The ratio calculated BMR/measured BMR according to the Nelson equation in this subgroup was 101.0 (12.1/0.74). In the group of the 22 subjects with a body weight > or = 120 kg ratio of estimated values for BMR using original Harris-Benedict equation to measured BMR was 102.2 +/- 15.4% (mean +/- SD%, r = 0.61), respectively 93.2 +/- 14.5% (r = 0.50) when weight was set at 120 kg due to current recommendations. The predictive value of the original Harris-Benedict equation was slightly different from modified Harris-Benedict equation, which was recalculated by Roza et al. The best fitting equations for predicting BMR in these 43 severe und morbidly obese subjects were the Harris-Benedict (ratio calculated BMR to measured, BMR mean +/- SD% correlation coefficient r = 101 +/- 12.9 0.69), the Jensen (101.5 +/- 12.3 0.74), the Nelson (99.3 +/- 11.4 0.76) and the Cunningham equation (98.9 +/- 11.7 0.74).
Calculated BMR was compared with measured BMR.
In 43 patients (age range 18 to 61 years, 5 men, 38 women) with obesity class II and III (body mass index, BMI, mean +/- SD 45.6 kg/m2 +/- 5.4 kg/m2, range 37.1-58.6 kg/m2) basal metabolic rate BMR was determined using indirect calorimetry (Deltatrac MBM, Datex, Instrumentarium Corp., Helsinki, Finnland) and the components of body composition were determined using the bioelectrical-impedance-analysis (BIA) method (BIA, Akern-Gerät, RJL Systems, Detroit). A second objective was to examine the reliability of the Harris Benedict equation in obese subjects, especially with a weight > or = 120 kg. doi:10.20463/ aim of this study was to evaluate the currently available predictive equations for basal metabolic rate (BMR) in subjects with obesity class II and III, and to assess the contribution by the components of a two-compartment model of body composition, namely the lean body mass (LBM) and the fat mass (FM) to the prediction. Non-exercise activity thermogenesis (Neat): a component of total daily energy expenditure. Harris Benedict Basal Metabolic Rate Calculator.Ĭhung N, Park MY, Kim J, et al. Indirect calorimetry: from bench to bedside. Gupta RD, Ramachandran R, Venkatesan P, Anoop S, Joseph M, Thomas N. Indirect calorimetry: history, technology, and application. Mtaweh H, Tuira L, Floh AA, Parshuram CS. Prediction of resting energy expenditure in healthy older adults: A systematic review.
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The centenary of the Harris-Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group. Examining variations of resting metabolic rate of adults: a public health perspective. McMurray RG, Soares J, Caspersen CJ, McCurdy T.
Cellular metabolism and disease: what do metabolic outliers teach us? Cell.
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