Predictive equations for body fat and abdominal fat with DXA and MRI as reference in Asian Indians

Objective: To develop accurate and reliable equations from simple anthropometric parameters that would predict percentage of total body fat (%BF), total abdominal fat (TAF), subcutaneous abdominal adipose tissue (SCAT), and intra‐abdominal adipose tissue (IAAT) with a fair degree of accuracy. Methods and Procedures: Anthropometry, %BF by dual‐energy X‐ray absorptiometry (DXA) in 171 healthy subjects (95 men and 76 women) and TAF, IAAT, and SCAT by single slice magnetic resonance imaging (MRI) at L3–4 intervertebral level in 100 healthy subjects were measured. Mean age and BMI were 32.2 years and 22.9 kg/m², respectively. Multiple regression analysis was used on the training data set (70%) to develop equations, by taking anthropometric and demographic variables as potential predictors. Predicted equations were applied on validation data set (30%). Results: Multiple regression analysis revealed the best equation for predicting %BF to be: %BF = 42.42 + 0.003 × age (years) + 7.04 × gender (M = 1, F = 2) + 0.42 × triceps skinfold (mm) + 0.29 × waist circumference (cm) ? 0.22 × weight (kg) ? 0.42 × height (cm) (R ² = 86.4%). The most precise predictive equation for estimating IAAT was: IAAT (mm²) = ?238.7 + 16.9 × age (years) + 934.18 × gender (M = 1, F = 2) + 578.09 × BMI (kg/m²) ? 441.06 × hip circumference (cm) + 434.2 × waist circumference (cm) (R ² = 52.1%). SCAT was best predicted by: SCAT (mm²) = ?49,376.4 ? 17.15 × age (years) + 1,016.5 × gender (M = 1, F = 2) +783.3 × BMI (kg/m²) + 466 × hip circumference (cm) (R ² = 67.1). Discussion: We present predictive equations to quantify body fat and abdominal adipose tissue sub‐compartments in healthy Asian Indians. These equations could be used for clinical and research purposes.     

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.     

VAT=TAAT‐SAAT: Innovative anthropometric model to predict visceral adipose tissue without resort to CT‐Scan or DXA

Objective:

To investigate whether a combination of a selected but limited number of anthropometric measurements predicts visceral adipose tissue (VAT) better than other anthropometric measurements, without resort to medical imaging.

Hypothesis:

Abdominal anthropometric measurements are total abdominal adipose tissue indicators and global measures of VAT and SAAT (subcutaneous abdominal adipose tissue). Therefore, subtracting the anthropometric measurement the more correlated possible with SAAT while being the least correlated possible with VAT, from the most correlated abdominal anthropometric measurement with VAT while being highly correlated with TAAT, may better predict VAT.

Design and Methods:

BMI participants' range was from 16.3 to 52.9 kg m^?2. Anthropometric and abdominal adipose tissues data by computed tomography (CT‐Scan) were available in 253 patients (18‐78 years) (CHU Nord, Marseille) and used to develop the anthropometric VAT prediction models.

Results:

Subtraction of proximal thigh circumference from waist circumference, adjusted to age and/or BMI, predicts better VAT (Women: VAT = 2.15 × Waist C ? 3.63 × Proximal Thigh C + 1.46 × Age + 6.22 × BMI ? 92.713; R² = 0.836. Men: VAT = 6 × Waist C ? 4.41 × proximal thigh C + 1.19 × Age ? 213.65; R² = 0.803) than the best single anthropometric measurement or the association of two anthropometric measurements highly correlated with VAT. Both multivariate models showed no collinearity problem. Selected models demonstrate high sensitivity (97.7% in women, 100% in men). Similar predictive abilities were observed in the validation sample (Women: R² = 76%; Men: R² = 70%). Bland and Altman method showed no systematic estimation error of VAT.

Conclusion:

Validated in a large range of age and BMI, our results suggest the usefulness of the anthropometric selected models to predict VAT in Europides (South of France).

     

Resting Metabolic Rate in Severely Obese Diabetic and Nondiabetic Subjects

Objectives : To compare the resting metabolic rate (RMR) between diabetic and nondiabetic obese subjects and to develop a predictive equation of RMR for these subjects. Research Methods and Procedures : Obese adults (1088; mean age = 44.9 ± 12.7 years) with BMI ≥ 35 kg/m² (mean BMI = 46.4 ± 8.4 kg/m²) were recruited. One hundred forty‐two subjects (61 men, 81 women) were diagnosed with type 2 diabetes (DM), giving the prevalence of DM in this clinic population as 13.7%. RMR was measured by indirect calorimetry, and several multivariate linear regression models were performed using age, gender, weight, height, BMI, fat mass, fat mass percentage, and fat‐free mass as independent variables. Results : The severely obese patients with DM had consistently higher RMR after adjustment for all other variables. The best predictive equation for the severely obese was RMR = 71.767 ? 2.337 × age + 257.293 × gender (women = 0 and men = 1) + 9.996 × weight (in kilograms) + 4.132 × height (in centimeters) + 145.959 × DM (nondiabetic = 0 and diabetic = 1). The age, weight, and height‐adjusted least square means of RMR between diabetic and nondiabetic groups were significantly different in both genders. Discussion : Severely obese patients with type 2 diabetes had higher RMR than those without diabetes. The RMR of severely obese subjects was best predicted by an equation using age, gender, weight, height, and DM as variables.     

Association of the myostatin gene with obesity, abdominal obesity and low lean body mass and in non-diabetic asian indians in north India

Background

To determine the association of the A55T and K153R polymorphisms of the Myostatin gene with obesity, abdominal obesity and lean body mass (LBM) in Asian Indians in north India.

Materials and Methods

A total of 335 subjects (238 men and 97 women) were assessed for anthropometry, % body fat (BF), LBM and biochemical parameters. Associations of Myostatin gene polymorphisms were evaluated with anthropometric, body composition and biochemical parameters. In A55T polymorphism, BMI (p = 0.04), suprailiac skinfold (p = 0.05), total skinfold (p = 0.008), %BF (p = 0.002) and total fat mass (p = 0.003) were highest and % LBM (p = 0.03) and total LBM (Kg) were lowest (p = 0.04) in subjects with Thr/Thr genotype as compared to other genotypes. Association analysis of K153R polymorphism showed that subjects with R/R genotype had significantly higher BMI (p = 0.05), waist circumference (p = 0.04), %BF (p = 0.04) and total fat mass (p = 0.03), and lower %LBM (p = 0.02) and total LBM [(Kg), (p = 0.04)] as compared to other genotypes. Using a multivariate logistic regression model after adjusting for age and sex, subjects with Thr/Thr genotype of A55T showed high risk for high %BF (OR, 3.92, 95% Cl: 2.61–12.41), truncal subcutaneous adiposity (OR, 2.9, 95% Cl: 1.57–6.60)] and low LBM (OR, 0.64, 95% CI: 0.33–0.89) whereas R/R genotype of K153R showed high risk of obesity (BMI; OR, 3.2, 95% CI: 1.2–12.9; %BF, OR, 3.6, 95% CI: 1.04–12.4), abdominal obesity (OR, 2.12, 95% CI: 2.71–14.23) and low LBM (OR, 0.61, 95% CI: 0.29–0.79).

Conclusions/Significance

We report that variants of Myostatin gene predispose to obesity, abdominal obesity and low lean body mass in Asian Indians in north India.     

A QTL for genotype by sex interaction for anthropometric measurements in Alaskan Eskimos (GOCADAN Study) on chromosome 19q12-13

Variation in anthropometric measurements due to sexual dimorphism can be the result of genotype by sex interactions (G×S). The purpose of this study was to examine the sex-specific genetic architecture in anthropometric measurements in Alaskan Eskimos from the Genetics of Coronary Artery Disease in Alaska Natives (GOCADAN) study. Maximum likelihood-based variance components decomposition methods, implemented in SOLAR, were used for G×S analyses. Anthropometric measurements included BMI, waist circumference (WC), waist/height ratio, percent body fat (%BF), and subscapular and triceps skinfolds. Except for WC, mean values of all phenotypes were significantly different in men and women (P < 0.05). All anthropometric measures were significantly heritable (P < 0.001). In a preliminary analysis not allowing for G×S interaction, evidence of linkage was detected between markers D19S414 and D19S220 on chromosome 19 for WC (logarithm of odds (lod) = 3.5), %BF (lod = 1.7), BMI (lod = 2.4), waist/height ratio (lod = 2.5), subscapular (lod = 2.1), and triceps skinfolds (lod = 1.9). In subsequent analyses which allowed for G×S interaction, linkage was again found between these traits and the same two markers on chromosome 19 with significantly improved lod scores for: WC (lod = 4.5), %BF (lod = 3.8), BMI (lod = 3.5), waist/height ratio (lod = 3.2), subscapular (lod = 3.0), and triceps skinfolds (lod = 2.9). These results support the evidence of a G×S interaction in the expression of genetic effects resulting in sexual dimorphism in anthropometric phenotypes and identify the chromosome 19q12-13 region as important for adiposity-related traits in Alaskan Eskimos.     

Development and Validation of Computed Tomography Derived Anthropometric Regression Equations for Estimating Abdominal Adipose Tissue Distribution

The purpose of this study was twofold: (1) to develop multiple regression equations for predicting computed tomography (CT) derived intra-abdominal (IAF), subcutaneous (SCF), and total (TOTF= IAF+SCF) abdominal adipose tissue areas from anthropometric measures in adult white males with a large range of age (18–71 years) and percent body fat (2.0–40.6); and (2) to validate the new and existing equations that used similar Hounsfield Units (HU) for determining IAF for estimating these fat depots. One hundred fifty-one white male subjects had IAF, SCF, and TOTF determined by a single CT scan, skinfold and circumference measures taken and body density determined. Linear intra-correlations and factor analysis procedures were used to identify variables for inclusion in stepwise multiple regression solutions. IAF was estimated from age, waist circumference, the sum of mid-thigh and lower thigh circumferences, and vertical abdominal skinfold. SCF was estimated from age, umbilicus circumference, chest and suprailiac skinfolds. TOTF was estimated from age, body mass index (BMI), chest skinfold, and umbilicus circumference. R² for IAF, SCF, and TOTF was .73, .77, and .86 respectively. The existing and the new equations were validated on an independent sub-sample of 51 subjects. The only existing equation that met validation criteria had a validation R² = .67 for IAF. All three new equations met validation criteria with R ² validations of .75, .79, and .85 for IAF, SCF, and TOTF respectively. It is concluded that the new equations might be used as an inexpensive estimation of IAF, SCF, and TOTF in adult white males varying greatly in age and percent body fat.     

Interaction of FTO and physical activity level on adiposity in African-American and European-American adults: the ARIC study

Physical inactivity accentuates the association of variants in the FTO locus with obesity‐related traits but evidence is largely lacking in non‐European populations. Here we tested the hypothesis that physical activity (PA) modifies the association of the FTO single‐nucleotide polymorphism (SNP) rs9939609 with adiposity traits in 2,656 African Americans (AA) (1,626 women and 1,030 men) and 9,867 European Americans (EA) (5,286 women and 4,581 men) aged 45–66 years in the Atherosclerosis Risk in Communities (ARIC) study. Individuals in the lowest quintile of the sport activity index of the Baecke questionnaire were categorized as low PA. Baseline BMI, waist circumference (WC), and skinfold measures were dependent variables in regression models testing the additive effect of the SNP, low PA, and their interaction, adjusting for age, alcohol use, cigarette use, educational attainment, and percent European ancestry in AA adults, stratified by sex and race/ethnicity. rs9939609 was associated with adiposity in all groups other than AA women. The SNP × PA interaction was significant in AA men (P ≤ 0.002 for all traits) and EA men (P ≤ 0.04 for all traits). For each additional copy of the A (risk) allele, WC in AA men was higher in those with low PA (β_lowPA: 5.1 cm, 95% confidence interval (CI): 2.6–7.5) than high PA (β_highPA: 0.7 cm, 95% CI: ?0.4 to 1.9); P (interaction) = 0.002). The interaction effect was not observed in EA or AA women. FTO SNP × PA interactions on adiposity were observed for AA as well as EA men. Differences by sex require further examination.     

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.     

Effects of Obesity and Body Fat Distribution on Lipids and Lipoproteins in Nondiabetic American Indians: The Strong Heart Study

Objectives: To examine the relationship between obesity and lipoprotein profiles and compare the effects of total obesity and central adiposity on lipids/lipoproteins in American Indians. Research Methods and Procedures: Participants were 773 nondiabetic American Indian women and 739 men aged 45 to 74 years participating in the Strong Heart Study. Total obesity was estimated using body mass index (BMI). Central obesity was measured as waist circumference. Lipoprotein measures included triglycerides, high‐density lipoprotei in (HDL) cholesterol, low‐density lipoprotein (LDL) cholesterol, apolipoprotein AI (apoAI), and apolipoprotein B (apoB). Partial and canonical correlation analyses were used to examine the associations between obesity and lipids/lipoproteins. Results: Women were more obese than men in Arizona (median BMI 32.1 vs. 29.2 kg/m²) and South Dakota and North Dakota (28.3 vs. 28.0 kg/m²), but there was no sex difference in waist circumference. Men had higher apoB and lower apoAI levels than did women. In women, when adjusted for center, gender, and age, BMI was significantly related to HDL cholesterol (r = ?0.24, p < 0.001). There was a significant but weak relation with apoAI (r = ?0.14 p < 0.001). Waist circumference was positively related to triglycerides (r = 0.14 p < 0.001) and negatively related to HDL cholesterol (r = ?0.23, p < 0.001) and apoAI (r = ?0.13, p < 0.001). In men, BMI was positively correlated with triglycerides (r = 0.30, p < 0.001) and negatively correlated with HDL cholesterol (r = ?0.35, p < 0.001) and apoAI (r = ?0.23, p < 0.001). Triglycerides increased with waist circumference (r = 0.30, p < 0.001) and HDL cholesterol decreased with waist circumference (r = ?0.36 p < 0.001). In both women and men there was an inverted U‐shaped relationship between obesity and waist with LDL cholesterol and apoB. In canonical correlation analysis, waist circumference received a greater weight (0.86) than did BMI (0.17) in women. However, the canonical weights were similar for waist (0.46) and BMI (0.56) in men. Only HDL cholesterol (?1.02) carried greater weight in women, whereas in men, triglycerides (0.50), and HDL cholesterol (?0.64) carried a large amount of weight. All the correlation coefficients between BMI, waist circumference, and the first canonical variable of lipids/lipoproteins or between the individual lipid/lipoprotein variables and the first canonical variable of obesity were smaller in women than in men. Triglycerides and HDL cholesterol showed clinically meaningful changes with BMI and waist circumference in men. All lipid/lipoprotein changes in women in relation to BMI and waist circumference were minimal. Discussion: The main lipoprotein abnormality related to obesity in American Indians was decreased HDL cholesterol, especially in men. Central adiposity was more associated with abnormal lipid/lipoprotein profiles than general obesity in women; both were equally important in men.     

Exercise effect on weight and body fat in men and women

Objectives: The effect of national exercise recommendations on adiposity is unknown and may differ by sex. We examined long‐term effects of aerobic exercise on adiposity in women and men. Research Methods and Procedures: This was a 12‐month randomized, controlled clinical trial testing exercise effect on weight and body composition in men (N = 102) and women (N = 100). Sedentary/unfit persons, 40 to 75 years old, were recruited through physician practices and media. The intervention was facility‐ and home‐based moderate‐to‐vigorous intensity aerobic activity, 60 min/d, 6 days/wk vs. controls (no intervention). Results: Exercisers exercised a mean 370 min/wk (men) and 295 min/wk (women), and seven dropped the intervention. Exercisers lost weight (women, ?1.4 vs. +0.7 kg in controls, p = 0.008; men, ?1.8 vs. ?0.1 kg in controls, p = 0.03), BMI (women, ?0.6 vs. +0.3 kg/m² in controls, p = 0.006; men, ?0.5 kg/m² vs. no change in controls, p = 0.03), waist circumference (women, ?1.4 vs. +2.2 cm in controls, p < 0.001; men, ?3.3 vs. ?0.4 cm in controls, p = 0.003), and total fat mass (women, ?1.9 vs. +0.2 kg in controls, p = 0.001; men, ?3.0 vs. +0.2 kg in controls, p < 0.001). Exercisers with greater increases in pedometer‐measured steps per day had greater decreases in weight, BMI, body fat, and intra‐abdominal fat (all p trend < 0.05 in both men and women). Similar trends were observed for increased minutes per day of exercise and for increases in maximal oxygen consumption. Discussion: These data support the U.S. Department of Agriculture and Institute of Medicine guidelines of 60 min/d of moderate‐to‐vigorous physical activity.     

Validation of DXA Body Composition Estimates in Obese Men and Women

The aim of this study was to determine the accuracy of dual‐energy X‐ray absorptiometry (DXA)‐derived percentage fat estimates in obese adults by using four‐compartment (4C) values as criterion measures. Differences between methods were also investigated in relation to the influence of fat‐free mass (FFM) hydration and various anthropometric measurements. Six women and eight men (age 22–54 years, BMI 28.7–39.9 kg/m², 4C percent body fat (%BF) 31.3–52.6%) had relative body fat (%BF) determined via DXA and a 4C method that incorporated measures of body density (BD), total body water (TBW), and bone mineral mass (BMM) via underwater weighing, deuterium dilution, and DXA, respectively. Anthropometric measurements were also undertaken: height, waist and gluteal girth, and anterior‐posterior (A‐P) chest depth. Values for both methods were significantly correlated (r² = 0.894) and no significant difference (P = 0.57) was detected between the means (DXA = 41.1%BF, 4C = 41.5%BF). The slope and intercept for the regression line were not significantly different (P > 0.05) from 1 and 0, respectively. Although both methods were significantly correlated, intraindividual differences between the methods were sizable (4C‐DXA, range = ?3.04 to 4.01%BF) and significantly correlated with tissue thickness (chest depth) or most surrogates of tissue thickness (body mass, BMI, waist girth) but not FFM hydration and gluteal girth. DXA provided cross‐sectional %BF data for obese adults without bias. However, individual data are associated with large prediction errors (±4.2%BF). This error appears to be associated with tissue thickness indicating that the DXA device used may not be able to accurately account for beam hardening in obese cohorts.     

Combination of BMI and Waist Circumference for Identifying Cardiovascular Risk Factors in Whites

Objective: BMI (kilograms per meters squared) and waist circumference (WC) (measured in centimeters) are each associated with the risk of developing cardiovascular disease (CVD). Therefore, a combination of the two may be more effective in identifying subjects at risk than either alone. The present study sought to identify the combination of BMI and WC that has the strongest association with CVD risk factors in whites. Research Methods and Procedures: Subjects were 8712 white men and women from the Third National Health and Nutrition Examination Survey. The optimal combination of BMI and WC was developed using logistic regression models with BMI and WC as predictors and CVD risk factors as outcomes. The combined measure of BMI and WC using current cut‐off points was also examined. Sensitivity, specificity, and receiver operating characteristics curves were compared between the combined measures and BMI alone. Results: For white men, the optimal combination of BMI and WC for identifying CVD risk factors was 0.68 × BMI + 0.32 × WC. This combination generated a score that better estimated the odds of having CVD risk factors than either alone. For white women, WC alone largely determined the likelihood of having CVD risks. The combination of BMI and WC using current cut‐off points may provide an improved measure of CVD risk. Combined measures showed a higher sensitivity or a shorter distance in receiver operating characteristic curves in the identification of CVD risk factors. Discussion: Combined measures of BMI and WC may provide a higher overall test performance for CVD risk factors and may be useful in some ethnic groups as an improved means of screening subjects for further evaluation in the clinical setting.     

Brief communication: Body mass index,body adiposity index,and percent body fat in asians

Human obesity is a growing epidemic throughout the world. Body mass index (BMI) is commonly used as a good indicator of obesity. Body adiposity index (BAI = hip circumference (cm)/stature (m)^1.5 ? 18), as a new surrogate measure, has been proposed recently as an alternative to BMI. This study, for the first time, compares BMI and BAI for predicting percent body fat (PBF; estimated from skinfolds) in a sample of 302 Buryat adults (148 men and 154 women) living in China. The BMI and BAI were strongly correlated with PBF in both men and women. The correlation coefficient between BMI and PBF was higher than that between BAI and PBF for both sexes. For the linear regression analysis, BMI better predicted PBF in both men and women; the variation around the regression lines for each sex was greater for BAI comparisons. For the receiver operating characteristic (ROC) analysis, the area under the ROC curve for BMI was higher than that for BAI for each sex, which suggests that the discriminatory capacity of the BMI is higher than the one of BAI. Taken together, we conclude that BMI is a more reliable indicator of PBF derived from skinfold thickness in adult Buryats. Am J Phys Anthropol 152:294–299, 2013. © 2013 Wiley Periodicals, Inc.     

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 abdominal adipose tissue with DXA and anthropometry

Objective: To identify an anatomically defined region of interest (ROI) from DXA assessment of body composition that when combined with anthropometry can be used to accurately predict intra‐abdominal adipose tissue (IAAT) in overweight/obese individuals. Research Methods and Procedures: Forty‐one postmenopausal women (age, 49 to 66 years; BMI, 26 to 37 kg/m²) underwent anthropometric and body composition assessments. ROI were defined as quadrilateral boxes extending 5 or 10 cm above the iliac crest and laterally to the edges of the abdominal soft tissue. A single‐slice computed tomography (CT) scan was measured at the L3 to L4 intervertebral space, and abdominal skinfolds were taken. Results: Forward step‐wise regression revealed the best predictor model of IAAT area measured by CT (r² = 0.68, standard error of estimate = 17%) to be: IAAT area (centimeters squared) = 51.844 + DXA 10‐cm ROI (grams) (0.031) + abdominal skinfold (millimeters) (1.342). Interobserver reliability for fat mass (r = 0.994; coefficient of variation, 2.60%) and lean mass (r = 0.986, coefficient of variation, 2.67%) in the DXA 10‐cm ROI was excellent. Discussion: This study has identified a DXA ROI that can be reliably measured using prominent anatomical landmarks, in this case, the iliac crest. Using this ROI, combined with an abdominal skinfold measurement, we have derived an equation to predict IAAT in overweight/obese postmenopausal women. This approach offers a simpler, safer, and more cost‐effective method than CT for assessing the efficacy of lifestyle interventions aimed at reducing IAAT. However, this warrants further investigation and validation with an independent cohort.     

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

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

Associations of Fat Distribution and Obesity with Hypertension in a Bi‐ethnic Population: The ARIC Study

Objective: To examine associations of hypertension with obesity and fat distribution among African American and white men and women. Research Methods and Procedures: The analysis sample included 15,063 African American and white men and women between the ages of 45 and 64 years who were participants in the 1987 through 1989 examination of the Atherosclerosis Risk in Communities Study (ARIC). Odds ratios and adjusted prevalences of hypertension were calculated across sexspecific quintiles of body mass index (BMI), waist‐to‐hip ratio (WHR), waist circumference, and waist‐to‐height ratio (waist/height) and adjusted for age, research center, smoking, education, physical activity, alcohol consumption, hormone replacement therapy, and menopausal status. Results: The prevalence of hypertension was higher among African Americans than whites. In the lowest quintile of BMI, 41% of African American women and 43% of African American men had hypertension compared with 14% of white women and 19% of white men. Elevated BMI, WHR, waist circumference, and waist/height were associated with increased odds of hypertension in African American and white men and women. In women, but not in men, there were significant interactions between ethnicity and the anthropometric variables studied here. The direction of the interaction indicated larger odds ratios for hypertension with increasing levels of anthropometric indices in white compared with African American women. Discussion: Obesity and abdominal fat preponderance were associated with increased prevalence of hypertension in African American and white men and women. Associations were similar among African American and white men, but obesity and fat patterning were less strongly associated with hypertension in African American than in white women.     

Eating Behaviors and Indexes of Body Composition in Men and Women from the Québec Family Study

Objective: To put into relationship the dietary and anthropometric profile of men and women with their eating behaviors (cognitive dietary restraint, disinhibition, and susceptibility to hunger) and to assess whether gender and obesity status influence these associations. Research Methods and Procedures: Anthropometric measurements (including visceral adipose tissue accumulation), dietary profile (3‐day food record), and eating behaviors (Three‐Factor Eating Questionnaire) were determined in a sample of 244 men and 352 women. Results: Women had significantly higher cognitive dietary restraint and disinhibition scores than men (p < 0.0001). In both genders, scores for disinhibition and susceptibility to hunger, but not for cognitive dietary restraint, were higher in obese subjects than in overweight and nonobese subjects (p < 0.05). Positive correlations were observed between rigid restraint and most of the anthropometric variables studied (0.12 ≤ r ≤ 0.16). Moreover, in women, flexible restraint was negatively associated with body fat and waist circumference (r = ?0.11). Cognitive dietary restraint and rigid restraint were positively related to BMI among nonobese women (0.19 ≤ r ≤ 0.20), whereas in obese men, cognitive dietary restraint and flexible restraint tended to be negatively correlated with BMI (?0.20 ≤ r ≤ ?0.22; p = 0.10). Discussion: Gender could mediate associations observed between eating behaviors and anthropometric profile. It was also found that disinhibition and susceptibility to hunger are positively associated with the level of obesity. On the other hand, cognitive dietary restraint is not consistently related to body weight and adiposity, whereas rigid and flexible restraint are oppositely associated to obesity status, which suggests that it is important to differentiate the subscales of cognitive dietary restraint. Finally, counseling aimed at coping with disinhibition and susceptibility to hunger could be of benefit for the long‐term treatment of obesity.     

Two-dimensional predictive equation to classify visceral obesity in clinical practice

Objective: Visceral obesity assessment is not easy, and although computed tomography (CT) is an accurate tool, this technique is expensive and sometimes not suitable in clinical practice. We developed a new two‐dimensional elliptical anthropometric equation to classify visceral obesity and evaluated the validity and the reliability of the new equation compared with CT. Research Methods and Procedures: We collected anthropometric and CT data from overweight/obese subjects (n = 61, BMI = 32.4 ± 3.7 kg/m²). A validation group of 32 subjects was also selected. An equation for the assessment of visceral obesity was developed using multiple regression analysis. Once validated, the equation was compared with previous models. Tests for accuracy included mean differences, analysis of diagnostic, R², Snedecor's F‐test, and Bland‐Altman plot. Results: Multiple regression analysis revealed that the sagittal and coronal diameters and the triceps skinfold were significant contributors to the model. The final equation was: visceral area (VA)/subcutaneous area (SA)_predicted = 0.868 + 0.064 × sagittal diameter ?0.036 × coronal diameter ?0.022 × triceps skinfold. Patients with visceral‐subcutaneous area ratio (VA/SA) >0.42 were classified as having visceral obesity. The predictive equation was valid, showing a significant association with VA/SA assessed by CT (VA/SA_CT; r = 0.68; p < 0.0001). Paired Student's t test showed no significant differences with VA/SA_CT (p = 0.541). The reliability was high [F(24/60) = 2.12; p = 0.01]. Discussion: The new two‐dimensional and elliptical predictive equation is valid to assess visceral obesity and is more precise than previous models.