- Click here for explanation of classification scheme.

• Mathematically derive a predictive equation for REE based on a sample of 498 healthy normal-weight and obese individuals
• Assess the usefulness of the more recent body composition measures of body composition (%BF) and distribution (WHR) in predicting REE
• Assess the predictive value as well as overall practicality of the new equations compared with those currently being applied.

• Recruitment by a 2x2x5 factorial design where sex and weight were stratified by five different age groups according to decade
• Bias toward the working class (defined as either the subject or spouse being employed at least half-time for the past year)
• Understand and give written consent
• Either the subject or spouse being employed the equivalent of half-time
• Good health with less than one sick day per month for the past year
• No major current illnesses or psychological problems
• Extreme underweight (<80% IBW) or morbidly obese (>180% IBW).

• Refusal to consent
• In poor health.

Recruitment   

• Data from subjects enrolled in the five-year RENO Diet-Heart study
• Recruitment by a 2x2x5 factorial design where sex and weight were stratified by five different age groups according to decade
• Bias toward working-class individuals.

Design

Cross-sectional study

Blinding used

Not applicable

Intervention

Not applicable

Statistical Analysis

• Pearson correlation coefficients and simple and stepwise multiple-regression analyses to assess relationships between measured resting energy expenditure and weight, height, age, sex, fat-free mass (FFM), percent ideal body weight ( percent IBW), body mass index (BMI) and waist-hip ratio (WHR)
  
  • Predictive equations were developed and compared with commonly used equations
  • Sex was entered as a dummy variable
  • Included estimates of FFM
  • Data for 482 of 498 subjects.

Timing of Measurements

One measurement time

Dependent variables

• Measured resting energy expenditure (REE) [(VO₂, liters per minute), CO₂ (liters per minute; ml/kg per minute), RER, ventilation (liters per minute)]
  • IC type: Metabolic measurement care with a canopy hood; Standard computer programs converted O₂ and CO₂ gas exchanged into REE
  • Rest before measure: Relaxed, supine position with a standardized relaxation tape
  • Measurement length: Approximately 20 minutes per subject; measurements repeated on all subjects until a three-minute steady state was achieved
  • Fasting length: Overnight
  • Exercise conditioning 24 prior to test? Abstain from exercise 12 hours before the test
  • Room temp: Not reported
  • No. of measures were they repeated? Only one day for obtaining IC measurement, described in measurement length
  • Coefficient of variation? None reported
  • Equipment of Calibration: “SF was key to our successful measurement by his training of our staff and high quality research methodology commitment”
  • Training of measurer? Trained and certified nutritionists using a standardized protocol
  • Subject training of measuring process? None reported
  • Other: Refrain from smoking more than one hour before testing but for 12 hours if possible
  • Monitored heart rate? Not reported
  • Body temperature? Not reported
• Predicted REE.

Independent variables

• Age
• Weight: Measured to nearest 0.55kg on standard physician's beam scale in street clothes and without shoes
• Height: measured to nearest 0.63cm on standardized wall-mounted height board without shoes and using standard protocol
• Percent ideal body weight (%IBW): Determined using 1959 Metropolitan tables
• BMI: kg/m²
• FFM: Body fat (BF) determined from three skinfolds using Jockson-Pollock method; FFM calculated by weight (kg) - fat (kg) where fat (kg) is weight (kg) x %BF
• Waist: Hip ratio: Waist measured at obvious indentation or smallest circumference at mid-torso; hip measurement taken at widest circumference on the torso.

• Initial N: N=508
• Attrition (final N): N=498 (N=247 females; N=251 males) (10 dropouts due to inability to obtain REE measurements)
• Age:
  • Females: 44.6±14.0 years (range: 20-76)
  • Males: 44.4+14.3 years (range: 19-78)
• Ethnicity: Not reported
• Other relevant demographics: None reported
• Anthropometrics:
  • <100% Ideal Body Weight

	Women N=52 Mean±SD	Men N=26 Mean±SD
Weight, kg	54.9±4.5	68.5±5.8
Height, cm	164.8±5.2	178.9±6.3
Percent IBW	92.8±4.4	94.8±4.0
BMI	20.3±1.0	21.5±0.9
W:H ratio	0.73±0.05	0.86±0.04
Percent Body fat	30.0±4.5	10.7±5.3

• 100-119% Ideal Body Weight

	Women N=83 Mean±SD	Men N=103 Mean±SD
Weight, kg	63.7±5.5	80.2±7.2
Height, cm	163.9±5.8	178.5±7.1
Percent IBW	108.8±5.9	110.9±5.2
BMI	23.8±1.3	25.2±1.1
W:H ratio	0.76±0.06	0.90±0.05
Percent Body fat	28.7±5.5	17.6±4.9

• 120-139% Ideal Body Weight

	Women N=61 Mean±SD	Men N=82 Mean±SD
Weight, kg	76.2±6.6	92.2±7.3
Height, cm	164.7±6.4	178.0±6.3
Percent IBW	129.5±6.4	128.5±5.4
BMI	28.4±1.5	29.1±1.2
W:H ratio	0.80±0.07	0.93±0.05
Percent Body fat	36.1±4.5	21.9±5.1

• =140% Ideal Body Weight

	Women N=51 Mean±SD	Men N=40 Mean±SD
Weight, kg	89.4±11.0	108.7±13.1
Height, cm	163.7±7.7	178.3±7.3
Percent IBW	153.1±11.3	151.9±10.8
BMI	33.5±2.5	34.4±2.4
W:H ratio	0.82±0.07	0.96±0.06
Percent Body fat	41.5±5.5	28.4±6.0

• Unplanned exposure factor: N=1 individual was <80% IBW and N=2 were >180% IBW.
•  Location: University of Nevada, Reno NV.

Pearson Correlation Coefficients with REE

• Percent FFM was shown to correlate most highly with REE (R=0.80) for the entire group of males and females
  • FFM and weight were correlated (R=0.79)
  • FFM and height were correlated (R=0.81)
  • Sex and FFM were correlated (R=0.83)
• Weight: 0.72607
• Height: 0.68669
• Age: -0.26104
• Percent IBW: 0.35497
• WHR: 0.54599.

Stepwise multiple regression for predictive equations

• Stepwise multiple regression analysis including all variables yielded a predictive equation for REE in which FFM alone yielded an R² value of 0.64
  • The stepwise addition of weight, age, height, and WHR increased the R² value to 0.70
  • The remaining variables (sex,  percent IBW and BMI) did not contribute further to the predictive value of the equation
• To develop a practical equation for predicting REE, FFM was excluded and weight alone was used: Weight alone yielded a R² value of 0.56
  • The addition of percent IBW, age and sex increased the correlation to R²=0.71
  • Further, a more practical equation that included weight, height and age had a predictive value that equaled equations containing FFM and percent IBW (R²=0.71
    • REE=9.88 x weight+6.25 x height-4.92 x age+166 x sex (males, one; females, zero)-161
• The R² value of approximately 0.7 seemed to be a barrier above which REE could not be more accurately predicted, suggesting that there is a variability of >30% in REE that cannot be explained on the basis of the variables assessed in this study... [possibly] due to individual differences in genetically determined or acquired metabolic efficiency.
• The effects of sex and obesity on REE were extensively explored by analyzing men and women, normal (<120% IBW) and overweight (≥120% IBW)  individuals separately. No significant differences were observed between simple regression lines for FFM in men and women within the range of values studied (males R²=0.44; females R²=0.36).
• A stepwise multiple-regression analyses of subsets of the population based on sex and %IBW did not reveal any relationships or equations surpassing the predictive value of the equation considering measured weight and height and age.
• The R² of 0.71 is obtained in using gender-specific equation of Mifflin-Jeor (above)
• Proposed equations (MSJE)  for males and females after simplification by rounding of numbers:
  • Females: REE=10 x weight (kg)+6.25 x height (cm)-5 x age (years)-161
  • Males: REE=10 x weight (kg) + 6.25 x height (cm)-5 x age (years)+5.

Clinical

• Measured REE for all women (N=247) had a mean of 1,349±214kcal per day and a range of 927-2,216kcal per day
  • MSJE equation predicted 1,348+179kcal per day
• Measured REE for all men (N=251) had a mean of 1,776±297kcal per day and a range of 1,030-2,849kcal per day.
  • MSJE equation predicted 1,775+192kcal per day.

Correlation between measured and other predictive equations:

• Mifflin-St Jeor (this study) had the best predictors of REE with the Owen equations having the least mean difference from measured REE (-4% in females and 0.1% in males)
• HB significantly overpredicted REE in studies population (paired T-tests on individual means, P<0.01) by 5% and Cunningham equations significantly overpredicted REE in study population by 14-15% (by paired T-tests on individual means, P<0.01).

As stated by the author in body of report:

• The relationships between REE and several variables were studied to derive predictive equations for practical, clinical use in weight management today. The result of this process was to confirm EE is determined largely by FFM but is highly correlated with total body weight as well; adding height, age and sex builds on the predictive value of weight in determining REE
• Previous findings suggest HB equation overpredicts measured REE by an average of >15% in modern populations; in our study, the are marked differences between means for weights and ages.
• Our equations are limited to their derivation from our study population, their clinical utility can be assessed by testing in other populations. Their strength and significance lie in
  • Derivation from a larger, modern-day population (stratified by age, weight and sex)
  • Use of advanced equipment and technology for the measurement of actual REE
  • A reasonably high correlation between REE and weight, height and age (R2=0.71)
• Direct metabolic measurements are preferable in individuals where a precise determination of REE is indicated. Other components of 24-EE (physical activity and thermic effect of food should be studied to assess more accurately the individual total daily caloric requirements.
• 30% unexplained variability in REE observed should be studied further.

Government:	NIDDK, NIH
University/Hospital:	University of Nevada School of Medicine, and the Graduate School University of Nevada,

Strengths

• Large sample size in comparison to other studies and stratified for age, sex and weight
• Standardized measurements.

Generalizability/Weaknesses

• Generalizability to study population with potential clinical utility with further validation testing
• Potential for small IC inaccuracy due to lack of subject training on the procedure.