<i>Density of Body Fat in Man and Other Mammals</i>

Fidanza, F; Keys, Ancel; Anderson, Joseph T.

doi:10.1152/jappl.1953.6.4.252

Cited by 235 publications

(78 citation statements)

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“…The former model assumes that the body can be partitioned into the FM and FFM whose respective densities are 0.9007 (Fidanza et al, 1953) and 1.1000 gacm 3 (Broz Ïek et al, 1963). The FFM compartment comprises four components whose percentages and densities (in parentheses) at 36 C are assumed (Broz Ïek et al, 1963) to be as follows: 73.72% water (0.99371 gacm 3 ), 19.41% protein (1.34 gacm 3 ), 5.63% bone mineral (2.982 gacm 3 ) and 1.24% non-bone mineral (3.317 gacm 3 ).…”

Section: Discussionmentioning

confidence: 99%

“…Brie¯y, the masses and volumes for TBW and BMM were subtracted from those determined for the whole body using hydrodensitometry (BD massavolume). This enabled the remainder to be partitioned into fat and residual (protein, nonbone mineral and glycogen) masses whose respective densities were assumed to be 0.9007 (Fidanza et al, 1953) and 1.404 gacm 3 (Allen et al, 1959; 37 C only samples). The %BF was also estimated using the two-compartment hydrodensitometric body composition model (Broz Ïek et al, 1963;%BF 497.1aBD 7 451.9).…”

Section: Body Compositionmentioning

confidence: 99%

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Predicting the resting metabolic rate of young Australian males

et al. 2001

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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) crossvalidate 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 (a 0.05, power 0.80) statistically and physiologically signi®cant differences of 8% between our predictedameasured RMRs and those predicted from the equations of other investigators. Subjects: Thirty-eight males (X AE s.d.: 24.3 AE 3.3 y; 85.04 AE 13.82 kg; 180.6 AE 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 kJaday. 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 kJaday) and that predicted from skinfold thicknesses (R 0.886, SEE 440 kJaday). The regression equations of Harris & Benedict (1919) and Scho®eld (1985) all overestimated the mean RMR of our subjects by 518 ± 600 kJaday (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 signi®cant 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-yold 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.

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Section: Discussionmentioning

confidence: 99%

Section: Body Compositionmentioning

confidence: 99%

Predicting the resting metabolic rate of young Australian males

et al. 2001

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“…Assuming the fraction of fat in adipose tissue to be 0.85 (Brozec et al, 1963) and its density of 0.9007 g=cm 3 (Fidanza et al, 1953) fat mass was determined from adipose tissue volume as follows:…”

Section: Body Compositionmentioning

confidence: 99%

Body composition and energy expenditure in Duchenne muscular dystrophy

et al. 2003

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Objective: To investigate the relationship between resting energy expenditure (REE) and body composition in Duchenne Muscular Dystrophy (DMD). Design: An observational study. Setting: University Research Centre. Subjects: Nine Duchenne children (age range 6 -12 y), mean relative weight 128%, agreed to undergo the investigation and all of them completed the study; Interventions: Assessment of body composition (total body fat and skeletal muscle mass) by magnetic resonance imaging and resting energy expenditure by indirect calorimetry. Main outcome measures: Fat mass (FM; kg and percentage weight), fat-free mass (FFM; kg and percentage weight), muscle mass (kg and percentage weight), resting energy expenditure (kJ=kg body weight and kJ=kg fat-free mass). Results: In Duchenne children fat mass averages 32% and total skeletal muscle mass 20% of body weight. Resting energy expenditure per kg of body weight falls within the normal range for children of the same age range, while when expressed per kg of FFM is significantly higher than reference values. No relationship was found between REE and total skeletal muscle mass. Conclusions:Our results do not demonstrate a low REE in DMD boys; on the contrary REE per kg of FFM is higher than normal, probably due to the altered FFM composition. We suggest that the development of obesity in DMD children is not primarily due to a low REE but to other causes such as a reduction in physical activity and or overfeeding.

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“…This permits the remainder to be separated into fat and residual masses with assumed densities of 0.9007 (Fidanza et al, 1953) and 1.404 gacm 3 (Allen et al, 1959; 37 C samples only), respectively. The foregoing enables us to appreciate why the bodybuilders' FFM density was lower (P 0.014) than that for the control subjects.…”

Section: Body Composition Of Female Bodybuildersmentioning

confidence: 99%

“…The three-compartment body composition model controls for inter-individual variability in the large and acutely variable TBW because the mass and volume of this component are subtracted from the mass and volume of the total body. This then enables the remainder to be partitioned into fat and fat free dry masses with assumed densities of 0.9007 (Fidanza et al, 1953) and 1.569 gacm 3 (Broz Ïek et al, 1963), respectively. The three-compartment model is therefore based on determinations of body density and TBW, whereas the two-compartment hydrodensitometric model is based only on the former measurement.…”

Section: Body Composition Of Female Bodybuildersmentioning

confidence: 99%

Body composition changes in female bodybuilders during preparation for competition

et al. 2001

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Objective: To determine anthropometric and body composition changes in female bodybuilders during preparation for competition. Design: There was an attempt to match subjects in the control and experimental groups for height and percentage body fat (%BF) for the initial test of this longitudinal study. Subjects: Five competitive bodybuilders (X AE s.d.: 35.3 AE 5.7 y; 167.3 AE 3.7 cm; 66.38 AE 6.30 kg; 18.3 AE 3.5 %BF) and ®ve athletic females (X AE s.d.: 30.9AE 13.0 y; 166.9 AE 3.9 cm; 55.94 AE 3.59 kg; 19.1 AE 3.3 %BF) were recruited from advertisements in a bodybuilding newsletter and placed on sports centre noticeboards. Interventions: The following measurements were conducted 12 weeks, 6 weeks and 3 ± 5 d before the bodybuilders' competitions: anthropometric pro®le, body density by underwater weighing, total body water via deuterium dilution and bone mineral mass from a dual-energy X-ray absorptiometry scan. A combination of the last three measurements enabled the %BF to the determined by a four compartment model. Results: A signi®cant (P 0.001) 5.80 kg body mass loss by the bodybuilders as they prepared for competition was primarily due to a reduction in fat mass (FM; À4.42 kg; 76.2%) as opposed to fat-free mass (FFM; À1.38 kg; 23.8%). The decreases in body mass and FM over the ®nal 6 weeks were greater than those over the ®rst 6 weeks. Their %BF decreased (P`0.001) from 18.3 to 12.7, whereas the values for the control group remained essentially unchanged at 19.1 ± 19.6 %BF. These body composition changes by the bodybuilders were accompanied by a signi®cant decline (P`0.001) of 25.5 mm (76.3 ± 50.8 mm) in the sum of eight skinfold thicknesses (triceps subscapular biceps iliac crest supraspinale abdominal front thigh medial calf). Conclusions: Although the bodybuilders presented with low %BFs at the start of the experiment, they still signi®cantly decreased their body mass during the 12 week preparation for competition and most of this loss was due to a reduction in FM as opposed to FFM. Sponsorship: Australian Research Council (small grants scheme). Descriptors: four-compartment body composition model; dual energy X-ray absorptiometry; somatotype

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Density of Body Fat in Man and Other Mammals

Cited by 235 publications

References 0 publications

Predicting the resting metabolic rate of young Australian males

Predicting the resting metabolic rate of young Australian males

Body composition and energy expenditure in Duchenne muscular dystrophy

Body composition changes in female bodybuilders during preparation for competition

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