Volume 7 Supplement 1

20th Brazilian Diabetes Society Congress

Open Access

Performance of resting metabolic rate estimation equations in obese patients

  • Milene Moehlecke1Email author,
  • Manoel Roberto Maciel Trindade1,
  • Ana Carolina Mazzuca1,
  • Carina Andriatta Blume1,
  • Jakeline Rheinheimer1 and
  • Cristiane Bauermann Leitão1
Diabetology & Metabolic Syndrome20157(Suppl 1):A231

https://doi.org/10.1186/1758-5996-7-S1-A231

Published: 11 November 2015

Background

Weight gain may be associated with an imbalance between energy intake and energy expenditure. The resting metabolic rate (RMR) is the main component of total energy expenditure, and is related mainly to lean mass (LM), as well as to other factors such as fat mass (FM), age, sex and genetic factors. A RMR lower than expected may be a risk factor for weight gain. RMR is estimated by equations that use patient's weight, sex, age and height to calculate energy needs. Several studies have shown that these equations have a poor agreement with RMR measured by indirect calorimetry (IC) in obese patients once their excess fat-free mass (FFM) is usually not taken into account.

Objective

To evaluate the accuracy of five equations in predicting RMR in obese subjects. Results were compared with measured RMR (mRMR) determined by IC.

Materials and methods

Cross-sectional study was conducted in obese Southern Brazilian volunteers recruited from community. Body mass index (BMI) was calculated by dividing weight (in kilograms) by squared height (in meters). Body composition was evaluated by dual-energy X-ray. RMR was measured by IC (Weir equation) and estimated (eRMR) by Mifflin–St. Jeor, Owen, Harris-Benedict, Ireton-Jones and Horie-Waitzberg & Gonzalez (H & WG) equations (Figure 1). The latter takes into consideration the FFM. Equations performance were determined by bias (mean difference between mRMR and eRMR); precision (standard deviation of bias) and by accuracy (percentage of estimates within 5% of mRMR).
Figure 1

Prediction equations for comparison with indirect calometry in obese subjects.

Results

Sixty individuals (46 women [75%], 48 white [84%]) aged 46±13 yrs. (range, 21-83 yrs.) were evaluated. Overall, mRMR was 1941±642 kcal/day. mRMR increased along with BMI (Figure 2), but the association was lost when corrected for LM (P=0,859). H & WG equation was the only equation unbiased (P=0.801) (Figure 3). The Harris-Benedict, Owen and Mifflin–St. Jeor equations were biased overall toward underestimation, while Ireton-Jones equation was biased toward overestimation (Figure 4). Bias was significantly higher in women for Harris-Benedict, Mifflin St. Jeor and Owen equations. Accuracy to estimate RMR at ±5% was suboptimal for all equations, except for H & WG.
Figure 2

Obese patients distributed by anthropometric and body composition parameters.

Figure 3

Comparison between estimated RMR from several equations and measured RMR in obese patients.

Figure 4

Individual resting metabolic rate values, expressed as mean ± 2SD (dashed lines)

Conclusion

In this sample of obese subjects, the available RMR estimate equations that do not take into account the FFM have poor accuracy when compared with mRMR.

Authors’ Affiliations

(1)
UFRGS

Copyright

© Moehlecke et al. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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