Simplified resting metabolic rate-predicting formulas for normal-sized and obese individuals

Obes Res. 2005 Jul;13(7):1255-62. doi: 10.1038/oby.2005.149.

Abstract

Objective: Resting metabolic rate (RMR) is known to be proportional to body weight and to follow allometric scaling principles. We hypothesized that RMR can be predicted from an allometric formula with weight alone as an independent variable.

Research methods and procedures: An allometric, power-law scaling model was fit to RMR measurements obtained from a cohort of patients being treated for weight loss. This, as well as many of the commonly used RMR-predicting formulas, was tested for RMR prediction ability against a large publicly available RMR database. Bland-Altman analysis was used to determine the efficacy of the various RMR-predicting formulas in obese and non-obese subjects.

Results: Power law modeling of the RMR-body weight relationship yielded the following RMR-predicting equations: RMR(Women) = 248 x Weight(0.4356) - (5.09 x Age) and RMR(Men) = 293 x Weight(0.4330) - (5.92 x Age). Partial correlation analysis revealed that age significantly contributed to RMR variance and was necessary to include in RMR prediction formulas. The James, allometric, and Harris-Benedict formulas all yielded reasonable RMR predictions for normal sized and obese subjects.

Discussion: A simple power formula relating RMR to body weight can be a reasonable RMR estimator for normal-sized and obese individuals but still requires an age term and separate formulas for men and women for the best possible RMR estimates. The apparent performance of RMR-predicting formulas is highly dependent on the methodology employed to compare the various formulas.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Basal Metabolism / physiology*
  • Body Height / physiology
  • Body Weight / physiology
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Mathematics
  • Middle Aged
  • Models, Biological
  • Obesity / metabolism*
  • Predictive Value of Tests
  • Reproducibility of Results
  • Sensitivity and Specificity