Adult Weight Management

Healthy Non-Obese Older Adults (2010-2012)

Citation:

Taaffe DR, Thompson J, Butterfield G, Marcus R. Accuracy of equations to predict basal metabolic rate in older women. J Am Diet Assoc. 1995; 95 (12): 1,387-1,392.

PubMed ID: 7594140
 
Study Design:
Cross-Sectional Study
Class:
D - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:

To assess the accuracy and compare the results of several published equations for predicting basal metabolic rate (BMR) in healthy, older women.

Inclusion Criteria:
  • Understand and give written consent
  • Healthy, free of systemic disease and metabolic disorders known to affect metabolic rate
  • Weight stable.
Exclusion Criteria:
  • Refusal to consent
  • Not meeting inclusion criteria.
Description of Study Protocol:

Recruitment

Subjects recruited from Palo Alto, CA, and adjoining communities to participate in various studies. Specific methods not described.

Design

Cross-sectional study.

Blinding Used 

Not applicable.

Intervention

Not applicable.

Statistical Analysis

  • Simple and stepwise multiple regression analyses were performed to examine the relationship between body composition parameters and measured BMR
  • Linear regression and the root mean squared prediction error (RMSPE) were used to determine accuracy and precision of predicted BMR vs. measured BMR
  • All tests were two-tailed, and significance was at P=0.05.
Data Collection Summary:

Timing of Measurements 

  • Screening visit: Health history questionnaire, physical examination, multi-phasic lab profile and resting ECG
  • BMR determined at 6 a.m. after an overnight stay and a 10-hour fast
  • Repeat measures were made on three consecutive mornings to establish coefficient of variation (CV).

Dependent Variables

  • Measured BMR  [(VO2, l/min), CO2 (l/min; ml/kg/min), ventilation (l/min)] (Weir equation)]
    • C type: Douglas bag then using an O2 and CO2 analyzer
    • Rest before measure: Rested overnight for seven to eight hours; and before rising
    • Measurement length: After an adjustment period of five minutes; expired air was collected in a Douglas bag for 10 minutes
    • Steady state: An adjustment period of 5 minutes
    • Fasting length: 10 hours
    • Exercise conditioning 24 hours prior to test: None reported
    • Room temperature: 23 degrees C
    • Number of measures and were they repeated: Yes, on three consecutive mornings
    • Coefficient of variation: Precision error for triplicate measures was 3.5%
    • Equipment of calibration: Yes
    • Training of measurer: Not reported
    • Subject training of measuring process: Not reported
    • Dietary: None reported.
    • Monitored heart rate: Yes, on screening in physical exam
    • Body temperature: No
  • Accuracy and precision of measured BMR vs. predicted BMR.

Independent Variables

  • Body composition [fat mass (kg), fat-free mass (kg), percentage body fat]: Dual energy X-ray absorptiometry (DXA, Hologic QDR 1000/W, whole body version 5.47)
    • Body fat: Calculated from equation of Siri
    • Fat free mass equals total body weight minus fat mass
    • Dual energy X-ray absorptiometry in 81 subjects (CV 1%)
    • Body density assessed by hydrostatic weighing (N=35) w/residual lung volume measured by nitrogen washout method (Wilmore); body fat (Siri) and FFM (total body weight minus fat mass) computed.
  • Weight: procedures not specified
  • Height: procedures not specified
  • Body mass index (BMI)
  • Prediction equations
    • Harris-Benedict (1918), Owen et al, 1986, Mifflin et al, 1990: Prediction equations all meet project definitions
    • WHO/FAO (1985): Not identical due to reported height in cm NOT meters; hence height factor is adjusted from 644 to 6.4.
    • Fredrix et al, 1990: 1641+10.7(W)-9(A)-203(G)
    • Arciero et al, 1993: 144+7.8(W)+4.7 (H)-39.5(MS).
      • W=wt(kg); H=ht (cm); A=age (y); G=gender: 1=male; 2=female; MS=menopausal status. 1=pre-menopause; 2=peri-menopause and 3=post-menopause.
    • Age.
Description of Actual Data Sample:
  • Initial N: Not given
  • Final N: N=116 females (N=45 taking exogenous estrogen)
  • Age: Mean 67.1±4.4 years (range: 60-82 years)
  • Ethnicity: Not specified. All subjects were Caucasian
  • Other relevant demographics: None reported
  • Anthropometrics:
  Mean±SD Range
Height, cm 162.5±6.3 137.2-179.6 
Weight, kg 70.4±10.4 47.4-101.2
BMI 26.7±4.2 18.9-39.4
FFM, kg 43.8±4.7 32.5-55.1
Fat mass, kg 26.6±8.4 8.9-46.5
Body fat, percent 37.0±7.6 18.0-54.9
BMR, kcal per day 1,285±155 978-1,843
  • Location: Palo Alto, CA, United States. 

 

Summary of Results:

The relationship between age and BMR of the study population was that as individuals aged, measured BMR (kcal per day) decreased (R= -0.17, P=0.064).

Anthropometric

There was no difference in height, body weight, FFM or fat mass between women taking exogenous estrogen and those not taking estrogen; subjects taking estrogen were young (P<0.01) than those NOT taking estrogen. So, results were pooled.

  • Correlations between:
    • BMR and weight: R=0.72 (P-0.001)
    • BMR and FFM: R=0.55 (P=0.0001)
    • BMR and fat mass: R=0.59 (P=0.0001)
  • Body weight and percent body fat were independent predictors for 54% of the variance in BMR; percent body fat accounted for only 2% of variance.

Resting energy expenditure

The mean±SD for measured BMR=1,285±155kcal per day; range=978-1,843kcal per day.

The Sleeping Energy Expenditure (SEE), R2 and P-value for regression of predicted BMR (X-variable) on measured BMR (Y-variable):

   SEE  R2  P-value
 HB 111 0.50 0.0001 
 Owen 108 0.52 0.0001 
 Fredrix 114 0.47 0.0001 
 Arciero 115 0.46 0.0001 
 Mifflin 117 0.44 0.0001 
 Cunningham 130 0.30 0.0001 
 WHO/FAO 117 0.44 0.0001 
  • All predicted BMRs correlated with measured BMR (P=0.0001) The explained variance in actual BMR ranged from 30% (Cunningham)-52% (Owen)
  • When group means are examined, the equation of Owen (i.e. uses body weight) and Fredrix (uses body weight and age) were similar to measured BMR. The difference was 2.2% and -0.5%, respectively. The group mean difference for Mifflin was 3.9% to 13.7% for that proposed by Cunningham. 

Differences between mBMR and calculated estimates

  Difference, kcal per day Difference, percent RMSPE (kcal)
HB 30 3.1±8.8  114 
Owen 16 2.2±8.8 115
Fredrix -15 -0.5±9.1 116
Arciero 53 5.1±9.2 127
Mifflin -57 -3.9±9.0 131
Cunningham 30 3.3±10.4 134
Cunningham 162 13.7±11.4 208
WHO/FAO 101 8.7±9.6 154
  • Percent difference=(predicted-measured/measured) x 100
  • RMSPE =root mean squared prediction error- determines the accuracy of prediction for each subject
  • Analysis by RMSPE revealed that the equations of Owen et al and Fredrix et al, and Harris Benedict were the most accurate-predicting actual BMR within 116kcal per day whereas the original Cunningham equation (before an adjustment of the intercept) resulted in the largest prediction error at 208kcal per day.

 

Author Conclusion:

As stated by the author in body of report:

  • “The results of this study suggest that the equations of Owen et al, Fredrix et al and Harris-Benedict which use the easily measured variable of body weight are most appropriate for a population of healthy older women”
  • “Previous findings of Arciero report that Owen et al and Mifflin et al under predicted energy expenditure in the Arciero group by 5% and 10%, respectively and the equation of Fredrix over predicted by 3%. We also report that the equation of Mifflin et al under predicted group mean BMR by 4 to 5%, with an individual error of 131kcal per day."
  • “As observed by Owen et al, we found that body weight correlated best with BMR. [and] although Mifflin et al found FFM to be the best single predict of metabolic rate for their large population of men and women, they also found weight to be a better predictor than FFM in women (R2=0.50 for BMR and body weight vs. 0.36 for BMR and FFM), which agrees with our study.”
  • “Conditions that vary in studies are the several terms used to describe energy expenditure under resting conditions. As all predictive equations in this article are classified as RMR or REE, adjusting by either 7% or 10% to equate to BMR measures does not improve predictive capacity. For the equation of Mifflin et al, the predictive error is unchanged and for all other equations, the predictive error is increased.”
Funding Source:
Government: Department of Veterans Affairs
Reviewer Comments:

Strengths 

  • Large, heterogeneous population
  • Statistical information examined differences between group means AND individual differences
  • Standardized IC procedures
  • Measured BMR (i.e., subjects did not “rise in the a.m.”)
  • Selected an important ethnic group within a healthy population.

Generalizability/Weaknesses

  • “Study biases: Researchers “selected” participants after a screening (that included a thorough physical examination) making the sampling unit individuals more likely motivated than the general population”
  • Accuracy of IC measurements improve understanding of the population differences between measured and predicted.”

[NOTE: Root mean squared prediction error (RMSPE) determines how close the predicted BMR value for each subject was to the actual BMR. A low RMSPE value indicates that the predicted value obtained from the JTH prediction equation is close to the actual BMR.]

Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) Yes
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) Yes
 
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? N/A
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? N/A
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? No
  4.1. Were follow-up methods described and the same for all groups? N/A
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) ???
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? ???
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? No
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) N/A
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? No
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? Yes
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? Yes
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? Yes
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? Yes
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? Yes
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? Yes
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? Yes
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? Yes
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes