Healthy NonObese Adults (20102012)
Scalfi L, Coltorti A, Sapio C, DiBiase G, Borrelli R, Contaldo F. Predicted and measured resting energy expenditure in healthy young women. Clin Nutr. 1993; 12: 17.
PubMed ID: 16843268To crossvalidate several equations developed to predict BEE in a sample of young women with very different body sizes.
 Understand and give written consent
 No clinical signs or history of diabetes mellitus or endocrine disorders
 Pharmacological or hormonal therapy
 Nonsmokers or smoked less than five cigarettes a day
 Not under any physical training.
None specified.
Recruitment
Subjects were recruited from the staff members and the students of the Second Medical School of the University of Naples; specific procedures were not specified.
Design
Crosssectional study.
Statistical Analysis
 Twoway analysis of variance (ANOVA) and Tukey's test for pairwise comparisons were used to evaluate differences between measured basal energy expenditure (mBEE) and predicted basal energy expenditure (pBEE)
 Agreement between mBEE and pBEE evaluated using procedures suggested by Bland and Altman
 PMdiff = predicted minus measured
 95% limits of agreement: Values within which 95% of the PMdiff lay; calculated as mean±SD
 %MP = 100 x (mBEE/pBEE)
 PMdiff and %MP were related to the corresponding mean estimate of BEE calculated as PMmean = (mBEE +pBEE) divided by two
 Linear regression analysis: Slope significantly different from zero indicated that PMdiff and %MP varied according to the magnitude of BEE, with a proportional disagreement between pBEE and mBEE
 Linear regression analysis and simple correlation coefficient used to evaluate association of PMdiff and pMP% with age and anthropometric variables.
Timing of Measurements
One measurement.
Dependent variables
Agreement between measured and predicted values:
 PMdiff equal predicted BEE minus measured BEE
 95% limits of agreement: Values within which 95% of the PMdiff lay
 %MP = 100 x (mBEE/pBEE).
Independent Variables
 Measured BMR [(VO_{2}, L per minute), CO_{2} (L per minute; ml per kg per minute), RQ:
 IC type: Open circuit canopy system and calculated according to indirect calorimetry from oxygen consumption and carbon dioxide production, neglecting protein oxidation
 Rest before measure: 30minute rest; measurement began from 8:30 a.m. to 9:00 a.m. onward with subjects lying supine on the bed
 Measurement length: 75 minutes; the last 60 minutes were used for calculations
 Steady state: Likely
 Fasting length: 12hour fast
 Exercise conditioning 24 prior to test? “No physical training”
 Room temp: 24° to 26° C
 Number of measures were they repeated? None reported
 Coefficient of variation? Yes, the betweenperiod CV was always below 3%. No detectable timerelated trend in any subject when the linear regression analysis between energy expenditure and time was considered (P>0.25).
 Equipment of Calibration: Yes, with burning ethanol or butane in the canopy at a known rate with oxygen and carbon dioxide analyzers calibrated using 100% N, a mixture of 20% O_{2} and 0.80 CO_{2} in N and outside atmospheric air
 Training of measurer? None reported
 Subject training of measuring process? None reported
 Monitored heart rate? No
 Body temperature? No
 Predicted BMR using weightonly formulas of Owen et al, Schofield et al and FAO/WHO/UNU
 Predicted BMR based on weight and height: Mifflin et al, HARRIS, KLEIBER, SCHOF2, and FAO/WHO/UNU 2.
Control Variables
 Age
 Height
 Weight
 Body mass index: kg/m^{2}
 Two study groups defined:
 Lean and overweight (NWt): BMI 19kg/m^{2} to 29kg/m^{2}
 Obese (Ob): BMI 30kg/m^{2} or higher.
 Two study groups defined:
 Final N: N=104 women
 Age: 18 to 32 years
 Ethnicity: Not specified, although study was done in Italy.
Anthropometrics
 Weight: 45.3 to 105.0
 Height: 149cm to 178cm
 BMI: 18.6kg/m^{2} to 41.6kg/m^{2}.
Mean ± SD  
Lean and Overweight N=74  
Age, year  23.3±4.0 
Weight, kg  59.9±10 
Height, cm  160.4±5.6 
BMI, kg/m^{2}  21.6±2.7 
Very Obese N=30  
Age, year  22.3±3.9 
Weight, kg  88.6±18.7 
Height, cm  160.4±5.6 
BMI, kg/m^{2}  33.7±3.3 
Location
Italy.
 Measured BEE:
 NWt: 5,252kJ±690kJ per day (1,255kcal±165kcal per day)
 Ob: 6,777kJ±603kJ per day (1,620kcal±144kcal per day)
 Variability for the entire sample: 3,658kJ per day to 7,946kJ per day (874kcal per day to 1,899kcal per day).
 Predicted BEE mean:
 NWt: 5,127±302 (1,226kcal±72kcal per day ) (Owen equation) to 6,018kJ±611kJ per day (1,438±146) (Kleiber equation)
 Ob group: 5,991±263(1,431kcal±63kcal per day) (Owen equation), 7,458kJ±521kJ per day (1,782±124) (Kleiber equation)
 Fasting RQ was similar in the NWT group (0.831±0.033) and in the Ob group (0.829±0.049).
Mean PMdiff with the Corresponding 95% Limits of Agreement for the Different Predictive Equations in 74 Lean and Overweight Young Women
Mean kJ per Day (Kcal per Day)  95% Limits of Agreement (Kcal per Day)  
Equations Based on Weight  
OWEN  128 (32)  1,126 to 838 (269 to 200) 
SCOF1  520  468 to 1,532 
FAO1  472 (113)  457 to 1,429 (109 to 341) 
Equations Based on Weight and Height  
MIFFLIN  276 (66)  642 to 1,198 (152 to 286) 
HARRIS  658 (155)  262 to 1,570 (62 to 375) 
KLEIBER  740  188 to 1,688 
SCHOF2  438  515 to 1,417 
FAO2  465 (111)  709 to 1,560 (170 to 373) 
 The values within which 95% prediction errors occur for Owen is 469.4kcals per day, FAO1 is 450kcals per day, Mifflin St. Jeor is 439.8kcals per day, HB is 437.8kcals per day and FAO2 is 542.3kcals per day
 In NWt women percent MP (Measured BEE expressed as a percentage of the predicted value) was 100% for the Owen equation and around 90% for most other formulas. A significant relationship (P<0.08) between MPdiff and PMdiff was apparent fro all equations in the NWt group and for Owen equation and the HB equation in the obese group.
The Relationship (Correlation Coefficient and Slope of PMdiff vs. PMmean) for the Different Predictive Equations in 74 Lean and Overweight Young Women
(To determine kcal per day, multiply by 0.239)
Equation  PMdiff R  vs. PM Mean Slope 
OWEN  0.82*  0.867 
SCHOF1  0.08  0.061 
FAO1  0.17  0.124 
MIFFLIN  0.41*  0.324 
HARRIS  0.58*  0.492 
KLEIBER  0.19  0.138 
SCHOF2  0.21^{@}  0.151 
FAO2  0.24^{@}  0.191 
* P<0.001.
^{@} P<0.10.
Mean PMdiff with the Corresponding 95% Limits of Agreement for the Different Predictive Equations in 30 Young Obese Women
Equation  Mean (kJ per Day)  95% Limits of Agreement 
OWEN  786  1,951 to 380 
SCHOF1  831  157 to 2,119 
FAO1  831  457 to 2,119 
MIFFLIN  41  1,169 to 1,251 
HARRIS  333  851 to 1,517 
KLEIBER  861  558 to 1,919 
SCHOF2  607  717 to 1,930 
FAO2  468  1,003 to 1,939 
95% of the prediction limits were wide and was 1,964kJ per day (469kcal per day) for the Owen equation.
The Relationship (Correlation Coefficient and Slope of PMdiff vs. PMmean) for the Different Predictive Equations in 30 Young Obese Women
Equation  PMdiff R  vs. PM Mean Slope 
OWEN  0.70*  1.122 
SCHOF1  0.10  9,132 
FAO1  0.10  0.132 
MIFFLIN  0.27  0.362 
HARRIS  0.45*  0.652 
KLEIBER  0.16  0.206 
SCHOF2  0.13  0.187 
FAO2  0.13  0.222 
* P<0.001.
^{#} P<0.01.
 Measured BEE expressed as a percentage of the predicted value was above 100% for the Owen equation and around 90% for most of the other formulae in the NWt women
 Mean percent MP was higher in the Ob group than in the NWt group for the equations OWEN, MIFFLIN, HARRIS and KLEIBER with minor differences for other formulae
 A significant (P<0.05) relationship between percent MP and PMdiff was apparent for all the equations in the NWt group and for the OWEN and HARRIS equations in the Ob group
 When PMdiff were considered independently of the positive or negative sign, the mean deviation from measured BEE was significant (P<0.01) and lower for the Owen equation (380kJ±311kJ per day or 91kcal per day) and the Mifflin equation was 423kJ±308kJ per day (or 101kcal±74kcal per day). Other formulae were more than 550kJ per day or 132kcal per day).
Anthropometric
 PMdiff or percent MP was not related to age, independently of the equation chosen
 In NWt women, PMdiff was significantly correlated with height for the OWEN equation (0.263, P<0.05) and KLEIBER equation (0.213, P<0.10)
 Correlation coefficient of predictedmeasured difference varied as a function of weight and BMI for many of the predictive formulae, particularly based on weight alone. The relationships between PMdiff and weight were much stronger in Ob women than NWt women [for FAO1 (R=0.531) and FAO2 (R=0.427) was P<0.005] for all but the OWEN equation.
As stated by the author in body of report:
 In a sample of young women with different body sizes, most predictive formulae overestimate mBEE by at least 430kJ per day (100kcal per day), corresponding to more than 7% of mean BEE. This could lead to a significant bias in estimating BEE and energy requirements in samples or populations.
 For application to the single individual, evaluate the 95% limits of agreement (i.e., the values within which 95% of the Predicted to Measured differences lie). Both study groups had wide 95% limits of agreement (more than 1,700kJ per day (406kcal per day) in NWt women and more than 2,300kJ per day (549.7kcal per day in Ob women) and is of limited usefulness.
 Our results confirm that, in both lean and obese women, the poor accuracy of all predictive formulae is due to the wide variability in the PMdiff rather than to the extent of the mean PMdiff
 When evaluating the association between prediction error (PMdiff) and PMmean, it indicates the occurrence of a proportional bias, making the prediction error dependent on the magnitude of mBEE. Thus, inconsistent conclusions on PMdiff can be reached because of either the differences in body weight and BMI or the specific bias due to the predictive equation chosen
 Most of the predictive formulae tend to define a subject as hypometabolic, while it is much more unlikely to detect correctly a hypermetabolic condition. This is less marked, but no absent, for the OWEN equation and the MIFFLIN equation
 In the NWt group, the MIFFLIN equation yielded the best estimate of BEE in terms of prediction error, percent MP and no relationship between prediction error and body size
 Need to crossvalidate in different samples with respect to age and gender
 The accuracy in the single individual of all the formulae remains poor either because of the very large limits of agreement for the predictedmeasured differences or the relationship between prediction error and body size (observed especially in the Ob group).
University/Hospital:  Federico II University (Italy) 
Strengths
 Statistical analyses evaluated agreement using 95% limits of agreement
 Assigned into two groups: Lean and overweight vs. obese
 Reviewed UK and US developed equations.
Generalizability/Weaknesses
 Questionable validity of indirect calorimeter
 Limited to Italian working class and student population since convenience sampling of Medical School staff and students
 Limited generalizability to US population due to lifestyle and possible SES differences
 Sampling is not discussed in detail
 Measurement reliability is unclear due to no mention of training of measurer. While they label study as BMR, subjects actually had RMR measured as they did not sleep in facility overnight
 Study variables did not address possible biases due to stage in menstrual cycle or control for biases of smoking
 An important intervening variable not measured are women in older age.
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?  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 crosssectional 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?  ???  
4.1.  Were followup methods described and the same for all groups?  ???  
4.2.  Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (crosssectional 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?  ???  
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 crosssectional 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?  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 cointerventions (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 followup 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 doseresponse 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?  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  