Relationship between anthropometric measures and body composition among Muslim females of West Bengal, India

The human body composition is assessed to determine percent body fat (PBF), fat mass (FM), and lean body mass or fat free mass (FFM). The topological distribution of body fat has been the subject of many studies in the world and India. To the best of our knowledge the present paper is the first report on the body composition in terms of PBF and FM, and their relationship with anthropometric measures in Muslim females in India. The present study examines anthropometric measurements and their relationship with the body composition among Muslim females of West Bengal, India. A cross-sectional study of 100 female, Muslim students of Howrah and Kolkata was undertaken to compare the relationships of biceps and triceps skinfold, waist, hip and upper arm circumference, waist hip ratio and conicity index with their body composition variables (PBF and FM). All anthropometric measures displayed significant (p < 0.05) correlation with body composition measures. The triceps skinfold, however, demonstrated a significant correlation with PBF (r = 0.90) and FM (r = 0.93). The greatest amount of variation of PBF (81.3 %) and FM (89.2 %) was explained by the triceps skinfold. In addition, a considerable amount of variation of PBF (72.8 %) and FM (86.0 %) was explained by the mid upper arm circumference. In conclusion, the present study displays a tendency of regional adiposity in the upper arm, triceps skinfold and mid upper arm circumference are much more strongly associated with body fat.     

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.     

Income, birth order, siblings, and anthropometry

The purpose of the present cross-sectional study was to examine the relationship and effect of monthly household income, birth order, and number of siblings on adult body dimensions, adiposity index, and body composition among adult Bengali females. One hundred seventy-one adult Bengali females, age 20.35 +/- 1.51 years (mean +/- SD; range: 18-21 years) from Kolkata (formerly Calcutta) were studied. Anthropometric measures (weight, height, waist circumference, hip circumference, and triceps, biceps, subscapular, suprailiac, and medial calf skinfold thicknesses) were taken from all participants using standard protocols. BMI and log10 of the sum of the five skinfold thicknesses were computed subsequently. Percentage of body fat was estimated from the triceps skinfold thickness following the equation of Durnin and Womersley (1974), and fat mass was then calculated. Results of the correlation analysis revealed that monthly household income had significant (p < 0.05) positive association with all anthropometric variables. Birth order and number of siblings showed significant (p < 0.05) inverse association. The correlation of monthly household income with anthropometric variables was much stronger for number of siblings and birth order. The results of the analysis of variance showed that monthly household income, birth order, and number of siblings (tertiles used to categorize all variables) had significant effects (p < 0.05) on anthropometric variables, indicating differences in adult body dimensions, the adiposity index, and body composition in relation to income, birth order, and number of siblings.     

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).

     

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.     

Relationships among body composition measures in community-dwelling older women

Objective: To examine whether simple anthropometric measures provide a good estimate of total and visceral fat in 146 community‐dwelling, older white women (mean age, 74.0 ± 4.1 years). Research Methods and Procedures: Total body fat and visceral fat were measured using electron beam computed tomography (EBT). Anthropometric parameters (height, weight, BMI, sagittal diameter, and waist circumference) were measured using standard techniques. Total percentage body fat was assessed using DXA. Spearman correlations were used to examine the association between the measures. Linear regression, controlling for age, was used to examine the associations between the anthropometric parameters and total and visceral body fat measured by EBT. Results: Correlations among body composition measures ranged from ρ = 0.46 to 0.93 (p < 0.0001). EBT total fat was strongly correlated with both DXA estimates of total percentage fat (ρ = 0.86) and BMI (ρ = 0.89). Separate linear regression models indicated that BMI, waist circumference, sagittal diameter, and DXA total percentage fat were each independently related to EBT total fat. BMI had the strongest linear relationship, explaining 80% of the model variance (p < 0.0001). Linear regression indicated that BMI, waist circumference, and sagittal diameter were each independently related to EBT visceral fat, with BMI and sagittal diameter explaining ～53% of the model variance (p < 0.0001). Discussion: The use of simple anthropometric measures such as BMI, sagittal diameter, and waist circumference may be an appropriate alternative for more expensive techniques when assessing total fat but should be used with caution when estimating visceral body fat.     

Anthropometric dimensions provide reliable estimates of abdominal adiposity: A validation study

Abdominal fat accumulation is a major risk factor for cardiometabolic morbidity and mortality. The purpose of the study is to assess the possibility of developing accurate estimation equations based on body measurements to determine total abdominal (TFA), subcutaneous (SFA) and visceral fat area (VFA). Hungarian volunteers (n = 198) aged between 20 and 81 years were enrolled in the study, which was conducted between July and November 2014. All persons underwent anthropometric measurements and computer tomographic (CT) scanning. Sex-specific multiple linear regression analyses were conducted in a subgroup of 98 participants to generate estimation models, then Bland–Altman's analyses were applied in the cross-validation group to compare their predictive efficiency. The variables best predicting VFA were hip circumference, calf circumference and waist-to-hip ratio (WHR) for males (R² = 0.713; SEE = 5602.1 mm²) and sagittal abdominal diameter (SAD), WHR, thigh circumference and triceps skinfold for females (R² = 0.845; SEE = 3835.6 mm²). The SFA prediction equation included SAD, thigh circumference and abdominal skinfold for males (R² = 0.848; SEE = 4124.1 mm²), body mass index and thigh circumference for females (R² = 0.861; SEE = 5049.7 mm²). Prediction accuracy was the highest in the case of TFA: hip circumference and WHR for males (R² = 0.910; SEE = 5637.2 mm²), SAD, thigh circumference and abdominal skinfold for females (R² = 0.915; SEE = 6197.5 mm²) were used in the equations. The results suggested that deviations in the predictions were independent of the amount of adipose tissue. Estimation of abdominal fat depots based on anthropometric traits could provide a cheap, reliable method in epidemiologic research and public health screening to evaluate the risk of cardiometabolic events.     

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.     

Diabetes in Urban African Americans. XII. Anthropometry for Assessing Municipal Hospital Outpatients Recently Diagnosed with Type 2 Diabetes

Objective : Abdominal obesity is associated with insulin resistance and cardiovascular risk factors, but there has been little information published to advance the use of abdominal anthropometry in the care of diabetic patients. Research Methods and Procedures : A cross-sectional survey of municipal hospital outpatients recently diagnosed with type 2 diabetes (73 men and 142 women of whom 89% were African Americans). Age-adjusted linear regression was used to compare the supine sagittal abdominal diameter (SAD), supine waist circumference, four anthropometric ratios, and the body mass index (kg/m²) for their ability to predict serum fasting C-peptide and lipid levels. Results : The best predictor of log-transformed C-peptide was SAD/height (p<0.0001 for men; p = 0.0003 for women). SAD/thigh circumference was the best predictor of log-transformed triglycerides for men (p = 0.002) and of total cholesterol/HDL cholesterol for women (p = 0.043). The body mass index was less able to predict C-peptide, HDL cholesterol and total cholesterol/HDL cholesterol than was SAD/height or SAD/thigh circumference or waist circumference/height. Discussion : Anthropometric indices of abdominal obesity appear to be correlated with insulin production and lipid risk factors among municipal-hospital, type 2 diabetic patients much as they are in other studied populations. Since anthropometric data are inexpensively obtained and immediately available to the practitioner, their utility for preliminary clinical assessment deserves to be tested in prospective outcome studies.     

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

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

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.     

Larger Thigh and Hip Circumferences Are Associated with Better Glucose Tolerance: The Hoorn Study

Objective: A higher waist‐to‐hip ratio, which can be due to a higher waist circumference, a lower hip circumference, or both, is associated with higher glucose levels and incident diabetes. A lower hip circumference could reflect either lower fat mass or lower muscle mass. Muscle mass might be better reflected by thigh circumference. The aim of this study was to investigate the contributions of thigh and hip circumferences, independent of waist circumference, to measures of glucose metabolism. Research Methods and Procedures: For this cross‐sectional study we used baseline data from the Hoorn Study, a population‐based cohort study of glucose tolerance among 2484 men and women aged 50 to 75. Glucose tolerance was assessed by a 75‐g oral glucose tolerance test; hemoglobin A_1c and fasting insulin were also measured. Anthropometric measurements included body mass index (BMI) and waist, hip, and thigh circumferences. Results: Stratified analyses and multiple linear regression showed that after adjustment for age, BMI, and waist circumference, thigh circumference was negatively associated with markers of glucose metabolism in women, but not in men. Standardized β values in women were ?0.164 for fasting, ?0.206 for post‐load glucose, ?0.190 for hemoglobin A_1c (all p < 0.001), and ?0.065 for natural log insulin levels (p = 0.061). Hip circumference was negatively associated with markers of glucose metabolism in both sexes (standardized betas ranging from ?0.093 to ?0.296, p < 0.05) except for insulin in men. Waist circumference was positively associated with glucose metabolism. Discussion: Thigh circumference in women and hip circumference in both sexes are negatively associated with markers of glucose metabolism independently of the waist circumference, BMI, and age. Both fat and muscle tissues may contribute to these associations.     

Waist Circumference as the Best Predictor of Noninsulin Dependent Diabetes Mellitus (NIDDM) Compared to Body Mass Index,Waist/hip Ratio and Other Anthropometric Measurements in Mexican Americans—A 7-Year Prospective Study

Although a number of obesity-related variables are recognized risk factors for NIDDM, few studies have addressed which one is the best predictor. A cohort of 721 Mexican Americans aged 25–64 years who were free of NIDDM at baseline were followed for an average of 7.2 years; 105 new cases of NIDDM were diagnosed. Body weight, body mass index (BMI), waist and hip circumferences, waist/hip ratio (WHR), triceps and subscapular skinfolds were all positively predictive of NIDDM independent of age and sex. There were modest to strong correlations between these anthropometric variables, however, waist circumference was the strongest predictor of NIDDM. The predictive power of a single measurement of waist circumference was at least equal to that of WHR and BMI combined. The risk of NIDDM for those in the highest quartile of waist circumference was 11 times greater than for those in the lowest quartile (95% confidence interval: 4.2–28.8). The waist-NIDDM relation was stronger in subjects with BMI ≤ 27 kg/m² (OR: 6.0 for a 1 SD difference) than in subjects with BMI > 27 kg/m2 (OR: 1.7 for a 1 SD difference). In multivariate analysis, waist circumference was the only significant predictor of NIDDM in models that included other anthropometric variables either separately or simultaneously. WHR and BMI were independent predictors of NIDDM after adjustment for each other, however, their predictive abilities disappeared after adjustment for waist circumference. The data indicate that waist is the best obesity-related predictor of NIDDM. This finding suggests that the distribution of body fat, especially abdominal localization, is a more important determinant than the total amount of body fat of the development of NIDDM in Mexican Americans.     

Body adiposity index and other indexes of body composition in the SAPHIR study: Association with cardiovascular risk factors

Objective:

The accuracy of anthropometric surrogate markers such as the body adiposity index (BAI) and other common indexes like the body mass index (BMI), waist‐to‐hip ratio (WHR) and waist‐to‐height ratio (WHtR) to predict metabolic sequelae is essential for its use in clinical practice.

Design and Methods:

Thus, we evaluated the strength of BAI and other indexes to relate with anthropometric parameters, adipocytokines, blood lipids, parameters of glucose‐homeostasis and blood pressure in 1,770 patients from the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR) study in a crosssectional design. Measurements were BAI, BMI, WHR, WHtR, abdominal subcutaneous and visceral adipose tissue (aSAT and VAT), total body adipose tissue mass, body weight, waist‐ and hip circumference (WC and HC), leptin, adiponectin, high‐density lipoprotein‐cholesterol (HDL‐C), low‐density lipoprotein‐cholesterol (LDL‐C), triglycerides (TG), fasting plasma glucose, fasting plasma insulin, the homeostasis model assessment of insulin resistance (HOMAIR), systolic and diastolic blood pressure.

Results and Conclusions:

BAI was significantly associated with leptin and HC. We conclude that BAI was the best calculator for leptin. BAI was inferior to BMI to predict anthropometric parameters other than HC, adiponectin, blood lipids, parameters of glucose homeostasis, and blood pressure in this cross‐sectional study.     

Obesity in South Africa: The South African Demographic and Health Survey

Objectives: To ascertain the anthropometric profile and determinants of obesity in South Africans who participated in the Demographic and Health Survey in 1998. Research Methods and Procedures: A sample of 13,089 men and women (age, ≥15 years) were randomly selected and then stratified by province and urban and nonurban areas. Height, weight, mid-upper arm circumference, and waist and hip circumference were measured. Body mass index (BMI) was used as an indicator of obesity, and the waist/hip ratio (WHR) was used as an indicator of abdominal obesity. Multivariate regression identified sociodemographic predictors of BMI and waist circumference in the data. Results: Mean BMI values for men and women were 22.9 kg/m² and 27.1 kg/m², respectively. For men, 29.2% were overweight or obese (≥25 kg/m²) and 9.2% had abdominal obesity (WHR ≥1.0), whereas 56.6% of women were overweight or obese and 42% had abdominal obesity (WHR >0.85). Underweight (BMI <18.5 kg/m²) was found in 12.2% of men and 5.6% of women. For men, 19% of the variation of BMI and 34% of the variation in waist circumference could be explained by age, level of education, population group, and area of residence. For women, these variables explained 16% of the variation of BMI and 24% of the variation in waist circumference. Obesity increased with age, and higher levels of obesity were found in urban African women. Discussion: Overnutrition is prevalent among adult South Africans, particularly women. Determinants of overnutrition include age, level of education, ethnicity, and area of residence.     

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.     

The Sex and Race Specific Relationship between Anthropometry and Body Fat Composition Determined from Computed Tomography: Evidence from the Multi-Ethnic Study of Atherosclerosis

Background

Few studies have investigated the relationship of anthropometric measurements with computed tomography (CT) body fat composition, and even fewer determined if these relationships differ by sex and race.

Methods

CT scans from 1,851 participants in the population based Multi-Ethnic Study of Atherosclerosis were assessed for visceral and subcutaneous fat areas by semi-automated segmentation of body compartments. Regression models were used to investigate relationships for anthropometry with visceral and subcutaneous fat separately by sex and race/ethnicity.

Results

Participants were 50% female, 41% Caucasian, 13% Asian, 21% African American, and 25% Hispanic. For visceral fat, the positive relationship with weight (p = 0.028), waist circumference (p<0.001), waist to hip ratio (p<0.001), and waist to height ratio (p = 0.05) differed by sex, with a steeper slope for men. That is, across the range of these anthropometric measures the rise in visceral fat is faster for men than for women. Additionally, there were differences by race/ethnicity in the relationship with height (p<0.001), weight (p<0.001), waist circumference (p<0.001), hip circumference (p = 0.006), and waist to hip ratio (p = 0.001) with the Hispanic group having shallower slopes. For subcutaneous fat, interaction by sex was found for all anthropometric indices at p<0.05, but not for race/ethnicity.

Conclusion

The relationship between anthropometry and underlying adiposity differs by sex and race/ethnicity. When anthropometry is used as a proxy for visceral fat in research, sex-specific models should be used.     

Commingling in the distributions of fat-related measures in Punjabi families

The distribution of anthropometric measurements related to fatness levels is examined to determine if skewness alone accounts for the nonnormality of such measures. A mixture of two normal distributions or a single skewed distribution fit the data significantly better than a single normal in all cases. For maximum hip width, knee diameter, and weight, two skewed distributions give a better fit than one skewed distribution, rejecting the null hypothesis of a single distribution even when skewness is considered. There is evidence for three skewed component distributions for biceps skinfold. Abdomen circumference, upper arm circumference, triceps skinfold, and calf skinfold are best approximated by a one component log-normal distribution. Children and parents show slightly different patterns in skewness and kurtosis when considered separately, but differences between them do not account for the commingling found in the combined distributions.     

Prediction of Visceral Adipose Tissue Using Air Displacement Plethysmography in Children

Objective: To determine the ability of air displacement plethysmography (ADP) to predict visceral adipose tissue (VAT) volume in children. Research Methods and Procedures: Fifty‐five (33 boys/22 girls) white children 13 to 14 years old were studied. Anthropometric measures were collected for body mass, stature, BMI, and waist‐to‐hip ratio (WHR), and body fat percentage was estimated from triceps and subscapular skinfolds, bioelectrical impedance analysis, and ADP. VAT volume was determined using magnetic resonance imaging, using a multiple slice protocol at levels L1 to L5. Results: Boys had significantly (p ≤ 0.05) less VAT volume than girls [645.1 (360.5) cm³ vs. 1035.8 (717.3) cm³]. ADP explained the greatest proportion of the variance in VAT volume compared with the other anthropometric measures. Multiple regression analysis indicated that VAT volume was best predicted by ADP body fat percentage in boys [r² = 0.81, SE of the estimate (SEE) = 160.1, SEE coefficient of variation = 25%] and by WHR and BMI in girls (r² = 0.80, SEE = 337.71, SEE coefficient of variation = 33%). Discussion: Compared with the other anthropometric measures, ADP explains the greatest proportion of the variance in VAT volume in children 13 to 14 years old. For boys, ADP is the tool of choice to predict VAT volume, yet using the more simply collected measures of BMI and WHR is recommended for girls. However, large SE of the estimates remained, suggesting that if precision is needed, there is no surrogate for direct imaging of VAT.     

A comparison of adiposity measures as predictors of all-cause mortality: the Melbourne Collaborative Cohort Study

Objective: Our goal was to examine five different measures of adiposity as predictors of all‐cause mortality. Research Methods and Procedures: Subjects were 16,969 men and 24,344 women enrolled between 1990 and 1994 in the Melbourne Collaborative Cohort Study (27 to 75 years of age). There were 2822 deaths over a median follow‐up period of 11 years. BMI, waist circumference, and waist‐to‐hip ratio were obtained from direct anthropometric measurements. Fat mass and percentage fat were estimated by bioelectric impedance analysis. Results: Comparing the top quintile with the second quintile, for men there was an increased risk of between 20% and 30% for all‐cause mortality associated with each of the anthropometric measures. For women, there was an increased risk of 30% (95% confidence interval for hazard ratio, 1.1–1.6) observed for waist circumference and 50% (1.2–1.8) for waist‐to‐hip ratio, but little or no increased risk for BMI, fat mass, and percentage fat. Waist‐to‐hip ratio was positively and monotonically associated with all‐cause mortality for both men and women. There was a linear association between waist circumference and all‐cause mortality for men, whereas a U‐shaped association was observed for women. Discussion: Measures of central adiposity were better predictors of mortality in women in the Melbourne Collaborative Cohort Study compared with measures of overall adiposity. We recommend measuring waist and hip circumferences in population studies investigating the risk of all‐cause mortality associated with obesity. The use of additional measures such as bioelectric impedance is not justified for this outcome.