Adult Weight Management

Healthy Non-Obese Older Adults (2010-2012)

Citation:

Arciero PJ, Goran MI, Gardner AM, Ades PA, Tyzbir RS, Poehlman ET. A practical equation to predict resting metabolic rate in older females. J Am Geriatr Soc. 1993; 41 (4): 389-395.

PubMed ID: 8463525
 
Study Design:
Cross-Sectional Study
Class:
D - Click here for explanation of classification scheme.
Quality Rating:
Neutral NEUTRAL: See Quality Criteria Checklist below.
Research Purpose:
  • To develop a practical (i.e., based on easily measured variables) and accurate age-specific equation for predicting resting metabolic rate (RMR) in older women
  • Compare (cross-validate) the measured RMR values of the study participants with existing equations for predicting RMR in older females.
Inclusion Criteria:
  • Healthy
  • Female
  • Age 50-81
  • Able to give consent.
Exclusion Criteria:
  • Clinical evidence of CHD (i.e. ST-segment depression greater than 1mm at rest or exercise) or cardiomyopathy)
  • Hypertension (blood pressure >140/90mm Hg)
  • Medications that could affect cardiovascular function or metabolic rate
  • Medical history of diabetes or obesity
  • Instability of body weight during previous year
  • Exercise-limiting non-cardiac disease (arthritis, peripheral vascular disease, cerebral vascular disease, etc.)
  • History of oophorectomy.
Description of Study Protocol:

Recruitment

Methods not specified

Design

Cross-sectional study

Blinding used

Not used

Intervention

Not applicable

Statistical Analysis

  • Regression equations used to examine linear and non-linear relationships between RMR and other independent variables
    • Semi-partial F-tests performed to determine whether the quadratic models explained a significantly greater amount of variance in RMR than explained by linear regression models
    • Pearson correlation used to assess the degree of association between variables
  • Stepwise multiple regression used to predict RMR from the independent variables
    • Potential predictors of RMR: Standing height, body weight, body surface area, body mass index, fat mass, fat-free mass, skinfolds, VO2max, leisure time physical activity, daily energy and macronutrient intake, menstrual status, and age
    • Practical equations derived in which only easily measured variables were considered: Body weight standing height, age, skinfolds, menstrual status and leisure time physical activity
  • Dependent T-test used to compare the measured and predicted means of RMR
  • Independent z test used to compare correlation coefficient (r) between predicted RMR and measured RMR with the multiple R obtained from the regression equation of the original study
  • Statistical significance was set at P<0.05.

 

Data Collection Summary:

Timing of Measurements

All participants admitted to research center evening before metabolic testing and identical protocol for all participants

Dependent Variable

Resting Metabolic Rate (RMR): 

  • Measured: Indirect calorimetry (IC)
    • IC type: Used ventilated hood technique for 45 minutes
    • Rest before measure: IC done under inpatient conditions (in research laboratory and measurements done in same room where participant slept); recent work showed inpatient RMR 8% lower under inpatient conditions compared with outpatient condition
    • Measurement length: 45 minutes
    • Fasting length: Overnight
    • Exercise conditioning 24 hours prior to test? IC 36-48 hours after last exercise bout to minimize the effect of physical activity on RMR
    • Room temp: Not reported
    • No of measures repeated? Not reported
    • Coefficient of variation? Not reported
    • Equipment calibration: No machine calibration was mentioned
    • Training of measurer? Not reported
    • Subject training of measuring process: Participants given practice with ventilated hood the evening before to reduce apprehension with testing conditions
  • Estimated RMR: Validity of five commonly used prediction equations tested in this data set: 
    • WHO/FAO (>60 years)
    • Harris-Benedict
    • Owen
    • Fredrix
    • Mifflin.

Independent variables

  • Height and weight: Methods not specified
  • Body composition:
    • Body fat: Underwater weighing to determine body density with simultaneous measurement of residual lung volume by the helium dilution method using the formula of Siri
    • Fat free mass (FFM): Total body weight minus fat weight
    • Skinfolds: Triceps, chest, abdomen and thigh (Lange skinfold caliper); all measurements taken on right side of body; each value was the mean of three consecutive measurements by same investigator
  • Physical activity level: Minnesota Leisure Time Physical Activity Questionnaire
  • Maximum aerobic power (VO2 max): progressive and continuos treadmill test to volitional fatigue
  • Energy intake: Three-day dietary intake using weighed food records
  • Menopausal status: Classified as 1=pre-menopausal (6%); 2=peri-menopausal (6%); or 3=postmenopausal (88%).
Description of Actual Data Sample:
  • Initial N: Not given
  • Attrition (final N): N=75 females
  • Age: 61±8 years  (50-81)
  • Ethnicity: Not reported
  • Other relevant demographics: None reported
  • Anthropometrics:
Women Mean±SD Range

Weight, kg

63.3±7 51.8-92.3

Height, cm

163±7 149-178

Body fat, percent

30.4±5 20.1-43.2

Fat-free body mass, kg

43.8±4

36.4-54
Fat mass, kg 19.4±5 11.1-39.8

Measured RMR range, kcal per day

1,302±100

1,094-1,513

  • Location: University of Vermont.
Summary of Results:

Major Results

  • Potential predictors of RMR were: Standing height, body weight, body surface area, body mass index, fat mass, fat-free mass, four skinfolds, VO2 max, leisure time physical activity, daily energy and macronutrient intake, menstrual status and age
  • Correlations (r) with RMR:
    • Age: -0.26
    • Height: 0.48
    • Weight 0.68
    • Fat free mass: 0.89
    • Leisure-time activity: -0.09
    • VO2 max: 0.42
  • Fat-free mass was the strongest and only significant predictor of RMR in the total group of 75 older women (R=0.89; P<0.01); explaining 79% of the total variance. In a laboratory setting, the measurement of FFM predicted RMR in older women within ±46kcal per day.
    • Regression equation: RMR (kcal per day)=21 (FFM, kg) + 369 (r2=0.79) (SEE= ±46kcal per day)
  • Focus of the study was to develop a simple equation for predicting RMR after removal of variables deemed impractical or difficult to measure (i.e., FFM) in a clinical (non-research) setting. A practical equation was derived in which only easily measured variables were considered: Body weight, standing height, age, skinfolds, menstrual status and leisure time physical activity.
    • Of these variables, body weight (kg) accounted for 47% of total variance in RMR (R=0.68; P<0.01)
      • Standing height (cm) explained an additional 8% of variance in RMR
      • Menopausal status explained 4% of total variance
      • Together, the three variables—weight, standing height and menopausal status accounted for 59% (R2) of the variance in RMR and the new practical equation predicted RMR within ±66kcal per day, P<0.01
      • Regression equation for this sample: RMR (kcal per day)=7.8 (weight, kg) + 4.7 (standing height, cm) - 39.5 (menopausal status, 1,2,3) + 144 (R=0.77, R2=0.59, SEE=66kcal per day, P<0.01)

Cross-validation results

  • Harris-Benedict underestimated (7%) RMR in older population; differences not significant
  • Owen et al significantly (P<0.05) under-predicted RMR by an average of 5% (group); with a range of -27% to 15% on an individual basis
  • Mifflin et al significantly (P<0.05) under-predicted measured RMR by 11% (group); ranging from -31% to 7%
  • WHO/FAO and Fredrix et al (P<0.05) overestimated measured RMR by 3%; ranging from -8% to 12% and -24% to 20%, respectively
  • WHO/FAO predicted RMR in older women with SEE of ±94kcal per day compared to ±66kcal per day in this study
  • Outpatient conditions in Fredrix study may have contributed to overestimated RMR; in previous study done by author, outpatient overestimated by approximately 8% compared with inpatient conditions.
Comparison of the original prediction equation of the five studies used for cross-validation analyses and the practical equation from the present study
Study Original equation
WHO (females, N=NA>60 years) RMR (kcal per day)=[9.2 (W) + 6.4(H)] -302 (R2=0.67)
Harris Benedict (females, N=16>50 years) RMR (kcal per day)=[1.8 (H) + 9.6 (W) - 4.7 (A)] +655 (R2=0.59)
Owen et al (females, N=8>50 years) RMR (kcal per day)=[7.2 (W)] +795 (R2=0.55)
Fredrix et al (females, (N=22>50 years) RMR (kcal per day)=[10.7 (W) - 9 (A) - 203 (2)] + 1641 (R2=0/84)
Mifflin et al (females, N=50>50 years) RMR (kcal per day)=[10 (W) + 6.25 (H)=5 (A)] - 161 (R2=0.71)
Arciero et al (females, N=75>50 years) RMR (kcal per day)=[7.8 (W) + 4.7 (H) - 40 (M)] +144 (R2=0.59)

Units for equations: W: Weight, kg; H: Height, cm; A: Age; M: Menopausal, 1=pre, 2=peri, 3=post; NA=not available

Comparisons of measured RMR and RMR predicted by five published equations and the correlation coefficient of predicted and measured RMR vs. the multiple R from the original prediction equation

  Criterion #1     Criterion #2
Study Measured
(kcal per day)
Predicted
(kcal per day)
P-value r vs. R
WHO (>60 years) 1,286.6 1,320.0 0.12 0.79 vs. 0.82
Harris-Benedict 1,301.6 1,274.6 0.07 0.75 vs. 0.78
Owen 1,301.6 1,249.2 <0.01 0.68 vs. 0.74*
Fredrix 1,301.6 1,359.3 <0.01 0.69 vs. 0.92*
Mifflin 1,301.6 1,185.3 <0.01 0.75 vs. 0.84*

* correlation coefficient is significantly different from the multiple R, P<0.05

Author Conclusion:
  • Currently available equations to predict RMR in older individuals have not been age- or sex-specific, have generally been based on data extrapolated from younger individuals, and have relied generally on small sample sizes (N=50)
  • In a laboratory setting, the measurement of FFM predicted RMR in older women within ±46kcal per day
  • However, in a clinical or non-research setting; body weight alone best predicted RMR, accounting for 47% of the explained variance in RMR in older females. The use of body weight alone would permit an estimate of RMR with a standard error of estimate of ±74kcal per day
  • Standing height significantly (R2=8%) strengthened the prediction of RMR and addition of menopausal status contributed an additional 4% of explained variance. The independent two (variables (plus body weight) increased the R2 to 59%, which would permit an estimate of RMR within ±66kcal per day
  • There was a significant linear decline in RMR in females >50 years of age with onset of menopause
  • Because of finding that menopausal status is a significant factor contributing to the variation in RMR in older women (hormone status), authors suggested may need to develop separate equations for older men and women
  • When five previously published equations were applied to predict RMR in cross validation-independent predicted values deviated by -31% to 20% from the measured values
  • With the exception of the WHO/FAO equation and the Harris-Benedict equation, none of the published equations cross-validated successfully with the measured RMR values from the 75 older women. The WHO/FAO equation predicted RMR in older women with a standard error of estimate of ±94kcal per day compared with ±66kcal per day in the present age- and sex-specific equation.

Major Conclusion

“In summary, we offer an accurate equation to predict RMR in older women that is both age-specific and practical for use in the clinical or non-research setting using body weight, standing height and menopausal status.”

Funding Source:
Government: NIH
Reviewer Comments:

Strengths

  • Large sample size in comparison to other studies
  • Inpatient conditions
  • Measurement standardized for optimum basal conditions
  • IC measurements performed 36-48 hours after last exercise bout to avoid effects of exercise on RMR
  • Subjects familiarized with equipment prior to study to minimize anxiety.

Limitations

  • Caucasians
  • Healthy older women
  • So limited generalizability; can’t apply findings to females in other age or ethnic groups
  • Cross-sectional; no cause and effects
  • Small sample of “oldest old” >80 years
  • Direct measurements recommended for precise measurements of RMR
  • Self-selection bias with convenience sample; may be different than general population
  • With diet diaries and weighed diet; may alter diet; not usual diet; may pick foods easier to weigh and measure
  • Large range of calorie intake among participants; range 1,000-3,223kcal per day; mean 1,758±429kcal per day which could affect results; not mentioned what effect it had on RMR (?)
  • Large range of leisure time activity; range 77-839kcal per day; mean 263±166kcal per day); not mentioned what effect it had on RMR (?)
  • Not sure why collected data on variables that were considered by authors as impractical or hard to measure.

If height and weight were significant predictors, what about BMI?

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? ???
  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? ???
3. Were study groups comparable? Yes
  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? Yes
  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%.) No
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? ???
  4.4. Were reasons for withdrawals similar across groups? ???
  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? No
  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.) No
  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? N/A
  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? N/A
  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? ???
  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? ???
  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? N/A
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