What is Normal?
Predictive Equations for Resting Energy Expenditure (REE/RMR)

Background
What percent of patients are normal?
Normal Calculator
Frequently Asked Questions
Conclusion
References

BACKGROUND

Indirect Calorimetry is the Gold Standard for measuring Resting Energy Expenditure (REE/RMR). Before the Korr products came along, it was not practical to perform indirect calorimetry measurements on individuals. Therefore, predictive equations were developed to estimate what the indirect calorimetry measurement (REE/RMR) would be based on an individuals sex, age, height, and weight.

Predictive equations provide an estimate of what is "normal"
for a given population based on their sex, age, height, and weight.

Dozens of predictive (normal) equations have been developed over the years. The one used most often was developed in 1919 and is commonly called the Harris-Benedict equation1. The Korr Products use the Harris-Benedict as the "normal" value to compare the actual measured value against.

The Korr products measure the Resting Energy Expenditure (REE/RMR) using indirect calorimetry and then provide a comparison to an estimate of what is “normal” using the Harris-Benedict predictive equation.

What percent of patients are normal?

Numerous studies have been done to study how well predictive equations predict. Since the results are so varied, it is difficult to summarize all of the findings. In the table below is data presented in a recent study by Frankenfield2, et. al. The study compared indirect calorimetry measurements on 130 adult volunteers grouped by degree of obesity (BMI range 181.8 to 96.8) against four different predictive equations. The data is summarized by the percent that fall within ±10% and the percentage that fall above and below the ±10% window.

Agreement between measured and predictive RMRa in non obese and obese men and women.

Table adapted from: Frankenfield DC, Rowe WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and non obese people. J Am Diet Assoc. 2003;103:1152-1159
Equation and Group % of subjects within range of agreement (±10% of RMR) % of subjects above range of agreement % of subjects below range of agreement
Harris-Benedict      
    Not obese (BMI<30) 69 27 4
    Obese (BMI > 30) 64 30 6
       
Adjusted Harris-Benedict      
    Not obese (BMI<30) 26 2 72
    Obese (BMI > 30) 60 5 35
       
Owen3,4      
    Not obese (BMI<30) 73 6 21
    Obese (BMI > 30) 51 6 43
       
Miffin5      
    Not obese (BMI<30) 82 10 8
    Obese (BMI > 30) 70 9 21
       
aRMR=measured resting metabolic rate.

 

Normal Calculator (Web Only)

We created a simple calculator so you can determine the predicted value based on the 4 different studies represented in the table above. Also, if you have performed an actual measurement using indirect calorimetry, a comparison to the predicted results will be made.


Calculator Inputs
Sex:  Male
 Female
Age:    yrs.
Height:    in.
Weight:    lbs.
Ideal Weight:    lbs.
Measured REE:  

  


Calculator Results
Predictive Equation REEa
kcal / day		 % Differenceb
Measured REE N/A
Harris-Benedict
Adjusted Harris-Benedict
Owen
Miffin
aREE = Resting Energy Expenditure (same as RMR=Resting Metabolic Rate)
b%Difference = (measured – predicted)/predicted x 100%

Predictive Equations Used

Be glad Korr has made it possible for you to actually measure REE/RMR.
Below are the predictive equations used by those that are not as fortunate!

Harris-Benedict
MEN: kcal/day = 66 + 13.75(wt)+5.0(ht)-6.76(age)
WOMEN: kcal/day = 655 + 9.56(wt)+1.85(ht)-4.68(age)

Weight Adjusted Harris-Benedict
Adjusted Weight (kg) = [(actual body wt – ideal wt)x0.25] + ideal wt

Owen
MEN4: kcal/day = 879 + 10.2 (wt)
WOMEN3: kcal/day = 795 + 7.2 (wt)

Miffin5
MEN: kcal/day = 5 + 10(wt) + 6.25 (ht) – 5 (age)
WOMEN: kcal/day = -161 + 10 (wt) + 6.25 (ht) – 5 (age)

 

FAQ
Frequently Asked Questions

 

 

DISCUSSION / CONCLUSION

Indirect calorimetry is the gold standard for measuring Resting Energy Expenditure (REE/RMR). Korr’s products are indirect calorimeters that provide an accurate measure of Resting Energy Expenditure.

There are many predictive equations that can be used when indirect calorimetry is not available. These predictive equations were found in studies with a relatively small sample size. Nonetheless, they are still used as an estimate of what is “normal” for an individual of comparable age, height, weight, and sex.

Statistically speaking, it is impossible to predict whether a given individual will measure above or below the value calculated by the predictive equation. You can only think in terms of probabilities.

It is also difficult to determine why a given individual measures above or below the value calculated by the predictive equation. Sometimes the answer is obvious due to the individual’s body composition. Other times the answer is very counterintuitive.

The important thing to remember is that you can now measure the Resting Energy Expenditure using indirect calorimetry – the gold standard. The measurement is powerful. Comparing the measurement to a predictive equation is secondary, and may not be utilized by many users of indirect calorimetry.

 

REFERENCES

  1. Harris JA, Benedict FG. A Biometric Study of Basal Metabolism in Man. Washington, DC: Carnegie Institute of Washington, 1919. Publication No. 279.
  2. Frankenfield DC, Rowe WA, Smith JS, Cooney RN. Validation of several established equations for resting metabolic rate in obese and non obese people. J Am Diet Assoc. 2003;103:1152-1159
  3. Owen OE, Kavle E, Owen RS, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr. 1986;44:1-19.
  4. Owen OE, Holup JL, D’Alessio, et al. A reappraisal of caloric requirements of men. Am J Clin Nutr. 1987;46:75-85.
  5. Miffin MD, St Jeor ST, Hill LA, et al. A new predictive equation for resting energy expendeture in healthy individuals. Am J Clin Nutr. 1990;51:241-247