Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation

Cunningham, John J.

doi:10.1093/ajcn/54.6.963

Cited by 481 publications

(373 citation statements)

References 41 publications

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“…The relationship of age and gender with metabolic rates disappeared after adjustment for fat mass and fat-free mass, except for SMR-8h. Similar results have been reported showing that the effect of age and gender on metabolic rates is mainly due to fat-free mass (Ravussin et al, 1986;Astrup et al, 1990;Cunningham, 1991;Nelson et al, 1992) and fat mass (Dionne et al, 1999).…”

Section: Discussionsupporting

confidence: 88%

“…In our equations, fatfree mass (measured using skinfold thickness) accounted for 84À89% of the variation in SMR, which is better than previously reported (Ravussin et al, 1990;Toubro et al, 1996;Weyer et al, 1999). In addition, results for the BMR equations are in good agreement with those of others (Cunningham, 1991;Ravussin and Bogardus, 1989;Tataranni and Ravussin, 1995). After fat-free mass, fat mass predicted metabolic rate, but accounted for less than 1% of variation in SMR.…”

Section: Discussionsupporting

confidence: 77%

“…Many equations have been developed to estimate BMR or SMR from body size measurements (Cunningham, 1991;Frankenfield et al, 2005), which can be helpful when actual metabolic measurements are not available. Their accuracy and applicability to specific ethnic groups must be considered.…”

Section: Introductionmentioning

confidence: 99%

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Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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Introduction: Basal metabolic rate (BMR) or sleeping metabolic rate (SMR) is the largest component of total energy expenditure (EE). An accurate prediction of BMR or SMR is needed to accurately predict total EE or physical activity EE for each individual. However, large variability in BMR and SMR has been reported. Objectives: This study was designed to develop prediction equations using body size measurements for the estimation of both SMR and BMR and to compare the prediction errors with those in previous reports. Methods: We measured body size, height, weight and body composition (fat mass and fat-free mass) from skinfold thickness in adult Japanese men (n ¼ 71) and women (n ¼ 66). SMR was determined as the sum of EE during 8 h of sleep (SMR-8h) and minimum EE during 3 consecutive hours of sleep (SMR-3h) measured using two open-circuit indirect human calorimeters. BMR was determined using a human calorimeter or a mask and Douglas bag. Results: The study population ranged widely in age. The SMR/BMR ratio was 1.0170.09 (range 0.82-1.42) for SMR-8h and 0.9470.07 (range 0.77-1.23) for SMR-3h. The prediction equations for SMR accounted for a 3À5% larger variance with 2-3% smaller standard error of estimate (SEE) than the prediction equations for BMR. Discussion: SMR can be predicted more accurately than previously reported, which indicates that SMR interindividual variability is smaller than expected, at least for Japanese subjects. The prediction equations for SMR are preferable to those for BMR because the former exhibits a smaller prediction error than the latter.

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Section: Discussionsupporting

confidence: 88%

Section: Discussionsupporting

confidence: 77%

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Interindividual variability in sleeping metabolic rate in Japanese subjects

et al. 2007

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“…For the calculation of RMR, only data of subjects in apparently steady-state conditions (ie VO 2 and VCO 2 did not vary more than 5% from the mean value of the 30 min measurement period) were used. In addition to measured values, RMR was predicted using the equations formulated by Harris and Benedict (1919), Robertson and Reid (1952), Scho®eld (1985), Pavlou et al (1986), Owen et al (1986Owen et al ( ,1987, Mif¯in et al (1990) and Cunningham (1991). The equations are given in Table 2.…”

Section: Methodsmentioning

confidence: 99%

Measured and predicted resting metabolic rate in Italian males and females, aged 18–59 y

et al. 2001

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Objectives: To determine the resting metabolic rate in a sample of the Italian population, and to evaluate the validity of predictive equations for resting metabolic rate (RMR) from the literature in normal and obese subjects. Design: Cross-sectional observational study. Settings: Department of Human Physiology and Nutrition, University`Tor Vergata', Rome. Subjects: A total of 320 healthy subjects, 127 males and 193 females, aged 18 ± 59 y. Methods: Weight, height and resting metabolic rate by indirect calorimetry were measured. Resting metabolic rate was also predicted using equations from the literature. Results: Resting metabolic rate (mean AE s.d.) in normal weight subjects was 7983 AE 1007 kJa24 h (males) and 6127 AE 907 kJa24 h (females). Measured RMR and predicted RMR values using various equations from the literature were signi®cantly different in males and females, except for the Harris ± Benedict equation and the Scho®eld equations. Also, in overweight and obese subjects the prediction error was generally larger compared to normal-weight subjects for all formulas except for the Harris ± Benedict and Scho®eld formulas. In overweight and obese males but not in females, RMR was lower than in normal-weight subjects after correcting for weight and age differences. Stepwise multiple regression of resting metabolic rate against weight, height and age in males and females did not reveal a prediction formula with a lower prediction error than the Harris ± Benedict or Scho®eld formulas and thus was not further explored. Conclusions: The Harris ± Benedict formula and the Scho®eld formula provide a valid estimation of resting metabolic rate at a group level in both normal-weight and overweight Italians. However, the individual error can be so high that for individual use a measured value has to be preferred over an estimated value.

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“…Typically, the predictive relationships for BMR have r 2 values in the range from 60 to 70% (reviewed by Shetty et al, 1996) leading to residual errors averaging 600-900 kJ (coefficient of variation 8-12%) (e.g., Jequier and Schutz, 1981;Dallosso and James 1984;Soares and Shetty, 1986;Weyer et al, 2000). Studies employing more direct analyses of body composition, by for example dual energy X-ray absorptiometry, at the same time as BMR measurements, have revealed that the major predictor of BMR is fat-free mass (FFM) (Fukagawa et al, 1990;Cunningham 1991;Weinsier et al, 1992), and this explains approximately 70% of the total variation, with some studies finding a secondary, independent contribution of fat mass (FM) (Nelson et al, 1992;Svendsen et al, 1993). Because the 'Schofield equation' only includes body weight and height as predictors, with sex and age being accommodated by a series of different equations for various subgroups, the important roles played by FFM and FM in the determination of BMR raise the possibility that additional anthropogenic measures (such as circumferences or skinfolds) might enhance predictability, with a relatively trivial increase in the workload involved in the predictor measurements (Shetty et al, 1994).…”

Section: Introductionmentioning

confidence: 99%

Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects

Johnstone¹,

Rance²,

Murison³

et al. 2006

Eur J Clin Nutr

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Background:The most commonly used predictive equation for basal metabolic rate (BMR) is the Schofield equation, which only uses information on body weight, age and sex to derive the prediction. However, because body composition is a key influencing factor, there will be error in calculating an individual's basal requirements based on this prediction. Objective: To investigate whether adding additional anthropometric measures to the standard measures can enhance the predictability of BMR and to cross-validate this within a separate subgroup. Design: Cross-sectional study of 150 Caucasian adults from Scotland, with a body mass index range of 16.7-49.3 kg/m 2 . All subjects underwent measurement of BMR, body composition, and 148 also had basic skinfold and circumference measures taken. The resultant equation was tested in a subgroup of 39 obese males. Results: The average difference between the predicted (Schofield equation) and measured BMR was 502 kJ/day. There was a slight systematic bias in this error, with the Schofield equation underestimating the lowest values. The average discrepancy between predicted and actual BMR was reduced to 452 kJ/day, with the addition of fat mass, fat-free mass, an overall 10% improvement on the Schofield equation (P ¼ 0.054). Using an equation derived from principal components analysis of anthropometry measurements similarly decreased the difference to 458 kJ/day (P ¼ 0.039). Testing the equation in a separate group indicated a 33% improvement in predictability of BMR, compared to the Schofield equation. Conclusions: In the absence of detailed information on body composition, utilizing anthropometric data provides a useful alternative methodology to improve the predictability of BMR beyond that achieved from the standard Schofield prediction equation. This should be confirmed in more individuals, both within the obese and normal weight category.

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Body composition as a determinant of energy expenditure: a synthetic review and a proposed general prediction equation

Cited by 481 publications

References 41 publications

Interindividual variability in sleeping metabolic rate in Japanese subjects

Interindividual variability in sleeping metabolic rate in Japanese subjects

Measured and predicted resting metabolic rate in Italian males and females, aged 18–59 y

Additional anthropometric measures may improve the predictability of basal metabolic rate in adult subjects

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