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.     

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.     

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.     

Preferences Predict Food Intake From 5 to 11 Years,but Not in Girls With Higher Weight Concerns,Dietary Restraint,and %Body Fat

Food preferences (FP) predict food intake in childhood; however, the predictive power of FP may decline among girls as weight concerns (WC) and dietary restraint (DR) increase during preadolescence. To examine longitudinal change in the preference‐intake (P‐I) relation and assess whether this relation weakens among non‐Hispanic white girls (n = 197) with a history of WC and DR from age 5 to 11. Girls' preferences for and intake (kcal) of 10 palatable snack foods were assessed biennially. Height, weight, percent body fat (%BF), WC, and DR were measured. Individual correlation coefficients were calculated per girl to capture within‐person P‐I correlations at each time of measurement. Overall, FP predicted girls' snack food calorie intakes between 5 and 11 years, but latent profile analysis (LPA) revealed three distinct patterns of change in P‐I correlations over time: “strong/stable” P‐I correlations were relatively high and became stronger with age; “increasing/later null” P‐I correlations were initially weak and became stronger between 5 and 9 years, but dropped to near 0 at 11 years; “initially weak/later strong” P‐I correlations were initially null and increased with age. Mixed models revealed that the “increasing/later null” group had greater increases in %BF, and higher WC, DR, and BMI percentiles from 5 to 11 years, compared to the other groups. In summary, FP predicted snack food calorie intake among most girls during childhood, but waned as a predictor of calorie intake at age 11 for a subset of girls with increasing %BF, and higher WC, DR, and BMIs.     

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.     

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

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

Evaluation of the Influence of Cardiorespiratory Fitness on Diverse Health Risk Factors,Independent of Waist Circumference,in 40‐Year‐Old Flemish Males

Objective: To examine whether cardiorespiratory fitness is associated with health risk factors, independent of abdominal obesity (waist circumference). Research Methods and Procedures: Data from 141 40‐year‐old Flemish male subjects were used in a cross‐sectional design. The main outcome measure in this study was the health risk profile, composed of lipid parameters, blood pressure, and body composition parameters. Data were analyzed by regression analysis and by means of analysis of covariance with waist circumference (WC) as covariate and cardiorespiratory fitness as source of variance. Results: The data demonstrated that cardiorespiratory fitness was significantly associated with triglycerides, with the ratio of total cholesterol/serum high density lipoprotein cholesterol, and with percent body fat, independent of an association with WC. Discussion: Major findings of the present cross‐sectional study indicate that a high WC is associated with adverse health‐related lipid profile and body composition characteristics and that relatively high levels of aerobic fitness attenuate the health risk associated with a large WC.     

Intraperitoneal Fat and Insulin Resistance in Obese Adolescents

Obesity is epidemic among adolescents in the United States. We sought to analyze the anthropometric measures of adiposity and fasting indices of insulin resistance, including insulin‐like growth factor–binding protein‐1 (IGFBP‐1), and to provide a clinical estimate of intraperitoneal (IP) fat in obese adolescents (BMI ≥95th percentile), between ages 13 and 17 years. Subjects had baseline testing to determine eligibility for a subsequent randomized, placebo‐controlled trial of metformin XR therapy. Anthropometry and dual‐energy X‐ray absorptiometry (DXA) were used to quantify total body fat while abdominal computed tomography (CT) was used to measure IP (CT‐IP) and subcutaneous (CT‐SQ) fat. Using anthropometry and fasting laboratory data, we constructed regression models for both CT‐IP and CT‐SQ. A total of 92 subjects, 33 males, were evaluated. Of the 92 subjects, 19 were black. Fasting insulin concentrations were highly associated with other measures of insulin resistance. Median percent body fat across all subjects, as measured by DXA, was 41%. Using CT measures, 67% of abdominal cross‐sectional area was fat, 14% of which was IP fat. In multiple regression analysis, waist circumference (WC) and BMI, jointly and independently, were strongly associated with both CT‐IP and CT‐SQ fat. BMI and WC explained 62% of variance of CT‐SQ fat, but only 26% of variance of CT‐IP fat. Adding triglyceride:high‐density lipoprotein (TG:HDL) ratio and IGFBP‐1 (among nonblacks) to the regression model increased the explained variance for estimating CT‐IP fat to 45%. When evaluating the metabolic morbidity of an obese adolescent, a model using fasting IGFBP‐1, TG:HDL, BMI, and WC may be worthwhile as an estimate of IP fat.     

The influence of posture change on measurements of relative body fat in the bioimpedance analysis method

The purpose of this study was to clarify the influence of posture change on relative body fat in the bioelectrical impedance analysis (BIA) method. The subjects were 30 Japanese healthy young adult males (age: 19.8 +/- 1.4 years, height: 172.3 +/- 5.8 cm, weight: 67.1 +/- 8.2 kg). We used devices with different body segment inductions, between the hand and foot (H-F BIA) and between hands (H-H BIA), and set four measurement conditions differing in posture (supine or sitting), during rest and measurement. The reliabilities of %BF in the H-H and H-F BIA methods were very high (r = 0.995, 0.966), and the relationship in %BF between the UW method and each BIA method was mid-range (r = 0.767, 0.709). Although there were no differences in %BF among different measurement postures in the H-F BIA method, %BF in the H-H BIA method increased significantly when the posture was changed just before measurement. This indicated that it is necessary to pay attention to the posture change just before measurement in the H-H BIA method.     

Waist circumference correlates with metabolic syndrome indicators better than percentage fat

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

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.     

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.     

24h Urinary Sodium Excretion and Subsequent Change in Weight,Waist Circumference and Body Composition

Background

In the same period as the increasing obesity epidemic, there has been an increased consumption of highly processed foods with a high salt content, and a few studies have suggested that a diet with a high salt content may be associated with obesity.

Objective

To investigate the association between 24 h urinary sodium excretion and subsequent change in body weight (BW), waist circumference (WC), body fat (BF) and fat free mass (FFM) among adults.

Design

A longitudinal population study based on the Danish part of the MONICA project, with examinations in 1987–1988 and 1993–1994. Complete information on 24 h urinary sodium excretion along with repeated measures of obesity, as well as on potential confounders, was obtained from 215 subjects. Linear regression was used to examine the association between sodium excretion, as a measure of salt consumption, and subsequent changes in BW, WC, BF and FFM, and further evaluated by restricted cubic splines. Stepwise adjustments were made for selected covariates.

Results

Neither the crude nor the adjusted models showed any statistically significant associations between sodium excretion and change in BW or WC. Likewise, we found no significant association between sodium excretion and change in BF and FFM in the unadjusted models. However, after adjusting for potential baseline confounders and the concurrent BW change, we found a significant increase in BF of 0.24 kg (P = 0.015, CI: 0.05 to 0.43) per 100 mmol increase in 24 h urinary sodium excretion (equivalent to 6 g of salt), during the 6-year study period. Moreover, during the same period, we found a significant association with FFM of −0.21 kg (P = 0.041, CI: −0.40 to −0.01).

Conclusions

These results suggest that a diet with a high salt content may have a negative influence on development in body composition by expanding BF and reducing FFM.     

Segregation analysis of fat mass and other body composition measures derived from underwater weighing.

Segregation patterns of three body composition measures which were derived from underwater weighing were evaluated in a random sample of 176 French-Canadian families. Two of the variables can be considered as primary partitions of weight (fat mass [FM] and fat-free mass [FFM]), while the remaining variable (percent body fat [%BF]) is a derived index combining the measures of both fat and fat-free weight. This study represents the first report investigating major gene effects for these measures. Segregation analyses revealed that a major locus hypothesis could not be rejected for two of the three phenotypes. The single exception was FFM, for which nearly 60% of the variance was accounted for by a non-Mendelian major effect, which may reflect environmentally based commingling or may be in part a function of gene-environment interactions or correlations. In contrast to the results for FFM, the results for each of FM and %BF were similar and suggested a major locus which accounted for 45% of the variance, with an additional 22%-26% due to a multifactorial component. Given the similarity of the major gene characteristics for these two phenotypes, the possibility that the same gene underlies both measures warrants investigation. A reasonable hypothesis is to consider genes that may influence nutrient partitioning, as the family of candidate genes to receive the major attention.     

Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women

Objective: To compare estimates of total and truncal fatness from eight‐electrode bioelectrical impedance analysis equipment (BIA₈) with those from DXA in centrally obese women. The secondary aim was to examine BMI and waist circumference (WC) as proxy measures for percentage total body fat (%TBF) and truncal body fat percentage (tr%BF). Research Methods and Procedures: This was a cross‐sectional study of 136 women (age, 48.1 ± 7.7 years; BMI, 30.4 ± 2.9 kg/m²; %TBF_DXA, 46.0 ± 3.7%; WC, 104 ± 8 cm). Fatness was measured by DXA and Tanita BC‐418 equipment (Tanita Corp., Tokyo, Japan). Agreement among methods was assessed by Bland‐Altman plots, and regression analysis was used to evaluate anthropometric measures as proxies for total and abdominal fatness. Results: The percentage of overweight subjects was 41.9%, whereas 55.9% of the subjects were obese, as defined by BMI, and all subjects had a WC exceeding the World Health Organization cut‐off point for abdominal obesity. Compared with DXA, the BIA₈ equipment significantly underestimated total %BF (?5.0; ?3.6 to ?8.5 [mean; 95% confidence interval]), fat mass (?3.6; ?3.9 to ?3.2), and tr%BF (?8.5; ?9.1 to ?7.9). The discrepancies between the methods increased with increasing adiposity for both %TBF and tr%BF (both p < 0.001). Variation in BMI explained 28% of the variation in %TBF_DXA and 51% of %TBF_BIA8. Using WC as a proxy for truncal adiposity, it explained only 18% of tr%BF_DXA variance and 27% of tr%BF_BIA8 variance. The corresponding figures for truncal fat mass were 49% and 35%, respectively. No significant age effects were observed in any of the regressions. Discussion: BIA₈ underestimated both total and truncal fatness, compared with DXA, with higher dispersion for tr%BF than %TBF. The discrepancies increased with degree of adiposity, suggesting that the accuracy of BIA is negatively affected by obesity.     

Genetic and Environmental Influences on Body‐Fat Measures among African‐American Twins

Objective: To investigate the genetic and environmental influences on body‐fat measures including waist circumference (WC), waist‐to‐hip ratio (WHR), and body mass index (BMI) among African‐American men and women. Research Methods and Procedures: Measurements were taken as part of the Carolina African American Twin Study of Aging. This sample currently comprises 146 same‐sex African‐American twins with an average age of 50 years (range, 22 to 88 years). This analysis included 26 monozygotic and 29 dizygotic men and 45 monozygotic and 46 dizygotic women. Maximum likelihood quantitative genetic analysis was used. Results: In men, additive genetic effects accounted for 77% of the variance in WC, 59% in WHR, and 89% in BMI. In women, additive genetic effects accounted for 76% of the variance in WC, 56% in WHR, and 73% in BMI. The remaining variance in both men and women was attributed to unique environmental effects (WC, 21%; WHR, 36%; BMI, 11% in men and WC, 22%; WHR, 38%; BMI, 27% in women) and age (WC, 2%; WHR, 5% in men and WC, 2%; WHR, 6% in women). When BMI was controlled in the analysis of WC and WHR, it accounted for a portion of the genetic and environmental variance in WHR and over one‐half of the genetic and environmental variance in WC. Discussion: There are both genetic and environmental influences on WC, WHR, and BMI, and independent of BMI, there are genetic and environmental effects on WC and WHR among both genders. The results from this African‐American twin sample are similar to findings among white twin samples.     

Relationship of visceral adiposity to cardiovascular disease risk factors in black and white teens

Objective: We tested the hypothesis that visceral adiposity, compared with general adiposity, would explain more of the variance in cardiovascular disease (CVD) risk factors. Research Method and Procedures: Subjects were 464 adolescents (238 black and 205 girls). Adiposity measures included visceral adipose tissue (VAT; magnetic resonance imaging), percent body fat (%BF; DXA), BMI, and waist girth (anthropometry). CVD risk factors were fasting insulin, fibrinogen, total to high‐density lipoprotein‐cholesterol ratio, triglycerides (TGs), systolic blood pressure, and left ventricular mass indexed to height^2.7. Results: After adjustment for age, race, and sex, all adiposity indices explained significant proportions of the variance in all of the CVD risk factors; %BF tended to explain more variance than VAT. Regression models that included both %BF and VAT found that both indices explained independent proportions of the variance only for total to high‐density lipoprotein‐cholesterol ratio. For TGs, the model that included both %BF and VAT found that only VAT was significant. For systolic blood pressure and left ventricular mass indexed to height^2.7, anthropometric measures explained more of the variance than VAT and %BF. Discussion: The hypothesis that visceral adiposity would explain more variance in CVD risk than general adiposity was not supported in this relatively large sample of black and white adolescents. Only for TGs did it seem that VAT was more influential than %BF. Perhaps the deleterious effect of visceral adiposity becomes greater later in life as it increases in proportion to general adiposity.     

Body fat distribution of children and adolescents in Abeokuta,Southwest Nigeria

Excessive central fat in children and adolescents is a risk factor for cardiovascular and metabolic disorders. This study aimed to compare the body fat distribution patterns of children and adolescents in Abeokuta, Nigeria with international reference standards. Five hundred seventy children aged 5 to 19 years were selected from seven schools using multistage random sampling. Weight, height, triceps and subscapular skinfold thickness (TSF, SSF), and circumference at the waist and hips (WC, HC) were measured. Body mass index (BMI), subscapular:triceps skinfold ratio (STR), waist:hip circumference ratio (WHR), and waist: height ratio (WHtR) were derived. Females had higher mean BMI, TSF, SSF, WC, HC, WHR, and WHtR, while males had significantly higher STR. The mean BMI, WC, TSF, and SSF values were lower for our subjects than for African‐American subjects at all ages. On the other hand, in both sexes, STR was higher among Nigerian than African‐American subjects up to 12 years old. Thereafter the values were similar. The mean WC was similar to those reported for African‐American males up to 8 years, and females up to 7 years of age; thereafter, African‐American had higher values. The prevalence of central obesity using WC and WHtR measures was 4.4% and 5.8%, respectively. There is a need to validate each index against serum lipid profiles and other cardiovascular and metabolic risk factors. Am J Phys Anthropol 150:647–654, 2013. © 2013 Wiley Periodicals, Inc.     

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.     

Susceptibility Loci for Adiposity Phenotypes on 8p, 9p,and 16q in American Samoa and Samoa

Obesity is a complex phenotype affected by genetic and environmental influences such as sociocultural factors and individual behaviors. Previously, we performed two separate genome‐wide investigations for adiposity‐related traits (BMI, percentage body fat (%BF), abdominal circumference (ABDCIR), and serum leptin and serum adiponectin levels) in families from American Samoa and in families from Samoa. The two polities have a common evolutionary history but have lately been influenced by variations in economic development, leading to differences in income and wealth and in dietary and physical activity patterns. We now present a genome‐wide linkage scan of the combined samples from the two polities. We adjust for environmental covariates, including polity of residence, education, cigarette smoking, and farm work, and use variance component methods to calculate univariate and bivariate multipoint lod scores. We identified a region on 9p22 with genome‐wide significant linkage for the bivariate phenotypes ABDCIR–%BF (1‐d.f. lod 3.30) and BMI–%BF (1‐d.f. lod 3.31) and two regions with genome‐wide suggestive linkage on 8p12 and 16q23 for adiponectin (lod 2.74) and the bivariate phenotype leptin‐ABDCIR (1‐d.f. lod 3.17), respectively. These three regions have previously been reported to be linked to adiposity‐related phenotypes in independent studies. However, the differences in results between this study and our previous polity‐specific studies suggest that environmental effects are of different importance in the samples. These results strongly encourage further genetic studies of adiposity‐related phenotypes where extended sets of carefully measured environmental factors are taken into account.