Alice S Ryan1, Maria Novitskaya2, Alice L Treuth2. 1. Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD; Baltimore Veterans Administration Maryland Health Care System Geriatric Research, Education and Clinical Center (GRECC), Baltimore, MD. Electronic address: aryan@som.umaryland.edu. 2. Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD.
Abstract
OBJECTIVE: To measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke. DESIGN: Cross-sectional study. SETTING: Hospital. PARTICIPANTS: Survivors of stroke (N=71). INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: RMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorptiometry scan and treadmill test for peak oxygen consumption (V̇o2peak). RMR was calculated using 9 established equations. RESULTS: RMR measured (1552±319 kcal/d) was significantly lower than 9 predicted RMR values (all P<.001), with the best being McArdle-Katch (1652±233 kcal/d), Livingston (1677±230 kcal/d), and Mifflin (1707±242 kcal/d). The Institute of Medicine of the National Academies (2437±386 kcal/d) had the largest discrepancy with measured RMR. Predicted RMR determined with 8 of 9 equations was between 9% and 18% greater than measured RMR. Appendicular lean mass (r=0.64, P<.001), total lean mass (r=0.64, P<.001), and V̇o2peak (r=0.41, P<.001) were associated with measured RMR. CONCLUSIONS: RMR predictive equations established in adults without stroke are not appropriate for the population with stroke population, indicating the need to measure RMR until a more accurate predictive equation is developed. This could support modifications to nutritional intake guidelines in patients with conditions of muscle atrophy. If measurement of RMR is not feasible, the Katch-McArdle equation should be used to estimate RMR in a patient with stroke because on average it provides the lowest percentage overestimate compared with other equations.
OBJECTIVE: To measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke. DESIGN: Cross-sectional study. SETTING: Hospital. PARTICIPANTS: Survivors of stroke (N=71). INTERVENTION: Not applicable. MAIN OUTCOME MEASURES: RMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorptiometry scan and treadmill test for peak oxygen consumption (V̇o2peak). RMR was calculated using 9 established equations. RESULTS: RMR measured (1552±319 kcal/d) was significantly lower than 9 predicted RMR values (all P<.001), with the best being McArdle-Katch (1652±233 kcal/d), Livingston (1677±230 kcal/d), and Mifflin (1707±242 kcal/d). The Institute of Medicine of the National Academies (2437±386 kcal/d) had the largest discrepancy with measured RMR. Predicted RMR determined with 8 of 9 equations was between 9% and 18% greater than measured RMR. Appendicular lean mass (r=0.64, P<.001), total lean mass (r=0.64, P<.001), and V̇o2peak (r=0.41, P<.001) were associated with measured RMR. CONCLUSIONS: RMR predictive equations established in adults without stroke are not appropriate for the population with stroke population, indicating the need to measure RMR until a more accurate predictive equation is developed. This could support modifications to nutritional intake guidelines in patients with conditions of muscle atrophy. If measurement of RMR is not feasible, the Katch-McArdle equation should be used to estimate RMR in a patient with stroke because on average it provides the lowest percentage overestimate compared with other equations.
Authors: Alice S Ryan; Andrew Buscemi; Larry Forrester; Charlene E Hafer-Macko; Frederick M Ivey Journal: Neurorehabil Neural Repair Date: 2011-07-06 Impact factor: 3.919
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