Use of anthropometric variables to predict relative body fat determined by a four-compartment body composition model.

OBJECTIVE: To generate equations for the prediction of percent body fat (% BF) via a four-compartment criterion body composition model from anthropometric variables and age. DESIGN: Multiple regression analyses were used to predict % BF from the best-weighted combinations of independent variables. SUBJECTS: In all 79 healthy males (X+/-s.d.: 35.0+/-12.2 y; 84.24+/-12.53 kg; 179.8+/-6.8 cm) aged 19-59 y were recruited from advertisements placed in a university newsletter and on community centres' noticeboards. INTERVENTIONS: The following measurements were conducted: % BF using a four-compartment (water, bone mineral mass, fat and residual) model and a restricted anthropometric profile (nine skinfolds, five girths and two bone breadths). RESULTS: Stepwise multiple regression selected six (subscapular, biceps, abdominal, thigh, calf and mid-axilla) of the nine skinfold measurements to predict % BF and using the sum of these six produced a quadratic equation with a standard error of estimate (SEE) and R(2) of 2.5% BF and 0.89, respectively. The inclusion of age as a predictor further improved the equation (% BF=-0.00057 x ( summation operator 6SF)(2)+0.298 x summation operator 6SF+0.078 x age - 1.13; SEE=2.2% BF, R(2)=0.91). However, the best equation used only the sum of three skinfold thicknesses (mid-axilla, calf and thigh) and age but also included waist girth and biepicondylar femur breadth as predictors (% BF=-0.00258 x ( summation operator 3SF)(2)+0.558 x summation operator 3SF+0.118 x age+0.282 x waist girth - 2.100 x femur breadth - 2.34; SEE=1.8% BF, R(2)=0.94). Analyses of two age groups, <30 and >/=30 y, demonstrated that for the same % BF, the former exhibited a higher sum of skinfold thicknesses. CONCLUSIONS: Equations were generated for the prediction of % BF via the four-compartment criterion body composition model from anthropometric variables and age. Agewise differences for the sum of skinfold thicknesses may be related to an increase in internal fat for the older subjects.

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.

Comparison of two hydrodensitometric methods for estimating percent body fat.

This study compared the two following hydrodensitometric methods for estimating percent body fat (%BF): 1) estimation of residual volume (RV) by helium dilution before and after measurement of immersed mass at RV, and 2) determination of immersed mass at a comfortable level of expiration (approximately functional residual capacity) with measurement of the associated gas volume by oxygen dilution. Twelve men [27.9 +/- 7.5 (SD) yr; 79.32 +/- 12.79 kg; 180.5 +/- 9.9 cm] were tested for %BF via both methods on each of two separate visits within 3 days by using a counterbalanced design. The two helium dilution measurements yielded a technical error of measurement of 0.2% BF and an intraclass correlation coefficient of 0.999. Corresponding values for the oxygen dilution method were 0.4% BF and 0.999, respectively. There was no difference (P = 0.80) between the helium dilution (16.9 +/- 9.3% BF) and oxygen dilution (16.9 +/- 9.4% BF) methods, and the individual differences ranged from -0.7 to 0.6% BF. The interclass correlation coefficient between the two methods was 0.999 with a SE of estimate of 0.4% BF. Whereas both methods were precise and reliable and yielded similar results, the oxygen dilution technique was more expedient and was preferred by the subjects because they were not required to exhale to RV.