A Major Gene for Resting Metabolic Rate Unassociated with Body Composition: Results from the Québec Family Study

A major gene hypothesis for resting metabolic rate (RMR) was investigated using segregation analysis (POINTER) of data on families participating in Phase 2 of the Québec Family Study. Complete analyses were conducted on RMR adjusted for age, and also on RMR adjusted for age and other covariates, primarily fat mass (FM) and fat-free mass (FFM). Prior to adjustment for covariates, support for a major gene hypothesis was equivocal — i.e., there was evidence for either a major gene or a multifactorial component (i.e., polygenic and/or familial environment). The multifactorial model was preferred over the major gene model, although the latter did segregate according to Mendelian expectations. However, after the effects of FM and FFM were accounted for, a major gene effect was unambiguous and compelling. The putative locus accounted for 57% of the variance, affected 7% of the sample, and led to high values of RMR. The lack of a significant multifactorial effect suggested that the familial etiology of RMR adjusted for FM and FFM was likely to be entirely a function of the major locus. Comparing the RMR results from pre- and post-adjustment for FM and FFM suggests a plausible hypothesis. We know from earlier studies in this sample that there is a putative major gene for FM and a major non-Mendelian effect for FFM. The current study leads us to speculate that: (1) the gene(s) affecting body size and body composition also may have an effect on RMR, and further (2) removal of the effect of the major gene(s) for body size and composition allowed for detection of an additional major gene affecting only the RMR. Thus, RMR appears to be an oligogenic trait.     

Pleiotropic Relationships between Cortisol Levels and Adiposity: The HERITAGE Family Study

Objective: To investigate familial basis for the relationship between cortisol adiposity at baseline and their training responses. Research Methods and Procedures: Bivariate correlation and segregation analyses were employed between cortisol and several adiposity measures [body mass index, fat mass (FM), fat-free mass, percentage of body fat (% BF), abdominal visceral fat (AVF), abdominal subcutaneous fat (ASF), and abdominal total fat (ATF)] from 99 white families and 105 black families. Results: In both races, significant inverse phenotypic correlations were generally observed between cortisol and adiposity measures at baseline but not for training responses. Significant cross-trait familial correlations were found for cortisol with abdominal fat (ASF, AVF, ATF) and overall body adiposity (FM, % BF) measures at baseline, which accounted for 14% to 20% of the phenotypic variance in whites. The cross-trait correlations were not significant for baseline phenotypes in blacks, perhaps because of the small sample size. A bivariate segregation analysis showed evidence of polygenic pleiotropy for cortisol with both abdominal fat and overall adiposity measures that accounted for 14% to 17% of the phenotypic covariance, but major gene pleiotropy was not suggested in whites. However, when ASF, AVF, and ATF were additionally adjusted for FM, no familial cross-trait correlations or polygenic pleiotropy between cortisol and the abdominal fat measures remained. Discussion: Evidence was found for polygenic pleiotropy but not for pleiotropic major gene effects between cortisol and overall adiposity in whites. However, the covariation of cortisol with abdominal fat phenotypes is dependent on concomitant polygenic factors for total-body fat.     

Evidence of Pleiotropic Loci for Fasting Insulin,Total Fat Mass,and Abdominal Visceral Fat in a Sedentary Population: The HERITAGE Family Study

Objective: To examine whether there is a major gene effect on fasting insulin and pleiotropic loci for fasting insulin, total fat mass (FM), and abdominal visceral fat (AVF). Research Methods and Procedures: A major gene hypothesis for fasting plasma insulin levels was assessed using segregation analyses of data on 495 members in 98 normolipidemic sedentary families of white descent who participated in the HERITAGE Family Study. Results: Segregation analyses were performed on insulin adjusted for age, on insulin adjusted for age and FM, and on insulin adjusted for age and AVF. Before adjustment for AVF and FM, a major gene effect on fasting insulin levels was indicated. The putative locus accounted for 54% of the variance under a recessive inheritance pattern, affecting 11% of the sample (i.e., allele frequency = 0.33). However, after adjusting for the effects of AVF or FM, neither a major effect alone nor a multifactorial component alone could be rejected, and support for a major gene was equivocal, i.e., neither the hypothesis of Mendelian τ values or that of the equal τs were rejected and the equal τ model fit the data better than the Mendelian τ model. This pattern (i.e., major gene evidence for insulin before but not after adjustment for AVF or FM) suggests that there is a putative locus with pleiotropic effects on both insulin and FM and another pleiotropic locus for both insulin and AVF. Discussion: Although these data do not directly support an additional major gene for insulin independent of AVF and FM, such support cannot be ruled out because there is still a significant major effect on FM‐ or AVF‐adjusted insulin (albeit the Mendelian nature of this effect is ambiguous).     

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.     

Genetic Pleiotropy for Resting Metabolic Rate with Fat-Free Mass and Fat Mass: The Québec Family Study

Shared genetic and familial environmental causes for the associations among resting metabolic rate (RMR), fat-free mass (FFM), and fat mass (FM) were investigated in families participating in phase 2 of the Québec Family Study. A multivariate familial correlation model assessing the pattern of significant cross-trait correlations between family members (e.g., RMR in parents with FFM in offspring) was used to infer the etiology of the associations. For each of FM and FFM with RMR, significant sibling, parent-offspring, and intraindividual cross-trait correlations suggest the associations are familial. Furthermore, the lack of significant spouse cross-trait correlations suggests that the familial aggregation is primarily genetic. Bivariate heritability estimates suggest that as much as 45% to 50% of the shared variance between FFM and RMR may be genetic, and as much as 28% to 34% for FM and RMR. This study supports the notion that the gene(s) affecting each of FFM and FM also influence the RMR. Moreover, the lack of any familial associations between FFM and FM suggests that the effects of each body size component on RMR are independent, i.e., more than one genetic source on the RMR-body size association. The possibility that RMR is an oligogenic trait (i.e., more than one underlying genetic etiology) should be further investigated using more complex multivariate segregation methods until specific genes can be tested.     

Segregation Analysis of Abdominal Visceral Fat: The HERITAGE Family Study

RICE, TREVA, JP DESPRÉS, LOUIS PÉRUSSE, JACQUES GAGNON, ARTHUR S LEON, JAMES S SKINNER, JACK H WILMORE, DC RAO, CLAUDE BOUCHARD. Segregation analysis of abdominal visceral fat: The HERITAGE Family Study. A major gene hypothesis for abdominal visceral fat (AVF) level, both before and after adjustment for total body fat mass, was investigated in 86 white families who participated in the HERITAGE Family Study. In this study, sedentary families were tested for a battery of measures (baseline), endurance exercise trained for 20 weeks, and then remeasured again. The baseline measures reported here are unique in that the variance due to a potentially important environmental factor (activity level) was limited. AVF area was assessed at L4 to L5 by the use of computerized tomography scan, and total body fat mass was assessed with underwater weighing. For fat mass, a putative locus accounted for 64% of the variance, but there was no evidence of a multifactorial component (i.e., no polygenic and/or common familial environmental effects). For AVF area, both a major gene effect accounting for 54% of the variance and a multifactorial component accounting for 17% of the variance were significant. However, after AVF area was adjusted for the effects of total level of body fat, the support for a major gene was reduced. In particular, there was a major effect for fat mass-adjusted AVF area, but it was not transmitted from parents to offspring (i.e., the three transmission probabilities were equal). The importance of this study is twofold. First, these results confirm a previous study that suggested that there is a putative major locus for AVF and for total body fat mass. Second, the findings from the HERITAGE Family Study suggest that the factors underlying AVF area in sedentary families may be similar to those in the population at large, which includes both sedentary and active families. Whether the gene(s) responsible for the high levels of AVF area is the same as that which influences total body fat content remains to be further investigated.     

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.     

Major Gene Influence on the Propensity to Store Fat in Trunk Versus Extremity Depots: Evidence From the Québec Family Study

Regional fat distribution is related to higher risks of cardiovascular morbidity and mortality, independent of general obesity. In particular, a centralized pattern of fat deposition, characterized by greater abdominal stores relative to extremity stores, is associated with a higher propensity to metabolic complications. Motivated by these considerations, we have initiated a systematic investigation of several measures of regional fat distribution aimed at the identification of possible major gene effects. Two measures approximate the size of subcutaneous fat stores: the sum of six skinfold thicknesses (SF6 = abdominal + suprailiac + subscapular + calf + triceps-+ biceps), and the sum of three trunk skinfold thicknesses (TSF3 = abdominal + suprailiac + subscapular). Both of these phenotypes are highly correlated with total fat mass, 0. 83 and 0.78 for SF6 and TSF3, respectively. The trunk to extremity ratio [TER = TSF3 / (calf + triceps + biceps)] is perhaps the most important of these phenotypes insofar as it is an index of centralized obesity; it is modestly correlated with fat mass (r = 0.18). Each of these phenotypes was adjusted for total fat mass by regression prior to analysis so that we could examine genetic effects on these measures of regional fat distribution without the confounding influence of the determinants of fat mass itself. Segregation analysis of SF6 and TSF3 controlled for total fat mass suggests the presence of a major effect underlying the observed phenotypic distribution; however, tests on the transmission probabilities did not substantiate the segregation of a Mendelian gene. Despite the sexual dimorphism in the expression of the TER, the distribution of age-, generation-, sex-, and fat mass-adjusted TER was not significantly heterogeneous comparing males to females. Consistent evidence of a recessive major gene determinant was obtained, accounting for 37% of the phenotypic variance with the frequency of the gene leading to high values of the TER being 0.35. This finding suggests that further studies to investigate the role of specific candidate genes are warranted.     

QDR 4500A DXA overestimates fat-free mass compared with criterion methods.

This study evaluated the accuracy with which the dual-energy X-ray absorptiometer (Hologic QDR 4500A) measured fat-free mass (FFM), fat mass (FM), and hydration of FFM. In a study of 58 men and women (ages 70-79 yr), the QDR 4500A was found to provide a systematically higher estimate of FFM and lower estimate of FM than a four-component model of body composition. A correction factor from this study was developed and applied to two other samples (n = 13 and 37). We found mean corrected levels of FFM and FM to be equivalent to that obtained by the four-component model or total body water. In addition, the hydration of the corrected FFM was closer to the established hydration level in adult samples and that obtained from the four-component model. These findings suggest that the current calibration of the fan-beam system of the Hologic QDR 4500A provides an overestimate of FFM and underestimate of FM compared with reference methods.     

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.     

Major gene effects on exercise ventilatory threshold: the HERITAGE Family Study.

This study investigates whether there are major gene effects on oxygen uptake at the ventilatory threshold (VO(2VT)) and the VO(2VT) maximal oxygen uptake (VT%VO(2 max)), at baseline and in response to 20 wk of exercise training by using data on 336 whites and 160 blacks. Segregation analysis was performed on the residuals of VO(2VT) and VT%VO(2 max). In whites, there was strong evidence of a major gene, with 3 and 2% of the sample in the upper distribution, that accounted for 52 and 43% of the variance in baseline VO(2VT) and VT%VO(2 max), respectively. There were no genotype-specific covariate effects (sex, age, weight, fat mass, and fat-free mass). The segregation results were inconclusive for the training response in whites, and for the baseline and training response in blacks, probably due to insufficient power because of reduced sample sizes or smaller gene effect or both. The strength of the genetic evidence for VO(2VT) and VT%VO(2 max) suggests that these traits should be further investigated for potential relations with specific candidate genes, if they can be identified, and explored through a genome-wide scan.     

Use of bioelectrical impedance to assess body composition changes at high altitude.

This study determined the feasibility of using bioelectrical impedance analysis (BIA) to assess body composition alterations associated with body weight (BW) loss at high altitude. The BIA method was also evaluated relative to anthropometric assessments. Height, BW, BIA, skinfold (SF, 6 sites), and circumference (CIR, 5 sites) measurements were obtained from 16 males (23-35 yr) before, during, and after 16 days of residence at 3,700-4,300 m. Hydrostatic weighings (HW) were performed pre- and postaltitude. Results of 13 previously derived prediction equations using various combinations of height, BW, age, BIA, SF, or CIR measurements as independent variables to predict fat-free mass (FFM), fat mass (FM), and percent body fat (%Fat) were compared with HW. Mean BW decreased from 84.74 to 78.84 kg (P less than 0.01). As determined by HW, FFM decreased by 2.44 kg (P less than 0.01), FM by 3.46 kg (P less than 0.01), and %Fat by 3.02% (P less than 0.01). The BIA and SF methods overestimated the loss in FFM and underestimated the losses in FM and %Fat (P less than 0.01). Only the equations utilizing the CIR measurements did not differ from HW values for changes in FFM, FM, and %Fat. It was concluded that the BIA and SF methods were not acceptable for assessing body composition changes at altitude.     

Reliability and validity of body composition measures in female athletes.

The purpose of this investigation was to determine the reliability and validity of bioelectrical impedance (BIA) and near-infrared interactance (NIR) for estimating body composition in female athletes. Dual-energy X-ray absorptiometry was used as the criterion measure for fat-free mass (FFM). Studies were performed in 132 athletes [age = 20.4 +/- 1.5 (SD) yr]. Intraclass reliabilities (repeat and single trial) were 0.987-0.997 for BIA (resistance and reactance) and 0.957-0.980 for NIR (optical densities). Validity of BIA and NIR was assessed by double cross-validation. Because correlations were high (r = 0.969-0.983) and prediction errors low, a single equation was developed by using all 132 subjects for both BIA and NIR. Also, an equation was developed for all subjects by using height and weight only. Results from dual-energy X-ray absorptiometry analysis showed FFM = 49.5 +/- 6.0 kg, which corresponded to %body fat (%BF) of 20.4 +/- 3.1%. BIA predicted FFM at 49.4 +/- 5.9 kg (r = 0.981, SEE = 1.1), and NIR prediction was 49. 5 +/- 5.8 kg (r = 0.975, SEE = 1.2). Height and weight alone predicted FFM at 49.4 +/- 5.7 kg (r = 0.961, SEE = 1.6). When converted to %BF, prediction errors were approximately 1.8% for BIA and NIR and 2.9% for height and weight. Results showed BIA and NIR to be extremely reliable and valid techniques for estimating body composition in college-age female athletes.     

Cross-Validation of Fat-Free Mass Estimated From Body Density Against Bioelectrical Resistance: Effects of Obesity and Gender

GORAN, MICHAEL I AND M ABU KHALED. Cross-validation of fat-free mass estimated from body density against bioelectrical resistance: effects of obesity and gender. Obes Res. The major purpose of this study was to examine whether estimates of body composition from bioelectrical resistance were systematically biased by obesity and/or gender (using hydrodensitometry as a comparison method). We compared fat-free mass (FFM) by bioelectrical resistance (BR) using 5 equations (Lukaski, Kushner, Rising, Khaled, and Segal) to FFM by hydrodensitometry (HD) in 20 lean men, 30 lean women, 33 obese men and 22 obese women. None of the BR equations was successfully cross-validated against FFM by HD in all 4 sub-groups. The Lukaski equation significantly underestimated FFM in all 4 groups by 2.7 to 4.7 kg; the Kushner equation significantly underestimated FFM by 2.0 to 2.9 kg except in obese women; the Rising equation significantly overestimated FFM in obese women (5.3 kg) and men (2.9 kg); the Khaled equation successfully predicted FFM in all groups except obese men; and the Segal equation successfully predicted FFM in all groups except lean men. In some groups, a portion of the discrepancy could be explained by bias originating from body fat. Analysis of our data by forward regression analysis demonstrated that height²/resistance, body weight, gender and suprailiac skinfold thickness provide the most accurate estimates of FFM (R²=0.92; SEE = 3.58kg) that are free of bias originating from gender and body fat. We conclude that the estimation of fat-free mass by BR is significantly influenced by gender and obesity. An alternative equation is proposed for estimating fat-free mass based on measurement of height²/resistance, body weight, gender and suprailiac skinfold thickness.     

Dual-energy X-ray absorptiometry (DXA) can accurately and nondestructively measure the body composition of small, free-living rodents

Dual-energy x-ray absorptiometry (DXA) is a nondestructive technique that can potentially measure specific components of whole-body composition in free-living and lab-raised animals. Our aim was to test the ability of DXA to measure the composition of a common arvicoline rodent, the northern red-backed vole (Clethrionomys rutilus). We used a DXA apparatus to obtain measurements of fat mass (FM), lean mass (LM),bone mineral content, bone mineral density, and fat-free mass(FFM) in carcasses of free-living and lab-raised voles. We then used chemical carcass analysis to derive predictive algorithms for actual values of FM, total body water, total protein, total mineral, LM, and FFM. Unexplained error in the equations for all voles grouped collectively ranged from R(2) = 0.82 to R(2) = 0.98. The DXA FM measurement had the highest coefficient of variation, and it was higher for free-living voles than for lab-raised voles. However, FM can be determined by difference with excellent precision by using the FFM equation (R(2) = 0.98). We also derived corrective terms for passive integrated transponder-tagged animals. Thus, DXA is a nonlethal, nondestructive tool capable of precisely and accurately measuring many specific parameters of whole-body composition in small free-living and lab-raised rodents.     

Anthropometric and body composition characteristics in pre- and postmenopausal Asian Indian women: Santiniketan women study

The present cross-sectional study was aimed to investigate anthropometric and body composition characteristics in pre- and postmenopausal Asian Indian women. A total of 245 healthy women aged 25 to 65 years took part in the study. A random sampling procedure using a local voters' registration list was followed to select the participants. All participants belonged to the Bengalee population and were inhabitants of the Bolpur-Santiniketan area (lying in between 23 degrees 40' north latitude and 87degrees 43' east longitude) West Bengal, India. Before the actual commencement of the study, written information was communicated to select individuals, and an appointment was requested at their respective houses. Anthropometric measures, namely height, weight etc., were collected using standard techniques. Percentages of body fat (%BF) and body mass index (BMI) were measured using an Omron body fat analyser. All subjects were categorized into two groups: premenopausal (Group I; n = 145, mean age = 32.66 +/- 5.75 years) and postmenopausal (Group II; n = 100, mean age = 52.72 +/- 5.62 years). It was observed that 80.00% women were cohabited and 80.82% were housewife with 44.08% of them having an education up to secondary level. Furthermore, 62.45% subjects had monthly family expenditure of > or = 5000 Indian Rupees. One way ANOVA revealed that there was significant group difference for age, age at menarche, MWC, WHR, FM, FFM and %BF across the groups. Intercorrelation matrix (Pearson's correlation) showed that age had significantly positive association with MWC (p < 0.01), MHC (p < 0.05), WHR (p < 0.01), FM (p < 0.01), and %BF (p < 0.01), whereas FFM has had negative association with age. Most interestingly, it was observed that there was significant difference [chi2 (1) = 9.73] for central obesity status across the groups. It seems reasonable to argue that onset of menopause does play a vital role to alter body composition and in turn CVD risk factors.     

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.     

Waist Circumference Measurement by Site,Posture, Respiratory Phase,and Meal Time: Implications for Methodology

Waist circumference (WC) has been advocated as a simple, reliable, and cost‐effective measure to understand an individual's cardio‐metabolic risk. Although several protocols exist for measuring WC, the variation induced by a few factors has not been investigated. We compared several established and experimental WC measurement protocols to identify factors that may cause variations in WC measurement. In this cross‐sectional study, we examined the variations in the measurement of waist circumference (WC) measures carried out in 11 ways differing by anatomical site, posture, respiratory phase, and time since last meal, using repeated measure analysis of variance (using mixed models) after Tukey‐Kramer adjustment. We estimated the proportion of variance in percentage of body fat (%BF) and fat‐free mass (FFM) explained by each of the WC measures. We studied 123 apparently healthy Asian Indians (75 females), with mean (s.d.) age of 34 (8.7) years and BMI of 23.9 (4.8) kg/m². Overall, the mean of WCs measured using the 11 protocols were statistically different. Further, post hoc analysis showed statistically significant, yet mostly small, differences between most of the pairs. No single WC measure explained highest variance in %BF or FFM for both genders. Although, the National Institute of Health (NIH), USA, protocol was convenient and may be less prone to errors, at present it does not control for many variables tested in this study. Measures of WC measured using different protocols were statistically different. We suggest that the site of measurement, posture, phase of respiration, and time since last meal should be standardized for the development of a protocol for measurement of WC for worldwide use.     

Percent body fat via DEXA: comparison with a four-compartment model.

This study compared body composition by dual-energy X-ray absorptiometry (DEXA; Lunar DPX-L) with that via a four-compartment (4C; water, bone mineral mass, fat, and residual) model. Relative body fat was determined for 152 healthy adults [30.0 +/- 11.1 (SD) yr; 75.10 +/- 14.88 kg; 176.3 +/- 8.7 cm] aged from 18 to 59 yr. The 4C approach [20.7% body fat (%BF)] resulted in a significantly (P < 0.001) higher mean %BF compared with DEXA (18.9% BF), with intraindividual variations ranging from -2.6 to 7.3% BF. Linear regression and a Bland and Altman plot demonstrated the tendency for DEXA to progressively underestimate the %BF of leaner individuals compared with the criterion 4C model (4C %BF = 0.862 x DEXA %BF + 4.417; r(2) = 0.952, standard error of estimate = 1.6% BF). This bias was not attributable to variations in fat-free mass hydration but may have been due to beam-hardening errors that resulted from differences in anterior-posterior tissue thickness.