FNCE 2023
Session 357. Providing MNT for the Pediatric Type 1 Diabetes Population: What Does the Evidence Show?
Monday, October 9, 8:30 AM - 9:30 AM

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Healthy Non-Obese Older Adults (2010-2012)

Study Design:
- Click here for explanation of classification scheme.
Quality Rating:
Research Purpose:
  1. To develop a new equation for calculating RMR in the elderly on basis of the data determined in a group of older subjects that use easily and exactly determinable in practice.
  2. To cross-validate WHO equations by comparing measured RMR in the study with values for RMR calculated by WHO equations.



  • Harris-Benedict equation: Match project-referenced?
Inclusion Criteria:
  1. Understand and give written consent
  2. 60 y
  3. Physically mobile
  4. Live around Giessen on a long-term basis.
Exclusion Criteria:
  1. Refusal to consent
  2. Subjects who suffered from hypothyroidism, edema, took thyroid hormones or diuretics.
Description of Study Protocol:


  • Ht measured? Yes; by a stadiometer integrated in the scale to the nearest 0.5 cm with subjects in standing position w/o shoes.
  • Wt measured? Yes, on a calibrated digital scale to the nearest 0.1 kg after shoes, coats, and sweaters had been removed. 0.5-1.0 kg was subtracted for remaining clothes.
  • W:H ratio: Circumferences were measured with a tape to the nearest 1 cm in the upright position. Waist was assessed at the smallest point b/t the lower rib and the iliac crest; hip was at the widest point to the grater trochanter and buttocks area.
  • Body composition: Measured using a bioelectrical impedance analyzer. Coefficient of variation for body impedance in our laboratory was 1.5%. Fat-free and fat mass were calculated by applying the equation derived from the cross-validation study from Deurenberg et al, 1991.


Resting energy expenditure

  • IC type: Open-circuit, indirect calorimeter using a ventilated hood system
  • Rest before measure: In supine position; completely at rest
  • Measurement length: 25-35 minutes; an additional initial 10 minutes was discarded to allow participants to acclimatize
  • Fasting length: Overnight fast and measurement taken b/t 6:00-11:00 a.m.
  • Exercise conditioning 24 prior to test? None reported
  • Room temp: Thermoneutral environment
  • Smoking status: Yes via survey
  • No. of measures were they repeated? None reported
  • Coefficient of variation? Mean coefficient of variation in this research was 1.05%
  • Equipment of Calibration: Calibrations were performed immediately before each test
  • Training of measurer? Not reported
  • Subject training of measuring process? Yes; familiarized with experimental procedure.
Data Collection Summary:

Outcome(s) and other measures

  1. Measured REE [(VO2, l/min), CO2 (l/min; ml/kg/min)
  2. Predicted REE using WHO/FAO
  3. Independent variables of weight, height, age, BMI, fat-free mass, waist:hip ratio, waist circumference
  4. Subject characteristics: age, diseases, medication, and smoking status.

Blinding used: No.

Description of Actual Data Sample:
  • N=179 women (67.8±5.7 y)
  • 107 men (66.9±5.1 y)
  • 68.7% of females and 78.5 males were between 60-70 yrs



(Check if SD or SEM)

Men, n=107 Mean±SD

Wt, kg

Ht, cm 173.0±6.5





Fat-free body mass, kg


Fat mass


Fat mass, %


Women, n=107 Mean±SD

Wt, kg


Ht, cm



WHR 0.84±0.06

Fat-free body mass (kg)


Fat mass (kg)


Fat mass (%)



  • Statistical analyses: Normal distribution checked by Kolmogorow-Smirnow test; Differences b/t measured RMR and RMR predicted w/ WHO equations by Wilcoxon signed ranks test for paired samples; Associations b/t measured and predicted, Spearman’s rank correlations and Bland-Altman plots; Stepwise, multi-linear regression analysis was used to estimate the best predictors of RMR. Two models were developed: physiological; then variables which are easily and exactly measurable in practice. P value <0.05.
Summary of Results:

The mean measured RMR was 5504 kJ/d (1315.5 kcal/d) in females and 6831 kJ/d (1632.6 kcal/d) in males.


There was a significant positive correlation between measured RMR and RMR predicted with the WHO equations in women (R=0.75, P<0.001) and men (R=0.61, p <0.001).


In a Bland-Altman plot, the difference between measured and predicted RMR depends on the absolute values for measured RMR.


On average, there was no significant difference in females b/t measure RMR and RMR predicted with the WHO equation, whereas in males measured RMR was slightly but significantly higher than calculated RMR.


Measured RMR compared to RMR predicted with the WHO equations for separate BMI groups (mean ±SD)

BMI (kg/m2) <24.0 24.0-25.9 <26.0-27.9 =28.0


n=51 n=32 n=44 n=52


2.4±7.5 0.0±7.3 1.7±7.2 1.8±8.6


n=25 n=29 n=27 n=26


6.2±9.1* 5.2±8.2* 3.8±8.5+ 6.2±9.8*

+Significant difference P<0.05
* Significant difference P<0.01

Measured RMR compared to RMR predicted with the WHO equations for separate age groups


By Age. When comparing measured to predicted RMR using FAO/WHO/UNU equation at different ages (<65, 65-69 y, 70-74 y, and >/= 75 y), there wasn’t any statistically significant differences between measured and predicted RMR in females. However, the percent difference trend in females was underestimation (i.e., positive values) to overestimation (negative values) as age increased [i.e., % difference ±SD of 1.7% (±8.9), 1.2% (±6.1), -0.3% (±7.2), and -0.09% (±9.1) by age group, respectively]. In males, measured and predicted RMR was underestimated in <65 years, 65-69 years, and 70-74 years by 8.0% (±9.7), 3.8% (±7.9), 5.3% (±8.2), respectively and overestimated by 1.5% (±5.3) in ±75 years.  The measured RMR to RMR predicted differed significantly in the age groups of <65 y, 65-69 y and 70-74 y in males.


Fat-free mass was the strongest predictor of RMR explaining 72% of the total variance in RMR. Fat mass, WHR, and age proved to be significant predictors of RMR whereas sex had no influence on RMR in the regression model.


The parameters of the physiological equation explained 76% of the variability in RMR.


Stepwise regression analysis resulted in the following equation:

[RMR [kJ/d]=3169+50.0 x body weight (kg) -15.3 x age(y)+746 x sex (female=0, male=1.]


In practice, RMR is best calculated with an equation including body weight, sex, and age. These variables accounted for 74% of the variance in RMR and predicted RMR within ±486kJ/d (116.2 kcal/d) (SEE).


Author Conclusion:

As stated by the author in body of report:

  • “In our study, this is the largest group of older subjects investigated concerning RMR. Results show that significant difference between our measured and predicted RMR occur in men but not in women.”
  • “Whereas the difference in the total male group was only moderate on average (5.3%, the differences were in some cases more pronounced when the various age or BMI groups were considered separately. This concurs with two earlier studies (Arciero 1993a, 1993b).”
  • “The HB equation was based on RMR data of only 6 women and 3 men who were over 60”
  • “Most of the variance (72%) in RMR could be attributed to fat-free mass in our investigation; Fat mass and body fat distribution were also significant determinants, explaining 2% of variability. RMR increased with increasing abdominal fat mass independent of body composition, indicating that abdominal fat mass has a higher RMR compared with fat mass located in the gluteal-femoral region.”
  • “In the physiological regression model, age proved to be an additional significant predictor of RMR... [meaning] there is a decrease in RMR with aging that is not explained by changes in body composition, implying that the metabolic rate of various tissues declines in the course of aging.. The physiological model, including body composition, fat distribution, and age, accounts for 76% of the variance in RMR and predicts RMR within 466kJ/d.”
  • “Body weight is strongly correlated with the metabolic active fat-free mass (females: R=0.88, P<0.001; males R=0.85, P<0.001)."
  • “In the physiological model, all variables can account for 74% of the variance in RMR and predict RMR within ±486 kJ/d in the elderly.”
  • “Our results clearly show that, even within the elderly age group, age still has an influence on RMR. Therefore, age has to be considered when calculating RMR in the elderly."
  • “One limitation of our study is that it did not include volunteers who were older than 85 years of age and is specific to German subjects because ethnic differences in RMR exist. Therefore, our equation should not be generalized to subjects of older age groups or other ethnic backgrounds.”
Funding Source:
Reviewer Comments:


  • “Large sample size.”
  • “Measured and analyzed pertinent factors that affect accuracy of IC (e.g., BMI, smoking, and age)”
  • “Selected an important ethnic group within a healthy population."



  • “As reported by researchers, limited generalizability to North American older adults and other ethnic groups.”
  • “Study biases include not representing elderly over 85+ years."
  • "An intervening variable not measured the amount of time to allow for “elderly to acclimatize to the testing conditions.”
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? N/A
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? N/A
  1.3. Were the target population and setting specified? N/A
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? N/A
  2.2. Were criteria applied equally to all study groups? N/A
  2.3. Were health, demographics, and other characteristics of subjects described? N/A
  2.4. Were the subjects/patients a representative sample of the relevant population? N/A
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? 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? Yes
  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%.) N/A
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? N/A
  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? Yes
  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? N/A
  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? No
  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? N/A
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? N/A
  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? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? N/A
  7.2. Were nutrition measures appropriate to question and outcomes of concern? N/A
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? N/A
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? N/A
  7.5. Was the measurement of effect at an appropriate level of precision? N/A
  7.6. Were other factors accounted for (measured) that could affect outcomes? N/A
  7.7. Were the measurements conducted consistently across groups? N/A
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? N/A
  8.2. Were correct statistical tests used and assumptions of test not violated? N/A
  8.3. Were statistics reported with levels of significance and/or confidence intervals? N/A
  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)? N/A
  8.6. Was clinical significance as well as statistical significance reported? N/A
  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? N/A
  9.2. Are biases and study limitations identified and discussed? N/A
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? N/A
  10.2. Was the study free from apparent conflict of interest? N/A