Single prediction equation for bioelectrical impedance analysis in adults aged 20--94 years

Existing equations for bioelectrical impedance analysis (BIA) are of limited use when subjects age or become overweight because these equations were developed in young, normal-weight subjects and are not valid in elderly or overweight people. The purpose of this study was to validate a single BIA equation in healthy white subjects aged 22--94 y with a body mass index between 17.0 and 33.8 kg/m(2). Healthy subjects (202 men and 141 women) aged 20--94 y were measured by two methods: fat-free mass (FFM) was measured by dual-energy x-ray absorptiometry (Hologic QDR-4500) and by a bioelectrical impedance analyzer (Xitron 4000B). Validity of BIA was assessed by double cross validation. Because correlations were high (r = 0.986--0.987) and prediction errors low, a single equation was developed using all subjects, as follows: FFM = -4.104 + (0.518 x height(2)/resistance) + (0.231 x weight) + (0.130 x reactance) + (4.229 x sex: men = 1, women = 0). FFM predicted with dual-energy x-ray absorptiometry was 54.0 +/- 10.7 kg. BIA-predicted FFM was 54.0 +/- 10.5 kg (r = 0.986, standard error of the estimate = 1.72 kg, technical error = 1.74 kg). In conclusion, the new Geneva BIA equation was valid for prediction of FFM in healthy adults aged 22--94 y with body mass indexes between 17.0 and 33.8 kg/m(2). Inclusion of reactance in the single prediction equation appeared to be essential for use of BIA equations in populations with large variations in age or body mass.     

Assessment of fat-free mass in teen-agers: use of TOBEC methodology

The accuracy of total-body electrical conductivity (TOBEC) for body composition assessment was examined in 50 teen-agers (33 males and 17 females) aged 11-19 y. Body composition measures included densitometry, hydrometry, bone mineral density of the one-third distal radius, and TOBEC. Fat-free mass (FFM) was calculated by using one-, two-, and three-compartment models: densitometry, FFMd; densitometry and hydrometry, FFMdw; and densitometry, hydrometry, and bone mineral density, FFMdwb. Correlations between TOBEC variables and the three calculations of FFM were highly significant (r = 0.88-0.95; P less than 0.01). No significant differences were observed (ANOVA) between the TOBEC estimate of FFM (FFMT) and FFMd, FFMdw, and FFMdwb. Similar results were observed when the data were analyzed by gender. The TOBEC estimate of FFM was equivalent to the estimates of FFM derived from one-, two-, and three-component models. Its ease of measurement and its prediction accuracy (R2 = 0.933; SEE = 2.45 kg) in teen-agers make it a preferred technique for body composition assessment.     

Body composition by bioelectrical-impedance analysis compared with deuterium dilution and skinfold anthropometry in patients with chronic obstructive pulmonary disease

Body composition is an important measure of nutritional status in patients with chronic obstructive pulmonary disease (COPD). We generated a regression model for bioelectrical impedance (BI) by using deuterium dilution (2H2O) as a reference method in 32 COPD patients, aged 63 +/- 9 y (mean +/- SD), in stable pulmonary and cardiac condition. Height squared divided by resistance (Ht2/Res) correlated well with total body water (TBW) as measured by 2H2O (r = 0.93, P less than 0.001, SEE = 1.9 L). The best-fitting regression equation to predict TBW comprised Ht2/Res and body weight (r2 = 0.89, SEE = 1.8 L, P less than 0.001). BI-predicted TBW was used to estimate BI-fat-free mass (FFM) that was compared with skinfold-thickness-based FFM predictions (Anthr-FFM). Relative to BI-FFM a significant overestimation of 4.4 +/- 0.8 kg was found by Anthr-FFM. Our results suggest that BI is a useful measure of body composition in patients with severe COPD.     

A new predictive equation for resting energy expenditure in healthy individuals

A predictive equation for resting energy expenditure (REE) was derived from data from 498 healthy subjects, including females (n = 247) and males (n = 251), aged 19-78 y (45 +/- 14 y, mean +/- SD). Normal-weight (n = 264) and obese (n = 234) individuals were studied and REE was measured by indirect calorimetry. Multiple-regression analyses were employed to drive relationships between REE and weight, height, and age for both men and women (R2 = 0.71): REE = 9.99 x weight + 6.25 x height - 4.92 x age + 166 x sex (males, 1; females, 0) - 161. Simplification of this formula and separation by sex did not affect its predictive value: REE (males) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) + 5; REE (females) = 10 x weight (kg) + 6.25 x height (cm) - 5 x age (y) - 161. The inclusion of relative body weight and body-weight distribution did not significantly improve the predictive value of these equations. The Harris-Benedict Equations derived in 1919 overestimated measured REE by 5% (p less than 0.01). Fat-free mass (FFM) was the best single predictor of REE (R2 = 0.64): REE = 19.7 x FFM + 413. Weight also was closely correlated with REE (R2 = 0.56): REE = 15.1 x weight + 371.     

Bioimpedance or anthropometry?

The ability of bioimpedance (BIA) to predict body composition in comparison with anthropometric measurements (weight and height) was assessed on three groups of adult young women (n = 99) and one group of adult young men (n = 49). Body fat (BF) and fat-free mass (FFM) by densitometry were used as the reference data. Resistance and reactance separately or together were poor predictors of BF and FFM, explaining from 0 to a maximum of 21 per cent of the FFM variation in the different groups. BF followed the same pattern, though the percentage of variance explained by both variables was even lower. Height squared divided by resistance (H2/R) explained from 22 to 68 per cent of the FFM variation and from 0 to 40 per cent of BF variation. Height alone was comparable to H2/R explaining from 11 to 53 per cent of the FFM variance in the four groups studied. Body weight was found to be the best single predictor of body composition; it explained from 56 to 78 per cent of FFM and 37 to 82 per cent of BF variability. Using stepwise regression analysis with all women combined, weight accounted for 70 per cent of the total FFM variation, with height and H2/R contributing only another 5 per cent. The same was found in men (68 vs 73 per cent respectively). The reported equation of Segal et al. was applied to our group, yielding almost the same high FFM prediction (r2 greater than 0.7 and SEE less than 2.5 kg).(ABSTRACT TRUNCATED AT 250 WORDS)     

Body mass index as a measure of body fatness: age- and sex-specific prediction formulas

In 1229 subjects, 521 males and 708 females, with a wide range in body mass index (BMI; 13.9-40.9 kg/m2), and an age range of 7-83 years, body composition was determined by densitometry and anthropometry. The relationship between densitometrically-determined body fat percentage (BF%) and BMI, taking age and sex (males = 1, females = 0) into account, was analysed. For children aged 15 years and younger, the relationship differed from that in adults, due to the height-related increase in BMI in children. In children the BF% could be predicted by the formula BF% = 1.51 x BMI-0.70 x age - 3.6 x sex + 1.4 (R2 0.38, SE of estimate (SEE) 4.4% BF%). In adults the prediction formula was: BF% = 1.20 x BMI + 0.23 x age - 10.8 x sex - 5.4 (R2 0.79, SEE = 4.1% BF%). Internal and external cross-validation of the prediction formulas showed that they gave valid estimates of body fat in males and females at all ages. In obese subjects however, the prediction formulas slightly overestimated the BF%. The prediction error is comparable to the prediction error obtained with other methods of estimating BF%, such as skinfold thickness measurements or bioelectrical impedance.     

Validation of a bioelectrical impedance analysis equation to predict appendicular skeletal muscle mass (ASMM)

RATIONALE: Appendicular skeletal muscle mass (ASMM) is useful in the evaluation of nutritional status because it reflects the body muscle protein mass. The purpose of this study was to validate, against dual-energy X-ray absorptiometry (DEXA), a BIA equation to predict ASMM to be used in volunteers and patients. METHOD: Healthy men (n = 246 men, BMI 25.3+/-2.9 kg/m(2)) and women (n =198, 24.1+/-3.6 kg/m(2)), and heart, lung and liver transplant patients (213 men, BMI of 24.6+/-4.4 kg/m(2); 113 women, BMI 23.0+/-5.2 kg/m(2)) were measured by BIA (Xitron Technologies) and DEXA (Hologic QDR 4500). A BIA equation to predict ASMM (kg) that included height(2)/resistance, weight, gender, age and reactance, was developed by means of multiple regressions. [table: see text] Mean difference (Bland-Altman) for volunteers was 0.1+/-1.1 kg, r =0.95, SEE 1.12 kg and for patients -0.4+/-1.5 kg, r =0.91, SEE 1.5 kg.Best fitted multiple regression equation was -4.211 + (0.267 x height2 / resistance) + (0.095 x weight)+(1.909 x sex (men = 1, women = 0)) + (-0.012 x age) + (0.058 x reactance). CONCLUSIONS: BIA permits the prediction of ASMM in healthy volunteers and patients between 22 and 94 years of age. A slightly larger, though clinically not significant, error was noted in patients.     

Body composition estimated by bioelectrical impedance in the Swedish elderly. Development of population-based prediction equation and reference values of fat-free mass and body fat for 70- and 75-y olds

OBJECTIVE: To develop a bioelectrical impedance (BIA) prediction equation for fat-free mass (FFM(BIA)) and present reference values of FFM and body fat (BF) for healthy Swedish elderly from population-based representative samples. SUBJECTS: This study is based on 823 (344 males, 479 females) participants from two systematic samples of birth cohorts in G?teborg aged 70 (cohort H70V, 201 males and 299 females) and 75 (cohort NORA75, 143 males and 180 females). METHODS: Body composition was measured with BIA (BIA-101, RJL system, Detroit) in both cohorts and was estimated by a four-compartment (4C) model from total body water (TBW) and total body potassium (TBK) in a sub-sample of the NORA75 cohort. The FFM(BIA) was validated against the FFM from the 4C model (FFM(4C)). RESULTS: The FFM(BIA) correlated well with FFM(4C) (r=0.95, SEE=2.64 kg). The FFM(BIA) (kg) in 70-y-old males and females were 58.5+/-5.4 and 43.4+/-4.4, and for 75-y-old males and females were 56.1+/-4.7 and 42.5+/-4, respectively. The body fat in kg (FM) among 70-y-old males and females were 25.2+/-8.1 and 25.7+/-8.4, and for 75-y-old males and females were 21.7+/-7.1 and 22.8+7.2, respectively. The percent body fat (BF%) among 70-y-old males and females were 29.5+/-5.8 and 36.3+/-6.4, and for 75-y-old males and females were 27.3+/-6 and 34.1+/-6.1, respectively. CONCLUSION: The FFM, FM and BF% from this study might be used as reference values for Swedish elderly aged 70 and 75 y.     

生物电阻抗法测量肥胖者体脂含量的应用方程

生物电阻抗是近年来被广泛应用的一种快速、简便、安全测量体成分的方法。本研究采用水下称重测量 1 96名 (男性 :66名 ;女性 :1 30名 ) 1 8～ 67岁肥胖受试者的体脂含量 ,并用生物电阻抗方法测定生物电阻抗值。结果表明用水下称重法测量的体脂含量和去脂体重的测量结果与文献中不同国家生物电阻抗推算方程的推算结果有显著的统计学差异 (P <0 0 1 )。本文通过用多元线性逐步回归和方差分析方法建立了适合我国肥胖人群特点的体成分推算方程 :BF =0 846 Wt- 0 1 85 Ht2 z - 2 361 Sex - 2 4 977。Ht:身高(cm) ,Wt:体重 (kg) ,Z :生物电阻抗 (Ω) ,Sex :性别 (男性 =1 ,女性 =0 )。方程的相关系数 :r =0 92 3 ,标准误 :S x=3 43 ,方差分析 (ANOVA)具有统计学意义 (F =365 73 ,P <0 0 0 1 )。     

Relationship between blood adipocytokines and resting energy expenditure in young and elderly women

It has been demonstrated in a previous study that resting energy expenditure (REE) is associated with adiponectin levels in the blood. However, body composition was not taken into consideration in that study. The purpose of the present study was to again investigate the relationship between blood adipocytokines and REE, adjusted by body composition, in both young and elderly women. REE and blood adipocytokines were measured in 115 young (age: 22.3+/-2.1 y, BMI: 21.3+/-1.9 kg/m(2)) and 71 elderly (63.4+/-6.5 y, 22.9+/- 2.3 kg/m(2)) women. Dual energy X-ray absorptiometry was used to measure percent body fat. Fat mass and fat free mass (FFM) were calculated. REE (kcal/d and kcal/kg BW/d) was lower in elderly women than in young women, but no significant difference was observed in REE, expressed as kcal/kg FFM/d, between the two groups. Although elderly women had a higher percent body fat and higher serum leptin concentrations than young women, plasma adiponectin concentrations did not differ between young and elderly women. In elderly women, REE (kcal/d) was significantly and inversely correlated with plasma adiponectin concentration (r=-0.386, p<0.001), but REE expressed per kilogram of BW or FFM was not significantly correlated. Furthermore, no significant correlation was observed between REE (kcal/d) and concentrations of plasma adiponectin or serum leptin, after adjusting for potential confounders such as body composition and hormones, in either age group. These results suggest that adipocytokines do not influence REE in adult women.     

Body mass index as a measure of body fatness in the elderly

Body composition was assessed in a group of 35 apparently health elderly males and 37 elderly females, aged 60-83 years, by means of anthropometry and densitometry. Mean body mass index (BMI) of the males was 25.0 +/- 2.2 kg/m2 and of the females 25.9 +/- 3.2 kg/m2, which indicates normal weight to only minor overweight. Body fat as assessed by densitometry was 31 per cent in men and 44 per cent in women, a rather high value, especially when compared to the rather low BMI. Body fat percentage as calculated from the sum of four skinfolds (bicipitalis, tricipitalis, subscapularis and supra-iliacalis) using regression equations from the literature was 27.9 +/- 2.5 per cent and 38.7 +/- 3.2 per cent for men and women respectively. These values are probably an underestimation of the body fat, due to a higher proportion of internal fat in elderly subjects, which is not measured by skinfolds. Body fat percentage as determined by the BMI has an estimation error of about 4 per cent when derived from sex- and age-specific regression equations. The body fat percentage as predicted from skinfold thicknesses had a comparable error of estimate. These prediction errors in the body fat percentage in the elderly are comparable with the prediction errors found in young and middle-aged subjects as reported in the literature.     

Body water distribution in severe obesity and its assessment from eight-polar bioelectrical impedance analysis

OBJECTIVE: To measure body water distribution and to evaluate the accuracy of eight-polar bioelectrical impedance analysis (BIA) for the assessment of total body water (TBW) and extracellular water (ECW) in severe obesity. DESIGN: Cross-sectional study. SETTING: Obesity clinic. SUBJECTS: In all, 75 women aged 18-66 y, 25 with body mass index (BMI) between 19.1 and 29.9 kg/m(2) (ie not obese), 25 with BMI between 30.0 and 39.9 kg/m(2) (ie class I and II obese), and 25 with BMI between 40.0 and 48.2 kg/m(2) (ie class III obese). METHODS: TBW and ECW were measured by (2)H(2)O and Br dilution. Body resistance (R) was obtained by summing the resistances of arms, trunk and legs as measured by eight-polar BIA (InBody 3.0, Biospace, Seoul, Korea). The resistance index at a frequency of x kHz (RI(x)) was calculated as height (2)/R(x). RESULTS: ECW : TBW was similar in women with class III (46+/-3%, mean+/-s.d.) and class I-II obesity (45+/-3%) but higher than in nonobese women (39+/-3%, P<0.05). In a random subsample of 37 subjects, RI(500) explained 82% of TBW variance (P<0.0001) and cross-validation of the obtained algorithm in the remaining 38 subjects gave a percent root mean square error (RMSE%) of 5% and a pure error (PE) of 2.1 l. In the same subjects, RI(5) explained 87% of ECW variance (P<0.0001) and cross-validation of the obtained algorithm gave a RMSE% of 8% and a PE of 1.4 l. The contribution of weight and BMI to the prediction of TBW and ECW was nil or negligible on practical grounds. CONCLUSIONS: ECW : TBW is similar in women with class I-II and class III obesity up to BMI values of 48.2 kg/m(2). Eight-polar BIA offers accurate estimates of TBW and ECW in women with a wide range of BMI (19.1-48.2 kg/m(2)) without the need of population-specific formulae.     

Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models

BACKGROUND: Skeletal muscle (SM) is a large body compartment of biological importance, but it remains difficult to quantify SM with affordable and practical methods that can be applied in clinical and field settings. OBJECTIVE: The objective of this study was to develop and cross-validate anthropometric SM mass prediction models in healthy adults. DESIGN: SM mass, measured by using whole-body multislice magnetic resonance imaging, was set as the dependent variable in prediction models. Independent variables were organized into 2 separate formulas. One formula included mainly limb circumferences and skinfold thicknesses [model 1: height (in m) and skinfold-corrected upperarm, thigh, and calf girths (CAG, CTG, and CCG, respectively; in cm)]. The other formula included mainly body weight (in kg) and height (model 2). The models were developed and cross-validated in nonobese adults [body mass index (in kg/m(2)) < 30]. RESULTS: Two SM (in kg) models for nonobese subjects (n = 244) were developed as follows: SM = Ht x (0.00744 x CAG(2) + 0.00088 x CTG(2) + 0.00441 x CCG(2)) + 2.4 x sex - 0.048 x age + race + 7.8, where R:(2) = 0.91, P: < 0.0001, and SEE = 2.2 kg; sex = 0 for female and 1 for male, race = -2.0 for Asian, 1.1 for African American, and 0 for white and Hispanic, and SM = 0.244 x BW + 7.80 x Ht + 6.6 x sex - 0.098 x age + race - 3.3, where R:(2) = 0.86, P: < 0.0001, and SEE = 2.8 kg; sex = 0 for female and 1 for male, race = -1.2 for Asian, 1.4 for African American, and 0 for white and Hispanic. CONCLUSION: These 2 anthropometric prediction models, the first developed in vivo by using state-of-the-art body-composition methods, are likely to prove useful in clinical evaluations and field studies of SM mass in nonobese adults.     

Eight-year longitudinal changes in body composition in healthy Swiss adults

OBJECTIVE: Significant changes in body composition occur during lifetime. This longitudinal study (8.0 +/- 0.8 yrs) in a cohort of healthy sedentary and physically active men (n = 78) and women (n = 53), aged 20 to 74 yr describes: 1) the longitudinal changes in weight and body composition and 2) their associations with age and physical activity. Method: Fat-free mass (FFM) and body fat (BF) were assessed by bioelectrical impedance analysis (BIA). Subjects who regularly performed >3 hours per week of endurance type physical activity were classified as "Active". Others were classified as "Sedentary". Subjects were also separated by age (<45 yr vs > or =45 yr). RESULTS: FFM increased by 1.7 +/- 2.8 kg in men <45 yr who gained 4.0 +/- 5.0 kg of body weight and was maintained (0.5 +/- 1.6 kg) in women <45 y who gained 1.6 +/- 3.0 kg of weight. A weight gain of 1.2 +/- 3.3 kg in men > or =45 yr was accompanied by stable FFM (-0.1 +/- 2.3 kg), and of 1.0 +/- 3.2 kg was accompanied by a loss of FFM in women > or =45 yr. In active men > or =45 yr, maintenance of FFM was associated with smaller weight gains than in sedentary; sedentary men > or =45 yr decreased FFM with larger weight gains than active subjects. Sedentary women <45 yr were able to gain FFM; the active women maintained, but did not gain FFM with smaller weight gains than in sedentary women. FFM decreased in >/=45 yr women despite of small weight gains. CONCLUSION: Weight change is clearly associated with a change in FFM. Weight gain is necessary to offset age-related FFM loss between 20 and 74 yrs. In active men, a FFM increase was associated with less weight gain than sedentary men. Future studies should evaluate the threshold of weight change and the level of physical activity necessary to prevent age-related losses of FFM.     

Body composition assessment in adults with cystic fibrosis: comparison of dual-energy X-ray absorptiometry with skinfolds and bioelectrical impedance analysis

OBJECTIVE: We compared body composition measurement in adults with cystic fibrosis (CF) by using non-invasive methods (skinfold thicknesses and bioelectrical impedance analysis [BIA]) with dual-energy X-ray absorptiometry (DXA). METHODS: Seventy-six adults with CF (mean age 29.9 +/- 7.9 y, mean body mass index 21.5 +/- 2.5 kg/m(2)) were studied. Body composition was measured to calculate fat-free mass (FFM) using DXA, the sum of four skinfold thicknesses, and BIA (predictive equations of Lukaski and of Segal). RESULTS: Mean FFM values +/- standard deviation measured using DXA were 54.8 +/- 7.3 kg in men and 41.2 +/- 3.9 kg in women. Mean FFM values measured using BIA/Lukaski were 51.5 +/- 7.8 kg in men and 40.4 +/- 4.9 kg in women (P < 0.0005 for men, not significant for women for comparison with DXA). Mean FFM values measured using BIA/Segal were 54.2 +/- 7.5 kg for men and 44.1 +/- 5.9 kg for women (not significant for men, P < 0.0005 for women for comparison with DXA). Mean FFM values measured using skinfolds were significantly higher than those for FFM with DXA (57.2 +/- 7.2 kg in men, 43.3 +/- 4.3 kg in women, P < 0.0005 for comparison with DXA). The 95% limits of agreement with FFM using DXA were, for men and women, respectively, -8.3 to 1.7 kg and -6.4 to 4.8 kg for BIA/Lukaski, -4.8 to 3.6 kg and -3.1 to 8.9 kg for BIA/Segal, and -2.8 to 7.3 kg and -1.5 to 5.7 kg for skinfolds. CONCLUSION: This study suggests that skinfold thickness measurements and BIA will incorrectly estimate FFM in many adults with CF compared with DXA measurements of FFM. These methods have limited application in the assessment of body composition in individual adult patients with CF.     

Body movement and physical activity energy expenditure in children and adolescents: how to adjust for differences in body size and age

BACKGROUND: Physical activity data in children and adolescents who differ in body size and age are influenced by whether physical activity is expressed in terms of body movement or energy expenditure. OBJECTIVE: We examined whether physical activity expressed as body movement (ie, accelerometer counts) differs from physical activity energy expenditure (PAEE) as a function of body size and age. DESIGN: This was a cross-sectional study in children [n = 26; (+/-SD) age: 9.6 +/- 0.3 y] and adolescents (n = 25; age: 17.6 +/- 1.5 y) in which body movement and total energy expenditure (TEE) were simultaneously measured with the use of accelerometry and the doubly labeled water method, respectively. PAEE was expressed as 1) unadjusted PAEE [TEE minus resting energy expenditure (REE); in MJ/d], 2) PAEE adjusted for body weight (BW) (PAEE. kg(-1). d(-1)), 3) PAEE adjusted for fat-free mass (FFM) (PAEE. kg FFM(-1). d(-1)), and 4) the physical activity level (PAL = TEE/REE). RESULTS: Body movement was significantly higher (P = 0.03) in children than in adolescents. Similarly, when PAEE was normalized for differences in BW or FFM, it was significantly higher in children than in adolescents (P = 0.03). In contrast, unadjusted PAEE and PAL were significantly higher in adolescents (P < 0.01). CONCLUSIONS: PAEE should be normalized for BW or FFM for comparison of physical activity between children and adolescents who differ in body size and age. Adjusting PAEE for FFM removes the confounding effect of sex, and therefore FFM may be the most appropriate body-composition variable for normalization of PAEE. Unadjusted PAEE and PAL depend on body size.     

Determination of skeletal muscle and fat-free mass by nuclear and dual-energy x-ray absorptiometry methods in men and women aged 51-84 y (1-3).

BACKGROUND: Skeletal muscle mass (SMM) and fat-free mass (FFM) are important variables in nutritional studies. Accurate techniques for measuring these variables have not been thoroughly validated in elderly subjects. OBJECTIVES: The objectives of this study were to 1) compare SMM values derived from dual-energy X-ray absorptiometry (DXA) with those calculated by a nuclear method from total body potassium (TBK) and total body nitrogen (TBN) measurement (both: KN) in older subjects, and 2) assess the accuracy of FFM measurement by DXA in these subjects. DESIGN: TBK, TBN, DXA (model XR36; Norland, Fort Atkinson, WI), bioimpedance, and anthropometric measurements were performed on healthy women (n = 50) and men (n = 25) aged 51-84 y. RESULTS: Mean SMM by KN was not significantly different from SMM by DXA in either sex. SMM by KN predicted SMM by DXA with an SEE of 2.1 kg (r = 0.95, P < 0.0001 for women and men together). In the men, FFM by DXA agreed well with FFM estimated by TBK, skinfold thicknesses, bioimpedance analysis, and a multicompartment model. In women, FFM by DXA was 4-5 kg less than that by the other methods (P < 0.01). Truncal fat was related to intermethod FFM differences (r = 0.58, P < 0.0001). CONCLUSIONS: These data indicate that 1) either the nuclear or the DXA method can be applied to estimate SMM in healthy older subjects, and 2) the Norland DXA instrument significantly underestimates FFM in older women, in part, because of the influence of truncal adiposity.     

Predicting the resting metabolic rate of young Australian males

OBJECTIVES: The aims of this study were: (a) to generate regression equations for predicting the resting metabolic rate (RMR) of 18 to 30-y-old Australian males from age, height, mass and fat-free mass (FFM); and (b) cross-validate RMR prediction equations, which are frequently used in Australia, against our measured and predicted values. DESIGN: A power analysis demonstrated that 38 subjects would enable us to detect (alpha = 0.05, power = 0.80) statistically and physiologically significant differences of 8% between our predicted/measured RMRs and those predicted from the equations of other investigators. SUBJECTS: Thirty-eight males (chi +/- s.d.: 24.3+/-3.3y; 85.04+/-13.82 kg; 180.6+/-8.3 cm) were recruited from advertisements placed in a university newsletter and on community centre noticeboards. INTERVENTIONS: The following measurements were conducted: skinfold thicknesses, RMR using open circuit indirect calorimetry and FFM via a four-compartment (fat mass, total body water, bone mineral mass and residual) body composition model. RESULTS: A multiple regression equation using the easily measured predictors of mass, height and age correlated 0.841 with RMR and the SEE was 521 kJ/day. Inclusion of FFM as a predictor increased both the R and the precision of prediction, but there was virtually no difference between FFM via the four-compartment model (R = 0.893, SEE = 433 kJ/day) and that predicted from skinfold thicknesses (R = 0.886, SEE = 440 kJ/day). The regression equations of Harris & Benedict (1919) and Schofield (1985) all overestimated the mean RMR of our subjects by 518 - 600 kJ/day (P < 0.001) and these errors were relatively constant across the range of measured RMR. The equations of Hayter & Henry (1994) and Piers et al (1997) only produced physiologically significant errors at the lower end of our range of measurement. CONCLUSIONS: Equations need to be generated from a large database for the prediction of the RMR of 18 to 30-y-old Australian males and FFM estimated from the regression of the sum of skinfold thicknesses on FFM via the four compartment body composition model needs to be further explored as an expedient RMR predictor.     

Resting metabolic rate, body composition and aerobic fitness comparisons between active and sedentary 54-71 year old males.

OBJECTIVE: To test the hypothesis that 55-70 y old male longterm exercisers (LE) have higher resting metabolic rates (RMR) than longterm nonexercisers (LNE). DESIGN: A power analysis demonstrated that this cross-sectional study required 12 subjects per group to detect a 10% RMR difference (kJ x kg FFM(-1) x d(-1)) between the LE and LNE (power = 0.8;alpha = 0.05). SUBJECTS: Twelve LE (X +/- s.d.; 63.5+/-3.4 y; 1.75+/-0.06 m; 69.01+/-8.24 kg; 20.4+/-4.9 %BF) and 12 LNE (63.6+/-5.6 y; 1.72+/-0.07 m; 79.44 12.4 kg; 29.6 4.4 %BF) were recruited from advertisements placed in a newspaper and on university and community noticeboards. INTERVENTIONS: Measurements were conducted for: RMR using the Douglas bag technique; body composition via a four compartment model which is based on determination of body density, total body water and bone mineral mass; and aerobic fitness using a submaximal work test on a cycle ergometer. RESULTS: The LE (93.00+/-7.16 kJ x kg(-1) x d(-1)) registered a significantly greater (P = 0.04) RMR than the LNE (84.70+/-11.23 kJ x kg(-1) x d(-1)) when energy expenditure was expressed relative to body mass, but this difference disappeared (P = 0.55) when the data were corrected for the non-zero intercept of the graph of RMR (MJ/d) against body mass. ANCOVA with FFM as the covariate also indicated that the RMR (MJ/d) difference between the groups was not statistically significant (P = 0.28). The adjusted means for the LE and LNE were 6.39 and 6.62 MJ/d, respectively. CONCLUSIONS: There are no RMR (MJ/d) differences between LE and LNE 54-71 y old males when statistical control is exerted for the effect of FFM and the higher value of the former group for RMR normalised to body mass disappears when this ratio is corrected for statistical bias.     

Physical activity energy expenditure predicts changes in body composition in middle-aged healthy whites: effect modification by age

BACKGROUND: It is unclear whether physical activity energy expenditure (PAEE) predicts changes in body composition. OBJECTIVE: The objective was to describe the independent associations between PAEE and changes in body composition in a population-based cohort. DESIGN: This was a prospective population-based study conducted in 739 (311 men and 428 women) healthy middle-aged (median age: 53.8 y) whites. The median follow-up was 5.6 y. PAEE (MJ/d) was assessed by heart rate monitoring, individually calibrated by using the FLEX heart rate method. Fat mass (FM) and fat-free mass (FFM) were assessed by bioimpedance. RESULTS: Body weight (BW) at follow-up was significantly related to baseline PAEE (P < 0.05) after adjustment for sex, baseline age, FM, FFM, and follow-up time. A significant interaction between PAEE and age (P = 0.023) was observed. After the subjects were stratified (above and below the median for age), BW increased by a mean (+/-SD) of 1.7 +/- 5.9 kg (P < 0.0001) in the younger cohort. In this group, follow-up FM was significantly associated with baseline PAEE (P = 0.036) after adjustment for confounders. In the older cohort, BW did not change between baseline and follow-up. In this group, in contrast with the younger population, follow-up BW, FM, and FFM were all significantly and positively associated with baseline PAEE (P < 0.01 for all). CONCLUSIONS: Baseline PAEE predicts a change in FM in younger adults, who as a group gained weight in this study. In contrast, baseline PAEE in older adults--who were on average weight stable--is associated with a gain in BW, which was explained by an increase in FM and FFM.