BACKGROUND: Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group. AIM: of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice. SUBJECTS AND METHODS: RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 +/- 5.7 y, BMI 26.4 +/- 3.7 kg/m(2)) and 107 male (age 66.9 +/- 5.1 y, BMI 26.3 +/- 3.1 kg/m(2)) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR. RESULTS: In females there was no significant difference between our measured RMR (5504 +/- 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 +/- 440 kJ/d), whereas in males measured RMR (6831 +/- 779 kJ/d) was significantly higher than calculated RMR (6490 +/- 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 x body weight [kg] - 15.3 x age [y] + 746 x sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R(2)) of the variance in RMR and predicted RMR within +/- 486 kJ/d (SEE). CONCLUSION: On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice.
BACKGROUND: Equations published in the literature for predicting resting metabolic rate (RMR) in older individuals were exclusively derived from studies with small samples of this age group. AIM: of the present investigation was therefore to compare the measured RMR of a relatively large group of older females and males with values for RMR calculated from the most commonly used WHO [1] equations. Furthermore, on the basis of the data collected by our study group a new equation for calculating RMR in the elderly was to be developed. Variables used in this equation should be easily and exactly determinable in practice. SUBJECTS AND METHODS: RMR was measured by indirect calorimetry after an overnight fast in a sample of 179 female (age 67.8 +/- 5.7 y, BMI 26.4 +/- 3.7 kg/m(2)) and 107 male (age 66.9 +/- 5.1 y, BMI 26.3 +/- 3.1 kg/m(2)) participants in the longitudinal study on nutrition and health status in an aging population of Giessen, Germany. The subjects were at least 60 years old, did not suffer from thyroid dysfunction, and were not taking thyroid hormones. Stepwise multiple linear regression analysis was used to estimate the best predictors of RMR. RESULTS: In females there was no significant difference between our measured RMR (5504 +/- 653 kJ/d) and RMR predicted with the WHO [1] equation (5458 +/- 440 kJ/d), whereas in males measured RMR (6831 +/- 779 kJ/d) was significantly higher than calculated RMR (6490 +/- 550 kJ/d). Results of regression analysis, considering body weight, body height, age, and sex, showed that RMR is best calculated by the following equation: RMR [kJ/d]= 3169 + 50.0 x body weight [kg] - 15.3 x age [y] + 746 x sex [female = 0, male = 1]. The variables of this equation accounted for 74 % (R(2)) of the variance in RMR and predicted RMR within +/- 486 kJ/d (SEE). CONCLUSION: On the basis of the data determined in a large group of older individuals, we offer a new equation for calculating RMR in the elderly that is both easy and accurate for use in practice.
Authors: Judi Porter; Kay Nguo; Jorja Collins; Nicole Kellow; Catherine E Huggins; Simone Gibson; Zoe Davidson; Dale Schoeller; Ross Prentice; Marian L Neuhouser; Linda Snetselaar; Helen Truby Journal: Am J Clin Nutr Date: 2019-12-01 Impact factor: 7.045