Validation of resting metabolic rate equations in obese and non-obese young healthy adults

Validation of predictive equations for resting energy expenditure in children and adolescents with different body mass indexes

BACKGROUND

Accurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents.

METHODS

275 participants (148 boys, 127 girls) aged 6-18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5-19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters.

RESULTS

When the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle's equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim's Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle's Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups.

CONCLUSION

Indirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle's equation appear to be most accurate equations for normal and overweight children and adolescents.

Cross-Validation of a New General Population Resting Metabolic Rate Prediction Equation Based on Body Composition

Current prediction equations for resting metabolic rate (RMR) were validated in a relatively small sample with high-individual variance. This study determined the accuracy of five common RMR equations and proposed a novel prediction equation, including body composition. A total of 3001 participants (41 ± 13 years; BMI 28.5 ± 5.5 kg/m²; 48% males) from nutrition clinics in Israel were measured by indirect calorimetry to assess RMR. Dual-energy X-ray absorptiometry were used to evaluate fat mass (FM) and free-fat mass (FFM). Accuracy and mean bias were compared between the measured RMR and the prediction equations. A random training set (75%, n = 2251) and a validation set (25%, n = 750) were used to develop a new prediction model. All the prediction equations underestimated RMR. The Cunningham equation obtained the largest mean deviation [-16.6%; 95% level of agreement (LOA) 1.9, -35.1], followed by the Owen (-15.4%; 95% LOA 4.2, -22.6), Mifflin-St. Jeor (-12.6; 95% LOA 5.8, -26.5), Harris-Benedict (-8.2; 95% LOA 11.1, -27.7), and the WHO/FAO/UAU (-2.1; 95% LOA 22.3, -26.5) equations. Our new proposed model includes sex, age, FM, and FFM and successfully predicted 73.5% of the explained variation, with a bias of 0.7% (95% LOA -18.6, 19.7). This study demonstrates a large discrepancy between the common prediction equations and measured RMR and suggests a new accurate equation that includes both FM and FFM.

Validation of Resting Energy Expenditure Equations in Older Adults with Obesity

It is unclear which energy expenditure prediction equation should guide weight loss interventions in older adults with obesity. We ascertained the validity of four equations commonly used in practice in a series of weight loss studies of adults aged ≥65 with a body mass index ≥30kg/m² using indirect calorimetry data. Diagnostic accuracy was defined as <10% discrepancy between predicted and measured resting metabolic rate (RMR). Mean was 73.4 years. RMR using the ReeVue was 1,643 kCal. With 59.0% accuracy, the WHO equation demonstrated the highest accuracy while the Harris-Benedict yielded 53.5% accuracy. The Owens equation demonstrated the least variability (21.5% overprediction, 27.8% underprediction) with 50.7% accuracy. A SECA bioimpedance analyzer noted the second lowest accuracy of 49.6%. Only 43.1% of measurements were within 10% of the gold-standard indirect calorimetry value using the Mifflin equation. All equations demonstrated <60% accuracy suggesting a great need for estimating energy needs.

Metabolic and Psychological Features are Associated with Weight Loss 12 Months After Sleeve Gastrectomy

CONTEXT

Laparoscopic sleeve gastrectomy (LSG) is a recognized effective procedure of bariatric surgery, but a poor response in weight loss may still represent a clinical problem. To date there are no validated predictors useful to better perform patient selection.

OBJECTIVE

To establish the association of baseline anthropometric, metabolic, and psychologic features with the percent total weight loss (%TWL) and percent excess weight loss (%EWL) 12 months after surgery.

DESIGN

Retrospective longitudinal analysis of a set of data about obese patients attending the outpatient service of a single obesity center from June 2016 to June 2019.

PATIENTS

A total of 106 obese patients underwent LSG with presurgery evaluation and follow-up at 12 months after surgery.

MAIN OUTCOME

Weight loss 12 months after LSG.

RESULTS

Patients who achieved a %TWL higher than the observed median (≥34%) were younger, with a lower fasting plasma glucose and glycated hemoglobin, with a lower prevalence of hypertension and with a lower score in the impulsiveness scale, compared with patients with a %TWL < 34%. Similar findings were found when %EWL was considered. Multivariable stepwise regression analysis showed that younger age, lower impulsiveness, higher-than-normal urinary free cortisol, and lower HbA1c were associated with higher %TWL, explaining about 31.5% of the weight loss.

CONCLUSION

Metabolic and psychologic features at baseline were independently associated with weight loss and explained a non-negligible effect on the response to LSG. These data suggest that careful metabolic and psychologic profiling could help in sharper indications and personalized pre- and postsurgical follow-up protocols in candidates for LSG.

Caloric restriction, resting metabolic rate and cognitive performance in Non-obese adults: A post-hoc analysis from CALERIE study

Physical activity (PA) has been proposed as a determinant of cognitive function and is one component of energy balance (EB). EB is the difference between energy intake (EI) and the total daily energy expenditure (TDEE). TDEE is a combination of resting metabolic rate (RMR), thermic effect of food and PA. The potential role of each of these components on cognitive function has not yet been systemically investigated. We aim to evaluate the association between each component of EB on cognition, using baseline and longitudinal data from a clinical trial of caloric restriction (CR). This is a parallel-group, randomized clinical trial comparing two years of 25% CR with two years of ad libitum diet (AL), with 220 healthy volunteers of both sex, aged between 21 and 50 years and initial BMI ≥ 22 kg/m2 and <28 kg/m2. Body weight, fat mass (FM), fat-free mass (FFM), and bone mineral content were evaluated, as well as RMR, TDEE, cognitive performance and baseline energy intake. A 30 min/day of a moderate level on a minimum of 5 days/week was advised as PA measure. Longitudinal analysis demonstrated that the influence of CR in the improvement of cognitive performance was moderated by changes in RMR, suggesting that in individuals submitted to CR, the cognitive performance and the RMR improved proportionally, independently of changes in EI and body mass. EB and homeostasis are crucial to modulate the RMR. Moreover, RMR presents an important influence on cognitive function in individuals submitted to CR in a long term.

Development of new predictive equations for basal metabolic rate in Iranian healthy adults: negligible effect of sex

Some studies have reported inaccuracy of predicting basal metabolic rate (BMR) by using common equations for Asian people. Thus, this study was undertaken to develop new predictive equations for the Iranian community and also to compare their accuracy with the commonly used formulas. Anthropometric measures and thyroid function were evaluated for 267 healthy subjects (18-60 y). Indirect calorimetry (InCal) was performed only for those participants with normal thyroid function tests (n = 252). Comparison of predicted RMR (both kcal/d and kcal.kg.wt^-1.d^-1) using current predictive formulas and measured RMR revealed that Harris-Benedict and FAO/WHO/UNU significantly over-estimated and Mifflin-St. Jeor significantly under-estimated RMR as compared to InCal measurements. In stepwise regression analysis for developing new equations, the highest r² (=0.89) was from a model comprising sex, height and weight. However, further analyses revealed that unlike the subjects under 30 y, the association between age and the measured RMR in subjects 30 y and plus was negative (r = -0.241, p = 0.001). As a result, two separate equations were developed for these two age groups. Over 80 percent of variations were covered by the new equations. In conclusion, there were statistical significant under- and over-estimation of RMR using common predictive equations in our subjects. Using the new equations, the accuracy of the calculated RMR increased remarkably.

Resting Energy Expenditure in Obese Women with Primary Hypothyroidism and Appropriate Levothyroxine Replacement Therapy

CONTEXT

Growing evidence suggests that appropriate levothyroxine (LT4) replacement therapy may not correct the full set of metabolic defects afflicting individuals with hypothyroidism.

OBJECTIVE

To assess whether obese subjects with primary hypothyroidism are characterized by alterations of the resting energy expenditure (REE).

DESIGN

Retrospective analysis of a set of data about obese women attending the outpatients service of a single obesity center from January 2013 to July 2019.

PATIENTS

A total of 649 nondiabetic women with body mass index (BMI) > 30 kg/m2 and thyrotropin (TSH) level 0.4-4.0 mU/L were segregated into 2 groups: patients with primary hypothyroidism taking LT4 therapy (n = 85) and patients with normal thyroid function (n = 564).

MAIN OUTCOMES

REE and body composition assessed using indirect calorimetry and bioimpedance.

RESULTS

REE was reduced in women with hypothyroidism in LT4 therapy when compared with controls (28.59 ± 3.26 vs 29.91 ± 3.59 kcal/kg fat-free mass (FFM)/day), including when adjusted for age, BMI, body composition, and level of physical activity (P = 0.008). This metabolic difference was attenuated only when adjustment for homeostatic model assessment of insulin resistance (HOMA-IR) was performed.

CONCLUSIONS

This study demonstrated that obese hypothyroid women in LT4 therapy, with normal serum TSH level compared with euthyroid controls, are characterized by reduced REE, in line with the hypothesis that standard LT4 replacement therapy may not fully correct metabolic alterations related to hypothyroidism. We are not able to exclude that this feature may be influenced by the modulation of insulin sensitivity at the liver site, induced by LT4 oral administration.

Resting Whole Body Energy Metabolism in Class 3 Obesity; from Preserved Insulin Sensitivity to Overt Type 2 Diabetes

CONTEXT

Insulin resistance and diabetes may influence separately or in combination whole body energy metabolism.

OBJECTIVE

To assess the impact of insulin resistance and/or overt type 2 diabetes on resting energy expenditure (REE) in class 3 obese individuals.

DESIGN AND SETTING

Retrospective, cross-sectional analysis of a set of data about individuals attending the outpatients service of a single center of bariatric surgery between January 2015 and December 2017.

PATIENTS

We screened 382 patients in which abnormal thyroid function was excluded, and segregated them in three groups of subjects: patients with type 2 diabetes (T2DM; n=70), non-diabetic insulin-resistant patients with HOMA-IR ≥ 3 (n=236), non-diabetic insulin-sensitive patients with HOMA-IR < 3 (n=75).

MAIN OUTCOME MEASURE

Resting energy expenditure (REE), body composition and insulin resistance assessed using indirect calorimetry, bioimpedance and HOMA-IR.

RESULTS

Non-diabetic insulin-sensitive patients resulted to be younger, with lower BMI and higher prevalence of female subjects; meanwhile, non-diabetic but insulin-resistant patients and T2DM patients were not different in terms of anthropometric parameters. REE was higher in T2DM than in non-diabetic insulin-resistant and insulin-sensitive individuals when expressed as percent of the predicted REE (based on Harris Benedict equation) (p<0.0001) or when adjusted for kg of free fat mass (p<0.0001) and was found to be higher also in insulin-resistant vs insulin-sensitive patients (p<0.001). The respiratory quotient was different between groups (0.87±0.11, 0.86±0.12 and 0.91±0.14 in T2DM, insulin-resistant and insulin-sensitive patients, respectively; p<0.03). Regression analysis confirmed that HOMA-IR was independently associated with the REE (R²=0.110, p<0.001).

CONCLUSION

Class 3 obese patients with normal insulin sensitivity are characterized by reduced fasting REE in comparison to insulin-resistant obese patients and obese patients with short duration of diabetes supporting the hypothesis that down-regulation of nutrients' oxidative disposal may represent an adaptation of energy metabolism in obese individuals with preserved insulin sensitivity.

Evaluating human basal metabolism: the erroneous and misleading use of so-called "prediction equations"

Prediction (regression) equations are widely used, but their reliability as predictive tools is questionable as they provide contradicting results. The key point is that values calculated by regression equations are not precisely defined numbers but lie within a range of possible values in the standard deviation interval, none of which can be considered as the most probable. Ignoring this point leads to illicit/improper calculations, generating wrong results, which may have adverse consequences for human health. To demonstrate this, we applied the equations of Harris and Benedict in a reverse method, i.e. calculating (predicting) the daily energy expenditure in the same subjects used to obtain the equations and comparing values with the original measured data. We used the Bland-Altman and frequency distribution analyses. We found large differences in both individual data and population characteristics, showing that prediction equations are not predictive tools.