Total energy expenditure measured using doubly labeled water compared with estimated energy requirements in older adults (≥65 y):Analysis of primary data

Background: Contemporary energy expenditure data are crucial to inform and guide nutrition policy in older adults to optimize nutrition and health. Objective: The aim was to determine the optimal method of estimating total energy expenditure (TEE) in adults (aged ≥65 y) through 1) establishing which published predictive equations have the closest agreement between measured resting metabolic rate (RMR) and predicted RMR and 2) utilizing the RMR equations with the best agreement to predict TEE against the reference method of doubly labeled water (DLW). Methods: A database consisting of international participant-level TEE data from DLW studies was developed to enable comparison with energy requirements estimated by 17 commonly used predictive equations. This database included 31 studies comprising 988 participant-level RMR data and 1488 participant-level TEE data. Mean physical activity level (PAL) was determined for men (PAL = 1.69, n = 320) and women (PAL = 1.66, n = 668). Bland-Altman plots assessed agreement of measured RMR and TEE with predicted RMR and TEE in adults aged ≥65 y, and subgroups of 65-79 y and ≥80 y. Linear regression assessed proportional bias. Results: The Ikeda, Livingston, and Mifflin equations most closely agreed with measured RMR and TEE in all adults aged ≥65 y and in the 65-79 y and ≥80 y subgroups. In adults aged ≥65 y, the Ikeda and Livingston equations overestimated TEE by a mean ± SD of 175 ± 1362 kJ/d and 86 ± 1344 kJ/d, respectively. The Mifflin equation underestimated TEE by a mean ± SD of 24 ± 1401 kJ/d. Proportional bias was present as energy expenditure increased. Conclusions: The Ikeda, Livingston, or Mifflin equations are recommended for estimating energy requirements of older adults. Future research should focus on developing predictive equations to meet the requirements of the older population with consideration given to body composition and functional measures.