Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

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.     

Are Ethnic and Gender Specific Equations Needed to Derive Fat Free Mass from Bioelectrical Impedance in Children of South Asian,Black African-Caribbean and White European Origin? Results of the Assessment of Body Composition in Children Study

Background

Bioelectrical impedance analysis (BIA) is a potentially valuable method for assessing lean mass and body fat levels in children from different ethnic groups. We examined the need for ethnic- and gender-specific equations for estimating fat free mass (FFM) from BIA in children from different ethnic groups and examined their effects on the assessment of ethnic differences in body fat.

Methods

Cross-sectional study of children aged 8–10 years in London Primary schools including 325 South Asians, 250 black African-Caribbeans and 289 white Europeans with measurements of height, weight and arm-leg impedance (Z; Bodystat 1500). Total body water was estimated from deuterium dilution and converted to FFM. Multilevel models were used to derive three types of equation {A: FFM = linear combination(height+weight+Z); B: FFM = linear combination(height²/Z); C: FFM = linear combination(height²/Z+weight)}.

Results

Ethnicity and gender were important predictors of FFM and improved model fit in all equations. The models of best fit were ethnicity and gender specific versions of equation A, followed by equation C; these provided accurate assessments of ethnic differences in FFM and FM. In contrast, the use of generic equations led to underestimation of both the negative South Asian-white European FFM difference and the positive black African-Caribbean-white European FFM difference (by 0.53 kg and by 0.73 kg respectively for equation A). The use of generic equations underestimated the positive South Asian-white European difference in fat mass (FM) and overestimated the positive black African-Caribbean-white European difference in FM (by 4.7% and 10.1% respectively for equation A). Consistent results were observed when the equations were applied to a large external data set.

Conclusions

Ethnic- and gender-specific equations for predicting FFM from BIA provide better estimates of ethnic differences in FFM and FM in children, while generic equations can misrepresent these ethnic differences.     

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.     

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.     

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.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

Reformulation of BMI and Percent Body Fat to Remove the Height Bias in 8‐year‐olds

BMI and percent body fat (%BF) are both related to height (Ht) in prepubertal children, so may misrepresent childhood adiposity, especially in tall or short children. We sought to construct replacement functions for BMI and %BF that are independent of Ht. Fat mass (FM) was measured using dual‐energy X‐ray absorptiometry, together with Ht and body mass (BM) in 746 healthy boys and girls aged 8 years (0.34 s.d.). Relationships between BM, FM, and Ht were measured and values of p and q derived such that the functions BM. Ht^?p and FM.BM^?q were unrelated to Ht. BM was not directly proportional to Ht², BMI being significantly related to Ht in both boys and girls (P < 0.001). BM was proportional to Ht³, BM. Ht^?3 being independent of Ht. Similarly, FM was not directly proportional to BM and %BF was significantly related to Ht (P < 0.001). While FM was proportional to BM², FM.BM^?1.5 was the function found to be independent of Ht. Using the 85th and 95th percentiles as the cutoffs for overweight and obesity respectively, 6.4% of the boys and 6.8% of the girls were classified differently by BMI and the Ht independent measure BM. Ht^?3. Similarly, 10.1% boys and 13.7% girls were classified differently by %BF and the Ht independent measure FM.BM^?1.5. We propose that improved diagnostic accuracy of body composition in 8‐year‐olds is provided by the BM function (BMF, BM. Ht^?3) and FM function (FMF, FM.BM^?1.5) replacing BMI and %BF, which both overestimate the adiposity of taller children and underestimate it in shorter children.     

Serial Measurements of Body Composition in Obese Subjects During a Very-Low-Energy Diet (VLED) Comparing Bioelectrical Impedance with Hydrodensitometry

Bioelectrical impedance analysis (BIA) is a convenient, inexpensive, and noninvasive technique for measuring body composition. BIA has been strongly correlated with total body water (TBW) and also has been validated against hydrodensitometry (HD). The accuracy and clinical utility of BIA and HD during periods of substantial weight loss remain controversial. We measured body composition in moderately and severely obese patients serially using both methods during a very-low-energy diet (VLED). Mean initial weight in these patients was 116 (± 30) kg (range, 74–196 kg). Mean weight loss was 24 (± 13) kg with a decrease in fat mass (FM) by HD of 20 kg (p<0.001) and a decrease in fat-free mass (FFM) of 3.6 kg (p<0.05). Loss of FFM is best predicted by the rate (kg/wk) of weight loss (r² = 0.86, p<0.0001). FFM, as predicted from BIA equations, was highly correlated with FFM as estimated by HD during all testing sessions (r=0.92-0.98). Although highly correlated, BIA overestimated FFM relative to HD and this difference appeared to be more pronounced for taller patients with greater truncal obesity. Although the discrepancy was no greater during weight-loss treatment, the level of disagreement was considerable. Therefore, the two methods cannot be used interchangeably to monitor relative changes in body composition in patients with obesity during treatment with VLED. The discrepancy between BIA and HD may be caused by body mass distribution considerations and by perturbations in TBW which affect the hydration quotient for FFM (BIA) and/or which affect the density constants for FFM and FM (HD).     

Comparison of body composition methods in obese African-American women

Objective : To compare the accuracy of percentage body fat (%BF) estimates between bioelectrical impedance analysis (BIA) and DXA in obese African‐American women. Research Methods and Procedures : Fifty‐five obese African‐American women (mean age, 45 years; mean BMI, 38; mean %BF, 48%) were studied. BF was assessed by both BIA (RJL Systems BIA 101Q; RJL Systems, Clinton Township, MI) and DXA (Hologic QDR‐2000 Bone Densitometer; Hologic Inc., Bedford, MA). Generalized and ethnicity‐ and obese‐specific equations were used to calculate %BF from the BIA. Bland‐Altman analyses were used to compare the agreement between the BIA and the DXA, with the DXA serving as the criterion measure. Results : Two of the generalized equations provided consistent estimates across the weight range in comparison with the DXA estimates, whereas most of the other equations increasingly underestimated %BF as BF increased. One of the generalized and one of the ethnicity‐specific equations had mean differences that were not significantly different from the DXA value. Discussion : The findings show that the Lukaski equation provided the most precise and accurate estimates of %BF in comparison with the QDR 2000 and provide preliminary support for the use of this equation for obese African‐American women.     

A Weight Reduction Program Preserves Fat‐Free Mass but Not Metabolic Rate in Obese Adolescents

Objective: To determine the effects of a multidisciplinary weight reduction program on body composition and energy expenditure (EE) in severely obese adolescents. Research Methods and Procedures: Twenty‐six severely obese adolescents, 12 to 16 years old [mean BMI: 33.9 kg/m²; 41.5% fat mass (FM)] followed a 9‐month weight reduction program including moderate energy restriction and progressive endurance and resistance training. Body composition was assessed by DXA, basal metabolic rate by indirect calorimetry, and EE by whole‐body indirect calorimetry with the same activity program over 36‐hour periods before starting and 9 months after the weight reduction period. Results: Adolescents gained (least‐square mean ± SE) 2.9 ± 0.2 cm in height, lost 16.9 ± 1.3 kg body weight (BW), 15.2 ± 0.9 kg FM, and 1.8 ± 0.5 kg fat‐free mass (FFM) (p < 0.001). Basal metabolic rate, sleeping, sedentary, and daily EE were 8% to 14% lower 9 months after starting (p < 0.001) and still 6% to 12% lower after adjustment for FFM (p < 0.05). Energy cost of walking decreased by 22% (p < 0.001). The reduction in heart rate during sleep and sedentary activities (?10 to ?13 beats/min), and walking (?20 to ?25 beats/min) (p < 0.001) resulted from both the decrease in BW and physical training. Discussion: A weight reduction program combining moderate energy restriction and physical training in severely obese adolescents resulted in great BW and FM losses and improvement of cardiovascular fitness but did not prevent the decline in EE even after adjustment for FFM.     

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.     

Body Fat and Fat‐Free Mass and All‐Cause Mortality

Objective: To investigate whether the association between BMI and all‐cause mortality could be disentangled into opposite effects of body fat and fat‐free mass (FFM). Research Methods and Procedures: All‐cause mortality was studied in the Danish follow‐up study “Diet, Cancer and Health” with 27, 178 men and 29, 875 women 50 to 64 years old recruited from 1993 to 1997. By the end of year 2001, the median follow‐up was 5.8 years, and 1851 had died. Body composition was assessed by bioelectrical impedance. Cox regression models were used to estimate the relationships among body fat mass index (body fat mass divided by height squared), FFM index (FFM divided by height squared), and mortality. All analyses were adjusted for smoking habits. Results: Men and women showed similar associations. J‐shaped associations were found between body fat mass index and mortality adjusted for FFM and smoking. The mortality rate ratios in the upper part of body fat mass were 1.12 per kg/m² (95% confidence interval: 1.07, 1.18) in men and 1.06 per kg/m² (95% confidence interval: 1.02, 1.10) in women. Reversed J‐shaped associations were found between FFM index and mortality with a tendency to level off for high values of FFM. Discussion: Our findings suggest that BMI represents joint but opposite associations of body fat and FFM with mortality. Both high body fat and low FFM are independent predictors of all‐cause mortality.     

Energy imbalance underlying the development of childhood obesity

Objective: To develop a model based on empirical data and human energetics to predict the total energy cost of weight gain and obligatory increase in energy intake and/or decrease in physical activity level associated with weight gain in children and adolescents. Research Methods and Procedures: One‐year changes in weight and body composition and basal metabolic rate (BMR) were measured in 488 Hispanic children and adolescents. Fat‐free mass (FFM) and fat mass (FM) were measured by DXA and BMR by calorimetry. Model specifications include the following: body mass (BM) = FFM + FM, each with a specific energy content, cff (1.07 kcal/g FFM) and cf (9.25 kcal/g FM), basal energy expenditure (EE), kff and kf, and energetic conversion efficiency, eff (0.42) for FFM and ef (0.85) for FM. Total energy cost of weight gain is equal to the sum of energy storage, EE associated with increased BM, conversion energy (CE), and diet‐induced EE (DIEE). Results: Sex‐ and Tanner stage–specific values are indicated for the basal EE of FFM (kff) and the fat fraction in added tissue (fr). Total energy cost of weight gain is partitioned into energy storage (24% to 36%), increase in EE (40% to 57%), CE (8% to 13%), and DIEE (10%). Observed median (10th to 90th percentile) weight gain of 6.1 kg/yr (2.4 to 11.4 kg/yr) corresponds at physical activity level (PAL) = 1.5, 1.75, and 2.0 to a total energy cost of weight gain of 244 (93 to 448 kcal/d), 267 (101 to 485 kcal/d), and 290 kcal/d (110 to 527 kcal/d), respectively, and to a total energy intake of 2695 (1890 to 3730), 3127 (2191 to 4335), and 3551 (2487 to 4930) kcal/d, respectively. If weight gain is caused by a change in PAL alone and PAL₀ = 1.5 at baseline t = 0, the model indicates a drop in PAL of 0.22 (0.08 to 0.34) units, which is equivalent to 60 (18 to 105) min/d of walking at 2.5 mph. Discussion: Halting the development or progression of childhood obesity, as observed in these Hispanic children and adolescents, by counteracting its total energy costs will require a sizable decrease in energy intake and/or reciprocal increase in physical activity.     

Ethnic‐Specific Differences in Abdominal Subcutaneous Adipose Tissue Compartments

South Asians have a higher prevalence of cardiovascular disease (CVD) than Europeans. Studies have identified distinct subcompartments of subcutaneous adipose tissue (SAT) that provide insight into the relationship between abdominal obesity and metabolic risk factors in different ethnic groups. Our objective was to determine the relationship between SAT compartments and fat‐free mass (FFM) between South Asian and European cohorts, and between men and women. Healthy Europeans and South Asians (n = 408) were assessed for FFM via dual energy X‐ray absorptiometry, and SAT areas by computed tomography (CT). SAT was subdivided into superficial subcutaneous abdominal adipose tissue (SSAT) and deep subcutaneous abdominal adipose tissue (DSAT). Linear regression analyses were performed using DSAT and SSAT as separate dependent variables and FFM and ethnicity as primary independent variables adjusting for age, gender, income, education, and smoking status. Results showed that South Asian men had significantly higher amounts of DSAT (median 187.65 cm² vs. 145.15 cm², P < 0.001), SSAT (median 92.0 cm² vs. 76.1 cm², P = 0.046), and body fat mass (BFM) (25.1 kg vs. 22.6 kg, P = 0.049) than European men. In a fully adjusted model, South Asians showed significantly greater DSAT at any FFM than Europeans. Women had more SSAT at any given FFM than men and less DSAT at any given FFM than men, irrespective of ethnic background. In conclusion, South Asians had more DSAT than Europeans and men had relatively more DSAT than women. These data suggest that specific fat depots are influenced by ethnicity and gender; therefore, could provide insight into the relationship between ethnicity, gender and subsequent risk for CVD.     

Effect of sarcopenia on cardiovascular disease risk factors in obese postmenopausal women

Objective: To compare sarcopenic‐obese and obese postmenopausal women for risk factors predisposing to cardiovascular disease (CVD) and determine whether there may be a relationship between muscle mass and metabolic risk in obese postmenopausal women. Research Methods and Procedures: In this cross‐sectional study, 22 healthy obese postmenopausal women (mean age, 66 ± 5 years; mean BMI, 27 ± 3 kg/m²) were divided into two groups matched for age (±2 years) and fat mass (FM) (±2%). Sarcopenia was defined as a muscle mass index of <14.30 kg fat‐free mass (FFM)/m² (which corresponds to 1 standard deviation below the values of a young reference population), and obesity was defined as an FM of >35% (which corresponds to the World Health Organization guidelines). FM, FFM (measured by DXA), daily energy expenditure (accelerometry), dietary intake (3‐day dietary record), and blood biochemical analyses (lipid profile, insulin, glucose, and C‐reactive protein) were obtained. Visceral fat mass (VFM) was calculated by the equation of Bertin, which estimates VFM from DXA measurements. Results: Obese women had more FFM (p = 0.006), abdominal FM (p = 0.047), and VFM (p = 0.041) and a worse lipid profile [p = 0.040 for triglycerides; p = 0.004 for high‐density lipoprotein (HDL); p = 0.026 for total cholesterol/HDL] than sarcopenic‐obese postmenopausal women. Obese women also ingested significantly more animal (p = 0.001) and less vegetal proteins (p = 0.013), although both groups had a similar total protein intake (p = 0.967). Discussion: Sarcopenia seems to be associated with lower risk factors predisposing to CVD in obese postmenopausal women. With the increase in the number of aging people, the health implications of being sarcopenic‐obese merit more attention.     

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.     

Birth weight is inversely associated with central adipose tissue in healthy children and adolescents

Objective: Previous studies have explored the association between birth weight and excess childhood body fat, but few have used precise measures of body composition, leading to equivocal and sometimes contradictory results. Research Methods and Procedures: Subjects included 101 children who underwent DXA measurements between 1995 and 2000. Birth weight and gestational age were assessed using maternal recall. Multiple linear regression analysis was used to determine the relationship between birth weight and the following four outcome variables: total fat mass (FM), truncal fat mass (TrFM), percentage body fat (%BF), and TrFM adjusted for FM (TrFMadj), controlling for current weight and Tanner stage. Results: The mean age of the children studied was 12.9 ± 2.4 years, and the mean birth weight reported by subjects’ mothers was 3.3 ± 0.5 kg. The FM and TrFM were 12.8 ± 8.7 kg and 5.1 ± 4.1 kg, respectively, and the mean %BF was 22.9 ± 10.3%. Birth weight was a significant predictor of FM (p = 0.02) and %BF (p = 0.038). However, birth weight adjusted for gestational age (BWTadj) was a significant (p = 0.03) negative predictor of TrFMadj, independently of race, sex, Tanner stage, and current weight. Discussion: These results provide evidence that, even in childhood and adolescence, a higher birth weight is associated with higher FM and %BF, while a low birth weight is associated with TrFM, adjusted for FM.     

Comparison of body adiposity index (BAI) and bmi with estimations of % body fat in clinically severe obese women

Objective:

Body adiposity index (BAI), a new surrogate measure of body fat (hip circumference/(height^1.5 – 18)), has been proposed as an alternative to body mass index (BMI). We compared BAI with BMI, and each of them with laboratory measures of body fat‐derived from bioimpedance analysis (BIA), air displacement plethysmography (ADP), and dual‐energy X‐ray absorptiometry (DXA) in clinically severe obese (CSO) participants.

Design and Methods:

Nineteen prebariatric surgery CSO, nondiabetic women were recruited (age = 32.6 ± 7.7 SD; BMI = 46.5 ± 9.0 kg/m²). Anthropometrics and body fat percentage (% fat) were determined from BIA, ADP, and DXA. Scatter plots with lines of equality and Bland–Altman plots were used to compare BAI and BMI with % fat derived from BIA, ADP, and DXA. BAI and BMI correlated highly with each other (r = 0.90, P < 0.001).

Results:

Both BAI and BMI correlated significantly with % fat from BIA and ADP. BAI, however, did not correlate significantly with % fat from DXA (r = 0.42, P = 0.08) whereas BMI did (r = 0.65, P = 0.003). BMI was also the single best predictor of % fat from both BIA (r² = 0.80, P < 0.001) and ADP (r² = 0.65, P < 0.001). The regression analysis showed that the standard error of the estimate (SEE), or residual error around the regression lines, was greater for BAI comparisons than for BMI comparisons with BIA, ADP, and DXA. Consistent with this, the Bland and Altman plots indicated wider 95% confidence intervals for BAI difference comparisons than for BMI difference comparisons for their respective means for BIA, ADP, and DXA.

Conclusions:

Thus, BAI does not appear to be an appropriate proxy for BMI in CSO women.     

Effects of Obesity on Substrate Utilization during Exercise

Objective: The capacity for lipid and carbohydrate (CHO) oxidation during exercise is important for energy partitioning and storage. This study examined the effects of obesity on lipid and CHO oxidation during exercise. Research Methods and Procedures: Seven obese and seven lean [body mass index (BMI), 33 ± 0.8 and 23.7 ± 1.2 kg/m², respectively] sedentary, middle‐aged men matched for aerobic capacity performed 60 minutes of cycle exercise at similar relative (50% Vo _2max) and absolute exercise intensities. Results: Obese men derived a greater proportion of their energy from fatty‐acid oxidation than lean men (43 ± 5% 31 ± 2%; p = 0.02). Plasma fatty‐acid oxidation determined from recovery of infused [0.15 μmol/kg fat‐free mass (FFM) per minute] [1‐¹³C]‐palmitate in breath CO₂ was similar for obese and lean men (8.4 ± 1.1 and 29 ± 15 μmol/kg FFM per minute). Nonplasma fatty‐acid oxidation, presumably, from intramuscular sources, was 50% higher in obese men than in lean men (10.0 ± 0.6 versus 6.6 ± 0.8 μmol/kg FFM per minute; p < 0.05). Systemic glucose disposal was similar in lean and obese groups (33 ± 8 and 29 ± 15 μmol/kg FFM per minute). However, the estimated rate of glycogen‐oxidation was 50% lower in obese than in lean men (61 ± 12 versus 90 ± 6 μmol/kg FFM per minute; p < 0.05). Discussion: During moderate exercise, obese sedentary men have increased rates of fatty‐acid oxidation from nonplasma sources and reduced rates of CHO oxidation, particularly muscle glycogen, compared with lean sedentary men.