Estimation of energy expenditure using prediction equations in overweight and obese adults: a systematic review

Anthropometric Trajectories and Dietary Compliance During a Personalized Ketogenic Program

BACKGROUND/OBJECTIVES

Ketogenic diets (KDs) have gained attention for their potential to promote weight loss and metabolic improvements. However, data on long-term body composition changes and adherence rates in real-world settings remain limited.

OBJECTIVE

This study aimed to assess the effects of a personalized ketogenic dietary program on anthropometric parameters over a 9-month period and to evaluate adherence across time.

METHODS

A total of 491 adults participated in a longitudinal intervention involving a structured ketogenic nutrition plan with follow-up at 3, 6, and 9 months. Body weight, fat mass (FM), skeletal muscle mass (SMM), and other composition metrics were measured at each visit.

RESULTS

Significant reductions in body weight (-12.6 kg) and fat mass (-10.3 kg) were observed after 3 months (p < 0.001), with minimal changes at 6 months and partial regain by Month 9. SMM remained relatively stable throughout the study. Retention dropped substantially after 3 months, dropping from 487 to 115 participants at Month 6 and 41 at Month 9. Despite this, participants who completed the program maintained significant anthropometric improvements.

CONCLUSIONS

A well-formulated ketogenic diet may promote rapid fat loss while preserving lean mass in the short term. However, long-term adherence poses significant challenges. Strategies to enhance dietary sustainability and retention are essential for maximizing the benefits of KDs in clinical practice.

Investigating the impact of body composition on the estimation of resting metabolic rate: new equations for adults aged ≥65 years developed using cross-sectional data

BACKGROUND

Due to changes in body composition during aging, the inclusion of body composition measures as a variable within equations to predict resting metabolic rate (RMR) may improve their predictive accuracy.

OBJECTIVES

This analysis of cross-sectional data aimed to develop and validate new RMR equations for older adults (≥65 y) incorporating variables for body composition, to predict performance and accuracy, and to explore the relative contribution of body composition variables acting directly or potentially via fat-free mass (FFM) to RMR.

METHODS

Analyses were conducted utilizing a unique international dataset of gold standard measures developed for this purpose. RMR was predicted from potential predictive variables using stepwise multiple regression. Predictive performance of the final model was assessed using double cross-validation. The new prediction equation was compared with published prediction equations for similar populations and with previously published RMR prediction equations that did not include FFM. Direct associations between the determined predictor variables and RMR with indirect effects mediated via FFM were examined using mediation final (or pathway) analysis.

RESULTS

The dataset contained 1238 participants. The predictive equations {utilizing either FFM (Equation 1) or lean body weight [LBW](Equation 2)} follow. Equation 1: RMR = 8.645 × height + 23.684 × weight - 29.717 × age + 38.213 × FFM + 209.637 × sex + 2693.223; Equation 2: RMR = -30.570 × age + 80.736 × LBW - 186.825 × sex + 3956.822 where RMR (kJ/d); height (cm); weight (kg); age (y); FFM (kg); LBW (kg); sex (M = 1, F = 0). The equation performed similarly to some anthropometric-based prediction equations. Predictors using FFM performed marginally better than those using LBW. All variables had significant (P < 0.001) direct effects upon RMR and significant (P < 0.001) indirect effects for sex, weight, and height.

CONCLUSIONS

New prediction equations predict RMR at the population level with minimal bias; however, the difference in performance with anthropometry-based equations is minimal. This may be explained by the contribution of FFM to weight, whereby equations that include weight are already accounting for FFM.

Development of New Equation for Predicting State of Normometabolism from Cohort of Hospitalized Patients with Obesity

Background/Objectives: Existing resting energy expenditure (REE) predictive equations, including Mifflin-St Jeor and Harris-Benedict, show limited accuracy, particularly in patients with a BMI over 35, often leading to overestimation or underestimation of REE. This study aimed to develop a new predictive equation specifically designed to identify normometabolic status in patients with obesity, enabling more precise qualitative assessments of basal metabolism through indirect calorimetry. Methods: A cohort of 89 hospitalized patients with obesity (BMI > 30) underwent REE measurement and comprehensive anthropometric assessments. Patients were classified as normometabolic if their REE was within ±10% of the Mifflin-St Jeor prediction or if their fat-free mass-specific REE fell between 23 and 30 kcal/kg. Results: The newly developed equation demonstrated high predictive accuracy (R2 = 0.923, root mean square error = 81.872 kcal/day), with a mean bias of -0.054 kcal/day and narrower limits of agreement (-156.834 to 156.725 kcal/day) compared to widely used models. Conclusions: These advancements could enhance follow-up and management of diet therapy in patients with obesity, allowing for a more tailored approach to their metabolic health over time.

Testing a proposed mathematical model of weight loss in women enrolled on a commercial weight-loss programme: the LighterLife study

Weight loss results in obligatory reductions in energy expenditure (EE) due to loss of metabolically active fat-free mass (FFM). This is accompanied by adaptive reductions (i.e. adaptive thermogenesis) designed to restore energy balance while in an energy crisis. While the '3500-kcal rule' is used to advise weight loss in clinical practice, the assumption that EE remains constant during energy restriction results in a large overestimation of weight loss. Thus, this work proposes a novel method of weight-loss prediction to more accurately account for the dynamic trajectory of EE. A mathematical model of weight loss was developed using ordinary differential equations relying on simple self-reported inputs of weight and energy intake to predict weight loss over a specified time. The model subdivides total daily EE into resting EE, physical activity EE, and diet-induced thermogenesis, modelling obligatory and adaptive changes in each compartment independently. The proposed model was tested and refined using commercial weight-loss data from participants enrolled on a very low-energy total-diet replacement programme (LighterLife UK, Essex). Mathematical modelling predicted post-intervention weight loss within 0.75% (1.07 kg) of that observed in females with overweight or obesity. Short-term weight loss was consistently underestimated, likely due to considerable FFM reductions reported on the onset of weight loss. The best model agreement was observed from 6 to 9 weeks where the predicted end-weight was within 0.35 kg of that observed. The proposed mathematical model simulated rapid weight loss with reasonable accuracy. Incorporated terms for energy partitioning and adaptive thermogenesis allow us to easily account for dynamic changes in EE, supporting the potential use of such a model in clinical practice.

Resting Energy Expenditure in Patients with Extreme Obesity: Comparison of the Harris-Benedict Equation with Indirect Calorimetry

Background: The main objective of the work was the analysis and description of data on body composition and resting energy expenditure (REE) values of selected groups of patients with obesity whose REE measurement results using indirect calorimetry reached a level below 95% of the predicted REE calculated using the Harris-Benedict (H-B) equation. The sub-goals were to describe the dependence of body composition on the size of the REE and to find out if the deviations between the number of the total measured REE and the REE calculated using H-B in the adapted group (patients with altered REE values, lower than expected caused by long caloric restriction) are significant. Methods: For the research, 71 (39 women and 32 men) patients treated in obesitology were selected. Patients underwent the measurement of resting metabolism using indirect calorimetry (IC) and body composition measurement on the bioimpedance device and, at the same time, the value of resting metabolism was calculated for everyone using the H-B equation. The whole group was divided into five groups according to the deviation of the measurement using IC and the calculation of the H-B equation. Results: In the total set of examined individuals, there were 32.4% with a reduced REE value compared to the REE calculation according to the H-B equation, which corresponds to 23 individuals. In the adapted group, the average measured REE was 2242 ± 616 kcal compared to the H-B calculation of 2638 ± 713 kcal. Statistically, these results were not significant, but a high case-to-case variation was found. The highest deviation from the H-B predictive calculation was -42% and +43% in the whole research group. The amount of muscle tissue in the adapted group averaged 44.3 ± 11.9 kg and the amount of fat-free mass (FFM) 77.9 ± 20.1 kg. When statistically testing the dependence of REE on FFM and muscle tissue in the adapted group, a strong correlation was found. Conclusions: The H-B equation alone is not suitable for setting a suitable diet therapy for an individual with obesity. In order to select and characterize a group of adapted individuals, it will be necessary to use other methods or a larger research sample, and preferably examine and divide patients with specific comorbidities or include their health status.

Comparative analysis of basal metabolic rate measurement methods in overweight and obese individuals: A retrospective study

The global prevalence of overweight and obesity is on the rise, presenting significant health challenges worldwide. Obesity is associated with various chronic diseases and imposes substantial economic burdens on society. Accurate assessment of basal metabolic rate (BMR) is crucial for effective weight management strategies. This retrospective study, conducted at Baskent University Hospital between October 2019 and October 2023, analyzed data from 133 overweight and obese individuals. Various methods including indirect calorimetry (IC), predictive equations (Harris-Benedict and Mifflin-St Jeor), and bioelectrical impedance analysis (BIA) were used to estimate BMR. Additionally, demographic, clinical, and body composition data were recorded. The mean BMR measured using IC was 1581 ± 322 kcal/day, which was significantly lower than the BMR estimated by other methods such as BIA (1765.8 ± 344.09 kcal/day), Harris-Benedict (1787.64 ± 341.4 kcal/day), and Mifflin-St Jeor equations (1690.08 ± 296.36 kcal/day) (P < .001). Among the predictive equations, the Mifflin-St Jeor method provided BMR estimates closest to the gold standard IC. When BMR measurement methods were compared to IC, 36.8% of measurements with the Harris-Benedict equation method, 50.4% with the Mifflin-St Jeor equation method, and 36.1% with the BIA method were within ± 10% agreement with IC measurements. Significant correlations were found between BMR and body composition parameters such as fat-free mass, muscle mass, and fat mass (R = 0.681, P < .001; R = 0.699, P < .001; R = 0.595, P < .001, respectively). Regression analysis identified that variables such as weight, height, body mass index, and muscle mass significantly predicted BMR measured by IC, accounting for 69.1% of the variance. This study underscores the challenges in assessing BMR in overweight and obese individuals. While IC remains the gold standard, predictive equations and BIA offer alternative methods. The Mifflin-St Jeor equation emerged as a practical option, closely aligning with IC results. However, discrepancies between methods and the influence of body composition highlight the importance of individualized approaches to BMR assessment in weight management strategies.

Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis

The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.

Measured Resting Energy Expenditure by Indirect Calorimetry and Energy Intake in Long-Term Growth Hormone-Treated Children with PWS

INTRODUCTION

Severe obesity can develop in children with PWS when food intake is not controlled. Maintenance of body weight requires an energy balance, of which energy intake and energy expenditure are important components. Previous studies described a decreased resting energy expenditure (REE) in growth hormone (GH)-untreated children with PWS. In short-term studies, no difference in REE was found between GH-treated and untreated children with PWS. However, there are limited data on REE in children with PWS who were GH-treated for a long period.

METHODS

This study describes measured REE (mREE), energy intake, and body composition during long-term GH treatment in children with PWS. Patients were treated with 1.0 mg GH/m2/day (∼0.035 mg/kg/day). REE was determined by indirect calorimetry; dietary energy intake was calculated using a 3-day dietary record. Body composition by dual-energy X-ray absorptiometry (DXA) scans.

RESULTS

We included 52 GH-treated children with PWS with a mean (SD) age of 8.53 (4.35) years and a median (IQR) GH-treatment duration of 7 (4-11) years. mREE increased with age, but was not associated with GH-treatment duration. A higher LBM was associated with higher mREE. Mean energy intake was significantly lower compared to daily energy requirements (DER) for age- and sex-matched healthy children (p < 0.001), ranging from 23 to 36% less intake in children aged 3.5-12 years to 49% less intake in children aged 12-18 years. Fifty percent of children had a normal REE, 17.3% a decreased REE, and 32.7% an elevated REE, according to predicted REE based on measured REE in a large group of healthy children.

CONCLUSION

In children with PWS, mREE increases with age. GH-treatment duration is not associated, whereas LBM is an important determinant of mREE. Children with PWS have a low to very low energy intake compared to DER for age- and sex-matched children, with a declining intake when becoming older.

INTRODUCTION

Severe obesity can develop in children with PWS when food intake is not controlled. Maintenance of body weight requires an energy balance, of which energy intake and energy expenditure are important components. Previous studies described a decreased resting energy expenditure (REE) in growth hormone (GH)-untreated children with PWS. In short-term studies, no difference in REE was found between GH-treated and untreated children with PWS. However, there are limited data on REE in children with PWS who were GH-treated for a long period.

METHODS

This study describes measured REE (mREE), energy intake, and body composition during long-term GH treatment in children with PWS. Patients were treated with 1.0 mg GH/m2/day (∼0.035 mg/kg/day). REE was determined by indirect calorimetry; dietary energy intake was calculated using a 3-day dietary record. Body composition by dual-energy X-ray absorptiometry (DXA) scans.

RESULTS

We included 52 GH-treated children with PWS with a mean (SD) age of 8.53 (4.35) years and a median (IQR) GH-treatment duration of 7 (4-11) years. mREE increased with age, but was not associated with GH-treatment duration. A higher LBM was associated with higher mREE. Mean energy intake was significantly lower compared to daily energy requirements (DER) for age- and sex-matched healthy children (p < 0.001), ranging from 23 to 36% less intake in children aged 3.5-12 years to 49% less intake in children aged 12-18 years. Fifty percent of children had a normal REE, 17.3% a decreased REE, and 32.7% an elevated REE, according to predicted REE based on measured REE in a large group of healthy children.

CONCLUSION

In children with PWS, mREE increases with age. GH-treatment duration is not associated, whereas LBM is an important determinant of mREE. Children with PWS have a low to very low energy intake compared to DER for age- and sex-matched children, with a declining intake when becoming older.

Basal metabolic rate using indirect calorimetry among individuals living with overweight or obesity: The accuracy of predictive equations for basal metabolic rate

BACKGROUND & AIMS

Weight reduction programs in people with overweight or obesity can be informed by indirect calorimetry (IC) which is the gold standard to measure basal metabolic rate (BMR). Since IC is labor intensive and expensive, predictive equations are often used as an alternative. In this study the accuracy rate was assessed and bias statistics of predictive equations were compared to IC among subjects with overweight or obesity. Secondly, differences in clinical features between individuals with over-, accurate or underestimation of their BMR were evaluated.

METHODS

This cross sectional study included 731 subjects from the outpatient obesity clinic of the Antwerp University Hospital, Belgium. Fourteen equations were evaluated. Overestimation and underestimation was defined as >10 % and <10 % of measured BMR.

RESULTS

In the total population, mean age was 43 ± 13 years, mean BMI 35.6 ± 5.8 kg/m2 and 79.5 % were female. The highest accuracy rates were reached by the Henry (73 %), Ravussin (73 %) and Mifflin St. Jeor (73 %) equations. In the total population, the Mifflin St. Jeor and Henry equation were unbiased. The Akern, Livingston and Ravussin equations were biased to underestimation. All other equations were biased to overestimation. Subjects with an underestimation of BMR had significantly higher waist-hip ratio (1.02 ± 0.13 vs 0.91 ± 0.11; P < 0.001), higher visceral adipose tissue (239 ± 96 vs 162 ± 93; P < 0.001), lower fat free mass (kg) (67.6 (45.4-95.9) vs 54.0 (39.6-95.5); P < 0.001) and a higher prevalence of the Metabolic Syndrome (24 (77.4) vs 112 (37.5); P < 0.001). Individuals with an overestimation of BMR had significantly higher subcutaneous adipose tissue (545 ± 149 vs 612 ± 149; P < 0.05), lower fasting plasma insulin (81 (10-2019) vs 67 (27-253); P < 0.001) and lower 2-h plasma glucose (132 (30-430) vs 116 (43-193); P < 0.001) during OGTT.

CONCLUSIONS

In this study, the Henry and Mifflin St. Jeor equations have the highest accuracy and lowest bias to estimate the basal metabolic rate in a Caucasian, predominantly female, population living with overweight or obesity. Visceral and subcutaneous adipose tissue and presence of metabolic syndrome were significantly different in individuals with over- or underestimation of BMR.

Estimates of Resting Energy Expenditure and Total Energy Expenditure Using Predictive Equations for Individuals After Bariatric Surgery: a Systematic Review with Meta-analysis

PURPOSE

Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS.

METHODS

MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist.

RESULTS

Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference =  - 39.71 kcal [95%CI =  - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference =  - 54.60 kcal [95%CI =  - 87.92; - 21.28]).

CONCLUSION

The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis

BACKGROUND & AIMS

To determine which resting energy expenditure (REE) predictive equation has the lowest bias in the aggregate level in individuals with excess weight during weight loss interventions.

METHODS

Searches were performed in MEDLINE, Web of Science, Scopus, CENTRAL and gray literature databases. Longitudinal studies on weight loss interventions which evaluated REE by predictive equations compared to that measured by indirect calorimetry in adults with excess weight at different follow-up times were included. Meta-analyses were performed with the differences between biases of predictive equations of the REE at the different follow-up times of weight loss.

RESULTS

Of the total of 2178 occurrences found in the databases, only eight studies were included. The Harris-Benedict (1919) equation showed the smallest differences between bias up to the third month (MD = 103.33 kcal; 95%CI = -39.01; 245.67), in the sixth month (MD = 59.16 kcal; 95%CI = 8.74; 109.57) and at the 12th month (MD = -71.41 kcal; 95%CI = -150.38; 7.55) of weight loss follow-up. Weight loss does not seem to have an effect on bias at different follow-up times.

CONCLUSION

Harris-Benedict (1919) equation seems to be the most adequate to assess the REE of individuals with excess weight during weight loss. However, the finding of large estimated predictive intervals may indicate that predictive equations may not be handy tools for individuals losing and regaining weight due to changes other than body weight.

Energy - a scoping review for the Nordic Nutrition Recommendations 2023 project

We need energy intake to provide energy and nutrients to our cells. The amount of daily energy intake should aim for energy balance, which results in good health. Under- or overconsumption of total daily energy over a longer period leads to increased risk of diseases. In this scoping review, the components of daily energy requirement are defined. Several methods to estimate energy requirements and the amount of total daily energy intake (kJ) related to health are also discussed. Reference values for energy intake in children, adults and pregnant and postpartum women, and older adults are evaluated. Results show that it is challenging to set reference values for energy intake since existing methods are not accurate and precise, and there are several factors that influence the estimated amount of energy. Energy requirement is increased during growth as in childhood, pregnancy and lactation. We conclude that more research in this area is needed, and that new high-quality studies in both Nordic and Baltic countries are needed to obtain new recommendation numbers for energy intake.

Implication of lipid turnover for the control of energy balance

The ongoing obesity epidemic is a consequence of a progressive energy imbalance. The energy-balance model (EBM) posits that obesity results from an excess in food intake and circulating fuels. A reversal in causality has been proposed recently in the form of the carbohydrate-insulin model (CIM), according to which fat storage drives energy imbalance. Under the CIM, dietary carbohydrates shift energy use in favour of storage in adipose tissue. The dynamics of lipid storage and mobilization could, therefore, be sensitive to changes in carbohydrate intake and represent a measurable component of the CIM. To characterize potential changes in lipid dynamics induced by carbohydrates, mathematical models were used. Here, we propose a coherent mathematical implementation of the CIM-energy deposition model (CIM-EDM), which includes lipid turnover dynamics. Using lipid turnover data previously obtained by radiocarbon dating, we build two cohorts of virtual patients and simulate lipid dynamics during ageing and weight loss. We identify clinically testable lipid dynamic parameters that discriminate between the CIM-EDM and an energy in, energy out implementation of the EBM (EBM-IOM). Using a clinically relevant two-month virtual trial, we additionally identify scenarios and propose mechanisms whereby individuals may respond differently to low-carbohydrate diets. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.

Accuracy of equations for estimating resting energy expenditure in children and adolescents living with phenylketonuria

BACKGROUND

Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population.

METHODS

An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations.

RESULTS

Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2  = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942.

CONCLUSION

Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

Measured vs estimated resting energy expenditure in children and adolescents with obesity

Pediatric obesity requires early targeted interventions consisting mainly of a low-calorie diet prescribed based on resting energy expenditure (REE), often estimated through predictive equations. The aim of this study was to define the prevalence of "hypo-", "normo-" and "hypermetabolic" in a large cohort of children and adolescents with obesity by comparing measured and estimated REE and to evaluate the characteristics related to these metabolic statuses in both males and females. The study population was divided into the three subgroups by comparing REE measured using indirect calorimetry and estimated using the Molnar equation, and subsequently analyzed. The majority of the participants (60.6%) were normometabolic, 25.5% hypermetabolic and 13.9% hypometabolic. No significant differences in age, Tanner stage, systolic blood pressure, or the presence of metabolic syndrome were found. However, the hypermetabolic subgroup was significantly lighter, shorter, with lower hip and waist circumferences, had a greater amount of fat-free mass and lower fat mass, significantly lower diastolic blood pressure, and a significantly higher frequency of non-alcoholic liver steatosis. Pediatric obesity is more associated with normal or increased REE than with a hypometabolic condition, suggesting that estimation of energy expenditure with predictive equations is still inadequate for prescribing the appropriate diet plan.

Resting energy expenditure in Korean type 2 diabetes patients: comparison between measured and predicted values

BACKGROUND/OBJECTIVES

Estimation of energy demand using resting energy expenditure (REE) is a reasonable approach for optimizing glycemic control and weight management in patients with type 2 diabetes mellitus (T2DM). This study aimed to compare REE predictions and objective measurements in patients with T2DM in Korea.

SUBJECTS/METHODS

This study enrolled 36 participants with T2DM (age range, 20-60 years). Anthropometric variables including height, weight, waist-hip ratio, blood pressure, body fat, body fat percentage, and total body weight were measured using bioimpedance. REE was evaluated using indirect calorimetry. The measured REE values were compared to values estimated using five predictive equations: the Harris-Benedict, Mifflin, Owen, Food and Agriculture Organization of the United Nations/World Health Organization (FAO/WHO), and Schofield equations. This study evaluated the associations between measured REE values and anthropometric/clinical data, including height, weight, and age, using multivariate linear regression.

RESULTS

The mean measured REE value was 1891.79 ± 288.03 kcal/day (male), 1,502.00 ± 202.96 kcal/day (female). REE estimates generated from the Mifflin equation showed the largest differences from measured REE values, whereas estimates derived from the FAO/WHO equation were the closest to the measured REE values. This study also identified associations between measured REE values and various anthropometric/clinical variables.

CONCLUSION

The accuracy of REE prediction equations is critically important in promoting the efficacy of dietary counseling and the effective treatment of diabetes. Our results indicate the need for additional studies informing more suitable methods for determining the energy requirements of Korean patients with T2DM.

Body composition affects the accuracy of predictive equations to estimate resting energy expenditure in older adults: An exploratory study

BACKGROUND

To investigate the accuracy of ten different predictive equations to estimate resting energy expenditure (REE) in a sample of Brazilian older adults and develop a predictive equation for estimating REE based on body composition data.

METHODS

A cross-sectional study with thirty-eight Brazilian older adults aged 60-84 years, who had their REE measured by indirect calorimetry (IC) and BC assessed by dual-energy x-ray absorptiometry (DXA). REE was compared to the estimation of ten predictive equations, and the differences between BC and anthropometric-based equations were investigated using Bland-Altman plots and Lin's concordance correlation. Accuracy was evaluated considering ±10% of the ratio between estimated and measured REE.

RESULTS

The sample was composed of 57.9% men, with a mean age of 68.1 (5.8) years, and a mean REE by IC of 1528 (451) kcal. The highest accuracy was 47.4% obtained by Luhrmann and Fredrix equations, and the lowest accuracy was 13.2% reached by Weigle equation. In general, the proportion of underestimation was higher than overestimation. All anthropometric-based equations presented a good agreement with REE from IC. For those equations derived from BC, however, three of them reached only a moderate agreement. In terms of accuracy, all equations presented lower than 50% of accurate prediction of REE.

CONCLUSIONS

In this sample of older adults, previous predictive equations to estimate REE did not show good accuracy, and those based on BC presented even worse results, showing that changes in BC related to aging could impact the accuracy of these equations.

Predictive Equations Overestimate Resting Metabolic Rate in Young Chilean Women with Excess Body Fat

Underestimating/overestimating resting metabolic rate (RMR) affects energy prescription. The objective was to compare RMR by indirect calorimetry (RMR IC) and RMR estimated by predictive equations in women with excess body fat. This was an analytical cross-sectional study with 41 women aged 18-28 with overnutrition according to body composition. The RMR IC was measured and RMR estimated using the FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations. The percentage of adequacy (90-110%), overestimation (>110%), and underestimation (<90%) were evaluated for RMR IC. Data were described by percentiles because of non-normal distribution according to the Shapiro-Wilk test. The Kruskal-Wallis test and Bland-Altman analysis were applied at a significance level of α < 0.05. The RMR IC was 1192 and 1183 calories/day (p = 0.429) in women with obesity and overweight, respectively. The FAO/WHO/UNU (1985), FAO/WHO/UNU (2004), Harris-Benedict, and Mifflin-St Jeor equations overestimated the RMR IC by 283.2, 311.2, 292.7, and 203.0 calories/day and by 296.7, 413.8, 280.0, and 176.6 calories/day for women with overweight and obesity (p < 0.001), respectively. The Harris-Benedict adjusted weight (0.5) equation underestimated RMR IC by 254.7 calories/day. The predictive equations overestimated RMR IC in women with excess body fat. The Mifflin-St Jeor equation showed less overestimation and better adequacy, but was not exempt from inaccuracy.

The 13C-bicarbonate technique as a tool for measurement of energy expenditure in overweight dogs undergoing body weight reduction and the effect of different dietary composition

Changes in body size and composition, i.e., body weight (BW) gain or loss, affect the daily energy expenditure (EE). To ensure an appropriate BW reduction and to find an efficient strategy to reduce and maintain a target BW, regular evaluations and adjustments of energy allowance are important. This study aimed to provide a detailed knowledge about the possible changes in resting EE using the oral 13C-bicarbonate technique (o13CBT) as a research tool in 16 overweight pet dogs undergoing BW reduction. Dietary composition (i.e., in % of dry matter [DM] being a high protein [33.3], low fat [9.6], and high crude fiber [18.0] diet [LFHFibre], and a high protein [37.9], high fat [52.0], carbohydrate-free diet [HFat]) during 16 wk of energy restriction were evaluated regarding effects on resting EE, rate of BW reduction, body composition, and plasma concentrations of metabolic hormones involved in energy metabolism and appetite regulation. The mean BW loss was higher (P < 0.05) for the dogs fed the LFHFibre diet (1.1%/wk) than that for dogs fed the HFat diet (0.8%/wk), but the total BW reduction of 14.6% and 12.0% of initial BW did not differ significantly (P > 0.05). Resting EE was lower (P < 0.02) after the BW reduction; 414 kJ (99 kcal)/kg BW0.75/d at the start (week 0) and 326 kJ (78 kcal)/kg BW0.75/d at the end (week 16) of the study. The BW reduction in both groups (P > 0.05) consisted of both fat mass (FM) and fat-free mass (FFM). Energy expenditure, calculated in relation to amount of FFM, was not significantly (P > 0.05) affected by BW reduction. Dietary composition did not significantly affect (P > 0.05) plasma concentrations of insulin, leptin, and ghrelin, and no effect (P > 0.05) of BW reduction was observed on hormone concentrations. In conclusion, the o13CBT proved to be a useful research method for studying short-term EE in overweight dogs. Even though all dogs lost BW, most dogs were still overweight at the end of the study. Due to a high individual variation among dogs, a longer experimental period with a larger sample size would be desirable.

Resting energy expenditure of a diverse group of South African men and women

BACKGROUND

In South Africa, overweight/obesity is a public health concern, disproportionally affecting Black females. A contributory role of a lower resting energy expenditure (REE) is suggested for African Americans. The present study assessed the REE of Black and White South African adults aiming to better understand the underlying predictors to overweight/obesity and transform this into locally appropriate recommendations.

METHODS

In 328 (63% female; 39% Black) healthy South African adults, REE was measured with indirect calorimetry and body composition with multifrequency bioelectrical impedance analysis. The REE was estimated with 30 sets of published equations. Black-White differences in REE, as measured and adjusted (analysis of covariance), were determined with quantile regression. Reliability/agreement of estimated (against measured) REE was determined with intra-class correlations (ICCs) and Bland-Altman analysis. A new equation was developed by median regression followed by preliminary validation.

RESULTS

Measured REE (adjusted for age along with fat-free mass [FFM], FFM index, FFM plus fat mass, FFM index plus fat mass index) in White subjects was significantly higher (p < 0.001) than in Black subjects for men and women alike, regardless of obesity class. None of the sets of estimation equations had good agreement with measured REE for Black, White, male and female subjects simultaneously. A new estimation equation, based on whole-body variables, had good reliability (ICC = 0.79) and agreement (mean difference: 27 kJ) and presents practical opportunities for groups at the local grass-roots level.

CONCLUSIONS

The REE in Black South African adults is lower than in White adults. Tailored REE equations may improve REE estimation of racially/ethnically diverse South African groups and contribute to improved obesity management.

Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women

Resting metabolic rate (RMR) depends on body fat-free mass (FFM) and fat mass (FM), whereas abdominal fat distribution is an aspect that has yet to be adequately studied. The objective of the present study was to analyze the influence of waist circumference (WC) in predicting RMR and propose a specific estimation equation for older Chilean women. This is an analytical cross-sectional study with a sample of 45 women between the ages of 60 and 85 years. Weight, height, body mass index (BMI), and WC were evaluated. RMR was measured by indirect calorimetry (IC) and %FM using the Siri equation. Adequacy (90% to 110%), overestimation (>110%), and underestimation (<90%) of the FAO/WHO/UNU, Harris−Benedict, Mifflin-St Jeor, and Carrasco equations, as well as those of the proposed equation, were evaluated in relation to RMR as measured by IC. Normal distribution was determined according to the Shapiro−Wilk test. The relationship of body composition and WC with RMR IC was analyzed by multiple linear regression analysis. The RMR IC was 1083.6 ± 171.9 kcal/day, which was significantly and positively correlated with FFM, body weight, WC, and FM and inversely correlated with age (p < 0.001). Among the investigated equations, our proposed equation showed the best adequacy and lowest overestimation. The predictive formulae that consider WC improve RMR prediction, thus preventing overestimation in older women.

Relative validity of a short screener to assess diet quality in patients with severe obesity before and after bariatric surgery

OBJECTIVE

To determine the relative validity and reproducibility of the Eetscore FFQ, a short screener for assessing diet quality, in patients with (severe) obesity before and after bariatric surgery (BS).

DESIGN

The Eetscore FFQ was evaluated against 3-d food records (3d-FR) before (T0) and 6 months after BS (T6) by comparing index scores of the Dutch Healthy Diet index 2015 (DHD2015-index). Relative validity was assessed using paired t tests, Kendall's tau-b correlation coefficients (τb), cross-classification by tertiles, weighted kappa values (k w ) and Bland-Altman plots. Reproducibility of the Eetscore FFQ was assessed using intraclass correlation coefficients (ICC).

SETTING

Regional hospital, the Netherlands.

PARTICIPANTS

Hundred and forty participants with obesity who were scheduled for BS.

RESULTS

At T0, mean total DHD2015-index score derived from the Eetscore FFQ was 10·2 points higher than the food record-derived score (P < 0·001) and showed an acceptable correlation (τb = 0·42, 95 % CI: 0·27, 0·55). There was a fair agreement with a correct classification of 50 % (k w = 0·37, 95 % CI: 0·25, 0·49). Correlation coefficients of the individual DHD components varied from 0·01-0·54. Similar results were observed at T6 (τb = 0·31, 95 % CI: 0·12, 0·48, correct classification of 43·7 %; k w = 0·25, 95 % CI: 0·11, 0·40). Reproducibility of the Eetscore FFQ was good (ICC = 0·78, 95 % CI: 0·69, 0·84).

CONCLUSION

The Eetscore FFQ showed to be acceptably correlated with the DHD2015-index derived from 3d-FR, but absolute agreement was poor. Considering the need for dietary assessment methods that reduce the burden for patients, practitioners and researchers, the Eetscore FFQ can be used for ranking according to diet quality and for monitoring changes over time.

Predicted estimates of resting energy expenditure have limited clinical utility in patients with cirrhosis

BACKGROUND & AIMS

Malnutrition is associated with adverse clinical outcomes in patients with cirrhosis. Accurate assessment of energy requirements is needed to optimize dietary intake. Resting energy expenditure (REE), the major component of total energy expenditure, can be measured using indirect calorimetry (mREE) or estimated using prediction equations (pREE). This study assessed the usefulness of predicted estimates of REE in this patient population.

METHODS

Individual mREE data were available for 900 patients with cirrhosis (mean [±1 SD] age 55.7±11.6 years-old; 70% men; 52% south-east Asian) and 282 healthy controls (mean age 36.0±12.8 years-old; 52% men; 18% south-east Asian). Metabolic status was classified using thresholds based on the mean ± 1 SD of the mREE in the healthy controls. Comparisons were made between mREE and pREE estimates obtained using the Harris-Benedict, Mifflin, Schofield and Henry equations. Stepwise regression was used to build 3 new prediction models which included sex, ethnicity, body composition measures, and model for end-stage liver disease scores.

RESULTS

The mean mREE was significantly higher in patients than controls when referenced to dry body weight (22.4±3.8 cf. 20.8±2.6 kcal/kg/24 hr; p <0.001); there were no significant sex differences. The mean mREE was significantly higher in Caucasian than Asian patients (23.1±4.4 cf. 21.7±2.9 kcal/kg/24 hr; p <0.001). Overall, 37.1% of Caucasian and 25.3% of Asian patients were classified as hypermetabolic. The differences between mREE and pREE were both statistically and clinically relevant; in the total patient population, pREE estimates ranged from 501 kcal/24 hr less to 548 kcal/24 hr more than the mREE. Newly derived prediction equations provided better estimates of mREE but still had limited clinical utility.

CONCLUSIONS

Prediction equations do not provide useful estimates of REE in patients with cirrhosis. REE should be directly measured.

LAY SUMMARY

People with cirrhosis are often malnourished and this has a detrimental effect on outcome. Provision of an adequate diet is very important and is best achieved by measuring daily energy requirements and adjusting dietary intake accordingly. Prediction equations, which use information on age, sex, weight, and height can be used to estimate energy requirements; however, the results they provide are not accurate enough for clinical use, particularly as they vary according to sex and ethnicity.

Clinical meaning of body temperatures in anorexia nervosa and bulimia nervosa

OBJECTIVE

Bradycardia is one of the common cardiac abnormalities in patients with eating disorders. It ensues from hypometabolism, which results from reduced caloric intake and consequential weight loss. Hypothermia is another consequence of hypometabolism. While at-rest metabolism and body mass index (BMI) are typically used to assess hypometabolism and estimate potential bradycardia, we hypothesised that body temperature, which is easy to measure, could also capture the presence of this threatening cardiovascular condition.

METHOD

We monitored heart rate continuously for 72 h, measured resting energy expenditure (REE) and assessed body temperature in 12 body parts for 58 patients with anorexia nervosa (AN) and 29 patients with bulimia nervosa (BN).

RESULTS

Palm temperature reflects bradycardia in both AN and BN, explaining 18% of its variance (p < 0.001), capturing this aspect even more efficiently than BMI. We also observe different correlations between palm temperature, abdominal temperature, BMI, REE and levels of physical activity.

CONCLUSION

Palm temperature could be used as a warning of bradycardia, a serious cardiovascular condition which can be difficult to detect in short visits with outpatients. Further studies are needed to determine how useful bradycardia and palm temperature could be to assess severity and prognosis of the disorder.

Dynamic changes in energy expenditure in response to underfeeding: a review

The observation that 64% of English adults are overweight or obese despite a rising prevalence in weight-loss attempts suggests our understanding of energy balance is fundamentally flawed. Weight-loss is induced through a negative energy balance; however, we typically view weight change as a static function, in that energy intake and energy expenditure are independent variables, resulting in a fixed rate of weight-loss assuming a constant energy deficit. Such static modelling provides the basis for the clinical assumption that a 14644 kJ (3500 kcal) deficit translates to a 1 lb weight-loss. However, this '3500 kcal (14644 kJ) rule' is consistently shown to significantly overestimate weight-loss. Static modelling disregards obligatory changes in energy expenditure associated with the loss of metabolically active tissue, i.e. skeletal muscle. Additionally, it disregards the presence of adaptive thermogenesis, the underfeeding-associated fall in resting energy expenditure beyond that caused by loss of fat-free mass. This metabolic manipulation of energy expenditure is observed from the onset of energy restriction to maintain weight at a genetically pre-determined set point. As a result, the observed magnitude of weight-loss is disproportionally less, followed by earlier weight plateau, despite strict compliance to a dietary intervention. By simulating dynamic changes in energy expenditure associated with underfeeding, mathematical modelling may provide a more accurate method of weight-loss prediction. However, accuracy at an individual level is limited due to difficulty estimating energy requirements, physical activity and dietary intake in free-living individuals. In the present paper, we aim to outline the contribution of dynamic changes in energy expenditure to weight-loss resistance and weight plateau.

Weight and body composition changes affect resting energy expenditure predictive equations during a 12-month weight-loss intervention

OBJECTIVE

Mathematical equations that predict resting energy expenditure (REE) are widely used to derive calorie prescriptions during weight-loss interventions. Although such equations are known to introduce group- and individual-level error into REE prediction, their validity has largely been assessed in weight-stable populations. Therefore, this study sought to characterize how weight change affects the validity of commonly used REE predictive models throughout a 12-month weight-loss intervention.

METHODS

Changes in predictive error of four models (Mifflin-St-Jeor, Harris-Benedict, Owen, and World Health Organization/Food and Agriculture) were assessed at 1-, 6-, and 12-month time points in adults (n = 66, 76% female, aged 18-55 years, BMI = 27-45 kg/m2 ) enrolled in a randomized clinical weight-loss trial.

RESULTS

All equations experienced significant negative shifts in bias (measured - predicted REE) toward overprediction from baseline to 1 month (p < 0.05). Three equations showed reversal of bias in the positive direction (toward underprediction) from baseline to 12 months (p < 0.05). Early changes in bias were correlated with decreased fat-free mass (p ≤ 0.01).

CONCLUSIONS

Changes in body composition and mass during a 12-month weight-loss intervention significantly affected REE predictive error in adults with overweight and obesity. Weight history should be considered when using mathematical models to predict REE during periods of weight fluctuation.

Resting Energy Expenditure Early after Cardiac Surgery and Validity of Predictive Equations: A Prospective Observational Study

OBJECTIVE

Several predictive equations have been used to estimate patients' energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations.

METHODS

This was a prospective observational study. Patients aged 18-80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson's correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA).

RESULTS

mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable.

CONCLUSIONS

The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.

Measured Versus Estimated Resting Metabolic Rate in Heart Failure With Preserved Ejection Fraction

BACKGROUND

Obesity is common in heart failure with preserved ejection fraction (HFpEF), and a hypocaloric diet can improve functional capacity. Malnutrition, sarcopenia, and frailty are also frequently present, and calorie restriction could harm some patients. Resting metabolic rate (RMR) is an essential determinant of caloric needs; however, it is rarely measured in clinical practice. The accuracy of commonly used predictive equations in HFpEF is unknown.

METHODS

RMR was measured with indirect calorimetry in 43 patients with HFpEF undergoing right heart catheterization at the University of Michigan, and among 49 participants in the SECRET trial (Study of the Effects of Caloric Restriction and Exercise Training in Patients With Heart Failure and a Normal Ejection Fraction); SECRET patients also had dual-energy X-ray absorptiometry body composition measures. Measured RMR was compared with RMR estimated using the Harris Benedict, Mifflin-St Jeor, World Health Organization, and Academy for Nutrition and Dietetics equations.

RESULTS

All predictive equations overestimated RMR (by >10%, P<0.001 for all), with mean (95% CI) differences Harris Benedict equation +250 (186-313), Mifflin-St. Jeor equation +169 (110-229), World Health Organization equation +300 (239-361), and Academy for Nutrition and Dietetics equation +794 (890-697) kcal/day. Results were similar across both patient groups, and the discrepancy between measured and estimated RMR tended to increase with body mass index. In SECRET, measured RMR was closely associated with lean body mass (ρ=0.74; by linear regression adjusted for age and sex: β=27 [95% CI, 18-36] kcal/day per kg, P<0.001; r²=0.56).

CONCLUSIONS

Commonly used predictive equations systematically overestimate measured RMR in patients with HFpEF. Direct measurement of RMR may be needed to effectively tailor dietary guidance in this population. Registration: URL: https://www.clinicaltrials.gov; Unique Identifier: NCT00959660.

Evaluation of Resting Energy Expenditure in Subjects with Severe Obesity and Its Evolution After Bariatric Surgery

Purpose

One major determinant of weight loss is resting energy expenditure (REE). However, data regarding REE is scarce in patients with severe obesity (SO)—BMI>50kg/m². Most studies used equation in order to estimate REE and not indirect calorimetry (IC) (gold standard). Additionally, there is no reliable data on the impact of bariatric surgery (BS) on REE.

Objectives

(a) To evaluate the REE in patients with SO; (b) to compare REE measured by IC (mREE) to that calculated by Mifflin St-Jeor equation (eREE); (c) to evaluate the impact of BS on REE and the relationship with evolution post-BS.

Material and Methods

Single-center observational study including consecutive patients with SO between January 2010 and December 2015, candidates for BS. mREE was determined at baseline, and 1 and 12 months post-BS by IC, using a Vmax metabolic monitor.

Results

Thirty-nine patients were included: mean age 46.5±11.77 years, 64.1%women. Preoperative mREE was 2320.38±750.81 kcal/day. One month post-BS, the mREE significantly decreased (1537.6 ± 117.46 kcal/day, p = 0.023) and remained unchanged at 12 months (1526.00 ± 123.35 kcal/day; p =0.682). Reduction in mREE after the BS was a predictor of reaching successful weight loss (nadir) and weight regain (5 years follow-up) (AUCROC of 0.841 (95%CI [0.655–0.909], p=0.032) and AUCROC of 0.855 (95% CI [0.639–0.901]), p= 0.027, respectively). eREE was not valid to identify these changes.

Conclusion

In patients with SO, a significant reduction of mREE occurs 1 month post-BS, unchanged at 12 months, representing the major conditioning of successful weight loss and maintenance post-BS.

Graphical abstract

Effects of a 3-Week In-Hospital Multidisciplinary Body Weight Reduction Program in Obese Females: Is Measured Resting Energy Expenditure Essential for Tailoring Adequately the Amount of Energy Intake?

In the obese population, the prescription of a proper diet plan is essential to ensure an appropriate and gradual weight loss, reduce the risk of weight cycling and favor an overall improvement of health conditions. Energy needs are commonly estimated using predictive equations, even if their accuracy is still debated, especially in severely obese subjects. In the present study, 850 severely obese females admitted to our hospital for a multidisciplinary body weight reduction program (BWRP) were divided into three subgroups, “hypo-,” “normo-,” and “hyper-metabolic,” based on the comparison between estimated resting energy expenditure (eREE, using the Mifflin equation) and measured REE (mREE, using indirect calorimetry). The majority of this study population was considered normo-metabolic (59.4%, mREE between 90 and 110% of eREE), 32.6% was hyper-metabolic (mREE > 110% of eREE) and only 8% was hypo-metabolic (mREE < 90% of eREE). The three subgroups were evaluated before and after a 3-week BWRP, entailing energy restricted diet, adapted physical activity, psychological counseling and nutritional education. Since the diet plan during the BWRP consisted of a 30% reduction of total energy expenditure (obtained by multiplying mREE by the physical activity level), each subgroup responded positively to the BWRP independently from the difference between mREE and eREE, the extent of BMI reduction and clinical, metabolic and physical amelioration being comparable among the three subgroups. By contrast, the restriction of the energy intake based on eREE during the BWRP would have determined a slighter caloric restriction in the hypo-metabolic subgroup, thus determining a smaller body weight reduction, and, by contrast, a more marked caloric restriction in the hyper-metabolic subgroup, probably difficult to be tolerated and maintained for prolonged period. In conclusion, the percentage of subjects with “slow metabolism” in a Caucasian female obese population seeking hospitalization for a BWRP is actually lower than expected, finding controverting the common notion that obesity is mostly due to reduced REE. The high percentage (40%) of inadequate eREE in these female obese populations further underlines the absolute need to include the measurement of REE in the clinical practice for the correct prescription of energy intake in severely obese populations.

Prediction of resting energy expenditure in healthy older adults: A systematic review

BACKGROUND & AIMS

Estimates of energy requirements, based on measured or predicted resting energy expenditure (REE), are needed to avoid undernutrition or overnutrition (and their clinical consequences) in elderly subjects. The aims of this systematic review were to evaluate the prediction accuracy of REE in healthy elderly subjects and to ascertain which equation is more reliable at group level and/or individual level.

METHODS

Studies assessing prediction of REE in general elderly population were systematically searched using PubMed, EMBASE, Web of Science and CINAHL until March 2020. Prediction accuracy of REE was assessed at both group (bias) and individual (precision) level for each equation.

RESULTS

Fourteen studies met the inclusion criteria of this systematic review. Bias was reported in 8 papers and calculated in another 5 from absolute values. There was a prevalent tendency towards an overestimation of REE across the studies. The least bias was observed for the Mifflin (-0.3%) and Harris-Benedict (+2.6%) equations, with values above 5% for the FAO/WHO/UNU, Fredrix and Muller equations. Precision widely varied between studies for the same equation. The higher precision was observed using the Harris-Benedict equation (~70%), while the Henry and Mifflin equations provided estimates within 10% of measured values in 65% and 61% of elderly individuals, respectively.

CONCLUSIONS

None of the prediction equations considered provides accurate and precise REE estimates in healthy older adults. However, the best prediction is given by the Mifflin equation at group level and by the Harris-Benedict equation at individual level. Further studies with strong quality design are needed to evaluate the variability and accuracy of REE in the elderly general population.

Evaluation of Measured Resting Metabolic Rate for Dietary Prescription in Ageing Adults with Overweight and Adiposity-Based Chronic Disease

The primary objective of this study was to compare weight changes in two groups of ageing Irish adults with overweight and adiposity-based chronic disease: participants who had dietary energy requirements prescribed on the base of measured RMR and participants whose RMR was estimated by a prediction equation. Fifty-four Caucasian adults (male n = 25; female n = 29, age 57.5 ± 6.3 years, weight 90.3 ± 15.1 kg, height 171.5 ± 9.5 cm, BMI 30.7 ± 4.6 kg/m²) were randomly assigned to a dietary intervention with energy prescription based on either measured RMR or estimated RMR. RMR was measured by indirect calorimetry after an overnight fast and predicted values were determined by the Mifflin et al. (1990) prediction equation. All participants received individual nutritional counselling, motivational interviewing and educational material. Anthropometric variables, blood pressure, blood glucose and blood lipid profile were assessed over 12 weeks. Body weight at week 12 was significantly lower (p < 0.05) for both groups following dietary interventions, mRMR: −4.2%; eRMR: −3.2% of initial body weight. There was no significant difference in weight loss between groups. Overall, 20.8% mRMR and 17.4% of eRMR participants experienced clinically meaningful (i.e., ≥5% of initial weight) weight reduction. Weight reduction in adults aged ≥50 years over the short term (12 weeks) favoured a reduction in blood pressure, triglycerides and glucose, thus reducing cardiovascular disease risk factors. This research indicates that employing a reduced-calorie diet using indirect calorimetry to determine energy needs when improving weight outcomes in adults (>50 years) with overweight and adiposity-based chronic disease is equal to employing a reduced-calorie diet based on the Mifflin et al. (1990) prediction equation. A reduced-energy diet based on mRMR or eRMR facilitates clinically meaningful weight reduction in adults (≥50 years) over the short term (12 weeks) and favours a reduction in blood pressure, triglycerides and glucose, thus reducing cardiovascular disease risk factors. Moreover, the addition of motivational interviewing and behaviour change techniques that support and encourage small behaviour changes is effective in short-term weight management.

Intermittent Versus Continuous Low-Energy Diet in Patients With Type 2 Diabetes: Protocol for a Pilot Randomized Controlled Trial

BACKGROUND

Intensive face-to-face weight loss programs using continuous low-energy diets (CLEDs) providing approximately 800 kcal per day (3347 kJ per day) can produce significant weight loss and remission from type 2 diabetes (T2D). Intermittent low-energy diets (ILEDs) and remotely delivered programs could be viable alternatives that may support patient choice and adherence.

OBJECTIVE

This paper describes the protocol of a pilot randomized controlled trial to test the feasibility and potential efficacy of remotely supported isocaloric ILED and CLED programs among patients with overweight and obesity and T2D.

METHODS

A total of 79 participants were recruited from primary care, two National Health Service hospital trusts, and a voluntary T2D research register in the United Kingdom. The participants were randomized to a remotely delivered ILED (n=39) or CLED (n=40). The active weight loss phase of CLED involved 8 weeks of Optifast 820 kcal/3430 kJ formula diet, followed by 4 weeks of food reintroduction. The active weight loss phase of ILED (n=39) comprised 2 days of Optifast 820 kcal/3430 kJ diet and 5 days of a portion-controlled Mediterranean diet for 28 weeks. Both groups were asked to complete 56 Optifast 820 kcal/3430 kJ days during their active weight loss phase with an equivalent energy deficit. The diets were isocaloric for the remainder of the 12 months. CLED participants were asked to follow a portion-controlled Mediterranean diet 7 days per week. ILED followed 1-2 days per week of a food-based 820 kcal/3430 kJ diet and a portion-controlled Mediterranean diet for 5-6 days per week. Participants received high-frequency (weekly, fortnightly, or monthly depending on the stage of the trial) multidisciplinary remote support from a dietitian, nurse, exercise specialist, and psychologist via telephone or the Oviva smartphone app. The primary outcomes of the study were uptake, weight loss, and changes in glycated hemoglobin at 12 months. An outcome assessment of trial retention was retrospectively added. Secondary outcomes included an assessment of adherence and adverse events. A qualitative evaluation was undertaken via interviews with participants and health care professionals who delivered the intervention.

RESULTS

A total of 79 overweight or obese participants aged 18-75 years and diagnosed with T2D in the last 8 years were recruited to the Manchester Intermittent and Daily Diet Diabetes App Study (MIDDAS). Recruitment began in February 2018, and data collection was completed in February 2020. Data analysis began in June 2020, and the first results are expected to be submitted for publication in 2021.

CONCLUSIONS

The outcomes of the MIDDAS study will inform the feasibility of remotely delivered ILED and CLED programs in clinical practice and the requirement for a larger-scale randomized controlled trial.

TRIAL REGISTRATION

International Standard Randomized Controlled Trial Number (ISRCTN) 15394285; http://www.isrctn.com/ISRCTN15394285.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/21116.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques

The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6–9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.

Changes of Body Weight and Body Composition in Obese Patients with Prader-Willi Syndrome at 3 and 6 Years of Follow-Up: A Retrospective Cohort Study

Few short-term studies of weight loss have been performed in adult patients with Prader–Willi syndrome (PWS) undergoing metabolic rehabilitation. We performed a retrospective cohort study of 45 adult obese PWS patients undergoing a long-term multidisciplinary metabolic rehabilitation program based on diet and physical activity. Body composition was evaluated by dual-energy X-ray absorptiometry in 36 (80%) patients. The mean (95% CI) weight change was −3.6 (−7.6 to 0.4, p = 0.08) kg at 3 years and −4.6 (−8.5 to −0.8, p = 0.02) kg at 6 years, and that of BMI was −1.7 (−3.4 to 0.1, p = 0.06) kg/m² at 3 years and −2.1 (−3.8 to −0.4, p = 0.02) kg/m² at 6 years. A decrease of about 2% in fat mass per unit of body mass was observed, which is in line with the expectations for moderate weight loss. A possibly clinically relevant decrease in total and low-density lipoprotein cholesterol was also observed. These long-term results are important for patients with PWS, which is characterized by severe hyperphagia, behavioral disturbances, and cognitive impairment and is generally considered “resistant” to classical weight loss interventions.

Are predictive equations a valid method of assessing the resting metabolic rate of overweight or obese former athletes?

Athletes retiring from their sports career, an understudied population, are susceptible to become overweight/obese because of decreased energy expenditure not followed by a reduction in energy intake. Therefore, their energy requirements, through resting metabolic rate (RMR), should be accurately addressed for weight management purposes. This study aimed to determine the validity of predictive equations (PEq) for RMR estimation using indirect calorimetry as the reference method in a sample of overweight/obese former athletes. The study uses cross-sectional data collected during baseline measurements of a lifestyle intervention (NCT03031951). The RMR of 56 overweight/obese (31.5 (4.0 kg/m2)) individuals (78.6% male, 37.5% obese, 95.8 (14.8 kg), 174.2 (8.7 cm)) was measured by indirect calorimetry and predicted using seven PEq: Harris-Benedict, Cunningham, Schofield, FAO/WHO/UNU, Owen, Mifflin-St. Jeor, and Katch-McArdle. Dual-energy X-ray absorptiometry was used to assess body composition. The PEq overestimated the RMR measured by the indirect calorimetry, 70-300 kcal/day (4.3-14.9%). The linear regression between the reference and each of the PEq did not differ from the identity line with estimated values explaining around 50% of the variability of the measured values. The agreement between the methods was weak for all the PEq showing wide limits of agreement. The Harris-Benedict equation was the only one in which the difference between the methods was not related to the magnitude of the measured RMR. Given the weak performance of the various RMR models in overweight/obese former athletes, an effective weight management intervention based on estimated resting energy requirements may be compromised.

Predictive equations for estimating resting energy expenditure in women with overweight and obesity at three postpartum stages

The objective was to investigate which predictive equations provide the best estimates of resting energy expenditure (REE) in postpartum women with overweight and obesity. Lactating women with overweight or obesity underwent REE measurement by indirect calorimetry, and fat-free mass (FFM) was assessed by dual-energy X-ray absorptiometry at three postpartum stages. Predictive equations based on body weight and FFM were obtained from the literature. Performance of the predictive equations were analysed as the percentage of women whose REE was accurately predicted, defined as a predicted REE within ±10 % of measured REE. REE data were available for women at 10 weeks (n 71), 24 weeks (n 64) and 15 months (n 57) postpartum. Thirty-six predictive equations (twenty-five weight-based and eleven FFM-based) were validated. REE was accurately predicted in ≥80 % of women at all postpartum visits by six predictive equations (two weight-based and four FFM-based). The weight-based equation with the highest performance was that of Henry (weight, height, age 30-60 years) (Henry_WH30-60), with an overall mean of 83 % accurate predictions. The Henry_WH30-60 equation was highly suitable for predicting REE at all postpartum visits (irrespective of the women's actual age), and the performance was sustained across changes in weight and lactation status. No FFM-based equation was remarkably superior to Henry_WH30-60 for the total postpartum period.

A systematic review and quantitative analysis of resting energy expenditure prediction equations in healthy overweight and obese children and adolescents

BACKGROUND

Resting energy expenditure (REE) estimates are often needed in young people and can be predicted using prediction equations based on body weight. However, these equations may perform poorly in those who are obese and overweight. The aim of this systematic review was to identify equations based on simple anthropometric and demographic variables that provide the most accurate and precise estimates of REE in healthy obese and overweight young people.

METHODS

Systematic searches for relevant studies in healthy obese and overweight young people aged ≤18 years were undertaken using PubMed, Scopus, Cinahl, OpenGrey and Cochrane Library (completed January 2018). Search terms included metabolism, calorimetry, obesity and prediction equation. Data extraction, study appraisal and synthesis followed PRISMA guidelines.

RESULTS

From 390 screened titles, 13 studies met inclusion criteria. The most accurate REE predictions (least biased) were provided by Schofield equations [+0.8% (3-18 years); 0% (11-18 years); +1.1% (3-10 years)]. The most precise REE estimations (percentage of predictions ± 10% of measured) for 11-18 years were provided by Mifflin equations (62%) and, for 7-18 years, by the equations of Schmelzle (57%), Henry (56%) and Harris Benedict (54%). Precision of Schofield predictions was 43% in both age groups. No accuracy data were available for those <3 years or for precision for those <7 years.

CONCLUSIONS

No single equation provided accurate and precise REE estimations in this population. Schofield equations provided the most accurate REE predictions so are useful for groups. Mifflin equations provided the most precise estimates for individuals aged 11-18 years but tended to underestimate REE.

Validity of predictive equations to estimate RMR in females with varying BMI

Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris-Benedict, Schofield, Henry, Mifflin-St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17-44 kg/m2). Agreement between methods was assessed by Bland-Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin-St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin-St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin-St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.

External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity: A Cross-Sectional Study

We performed an external cross-validation study of 10 equations to estimate resting energy expenditure (REE) in 2037 children with and 389 without obesity. Inclusion criteria were Caucasian ethnicity, age ≤ 18 years, and availability of REE. REE was measured using indirect calorimetry. The correct classification fraction (CCF) of an equation was defined as the fraction of subjects whose estimated REE was within 10% of measured REE. The Molnár equation was the most accurate REE prediction equation with CCFs of 0.70 (95% CI 0.65 to 0.76) in girls without obesity, 0.64 (95% CI 0.61 to 0.66) in girls with obesity, 0.76 (95% CI 0.67 to 0.83) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity. The Mifflin equation was the second most accurate equation with CCFs of 0.67 (95% CI 0.61 to 0.73) in girls without obesity, 0.61 (95% CI 0.58 to 0.64) in girls with obesity, 0.75 (95% CI 0.66 to 0.82) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity.

Evaluating steady-state resting energy expenditure using indirect calorimetry in adults with overweight and obesity

BACKGROUND

Determining a period of steady state (SS) is recommended when estimating resting energy expenditure (REE) using a metabolic cart. However, this practice may be unnecessarily burdensome and time-consuming in the research setting.

AIM

The aim of the study was to evaluate the use of SS criteria, and compare it to alternative approaches in adults with overweight and obesity.

METHODS

In this cross-sectional, ancillary analysis, participants enrolled in a bariatric (study 1; n = 13) and lifestyle (study 2; n = 51) weight loss intervention were included. Indirect calorimetry was performed during baseline measurements using a metabolic cart for 25 min, including a 5-min stabilization period at the start. SS was defined as the first 5-min period with a coefficient of variation (CV) ≤10% for both VO₂ and VCO₂ (hereafter REE_5-SS). Body composition was measured using bioelectrical impedance analysis in study 2 participants only. REE_5-SS was compared against the lowest CV (REE_CV-lowest), 5-min time intervals (REE_6-10, REE_11-15, REE_16-20, REE_21-25), 4-min and 3-min SS intervals (REE_4-SS and REE_3-SS), and time intervals of 6-15, 6-20 and 6-25 min (REE_6-15, REE_6-20, and REE_6-25) using repeated measures ANOVA and Bland-Altman analysis to test for bias, limits of agreement and accuracy (±6% measured REE).

RESULTS

Participants were 54 ± 13 years old, mostly women (75%) and had a BMI of 35 ± 5 kg/m². Overall, 54/63 (84%) of participants reached REE_5-SS, often (47/54, 87%) within the first 10-min (6-15 min). Alternative approaches to estimating REE had a relatively low bias (-16 to 13 kcals), narrow limits of agreement and high accuracy (83-98%) when compared to REE_5-SS, in particular, outperforming standard prediction equations (e.g., Mifflin St. Joer).

CONCLUSION

Indirect calorimetry measurements that utilize the 5-min SS approach to estimate REE are considered the gold-standard. Under circumstances of non-SS, it appears 4-min and 3-min SS periods, or fixed time intervals of atleast 5 min are accurate and practical alternatives for estimating REE in adults with overweight and obesity. However, future trials should validate alternative methods in similar populations to confirm these findings.

Do we need race-specific resting metabolic rate prediction equations?

Background

Resting metabolic rate (RMR) is a key determinant of daily caloric needs. Respirometry, a form of indirect calorimetry (IC), is considered one of the most accurate methods to measure RMR in clinical and research settings. It is impractical to measure RMR by IC in routine clinical practice; therefore, several formulas are used to predict RMR. In this study, we sought to determine the accuracy of these formulas in determining RMR and assess additional factors that may determine RMR.

Methods

We measured RMR in 114 subjects (67% female, 30% African American [AA]) using IC. Along with standard anthropometrics, dual-energy X-ray absorptiometry was used to obtain fat-free mass(FFM) and total fat mass. Measured RMR (mRMR) by respirometry was compared with predicted RMR (pRMR) generated by Mifflin–St.Joer, Cunningham, and Harris–Benedict (HB) equations. Linear regression models were used to determine factors affecting mRMR.

Results

Mean age, BMI, and mRMR of subjects were 46 ± 16 years (mean ± SD), 35 ± 10 kg/m², and 1658 ± 391 kcal/day, respectively. After adjusting for age, gender, and anthropometrics, the two largest predictors of mRMR were race (p < 0.0001) and FFM (p < 0.0001). For every kg increase in FFM, RMR increased by 28 kcal/day (p < 0.0001). AA race was associated with 144 kcal/day (p < 0.0001) decrease in mRMR. The impact of race on mRMR was mitigated by adding in truncal FFM to the model. When using only clinically measured variables to predict mRMR, we found race, hip circumference, age, gender, and weight to be significant predictors of mRMR (p < 0.005). Mifflin–St.Joer and HB equations that use just age, gender, height, and weight overestimated kcal expenditure in AA by 138 ± 148 and 242 ± 164 (p < 0.0001), respectively.

Conclusion

We found that formulas utilizing height, weight, gender, and age systematically overestimate mRMR and hence predict higher calorie needs among AA. The lower mRMR in AA could be related to truncal fat-free mass representing the activity of metabolically active intraabdominal organs.

Prevalence and characteristics of energy intake under-reporting among Australian adults in 1995 and 2011 to 2012

AIM

Misreporting of energy intake is a common source of measurement error found in dietary surveys, resulting in biased estimates and a reduction in statistical power. The present study aims to refine the conventional cut-off methods and to examine the extent to which Australian adults misreport their energy intake, and the characteristics of under-reporters between two time points.

METHODS

A revised Goldberg cut-off approach was used to identify those who reported implausible intake amounts in a secondary analysis of two large cross-sectional surveys. Identified low energy reporters were then used as the outcome variable in Poisson regressions to examine association with sex, age, body mass index (BMI), weight perceptions, education, relative household income, geographic remoteness and relative socioeconomic disadvantage.

RESULTS

The prevalence of under-reporting increased from 32% in 1995 to 41% in 2012, most of which can be attributed to an increase in men. Under-reporting has a positive association with BMI and relative socioeconomic disadvantage, but an inverse association with age, education, relative household income and residence in inner regional areas.

CONCLUSIONS

Under-reporting of energy intake is high in Australian adults, and appears have worsened over time in men, which could be partly explained by the upward trend in obesity. The use of conventional Goldberg method to identify under-reporters can greatly underestimate the prevalence of under-reporting, future studies should consider selecting a lower critical value to improve accuracy.