for Obese or Overweight Chinese Adults

The current study aimed to compare the estimates of body fat percentage (%BF) by performing bioelectrical impedance analysis (BIA) and dual energy X-ray absorptiometry (DXA) in a sample of obese or overweight Chinese adults who participated in a weight-loss randomized control trial stratified by gender to determine whether or not BIA is a valid measurement tool. Among 189 adults [73 males, 116 females; age  = 41 to 74 years; mean body mass index (BMI)  = 27.3 kg/m²], assessments of %BF at the baseline and six months from the baseline were conducted by performing BIA and DXA. Bland-Altman analyses and multiple regression analyses were used to assess the relationships between %BF_BIA and %BF_DXA. Compared with DXA, BIA underestimated %BF [in males: 4.6, –2.4 to 11.7 (mean biases, 95% limit of agreement) at the baseline, 1.4, –7.4 to 10.2 at the endpoint, and 3.2, –4.8 to 11.3 in changes; in females: 5.1, –2.4 to 12.7; 2.2, –6.1 to 10.4; and 3.0, –4.8 to 10.7, respectively]. For males and females, %BFDXA proved to be a significant predictor of the difference between DXA and BIA at the baseline, the endpoint, and in changes when BMI and age were considered (in males: p<0.01 and R ²  = 23.1%, 24.1%, 20.7%, respectively; for females: p<0.001 and R ²  = 40.4%, 48.8%, 25.4%, respectively). The current study suggests that BIA provides a relatively accurate prediction of %BF in individuals with normal weight, overweight, or obesity after the end of weight-loss program, but less accurate prediction of %BF in obese individuals at baseline or weight change during the weight-loss intervention program.     

Obese reproductive-age women exhibit a proatherogenic inflammatory response during hyperglycemia

Objective: The objective was to determine if physiological hyperglycemia induces a proatherogenic inflammatory response in mononuclear cells (MNCs) in obese reproductive‐age women. Research Methods and Procedures: Seven obese and 6 age‐matched lean women (20 to 39 years of age) underwent a 2‐hour 75‐g oral glucose tolerance test. The release of interleukin‐6 (IL‐6) and interleukin‐1β (IL‐1β) from MNCs cultured in the presence of lipopolysaccharide (LPS) was measured after isolation from blood samples drawn fasting and 2 hours after glucose ingestion. Reactive oxygen species (ROS) generation and intra‐nuclear nuclear factor κB (NFκB) from MNCs were quantified from the same blood samples. Insulin resistance was estimated by homeostasis model assessment of insulin resistance (HOMA‐IR). Total body fat and truncal fat were determined by DXA. Results: Obese women had a higher (p < 0.03) total body fat (42.2 ± 1.1 vs. 27.7 ± 2.0%), truncal fat (42.1 ± 1.2 vs. 22.3 ± 2.4%), and HOMA‐IR (3.3 ± 0.5 vs. 1.8 ± 0.2). LPS‐stimulated IL‐6 release from MNCs was suppressed during hyperglycemia in lean subjects (1884 ± 495 vs. 638 ± 435 pg/mL, p < 0.05) but not in obese women (1184 ± 387 vs. 1403 ± 498 pg/mL). There was a difference (p < 0.05) between groups in the hyperglycemia‐induced MNC‐mediated release of IL‐6 (?1196 ± 475 vs. 219 ± 175 pg/mL) and IL‐1β (?79 ± 43 vs. 17 ± 12 pg/mL). In addition, the obese group exhibited increased (p < 0.05) MNC‐derived ROS generation (39.3 ± 9.9 vs. ?1.0 ± 12.8%) and intra‐nuclear NFκB (9.4 ± 7.3 vs. ?23.5 ± 13.5%). Truncal fat was positively correlated with the MNC‐derived IL‐6 response (ρ = 0.58, p < 0.05) and intra‐nuclear NFκB (ρ = 0.64, p < 0.05). Discussion: These data suggest that obese reproductive‐age women are unable to suppress proatherogenic inflammation during physiological hyperglycemia. Increased adiposity may be a significant contributor to this pro‐inflammatory susceptibility.     

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

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

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.     

Birth Weight,Adult Body Composition,and Subcutaneous Fat Distribution

Objectives: To investigate if birth weight is related to both body mass index (BMI) and distribution of subcutaneous fat at adult age. Research Methods and Procedures: A 9‐year longitudinal study was performed in 229 subjects (192 women) with ages ranging from 27 to 36 years. Birth weight was retrieved by a questionnaire, and adult weight, height, skinfold thicknesses, and waist‐to‐hip ratio (WHR) were repeatedly measured at mean ages 27, 29, 31, and 36 years. BMI, sum of four skinfolds (S4S), the ratio between two truncal skinfolds and S4S (SS/S4S), and the ratio between WHR and the cross‐sectional area of the left thigh were calculated with the available data. Results: The adjusted model showed that in women, birth weight was significantly negatively related to adult S4S [β = ?5.211; (?9.768 to ?0.654)], waist circumference [β = ?1.449; (?2.829 to ?0.069)], and SS/S4S ratio [β = ?3.579; (?5.296 to ?1.862)]. In men, a significant negative association was observed between birth weight and adult WHR [β = ?1.096; (?2.092 to ?0.100)] only. Other relationships showed, although not significantly, the same negative trend, namely that lower birth weight is related to higher adult body fat mass (S4S) and a more truncal subcutaneous fat distribution (SS/S4S). No associations were found between birth weight and either adult BMI or the cross‐sectional area of the thigh. Discussion: Lower birth weight is, in both adult men and women, related to a higher adult subcutaneous fat mass and a more truncal distribution of subcutaneous fat, indicating a higher risk for obesity.     

Fitness Alters the Associations of BMI and Waist Circumference with Total and Abdominal Fat

Objective: We tested the following hypotheses in black and white men and women: 1) for a given BMI or waist circumference (WC), individuals with moderate cardiorespiratory fitness (CRF) have lower amounts of total fat mass and abdominal subcutaneous and visceral fat compared with individuals with low CRF; and 2) exercise training is associated with significant reductions in total adiposity and abdominal fat independent of changes in BMI or WC. Research Methods and Procedures: The sample included 366 sedentary male (111 blacks and 255 whites) and 462 sedentary female (203 blacks and 259 whites) participants in the HERITAGE Family Study. The relationships between BMI and WC with total fat mass (determined by underwater weighing) and abdominal subcutaneous and visceral fat (determined by computed tomography) were compared in subjects with low (lower 50%) and moderate (upper 50%) CRF. The effects of a 20‐week aerobic exercise training program on changes in these adiposity variables were examined in 86% of the subjects. Results: Individuals with moderate CRF had lower levels of total fat mass and abdominal subcutaneous and visceral fat than individuals with low CRF for a given BMI or WC value. The 20‐week aerobic exercise program was associated with significant reductions in total adiposity and abdominal fat, even after controlling for reductions in BMI and WC. With few exceptions, these observations were true for both men and women and blacks and whites. Discussion: These findings suggest that a reduction in total adiposity and abdominal fat may be a means by which CRF attenuates the health risk attributable to obesity as determined by BMI and WC.     

Cardiorespiratory fitness, different measures of adiposity, and cancer mortality in men

Objective: The purpose was to examine the prospective relationship among cardiorespiratory fitness level (CRF), different measures of adiposity, and cancer mortality in men. Research Methods and Procedures: Participants were 38,410 apparently healthy men who completed a comprehensive baseline health examination between 1970 and 2001. Clinical measures included BMI, waist circumference (WC), percent body fat, and CRF quantified as duration of a maximal treadmill exercise test. Participants were divided into fifths of CRF, BMI, WC, and percent body fat. Hazard ratios were computed with Cox regression analysis. Results: During a mean follow‐up period of 17.2 ± 7.9 years, 1037 cancer deaths occurred. Adjusted hazard ratios across incremental BMI quintiles were 1.0, 1.23, 1.15, 1.39, and 1.72; those of WC were 1.0, 1.05, 1.03, 1.31, and 1.64; those of percent body fat were 1.0, 1.24, 1.17, 1.23, and 1.50; and those of CRF were 1.0, 0.70, 0.67, 0.70, and 0.49 (trend p < 0.01 for each). Further adjustment for CRF eliminated the significant trend in mortality risk across percent body fat groups and attenuated the trend in risk across BMI and WC groups. Adjustment of CRF for adiposity measures had little effect on mortality risk. When grouped into categories of fit and unfit (upper 80% and lower 20% of CRF distribution, respectively), mortality rates (per 10,000 man‐years) were significantly lower in fit compared with unfit men within each stratum of BMI, WC, and percent body fat. Discussion: Higher levels of CRF are associated with lower cancer mortality risk in men, independently of several adiposity measures.     

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

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

The use of BMI and waist circumference as surrogates of body fat differs by ethnicity

Objective: To compare the prediction of percentage body fat using BMI and visceral adipose tissue (VAT) using waist circumference (WC) in individuals of Chinese, European, and South Asian origin. Research Methods and Procedures: Healthy men and women of Chinese, European, and South Asian origin (n = 627) between the ages of 30 and 65 years were recruited to ensure equal distribution of gender and representation across BMI ranges (18.5 to 24.9, 25 to 29.9, and ≥30 kg/m²). Participants were assessed for demographics, anthropometry, lifestyle, and regional adiposity. Percentage body fat and VAT were measured by DXA and computer tomography scan, respectively. Results: BMI and WC were highly correlated with total and regional measures of adiposity in each ethnic group. At any BMI, the percentage body fat of Chinese participants was similar to that of Europeans, but that of South Asians was greater by 3.9% (p < 0.001). Above a WC of 71.0 cm, the Chinese participants had an increasingly greater amount of VAT than the Europeans (p = 0.017 for interaction). South Asians had significantly more VAT than the Europeans at all but the most extreme WC (above 105 cm) (p < 0.05). Discussion: Compared with Europeans, percentage body fat was higher for a given BMI in South Asians, whereas VAT was higher for a given WC in both Chinese and South Asian men and women. These findings support the use of ethnic‐specific anthropometric targets.