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.     

Bias and limits of agreement between hydrodensitometry, bioelectrical impedance and skinfold calipers measures of percentage body fat

Previous research has often used correlations as a statistical method to show agreement; however, this is not a valid use of the statistic. The purpose of this study was to investigate the bias and limits of agreement for three methods of estimating percentage body fat for 117 male and 114 female university athletes: hydrodensitometry (HYD), bioelectrical impedance (BIA) and skinfold calipers (SKF). The mean (SD) percentage body fat for males as assessed by HYD, BIA and SKF methods, respectively, were 13.2 (3.3)%, 14.1 (3.3)% and 13.0 (3.2)%. Female body fat measurements were 22.5 (3.9)%, 23.7 (4.3)% and 23.8 (4.2)%, respectively. Pearson product moment correlations for male and female body fat percentages between the three methods were high, ranging from 0.81 to 0.86 (P < 0.05). However, compared to the criterion measure of body fat percentage (HYD), the magnitude of agreement BIA and SKF revealed a different pattern. The mean absolute difference between HYD and BIA measurements of body fat for males was −0.8 (2.0)% fat, and between HYD and SKF was it was 0.2 (1.7)% fat. The mean absolute difference for females between HYD and BIA was −1.2 (2.5)%; for HYD and SKF it was −1.4 (2.2)%. Compared to the HYD measures for males and females, the BIA and SKF measures were as much as a 3.8% underestimation and a 6.2% overestimation of body fat. This study provides evidence that the strength of a correlation does not indicate agreement between two methods. In future, reliability and validity studies should examine the absolute differences between two variables and calculate limits of agreement around which a practitioner can appreciate the precision of the methodologies. Accepted: 26 August 1997     

Assessing body composition with DXA and bioimpedance: effects of obesity, physical activity, and age

Objective: This study evaluated to what extent dual‐energy X‐ray absorptiometry (DXA) and two types of bioimpedance analysis (BIA) yield similar results for body fat mass (FM) in men and women with different levels of obesity and physical activity (PA). Methods and Procedures: The study population consisted of 37–81‐year‐old Finnish people (82 men and 86 women). FM% was estimated using DXA (GE Lunar Prodigy) and two BIA devices (InBody (720) and Tanita BC 418 MA). Subjects were divided into normal, overweight, and obese groups on the basis of clinical cutoff points of BMI, and into low PA (LPA) and high PA (HPA) groups. Agreement between the devices was calculated by using the Bland–Altman analysis. Results: Compared to DXA, both BIA devices provided on average 2–6% lower values for FM% in normal BMI men, in women in all BMI categories, and in both genders in both HPA and LPA groups. In obese men, the differences were smaller. The two BIA devices provided similar means for groups. Differences between the two BIA devices with increasing FM% were a result of the InBody (720) not including age in their algorithm for estimating body composition. Discussion: BIA methods provided systematically lower values for FM than DXA. However, the differences depend on gender and body weight status pointing out the importance of considering these when identifying people with excess FM.     

Validity of leg-to-leg bioelectrical impedance measurement in highly active women

The aim of this study was to compare the validity of the leg-to-leg bioelectrical impedance analysis (BIA) method with that of anthropometry using hydrostatic weighing (HW) as the criterion test. A secondary objective was to cross-validate previously developed anthropometric regression equations as well as to develop a new regression equation formula based on the anthropometric data collected in this study. Three methods for assessing body composition (HW, BIA, and anthropometric) were applied to 60 women university athletes. The means and standard deviations of age, weight, height, and body mass index (BMI) of athletes were as follows: age, 20.70 +/- 1.43; weight, 56.19 +/- 7.83 kg; height, 163.33 +/- 6.11 cm; BMI, 21.01 +/- 2.63 kg x m(-2). Leg-to-leg BIA (11.82 +/- 2.39) has shown no statistical difference between percentage body fat determined by HW (11.63 +/- 2.42%) in highly active women (p > 0.05). This result suggests that the leg-to-leg BIA and HW methods were somewhat interchangeable in highly active women (R = 0.667; standard error of estimate [SEE] = 1.81). As a result of all cross-validation analyses, anthropometric and BIA plus anthropometric results have generally produced lower regression coefficients and higher SEEs for highly active women between the ages of 18 and 25 years. The regression coefficients (0.903, 0.926) and SEE (1.08, 0.96) for the new regression formulas developed from this study were better than the all the other formulas used in this study.     

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.     

Validity of a New Abdominal Bioelectrical Impedance Device to Measure Abdominal and Visceral Fat: Comparison With MRI

Abdominal fat, and in particular, visceral adipose tissue (VAT), is the critical fat depot associated with metabolic aberrations. At present, VAT can only be accurately measured by computed tomography or magnetic resonance imaging (MRI). This study was designed to compare a new abdominal bioelectrical impedance (BIA) device against total abdominal adipose tissue (TAAT) and VAT area measurements made from an abdominal MRI scan, and to assess its reliability and accuracy. One‐hundred twenty participants were recruited, stratified by gender and BMI. Participants had triplicate measures of abdominal fat and waist circumference (WC) with the AB‐140 (Tanita, Tokyo, Japan) and WC measurements using a manual tape measure. A single abdominal MRI scan was performed as the reference method. Triplicate measures with the AB‐140 showed excellent precision for “visceral fat level,” trunk fat %, and WC. AB‐140 “visceral fat level” showed significantly stronger correlations with MRI TAAT area than with MRI VAT area (r = 0.94 vs. 0.65 in men and 0.92 vs. 0.64 in women). AB‐140 WC showed good correlation with manual WC measurements (r = 0.95 in men and 0.90 in women). AB‐140 and manual WCs showed comparable correlations with MRI TAAT area (r = 0.92 and 0.96 in men and 0.88 and 0.88 in women). AB‐140 is a simple, quick, and precise technique to measure abdominal fat and WC in healthy adults. It provides a useful proxy for TAAT measured by MRI, comparable to the correlation obtained with manual WC measurements. Neither the AB‐140 abdominal fat measures nor WC measurement appear to provide a useful proxy measure of VAT.     

Comparisons of fatness indicators in Budapest children

The aim of this study was to estimate the fatness level of Budapest children and youth in different ways and to compare these estimations using a large representative sample. Eighteen body measurements were taken on 2606 healthy boys and 2471 healthy girls aged between 3 and 18 years. About 20% of this sample was measured by the Futrex 5000A near infrared (NIR) spectrophotometer to assess the body fat percent (data of 419 boys and 462 girls aged between 5 and 18 years were analysed). Triceps skinfold thickness (TSF), sum of triceps, medial calf, subscapular and suprailiac skinfold thicknesses (SFS), body fat percent estimated according to Slaughter et al. (%BF), BMI (calculated from height and weight) and body fat percent assessed by NIR-method (NIR%BF) were compared. chi 2 tests of independence show significant connections among the distributions ranged by the five fatness indicators. However, correlation coefficients and standard errors indicate that strong relationships are only among the assessments based on skinfold thicknesses (r = 0.92-0.97, SEE = 1.8-2.6%). BMI and NIR%BF assess body fatness differently compared to skinfold thicknesses: r-values are moderate and SEE-values are relatively large (r = 0.59-0.87, SEE = 1.9-4.7%). These findings can be seen in both the boys and the girls. NIR%BF comparing to %BF significantly overpredicts body fat percent in the boys and significantly underpredicts it in the girls. BMI, height and weight are not in significant correlation with NIR%BF in the boys but there are moderate correlations in the girls. Our suggestion is that more research is needed with the use of NIR-method in children and adolescents, and it is necessary to refine prediction equations taking into consideration very carefully sex sand age differences.     

Estimating Body Composition in Adolescent Sprint Athletes: Comparison of Different Methods in a 3 Years Longitudinal Design

A recommended field method to assess body composition in adolescent sprint athletes is currently lacking. Existing methods developed for non-athletic adolescents were not longitudinally validated and do not take maturation status into account. This longitudinal study compared two field methods, i.e., a Bio Impedance Analysis (BIA) and a skinfold based equation, with underwater densitometry to track body fat percentage relative to years from age at peak height velocity in adolescent sprint athletes. In this study, adolescent sprint athletes (34 girls, 35 boys) were measured every 6 months during 3 years (age at start = 14.8 ± 1.5yrs in girls and 14.7 ± 1.9yrs in boys). Body fat percentage was estimated in 3 different ways: 1) using BIA with the TANITA TBF 410; 2) using a skinfold based equation; 3) using underwater densitometry which was considered as the reference method. Height for age since birth was used to estimate age at peak height velocity. Cross-sectional analyses were performed using repeated measures ANOVA and Pearson correlations between measurement methods at each occasion. Data were analyzed longitudinally using a multilevel cross-classified model with the PROC Mixed procedure. In boys, compared to underwater densitometry, the skinfold based formula revealed comparable values for body fatness during the study period whereas BIA showed a different pattern leading to an overestimation of body fatness starting from 4 years after age at peak height velocity. In girls, both the skinfold based formula and BIA overestimated body fatness across the whole range of years from peak height velocity. The skinfold based method appears to give an acceptable estimation of body composition during growth as compared to underwater densitometry in male adolescent sprinters. In girls, caution is warranted when interpreting estimations of body fatness by both BIA and a skinfold based formula since both methods tend to give an overestimation.     

Skinfolds and coronary heart disease risk factors are more strongly associated with BMI than with the body adiposity index

Objective:

A recent, cross‐sectional analysis of adults found that the hip circumference divided by height^1.5 minus 18 (the body adiposity index, BAI) was strongly correlated (r = 0.79) with percent body fat determined by dual energy X‐ray absorptiometry. The BAI was proposed as a more accurate index of body fatness than BMI. We examined whether BAI was more strongly related, than was BMI and waist circumference, to skinfold thicknesses and levels of various risk factors for coronary heart disease.

Design and Methods:

Cross‐sectional analyses of adults (n = 14,263 for skinfold thickness; n=6291 for fasting lipid levels) in the National Health and Nutrition Examination Survey (NHANES) III, 1988‐1994.

Results:

As compared with BMI and waist circumference, we found that BAI was less strongly associated with the skinfold sum and with risk factor levels. For example, correlations with the skinfold sum were r = 0.79 (BMI) vs. r = 0.70 (BAI) among men, and r = 0.86 (BMI) vs. r = 0.79 (BAI) among women; p < 0.001 for the difference between each pair of correlations. An overall index of the 7 risk factors was also more strongly associated with BMI and waist circumference than BAI in analyses stratified by sex, race‐ethnicity and age. Multivariable analyses indicated that if BMI was known, BAI provided little additional information on risk factor levels.

Conclusions:

Based on the observed associations with risk factor levels and skinfold thicknesses, we conclude that BAI is unlikely to be a better index of adiposity than BMI.     

Relationships Between the Body Mass Index and Body Composition

The aims of this study were to evaluate the Body Mass Index (BMI) (weight/stature²) as a proxy for percent body fat (%BF) and to determine its association with fat-free mass (FFM). Multivariate analysis of variance and partial correlations were used to examine relationships between BMI and %BF and FFM from densitometry for 504 men and 511 women, aged 20 to 45 years. Sensitivity/specificity analyses used cut offs of 28 kg/m² in men and 26 kg/m² in women for BMI, and 25% in men and 33% in women for %BF. Significantly higher associations existed in each gender between BMI and %BF in the upper BMI tertile than in the lower BMI tertiles. In the lower BMI tertiles, correlations between BMI and FFM were approximately twice as large as those between BMI and %BF. The BMI correctly identified about 44% of obese men, and 52% of obese women when obesity was determined from %BF. BMI is an uncertain diagnostic index of obesity. Results of Receiver Operator Characteristic (ROC) analyses using %BF and total body fat, both provided a BMI of 25 kg/m² in men and 23 kg/m² in women as diagnostic screening cut offs for obesity.     

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.     

Relationship between pituitary-thyroid axis hormones and anthropometric parameters in Czech adult population

Although the relationships between thyroid function and anthropometric parameters were studied in patients with thyroid disorders and in morbidly obese subjects, such data in normal healthy population are scarce. In our study, relationships between factors of body composition, fat distribution and age with hormones of the pituitary-thyroid axis were evaluated in a large, randomly selected sample of normal adult Czech population comprising of 1012 men and 1625 women. Our results exhibited weak, but significant relationships between body composition, body fat distribution and the parameters of pituitary-thyroid axis. Some of these associations were gender-specific. As shown by backward stepwise regression model, body fat distribution evaluated by centrality index (subscapular/triceps skinfold ratio) was negatively associated with free triiodothyronine (fT3) serum levels only in women, while a positive correlation of fT3 with BMI was specific for men. BMI was inversely related to free thyroxine (fT4) concentrations in women but not in men. The centrality index (CI) was positively related to TSH levels in both genders. The fT3/fT4 ratio, reflecting deiodinase activity, was inversely related to age and positively related to BMI in both genders, while the highly significant negative correlation between CI and fT3/fT4 ratio was specific for women.     

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.     

Critical evaluation of methods for determination of body fat content in children: back to basic parameters?

Considering the increasing prevalence of obesity among children and of obesity related disorders in the pediatric population, the reliable evaluation of body fat content in children is of critical importance in research and clinical medicine. In this study, we assessed the congruency of different estimates for body fat content in prepubertal children. We determined anthropometric parameters, such as BMI and skinfold thickness, and bioelectrical impedance in 676 prepubertal Caucasian children. We calculated body fat percentage (BF%) from these parameters applying 5 distinct algorithms and established raw centiles for these models. Expectedly, girls had significantly higher BF% regardless of the method applied. There were, however, significant variances in the calculated amount of BF% between the algorithms, with BIA based equations giving highest BF%, while skinfold based equations tended to provide lower BF% values. Direct comparison of the algorithms revealed a high degree of inconsistency and poor agreement in the assessment of body fat with variations of >10% BF%. Great differences in basic parameters, such as DeltaBMI (3.2 kg/m (2)) or Deltaskinfolds (1.75-fold), would be needed to reliably predict correct ranking of 10% difference in body fat with 95% probability. In summary, BF% strongly varies depending on both the method as well as the algorithm used. This questions the applicability of such field methods for the assessment of BF% for comparative analyses and the superiority of information over basic parameters such as BMI.     

The alpha 2-adrenergic receptor gene and body fat content and distribution: the HERITAGE Family Study

BACKGROUND: Among adrenergic receptor subtypes that regulate lipid mobilization, the alpha2-adrenergic receptor is involved in the inhibition of fatty acid mobilization from adipose tissue. A C-1291G polymorphism is located in the alpha2-adrenergic receptor gene (ADRA2A) but no association with body fat accumulation has been reported yet. MATERIALS AND METHODS: Body mass index (BMI), fat mass (FAT), percentage body fat (%FAT), trunk-to-extremity skinfold ratio (TER), sum of eight skinfolds (SF8), and abdominal subcutaneous (ASF), visceral (AVF), and total (ATF) fat areas assessed by CT scan have been measured in adult sedentary white (n = 503) and black (n = 276) subjects participating in the HERITAGE Family Study. Association between the C-1291G polymorphism and each phenotype was tested separately in men and women of each race using ANCOVA with the effects of age as covariate in addition to the effects of BMI for TER and of FAT for AVF, ASF, and ATF. RESULTS: The allele frequencies of the ADRA2A C-1291G polymorphism differed between races. No association was observed in white subjects, except for a moderate effect of the polymorphism accounting for less than 1% of the variance in AVF and ATF in women. In black subjects, however, the G-1291 allele was found to be associated with an increase of TER in men (3.8% of variance accounted for by the polymorphism), while in black women it was associated with a decrease in TER (2.9%) and in AVF (2.5%). CONCLUSION: These results suggest a role for the ADRA2A gene in determining the propensity to store fat in the abdominal area, independently of total body fatness.     

Which Obesity Indicators Are Better Predictors of Metabolic Risk?: Healthy Twin Study

No consensus exists as to the most sensitive and specific obesity indicator associated with metabolic risk factors. We aimed to validate anthropometry as the predictor for obesity-related metabolic risk factors through comparison with direct body composition measures in Korean adults. A total of 995 Korean women and 577 Korean men who participated in the Healthy Twin study were the subjects. Anthropometric measurements included BMI, waist circumference (WC), waist-to-hip ratio (WHR), and waist-to-height ratio (WHTR). Direct body composition measures included the percentage of body fat (%BF) measured using dual-energy X-ray absorptiometry scanners and bioelectrical impedance analyzer (BIA). The following criteria were used to define abnormal metabolic risk factors: blood pressure > or = 130/85 mm Hg, fasting glucose (> or = 100 mg/dl), insulin (> or = 25 microU/ml), homeostasis model assessment (HOMA) (> or = 2.61), high-density lipoprotein (HDL) (<40 mg/dl for men or <50 mg/dl for women), triacylglycerol (> or = 150 mg/dl), uric acid (>7 mg/dl for men or >6 mg/dl for women), high-sensitivity C-reactive protein (hs-CRP) (> or = 2.11 mg/l). In multiple regression analyses (adjusted for age, education, smoking, alcohol, exercise and past/current medical history, and treated families as a random effect), WC, WHTR, and BMI were consistently associated with all metabolic risk factors regardless of the subject's gender. Some of the areas under the receiver-operating characteristic curves regarding abnormal metabolic risk factors were significantly higher for the three indicators of central obesity than for %BF. Our study validates the usefulness of anthropometry over direct body fat measures to predict metabolic risks.     

Genome-wide association for abdominal subcutaneous and visceral adipose reveals a novel locus for visceral fat in women

Body fat distribution, particularly centralized obesity, is associated with metabolic risk above and beyond total adiposity. We performed genome-wide association of abdominal adipose depots quantified using computed tomography (CT) to uncover novel loci for body fat distribution among participants of European ancestry. Subcutaneous and visceral fat were quantified in 5,560 women and 4,997 men from 4 population-based studies. Genome-wide genotyping was performed using standard arrays and imputed to ~2.5 million Hapmap SNPs. Each study performed a genome-wide association analysis of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), VAT adjusted for body mass index, and VAT/SAT ratio (a metric of the propensity to store fat viscerally as compared to subcutaneously) in the overall sample and in women and men separately. A weighted z-score meta-analysis was conducted. For the VAT/SAT ratio, our most significant p-value was rs11118316 at LYPLAL1 gene (p = 3.1 × 10E-09), previously identified in association with waist-hip ratio. For SAT, the most significant SNP was in the FTO gene (p = 5.9 × 10E-08). Given the known gender differences in body fat distribution, we performed sex-specific analyses. Our most significant finding was for VAT in women, rs1659258 near THNSL2 (p = 1.6 × 10-08), but not men (p = 0.75). Validation of this SNP in the GIANT consortium data demonstrated a similar sex-specific pattern, with observed significance in women (p = 0.006) but not men (p = 0.24) for BMI and waist circumference (p = 0.04 [women], p = 0.49 [men]). Finally, we interrogated our data for the 14 recently published loci for body fat distribution (measured by waist-hip ratio adjusted for BMI); associations were observed at 7 of these loci. In contrast, we observed associations at only 7/32 loci previously identified in association with BMI; the majority of overlap was observed with SAT. Genome-wide association for visceral and subcutaneous fat revealed a SNP for VAT in women. More refined phenotypes for body composition and fat distribution can detect new loci not previously uncovered in large-scale GWAS of anthropometric traits.     

Pedometer-determined physical activity and indicators of health in Japanese adults

Recently, many cross-sectional studies observed that body mass index (BMI) and percentage of body fat (%BF) were inversely associated with pedometer-determined physical activities, but studies on Asian populations, including the Japanese, are sparse. Height, weight, body fat percentage (%BF, bioelectrical impedance analyzer), and waist circumference were measured on 117 women (62.8+/-4.5 years, 22.2+/-2.2 kg/m(2)) and 62 men (64.0+/-4.6 years, 23.6+/-2.5 kg/m(2)). Pearson correlations and partial correlation coefficients after controlling for age were calculated between steps/day and variables. Furthermore, participants were classified into four groups as follows: <5,000, 5,000-7,499, 7,500-9,999, and >or=10,000 steps/day, and analyzed using ANOVA across activity groups. In women, a significant correlation was found between steps/day and BMI (r=-0.217, p=0.018), %BF (r=-0.292, p=0.0014), and the relationship was still significant after controlling for age. The relationship between steps/day and waist circumference was not significant. In men, a significant relationship was not observed between steps/day and obesity indices. The correlations between steps/day and both BMI and %BF were significant in Japanese women, but weak compared with Caucasian and African-American women as reported previously. A possible cause is racial difference in degree of obesity and body shape. The effects of physical activity on body shape and composition may differ according to race.     

Ethnic differences in anthropometric characteristics of young children and their parents

The distribution of body fat, or fat patterning, is an important risk factor for cardiovascular disease and diabetes, independent of obesity. Furthermore, the incidence of cardiovascular disease and diabetes varies by ethnicity. We documented ethnic differences in anthropometric characteristics and body fat distribution between Anglo, Black, and Mexican American men (n = 101), women (n = 245), boys (n = 111), and girls (n = 111). We used aggregates of skinfold measures to examine ethnic differences in the deposition of fat in body compartments (body, trunk, leg, and arm) and analyzed trunk-extremity skinfold ratios to determine which best reflected ethnic differences in fat distribution. The results show that Mexican American mothers have larger skinfold ratios and more body fat (as determined by skinfold aggregates) than either Anglo or Black American mothers, whereas Black American mothers have larger ratios than Anglo American mothers. Mexican American fathers also have larger skinfold ratios but not more body fat (skinfold aggregates) than Anglo American fathers. Mexican American fathers have more body fat than Black American fathers, but we found no differences between skinfold ratios. The ethnic differences among children in skinfold ratios and aggregates are similar to those found among fathers, with more differences among girls than boys. Fat patterning differences do exist among the three ethnicities, with greater trunk fat among Mexican and Black Americans. Those ethnicities are known to be at higher risk for cardiovascular disease and diabetes.     

Measures of adiposity and cardiovascular disease risk factors

Objective: To determine which of five measures of adiposity maintains the strongest association with cardiovascular disease risk factors. Research Methods and Procedures: A nationally representative sample of 12,608 adult participants of the third National Health and Nutrition Examination Survey were examined. Waist circumference, total body fat, percent body fat, BMI, and skinfold thickness were measured following a standardized protocol. Results: In multivariable adjusted models including waist circumference and BMI as independent variables, waist circumference was a significantly better predictor. The odds ratios (95% confidence intervals) for each standard deviation higher waist circumference and BMI for men were as follows: 1.88 (1.43, 2.48) and 0.99 (0.76, 1.29), respectively, for hypertension; 1.51 (0.87, 2.59) and 1.23 (0.76, 1.99), respectively, for diabetes; and 1.85 (1.48, 2.32) and 1.00 (0.80, 1.24), respectively, for low high‐density lipoprotein‐cholesterol. The analogous odds ratios (95% confidence intervals) for women were as follows: 2.28 (1.74, 3.00) and 0.91 (0.69, 1.19), respectively, for hypertension; 2.72 (1.85, 4.00) and 0.82 (0.55, 1.23), respectively, for diabetes; and 1.90 (1.47, 2.47) and 1.07 (0.83, 1.38), respectively, for low high‐density lipoprotein‐cholesterol. Results were markedly similar for waist circumference in models adjusting for total body fat, percent body fat, and skinfold thickness separately. In contrast, waist circumference was not a significantly better predictor of elevated C‐reactive protein than the other measures of adiposity. Discussion: Waist circumference maintains a stronger association with cardiovascular disease risk factors than other measures of adiposity.