World Health Organization equations have shortcomings for predicting resting energy expenditure in persons from a modern, affluent population: generation of a new reference standard from a retrospective analysis of a German database of resting energy expenditure

Establishing a Prediction Model for Weight Loss Outcomes After LSG in Chinese Obese Patients with BMI ≥ 32.5 Kg/m2 Using Body Composition Data

Background

Laparoscopic sleeve gastrectomy (LSG) is associated with sustained and substantial weight loss. However, suboptimal results are observed in certain patients.

Objective

Drawing from body composition data at our center, clinically accessible predictive factors for weight loss outcomes were identified, leading to the development and validation of a preoperative predictive model for weight loss following LSG.

Methods and Materials

A retrospective analysis was conducted on the general clinical baseline and body composition data of obese patients (body mass index [BMI] ≥ 32.5 kg/m2) who underwent LSG between December 2016 and December 2022. Independent predictors for weight loss outcomes were selected through univariate logistic regression, random forest analysis, and multivariate logistic regression. Subsequently, a nomogram was developed to predict weight loss outcomes and was evaluated for discrimination, accuracy, and clinical utility, with validation performed in a separate cohort.

Results

A total of 473 patients with mean BMI were included. The preoperative resting energy expenditure to body weight ratio (REE/BW), fat-free mass index (FFMI), and waist circumference (WC) emerged as independent predictive factors for weight loss outcomes at one year post-LSG. These body composition parameters were incorporated into the construction of an Inbody predictive nomogram, which yielded area under the curve (AUC) values of 0.868 (95% CI: 0.826-0.902) for the modeling cohort and 0.829 (95% CI: 0.756-0.887) for the validation cohort. Calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC) from both groups demonstrated the model's robust discrimination, accuracy, and clinical utility.

Conclusion

In obese Chinese patients with a BMI ≥ 32.5 kg/m2, the Inbody-based nomogram integrating REE/BW, FFMI, and WC offers an effective preoperative tool for predicting weight loss outcomes one year after LSG, facilitating surgical planning and postoperative management.

A Comparison of Commonly Used Prediction Equations for Estimating Resting Metabolic Rate in Adults With Down Syndrome

BACKGROUND

Resting metabolic rate (RMR), the energy required by the body at rest, is the largest part of total daily energy expenditure. Commonly used prediction equations may overestimate RMR in adults with Down syndrome (DS). The purpose of this study was to assess the equivalency of prediction equations for estimating RMR in adults with DS.

METHODS

Twenty-five adults with DS (24 ± 5 years of age, 64% female) completed RMR assessments at an academic medical centre in the United States between November 2021 and July 2023. Measured RMR (kilocalories per day) was compared to estimated RMR from eight prediction equations using a null hypothesis significance (i.e., a paired t-test) and equivalence (i.e., a two one-sided test) tests. Bland-Altman plots, Pearson correlations and linear regressions were used to evaluate the bias between the measured and predicted RMR values.

RESULTS

Measured RMR in adults with DS was 1090 ± 136 kcal/day. Prediction equations overestimated RMR by 8 ± 16% (76 ± 165 kcal/day) to 45 ± 16% (488 ± 165 kcal/day) except for the Bernstein fat-free mass equation which underestimated RMR by 0.2 ± 11.5% (8 ± 123 kcal/day) and was statistically equivalent to measured RMR in our sample (p = 0.027).

CONCLUSIONS

The Bernstein fat-free mass equation offers better accuracy in adults with DS than other RMR prediction equations, but the equation needs to be evaluated in larger, more diverse samples of adults with DS.

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.

Predictive equations in determining resting energy expenditure in patients with head and neck cancer receiving home enteral nutrition

OBJECTIVE

It is important to cover energy targets among patients with head and neck cancer (HNC) to minimize weight and skeletal muscles loss. This study aimed to assess the agreement between indirect calorimetry (IC) and predictive equations for determining resting energy expenditures (REE) in HNC patients receiving home enteral nutrition (HEN).

RESEARCH METHODS AND PROCEDURES

Patients included in the study had to be diagnosed with HNC, be adults, have artificial access to the digestive tract, and participate in HEN. All measurements were conducted in the morning after prior patient preparation. Body weight and height were measured using a scale with an integrated height meter. A phase-sensitive, single-frequency bioimpedance analyzer was utilized to conduct bioelectrical impedance analysis. REE was measured using IC with a canopy hood and calculated using 27 different equations. Differences between variables were analyzed using appropriate t-tests and their nonparametric counterparts. The Bland-Altman test was used to assess the types of differences between measured REE (mREE) and predicted REE (pREE).

RESULTS

The examined patients (n = 71, 73.2% male) had a mean age of 63.99 ± 11.42 years and a BMI of 22.84 ± 3.59 kg/m2. The most common diagnosis was malignant tongue cancer. Most patients had stage III cancer. Treatment included surgery combined with radiotherapy or only chemoradiotherapy in most cases. The median duration of treatment and HEN was 206 days and 97 days, respectively. Men had a significantly higher REE than women. The Owen, Fredrix, Ireton-Jones, Korth, Weijs-Kruizenga, and Marraw,h equations estimated REE without significant statistical differences from IC and showed the smallest percentage error between pREE and mREE. The Korth equation had the smallest average mean difference between pREE and mREE, reducing the REE value average by 7 ± 274 kcal/day. The highest percentage of individual accurate predictions for pREE was obtained with the Fredrix (48%), Weijs-Kruizenga (48%), Korth (45%), and MarraPhA (45%) equations.

CONCLUSION(S)

The predictive equations examined in this study cannot replace IC for determining REE in HNC patients at the individual level. When equations are used, special attention should be given to planning HEN to account for possible discrepancies between pREE and mREE.

Resting Energy Expenditure Equations Have Lower Accuracy for Adolescents with Overweight/Obesity Versus Healthy-Weight Adolescents

Objective: The objectives of the study were (1) to assess whether resting energy expenditure (REE) equations have comparable validity for adolescents with overweight/obesity vs. adolescents with healthy weight and (2) to examine determinants of measured REE in adolescents with overweight/obesity vs. adolescents with healthy weight. Methods: Ten equations were used to predict REE for 109 adolescents (70% males; 36.7% with overweight/obesity); 95% equivalence testing was used to assess how well each equation agreed with the criterion measure of indirect calorimetry. Linear regression models were fitted to examine how much REE variance was accounted for by age, sex, race, fat-free mass (FFM), and fat mass. Results: For adolescents with healthy weight, all ten equations were significantly equivalent to the criterion measure within ±8.4% (p < 0.05), whereas for participants with overweight/obesity, only three equations were equivalent within the same range (p < 0.05). Controlling for age, sex, race, fat mass, and FFM accounted for 74% of REE variance. FFM explained the greatest amount (26%) of variance in REE, while weight status itself explained an additional 22%. Conclusions: Prediction equations tend to be more accurate for adolescents with healthy weight than adolescents with overweight/obesity unless the original sample specifically included participants with overweight/obesity. Determinants of REE are similar regardless of weight status.

Measurement of resting energy expenditure and its accuracy in women with breast cancer

BACKGROUND & AIMS

Breast cancer (BC) is frequently linked with obesity, metabolic syndrome, and sarcopenia. Therefore, measuring or accurately estimating resting energy expenditure (REE) is crucial for tailoring nutritional needs, managing weight and prevent under- or over-nutrition. We aimed to measure and compare REE between women with BC and a matched control group. Moreover, the prediction accuracy of selected formulas was evaluated.

METHODS

Women aged ≥18 years with newly diagnosis of BC (stage 0-III) and body mass index (BMI) ≤ 30 kg/m2 were included in this cross-sectional analysis. Anthropometry, indirect calorimetry, and bioelectrical impedance analysis (BIA) were performed. Patients with BC data were compared to healthy women with similar age and BMI range. Measured REE (mREE) was compared against 15 predictive equations. Agreement between methods was evaluated using Bland-Altman analysis.

RESULTS

We included 106 women with BC (age 49.9 ± 11.1 years and BMI 24.5 ± 2.8 kg/m2) and 75 women as control group. There were no differences in age, anthropometry, and BIA variables between groups, except for percentage fat mass. Measured REE values, alone and adjusted for fat-free mass (FFM) and age, were higher in patients with BC compared to controls (+4.3 % and +6.1 %, respectively). Regarding REE prediction, most of the selected equations underestimated mREE. Precision varied widely, with the two Marra equations showing the highest agreement (73 % and 74.5 %) along with the Müller equation (74 %), however, the wide limit of agreement range indicates substantial variability.

CONCLUSIONS

Women with early-stage BC exhibited higher mREE compared to controls, albeit its clinical significance is unknown. None of the selected predictive equations provided accurate and precise REE estimates in this group. Although the Marra equation displayed the highest agreement, further studies are needed to evaluate REE variability and its prediction in women with BC.

Clarifying main nutritional aspects and resting energy expenditure in children with Smith-Magenis syndrome

Our study aims to define resting energy expenditure (REE) and describe the main nutritional patterns in a single-center cohort of children with Smith-Magenis syndrome (SMS). REE was calculated using indirect calorimetry. Patients' metabolic status was assessed by comparing measured REE (mREE) with predictive REE (pREE). Patients also underwent multidisciplinary evaluation, anthropometric measurements and an assessment of average energy intake, using a 3-day food diary, which was reviewed by a specialized dietitian. Twenty-four patients (13 M) were included, the median age was 9 years (IC 95%, 6-14 years), 84% had 17p11.2 deletion, and 16% had RAI1 variants. REE was not reduced in SMS pediatric patients, and the mREE did not differ from the pREE. In patients with RAI1 variants (16%, n = 3/24), obesity was more prevalent than those with 17p11.2 deletion (100% vs 38%). Lower proteins intake and higher total energy intake were reported in obese and overweight patients, compared to healthy weight children. No significant difference was found between males and females in energy or macronutrient intake.

CONCLUSIONS

In SMS, the onset of obesity is not explained by REE abnormalities, but dietary factors seem to be crucial. Greater concern should be addressed to patients with RAI1 variants. A better understanding of the molecular mechanisms causing obesity in SMS patients could set the basis for possible future targeted therapies.

WHAT IS KNOWN

• More than 90% of SMS patients after the age of 10 are overweight or obese.

WHAT IS NEW

• Onset of overweight and obesity in SMS pediatric patients is not explained by abnormal resting energy expenditure. • The development of syndrome-specific dietary guidelines for SMS patients should be of utmost relevance and are highly needed.

Evaluating predictive equations for energy requirements throughout breast cancer trajectory: A comparative study

BACKGROUND & AIMS

Accurately estimating resting energy requirements is crucial for optimizing energy intake, particularly in the context of patients with varying energy needs, such as individuals with cancer. We sought to evaluate the agreement between resting energy expenditure (REE) predicted by 40 equations and that measured by reference methods in women undergoing active breast cancer treatment stage (I-IV) and post-completion (i.e., survivors).

METHODS

Data from 4 studies were combined. REE values estimated from 40 predictive equations identified by a systematic search were compared with REE assessed by indirect calorimetry (IC) using a metabolic cart (MC-REE N = 46) or a whole-room indirect calorimeter (WRIC-REE N = 44). Agreement between methods was evaluated using Bland-Altman and Lin's concordance coefficient correlation (Lin's CCC).

RESULTS

Ninety participants (24 % survivors, 61.1% had early-stage breast cancer I or II, mean age: 56.8 ± 11 years; body mass index: 28.7 ± 6.4 kg/m2) were included in this analysis. Mean MC-REE and WRIC-REE values were 1389 ± 199 kcal/day and 1506 ± 247 kcal/day, respectively. Limits of agreement were wide for all equations compared to both MC and WRIC (∼300 kcal for both methods), including the most commonly used ones, such as Harris-Benedict and Mifflin ST. Jeor equations; none had a bias within ±10% of measured REE, and all had low agreement per Lin's CCC analysis (<0.90). The Korth equation exhibited the best performance against WRIC and the Lvingston-Kohlstadt equation against MC. Similar patterns of bias were observed between survivors and patients and between patients with stages I-III versus IV cancer.

CONCLUSION

Most equations failed to accurately predict REE at the group level, and none were effective at the individual level. This inaccuracy has significant implications for women with or surviving breast cancer, who may experience weight gain, maintenance, or loss due to inaccurate energy needs estimations. Therefore, our research underscores the need for further efforts to improve REE estimation.

Development of a new equation and validation of earlier resting energy expenditure predicting equations in adults living in Tehran

Background

Predictive equations have been considered as a practical approach for estimating resting energy expenditure (REE) across multiple populations, but their accuracy for each community remains to be determined. Thus, the purposes of this study were to determine the validity of REE predictive equations and to develop a new REE predictive equation in adults living in Tehran.

Methods

The study included 284 subjects (158 females) aged 18-60 years old from two cross-sectional studies conducted in Tehrani populations. Anthropometric measurements were assessed using standard protocols. REE was measured using indirect calorimetry (IC) and was estimated using preexisting equations. A new equation was also developed based on the REE from IC and variables such as age, sex, height, and weight. Measured REE was compared to new equation and preexisting predictive equations via correlation, linear regression, and Bland-Altman tests.

Results

The new equation and the equations by Mifflin-St. Jeor, Livingston, Frankenfield, Nichols, Müller, and Ganpule demonstrated the best predictive value at a group level (mean percentage error=-2.2 to 2.4 %). At an individual level, the new equation and the equations by Mara, Frankenfield, Roza, Nikooyeh, and Harris & Benedict showed the greatest accuracies compared to measured REE (accuracy prediction=50-53%).

Conclusion

This study highlights the importance of considering race when predicting REE. It also demonstrates that the newly developed equation is more appropriate in a clinical setting at group but not individual level. Thus, further research is needed to examine the new equation in an independent sample.

Methods of Determining Energy Expenditure in Individuals with Prader-Willi Syndrome: A Systematic Literature Review

Prader-Willi syndrome (PWS) is a rare disorder characterised by varying nutritional phases that occur throughout the lifespan, ranging from failure to thrive to hyperphagia. If uncontrolled, the imbalance between energy intake and expenditure results in obesity development and increased morbidity and mortality risk. Although measures of energy requirements for accurate nutrition assessment are vital, the evidence appears sparse and heterogeneous; hence, the aim of this review was to examine the available literature on energy expenditure predicted or measured using various methods in individuals with PWS. Studies were sought that presented methods and results on resting energy expenditure or basal metabolic rate. A narrative synthesis was completed to present the study characteristics and results. Methods of determining energy requirements included predictive equations and indirect calorimetry. Differences amongst ages, growth hormone therapy, fasting status, and measures in which results were presented were limitations to appropriately summarising and identifying trends in energy expenditure. Indirect calorimetry was identified as the most accurate method; however, it is not widely available in all settings. Further research is encouraged to support the development of valid and reliable predictive equations that will better inform and improve the efficiency of clinical practice in supporting people with PWS.

Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis

BACKGROUND AND OBJECTIVES

Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations.

METHODS

Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations.

RESULTS

Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day.

CONCLUSION

This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Energy expenditure estimation during activities of daily living in middle-aged and older adults using an accelerometer integrated into a hearing aid

Background

Accelerometers were traditionally worn on the hip to estimate energy expenditure (EE) during physical activity but are increasingly replaced by products worn on the wrist to enhance wear compliance, despite potential compromises in EE estimation accuracy. In the older population, where the prevalence of hearing loss is higher, a new, integrated option may arise. Thus, this study aimed to investigate the accuracy and precision of EE estimates using an accelerometer integrated into a hearing aid and compare its performance with sensors simultaneously worn on the wrist and hip.

Methods

Sixty middle-aged to older adults (average age 64.0 ± 8.0 years, 48% female) participated. They performed a 20-min resting energy expenditure measurement (after overnight fast) followed by a standardized breakfast and 13 different activities of daily living, 12 of them were individually selected from a set of 35 activities, ranging from sedentary and low intensity to more dynamic and physically demanding activities. Using indirect calorimetry as a reference for the metabolic equivalent of task (MET), we compared the EE estimations made using a hearing aid integrated device (Audéo) against those of a research device worn on the hip (ZurichMove) and consumer devices positioned on the wrist (Garmin and Fitbit). Class-estimated and class-known models were used to evaluate the accuracy and precision of EE estimates via Bland-Altman analyses.

Results

The findings reveal a mean bias and 95% limit of agreement for Audéo (class-estimated model) of -0.23 ± 3.33 METs, indicating a slight advantage over wrist-worn consumer devices (Garmin: -0.64 ± 3.53 METs and Fitbit: -0.67 ± 3.40 METs). Class-know models reveal a comparable performance between Audéo (-0.21 ± 2.51 METs) and ZurichMove (-0.13 ± 2.49 METs). Sub-analyses show substantial variability in accuracy for different activities and good accuracy when activities are averaged over a typical day's usage of 10 h (+61 ± 302 kcal).

Discussion

This study shows the potential of hearing aid-integrated accelerometers in accurately estimating EE across a wide range of activities in the target demographic, while also highlighting the necessity for ongoing optimization efforts considering precision limitations observed across both consumer and research devices.

Multiple factor assessment for determining resting metabolic rate in young adults

Existing formulas cannot fully explain the variation of resting metabolic rate (RMR). This study aims to examine potential influencing factors beyond anthropometric measurements and develop more accurate equations using accessible parameters. 324 healthy adults (230 females; 18-32 years old) participated in the study. Height, fat-free mass (FFM), fat mass (FM) and RMR were measured. Menstrual cycle, stress levels, living habits, and frequency of consuming caffeinated foods were collected. Measured RMR were compared with predictive values of the new equations and previous 11 equations. Mean RMR for men and women was 1825.2 ± 248.8 and 1345.1 ± 178.7 kcal/day, respectively. RMR adjusted for FFM0.66FM0.066 was positively correlated with BMI. The multiple regression model showed that RMR can be predicted in this population with model 1 (with FFM, FM, age, sex and daily sun exposure duration) or model 2 (with weight and height replacing FFM and FM). The accuracy was 75.31% in the population for predictive model 1 and 70.68% for predictive model 2. The new equations had overall improved performance when compared with existing equations. The predictive formula that consider daily sun exposure duration improve RMR prediction in young adults. Additional investigation is required among individuals in the middle-aged and elderly demographic.

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.

Maximal sustainable energy intake during transatlantic ocean rowing is insufficient for total energy expenditure and skeletal muscle mass maintenance

Studies of extreme endurance have suggested that there is an alimentary limit to energy intake (EI) of ∼2.5 × resting metabolic rate (RMR). To gain further insight, this study aimed to simultaneously measure EI, total energy expenditure (TEE) body mass and muscle mass in a large cohort of males and females of varying ages during a transatlantic rowing race. Forty-nine competitors (m = 32, f = 17; age 24-67 years; time at sea 46 ± 7 days) in the 2020 and 2021 Talisker Whisky Atlantic Challenge rowed 12-18 hday-1 for ∼3000 miles. TEE was assessed in the final week of the row using 2 H2 18 O doubly labelled water, and EI was analysed from daily ration packs over this period. Thickness of relatively active (vastus lateralis, intermedius, biceps brachaii and rectus abdominus) and inactive (gastrocnemius, soleus and triceps) muscles was measured pre (<7 days) and post (<24 h) row using ultrasound. Body mass was measured and used to calculate RMR from standard equations. There were no sex differences in males and females in EI (2.5 ± 0.5 and 2.3 ± 0.4 × RMR, respectively, P = 0.3050), TEE (2.5 ± 1.0 and 2.3 ± 0.4 × RMR, respectively, P = 0.5170), or body mass loss (10.2 ± 3.1% and 10.0 ± 3.0%, respectively, P = 0.8520), and no effect of age on EI (P = 0.5450) or TEE (P = 0.9344). Muscle loss occurred exclusively in the calf (15.7% ± 11.4% P < 0.0001), whilst other muscles remained unchanged. After 46 days of prolonged ultra-endurance ocean rowing incurring 10% body mass loss, maximal sustainable EI of ∼2.5 × RMR was unable to meet total TEE suggesting that there is indeed a physiological capacity to EI.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

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.

High-protein diet with excess leucine prevents inactivity-induced insulin resistance in women

BACKGROUND AND AIMS

Muscle inactivity leads to muscle atrophy and insulin resistance. The branched-chain amino acid (BCAA) leucine interacts with the insulin signaling pathway to modulate glucose metabolism. We have tested the ability of a high-protein BCAA-enriched diet to prevent insulin resistance during long-term bed rest (BR).

METHODS

Stable isotopes were infused to determine glucose and protein kinetics in the postabsorptive state and during a hyperinsulinemic-euglycemic clamp in combination with amino acid infusion (Clamp + AA) before and at the end of 60 days of BR in two groups of healthy, young women receiving eucaloric diets containing 1 g of protein/kg per day (n = 8) or 1.45 g of protein/kg per day enriched with 0.15 g/kg per day of BCAAs (leucine/valine/isoleucine = 2/1/1) (n = 8). Body composition was determined by Dual X-ray Absorptiometry.

RESULTS

BR decreased lean body mass by 7.6 ± 0.3 % and 7.2 ± 0.8 % in the groups receiving conventional or high protein-BCAA diets, respectively. Fat mass was unchanged in both groups. At the end of BR, percent changes of insulin-mediated glucose uptake significantly (p = 0.01) decreased in the conventional diet group from 155 ± 23 % to 84 ± 10 % while did not change significantly in the high protein-BCAA diet group from 126 ± 20 % to 141 ± 27 % (BR effect, p = 0.32; BR/diet interaction, p = 0.01; Repeated Measures ANCOVA). In contrast, there were no BR/diet interactions on proteolysis and protein synthesis Clamp + AA changes in the conventional diet and the high protein-BCAA diet groups.

CONCLUSION

A high protein-BCAA enriched diet prevented inactivity-induced insulin resistance in healthy women.

Impact of methods for data selection on the day-to-day reproducibility of resting metabolic rate assessed with four different metabolic carts

BACKGROUND AND AIMS

Accomplishing a high day-to-day reproducibility is important to detect changes in resting metabolic rate (RMR) and respiratory exchange ratio (RER) that may be produced after an intervention or for monitoring patients' metabolism over time. We aimed to analyze: (i) the influence of different methods for selecting indirect calorimetry data on RMR and RER assessments; and, (ii) whether these methods influence RMR and RER day-to-day reproducibility.

METHODS AND RESULTS

Twenty-eight young adults accomplished 4 consecutive RMR assessments (30-min each), using the Q-NRG (Cosmed, Rome, Italy), the Vyntus CPX (Jaeger-CareFusion, Höchberg, Germany), the Omnical (Maastricht Instruments, Maastricht, The Netherlands), and the Ultima CardiO2 (Medgraphics Corporation, St. Paul, Minnesota, USA) carts, on 2 consecutive mornings. Three types of methods were used: (i) short (periods of 5 consecutive minutes; 6-10, 11-15, 16-20, 21-25, and 26-30 min) and long time intervals (TI) methods (6-25 and 6-30 min); (ii) steady state (SSt methods); and, (iii) methods filtering the data by thresholding from the mean RMR (filtering methods). RMR and RER were similar when using different methods (except RMR for the Vyntus and RER for the Q-NRG). Conversely, using different methods impacted RMR (all P ≤ 0.037) and/or RER (P ≤ 0.009) day-to-day reproducibility in all carts. The 6-25 min and the 6-30 min long TI methods yielded more reproducible measurements for all metabolic carts.

CONCLUSION

The 6-25 min and 6-30 min should be the preferred methods for selecting data, as they result in the highest day-to-day reproducibility of RMR and RER assessments.

Age But Not Menopausal Status Is Linked to Lower Resting Energy Expenditure

CONTEXT

It remains uncertain whether aging before late adulthood and menopause are associated with fat-free mass and fat mass-adjusted resting energy expenditure (REEadj).

OBJECTIVES

We investigated whether REEadj differs between middle-aged and younger women and between middle-aged women with different menopausal statuses. We repeated the age group comparison between middle-aged mothers and their daughters to partially control for genotype. We also explored whether serum estradiol and FSH concentrations explain REEadj in midlife.

METHODS

We divided 120 women, including 16 mother-daughter pairs, into age groups; group I (n = 26) consisted of participants aged 17 to 21, group II (n = 35) of those aged 22 to 38, and group III (n = 59) of those aged 41 to 58 years. The women in group III were further categorized as pre- or perimenopausal (n = 19), postmenopausal (n = 30), or postmenopausal hormone therapy users (n = 10). REE was assessed using indirect calorimetry, body composition using dual-energy X-ray absorptiometry, and hormones using immunoassays.

RESULTS

The REEadj of group I was 126 kcal/day [95% confidence interval (CI): 93-160] higher than that of group III, and the REEadj of group II was 88 kcal/day (95% CI: 49-127) higher. Furthermore, daughters had a 100 kcal/day (95% CI: 63-138 kcal/day) higher REEadj than their middle-aged mothers (all P < .001). In group III, REEadj was not lower in postmenopausal women and did not vary by sex hormone concentrations.

CONCLUSIONS

We demonstrated that REEadj declines with age in women before late adulthood, also when controlling partially for genetic background, and that menopause may not contribute to this decline.

Validation of the Updated GloboDiet Version by Protein and Potassium Intake for the German National Nutrition Monitoring

(1) Background: The German version of GloboDiet, a software for a computer-based assessment of 24 h recalls, was intensively updated. Therefore, validation is required prior to its use in the upcoming data collection within the German National Nutrition Monitoring. (2) Methods: For this purpose, the cross-sectional ErNst study with 109 participants (57 women and 52 men) was conducted. The study provided data on 24 h GloboDiet recalls and 24 h urine samples from the same day. Protein and potassium intake, known as eligible validation markers, were compared to the measured excretion in urine. To assess the agreement between intake and excretion, the following statistical methods were used: Wilcoxon rank tests, confidence intervals, Spearman correlations, and Bland-Altman plots. (3) Results: Overall, the updated German GloboDiet version showed valid estimates of protein intake. Regarding potassium, results were ambiguous and differed depending on the statistical method applied. While the Bland-Altman plot showed a good agreement between 24 h recalls and urine samples for potassium, the correlation was weak, suggesting that 24 h recalls may underestimate true intake. (4) Conclusions: Despite the partly ambiguous results, the updated GloboDiet version linked to the current German Nutrient Database provides valid estimates of nutrient intake.

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.

Does eating less or exercising more to reduce energy availability produce distinct metabolic responses?

When less energy is available to consume, people often lose weight, which reduces their overall metabolic rate. Their cellular metabolic rate may also decrease (metabolic adaptation), possibly reflected in physiological and/or endocrinological changes. Reduced energy availability can result from calorie restriction or increased activity energy expenditure, raising the following question that our review explores: do the body's metabolic and physiological responses to this reduction differ or not depending on whether they are induced by dietary restriction or increased activity? First, human studies offer indirect, contentious evidence that the body metabolically adapts to reduced energy availability, both in response to either a calorie intake deficit or increased activity (exercise; without a concomitant increase in food intake). Considering individual aspects of the body's physiology as constituents of whole-body metabolic rate, similar responses to reduced energy availability are observed in terms of reproductive capacity, somatic maintenance and hormone levels. By contrast, tissue phenotypic responses differ, most evidently for skeletal tissue, which is preserved in response to exercise but not calorie restriction. Thus, while in many ways 'a calorie deficit is a calorie deficit', certain tissues respond differently depending on the energy deficit intervention. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part I)'.

Circulating Neuronatin Levels Are Positively Associated with BMI and Body Fat Mass but Not with Psychological Parameters

Human genetic studies have associated Neuronatin gene variants with anorexia nervosa (AN) and obesity. Studies on the expression of the Neuronatin gene product, a proteolipid, are lacking. We investigated the relationship between circulating Neuronatin, body mass index (BMI), body composition (BC), physical activity (PA), and psychometric outcomes in patients with AN, normal weight, and obesity. Plasma Neuronatin was measured by ELISA in (1) 79 subjects of five BMI categories (AN/BMI < 17.5 kg/m2; normal weight/BMI 18.5-25 kg/m2; obesity/BMI 30-40 kg/m2; obesity/BMI 40-50 kg/m2; obesity/BMI > 50 kg/m2) with assessment of BC (bioimpedance analysis; BIA); (2) 49 women with AN (BMI 14.5 ± 1.8 kg/m2) with measurements of BC (BIA) and PA (accelerometry); (3) 79 women with obesity (BMI 48.8 ± 7.8 kg/m2) with measurements of anxiety (GAD-7), stress (PSQ-20), depression (PHQ-9) and eating behavior (EDI-2). Overall, a positive correlation was found between Neuronatin and BMI (p = 0.006) as well as total fat mass (FM; p = 0.036). In AN, Neuronatin did not correlate with BMI, FM, or PA (p > 0.05); no correlations were found between Neuronatin and psychometric outcomes in obesity (p > 0.05). The findings suggest an FM-dependent peripheral Neuronatin expression. The decreased Neuronatin expression in AN provides evidence that Neuronatin is implicated in the pathogenesis of eating disorders.

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.

Resting energy expenditure in adolescents with Down syndrome: a comparison of commonly used predictive equations

BACKGROUND

Adolescents with Down syndrome (DS) are two to three times more likely to be obese than their typically developing peers. When preventing or treating obesity, it is useful for clinicians to understand an individual's energy intake needs. Predictive resting energy expenditure (REE) equations are often recommended for general use in energy intake recommendations; however, these predictive equations have not been validated in youth with DS. The aim of this study was to compare the accuracy of seven commonly used predictive equations for estimating REE in adolescents who are typically developing to REE measured by indirect calorimetry in adolescents with DS.

METHODS

Adolescents with DS participated in a 90-min laboratory visit before 10:00 a.m. after a 12-h overnight fast and a 48-h abstention from aerobic exercise. REE was measured via indirect calorimetry, and estimated REE was derived using the Institute of Medicine, Molnar, Muller and World Health Organization equations. Mean differences between the measured and predicted REE for each equation were evaluated with equivalency testing, and P-values were adjusted for multiple comparisons using the Holm method.

RESULTS

Forty-six adolescents with DS (age: 15.5 ± 1.7 years, 47.8% female, 73.9% non-Hispanic White) completed the REE assessment. Average measured REE was 1459.5 ± 267.8 kcal/day, and the Institute of Medicine equations provided the most accurate prediction of REE with a 1.7 ± 11.2% (13.9 ± 170.3 kcal/day) overestimation. This prediction was not statistically different from the measured REE [P-value = 0.582; 95% confidence interval (CI): -64.5, 36.7], and the difference between the measured and predicted REE was statistically equivalent to zero (P-value = 0.024; 90% CI: -56.1, 28.3).

CONCLUSIONS

The results suggest that the Institute of Medicine equation may be useful in predicting REE in adolescents with DS. Future research should confirm these results in a larger sample and determine the utility of the Institute of Medicine equation for energy intake recommendations during a weight management intervention.

Poor performance of predictive equations to estimate resting energy expenditure in patients with Crohn's disease

Studies exploring the accuracy of equations calculating resting energy expenditure (REE) in patients with Crohn's disease (CD) are lacking. The aim of this study was to investigate the accuracy of REE predictive equations against indirect calorimetry in CD patients. REE was measured using indirect calorimetry (mREE) after an overnight fasting. Fourteen predictive equations, with and without body composition analysis parameters, were compared with mREE using different body weight approaches. Body composition analysis was performed using dual X-ray absorptiometry. One hundred and eighty-six CD outpatients (102 males) with mean age 41·3 (sd 14·1) years and 37·6 % with active disease were evaluated. Mean mREE in the total sample was 7255 (sd 1854) kJ/day. All equations underpredicted REE and showed moderate correlations with mREE (Pearson's r or Spearman's rho 0·600-0·680 for current weight, all P-values < 0·001). Accuracy was low for all equations at the individual level (28-42 and 25-40 % for current and adjusted body weight, respectively, 19-33 % for equations including body composition parameters). At the group level, accuracy showed wide limits of agreement and proportional biases. Accuracy remained low when sample was studied according to disease activity, sex, BMI and medication use. All predictive equations underestimated REE and showed low accuracy. Indirect calorimetry remains the best method for estimating REE of patients with CD.

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.

Comparison of Actual and Predicted Resting Metabolic Rate in Women with Lipedema

Background: An adequate dietary energy supply is particularly important in patients with lipedema as it promotes weight and fat loss. Accurate estimation of resting metabolic rate (RMR) allows implementing a proper calorie restriction diet in patients with lipedema. Our study aimed to compare actual resting metabolic rate (aRMR) with predicted resting metabolic rate (pRMR) in women with lipedema and to determine the association between individual body composition parameters, body mass index, and aRMR. Methods and Results: A total of 108 women diagnosed with lipedema were enrolled in the study. aRMR was obtained by indirect calorimetry (IC) using FitMate WM metabolic system (Cosmed, Rome, Italy). pRMR was estimated with predictive equations and bioelectric impedance analysis (BIA). All body composition parameters were based on BIA. The mean aRMR in the study group was 1705.2 ± 320.7 kcal/day. This study found the agreement of predictive equations compared to IC is low (<60%). Most methods of predicted RMR measurement used in our study significantly underpredicted aRMR in patients with lipedema. Therefore, the most applied equations remain useless in clinical practice in this specific population due to large individual differences among the studied women. Conclusions: IC is the best tool to evaluate RMR in evaluated patients with lipedema. It is necessary to propose a new equation to RMR determination in clinical practice.

Resting Energy Expenditure in the Critically Ill and Healthy Elderly-A Retrospective Matched Cohort Study

The use of indirect calorimetry to measure resting energy expenditure (mREE) is widely recommended as opposed to calculating REE (cREE) by predictive equations (PE). The aim of this study was to compare mREE with cREE in critically ill, mechanically ventilated patients aged ≥ 75 years and a healthy control group matched by age, gender and body mass index. The primary outcome was the PE accuracy rate of mREE/cREE, derived using Bland Altman plots. Secondary analyses included linear regression analyses for determinants of intraindividual mREE/cREE differences in the critically ill and interindividual mREE differences in the matched healthy cohort. In this retrospective study, 90 critically ill patients (median age 80 years) and 58 matched healthy persons were included. Median mREE was significantly higher in the critically ill (1457 kcal/d) versus the healthy cohort (1351 kcal/d), with low PE accuracy rates (21% to 49%). Independent predictors of mREE/cREE differences in the critically ill were body temperature, heart rate, FiO₂, hematocrit, serum sodium and urea. Body temperature, respiratory rate, and FiO₂ were independent predictors of interindividual mREE differences (critically ill versus healthy control). In conclusion, the commonly used PE in the elderly critically ill are inaccurate. Respiratory, metabolic and energy homeostasis variables may explain intraindividual mREE/cREE as well as interindividual mREE differences.

Physiologic and Behavioral Correlates of Energy Intake in Emerging Adulthood: Cross-Sectional Analysis of the RIGHT Track Health Study

BACKGROUND

Consistent findings have reported that FFM is associated with EI. However, conjoint assessments of physiologic (body composition, fasting serum leptin) and behavioral [eating behaviors and physical activity (PA)] correlates of EI during emerging adulthood have not been examined.

OBJECTIVES

We assessed associations between physiologic and behavioral correlates of EI within the context of one another in emerging adults (18-28 years old). We also assessed these associations in a subsample after the removal of probable EI underreporters.

METHODS

Cross-sectional data from 244 emerging adults (age = 19.6 ± 1.4 y; BMI = 26.4 ± 6.6 kg/m2; 56.6% female) from the RIGHT Track Health study were used. Measures included body composition (BOD POD), eating behaviors (Three-Factor Eating Questionnaire), objective and subjective PA (accelerometer-derived total activity counts and Godin-Shephard Leisure-Time Exercise Questionnaire), fasting serum leptin, and EI (three 24-hour dietary recalls). Correlates independently associated with EI were entered into a backward stepwise linear regression model. Correlates that met the criteria of P < 0.05 were retained. Analyses were repeated in a subsample after removing probable EI underreporters (n = 48). Effect modification by sex (male and female) and BMI (BMI < 25 kg/m2, BMI ≥ 25 kg/m2) categories was also assessed.

RESULTS

In the full sample, FFM (β: 18.4; 95% CI: 9.9, 26.8), leptin (β: -84.8; 95% CI: -154.3, -15.4), dietary restraint (β: -35.2; 95% CI: -59.1, -11.3), and subjective PA (β: 2.5; 95% CI: 0.04, 4.9) were significantly associated with EI. After the removal of probable underreporters, only FFM remained significantly associated with EI (β: 43.9; 95% CI: 27.2, 60.6). No evidence of effect modification by sex or BMI categories was noted.

CONCLUSIONS

Although physiologic and behavioral correlates were associated with EI in the full sample, only FFM remained a robust correlate of EI in a subsample of emerging adults after removing probable EI underreporters.

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.

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland−Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Assessment of Energy and Nutrient Intake and the Intestinal Microbiome (ErNst study): Protocol and Methods of a Cross-sectional Human Observational Study (Preprint)

BACKGROUND

On the national level, nutritional monitoring requires the assessment of reliable representative dietary intake data. To achieve this, standardized tools need to be developed, validated, and kept up-to-date with recent developments in food products and the nutritional behavior of the population. Recently, the human intestinal microbiome has been identified as an essential mediator between nutrition and host health. Despite growing interest in this connection, only a few associations between the microbiome, nutrition, and health have been clearly established. Available studies paint an inconsistent picture, partly due to a lack of standardization.

OBJECTIVE

First, we aim to verify if food consumption, as well as energy and nutrient intake of the German population, can be recorded validly by means of the dietary recall software GloboDiet, which will be applied in the German National Nutrition Monitoring. Second, we aim to obtain high-quality data using standard methods on the microbiome, combined with dietary intake data and additional fecal sample material, and to also assess the functional activity of the microbiome by measuring microbial metabolites.

METHODS

Healthy female and male participants aged between 18 and 79 years were recruited. Anthropometric measurements included body height and weight, BMI, and bioelectrical impedance analysis. For validation of the GloboDiet software, current food consumption was assessed with a 24-hour recall. Nitrogen and potassium concentrations were measured from 24-hour urine collections to enable comparison with the intake of protein and potassium estimated by the GloboDiet software. Physical activity was measured over at least 24 hours using a wearable accelerometer to validate the estimated energy intake. Stool samples were collected in duplicate for a single time point and used for DNA isolation and subsequent amplification and sequencing of the 16S rRNA gene to determine microbiome composition. For the identification of associations between nutrition and the microbiome, the habitual diet was determined using a food frequency questionnaire covering 30 days.

RESULTS

In total, 117 participants met the inclusion criteria. The study population was equally distributed between the sexes and 3 age groups (18-39, 40-59, and 60-79 years). Stool samples accompanying habitual diet data (30-day food frequency questionnaire) are available for 106 participants. Current diet data and 24-hour urine samples for the validation of GloboDiet are available for 109 participants, of which 82 cases also include physical activity data.

CONCLUSIONS

We completed the recruitment and sample collection of the ErNst study with a high degree of standardization. Samples and data will be used to validate the GloboDiet software for the German National Nutrition Monitoring and to compare microbiome composition and nutritional patterns.

TRIAL REGISTRATION

German Register of Clinical Studies DRKS00015216; https://drks.de/search/de/trial/DRKS00015216.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/42529.

Circulating Spexin Is Associated with Body Mass Index and Fat Mass but Not with Physical Activity and Psychological Parameters in Women across a Broad Body Weight Spectrum

Spexin (SPX) is a novel, widely expressed peptide, with anorexigenic effects demonstrated in animal models and negatively correlated with body mass index (BMI) in humans. It increases locomotor activity in rodents and is elevated in human plasma following exercise. Studies have also shown an effect of stress and anxiety on SPX’s expression in different brain structures in animals. The relationships between plasma SPX and physical activity, body composition, and patient-reported outcomes such as perceived stress, depressiveness, anxiety, and eating behaviors are unknown and were examined in this study over a wide BMI range. A total of 219 female (n = 68 with anorexia nervosa; n = 79 with obesity; n = 72 with normal weight) inpatients were enrolled. Perceived stress (PSQ 20), anxiety (GAD 7), depressiveness (PHQ 9), and eating disorder pathology (EDI 2), as well as BMI, bioimpedance analysis, and accelerometry, were measured cross-sectionally at the beginning of treatment and correlated with plasma SPX levels (measured by ELISA) obtained at the same time. Plasma SPX levels were negatively associated with BMI (r = −0.149, p = 0.027) and body fat mass (r = −0.149, p = 0.04), but did not correlate with perceived stress, anxiety, depressiveness, eating behavior, energy expenditure, and physical activity (p > 0.05). The results replicate the negative correlation of SPX with BMI and fat mass, but do not support the hypothesis that peripheral SPX plays a role in the regulation of stress, depressiveness, anxiety, eating behavior, or physical activity.

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.

Predictive equations for energy expenditure in adult humans: From resting to free-living conditions

Humans acquire energy from the environment for survival. A central question for nutritional sciences is how much energy is required to sustain cellular work while maintaining an adequate body mass. Because human energy balance is not exempt from thermodynamic principles, the energy requirement can be approached from the energy expenditure. Conceptual and technological advances have allowed understanding of the physiological determinants of energy expenditure. Body mass, sex, and age are the main factors determining energy expenditure. These factors constitute the basis for predictive equations for resting (REE) and total (TEE) energy expenditure in healthy adults. These equations yield predictions that differ up to ~400 kcal/d for REE and ~550 kcal/d for TEE. Identifying additional factors accounting for such variability and the most valid equations appears relevant. This review used novel approaches based on mathematical modeling of REE and analyses of the data from which REE predictive equations were generated. As for TEE, R² and SE were considered because only a few predictive equations are available. From these analyses, Oxford's and Plucker's equations appear valid for predicting REE and TEE in adults, respectively.

Dietary intake and physical activity of German university students

Aim

A balanced diet is important for health and well-being. The aim of this study was to investigate dietary intake and physical activity in German university students.

Subjects and methods

A cross-sectional nutrition study was conducted at the University of Education, Schwäbisch Gmünd (Germany). Food consumption (3-day estimated dietary record) and physical activity level (PAL, standardized questionnaire) were assessed in 329 female (f) (age 22.7±3.4 years) and 60 male (m) students (age 24.0±3.1 years) and compared with recommendations (one-sample t-test, α = 0.05).

Results

On average, students consumed less vegetables (f: 177±119, m: 152±163 g/day), fruit (f: 169±117, m: 146±158 g/day), and potatoes/grain products (f: 303±126, m: 362±163 g/day) than recommended. Meat consumption was too high in men (1155±651 g/week), but not in women (490±455 g/week). The consumption of milk/dairy products was slightly too low in women (190±129 g/day), but in the recommended range for men (279±253 g/day). Nutrient recommendations were almost achieved (exceptions: fiber, vitamin D and folate [f, m], fat, iron and vitamin E [f], carbohydrate [m]). Leisure time physical activity was 259±222 min/week (f) and 359±236 min/week (m), and PAL was 1.60±0.11 (f) and 1.65±0.12 (m).

Conclusions

Dietary intake and physical activity in university students is roughly equivalent to that in the average German population and needs to be improved.

Clinical Correlates of Measured and Predicted Resting Energy Expenditure in Patients with Anorexia Nervosa: A Retrospective Cohort Study

Resting energy expenditure (REE; i.e., the calorie amount required for 24 h during a non-active period) is an important parameter in nutritional rehabilitation of patients with anorexia nervosa (AN). This study determined whether age, body mass index, AN duration/subtype/specific symptoms/clinical severity, cognitive function alterations, and psychiatric comorbidities influenced REE or the difference between the calculated and estimated REE. Patients with AN who were followed at a daycare treatment facility between May 2017 and January 2020 (n = 138) underwent a complete assessment that included the MINI, Eating Disorder Examination Questionnaire, d2 test of attention, body fat composition by bioelectrical impedance analysis (BIA) and REE measurement by indirect calorimetry (REEIC). AN subtype (N = 66 for restrictive subtype and N = 69 for non-restrictive subtype; p = 0.005), free-fat mass (<0.001), and fat mass (<0.001) were associated with REEIC. Age (p < 0.001), height (p = 0.003), and AN duration (N = 46 for <3 years and N = 82 for ≥3 years; p = 0.012) were associated with the difference between estimated REE (using the Schebendach equation) and measured REEIC. Therefore, the Schebendach equation was adjusted differently in the two patients’ subgroups (AN duration ≤ or >3 years). Overall, REE was higher in patients with restrictive than non-restrictive AN. In the absence of BIA measures, REE-estimating equations should take into account AN duration.

Does adaptive thermogenesis occur after weight loss in adults? A systematic review

Adaptive thermogenesis (AT) has been proposed to be a compensatory response that may resist weight loss (WL) and promote weight regain. This systematic review examined the existence of AT in adults after a period of negative energy balance (EB) with or without a weight stabilisation phase. Studies published until 15 May 2020 were identified from PubMed, Cochrane Library, EMBASE, MEDLINE, SCOPUS and Web of Science. Inclusion criteria included statistically significant WL, observational with follow-up or experimental studies, age > 18y, sample size ≥10 participants, intervention period ≥ 1week, published in English, objective measures of total daily energy expenditure (EE) (TDEE), resting EE (REE) and sleeping EE(SEE). The systematic review was registered at PROSPERO (2020 CRD42020165348). A total of thirty-three studies comprising 2528 participants were included. AT was observed in twenty-seven studies. Twenty-three studies showed significant values for AT for REE (82·8 %), four for TDEE (80·0 %) and two for SEE (100 %). A large heterogeneity in the methods used to quantify AT and between subjects and among studies regarding the magnitude of WL and/or of AT was reported. Well-designed studies reported lower or non-significant values for AT. These findings suggest that although WL may lead to AT in some of the EE components, these values may be small or non-statistically significant when higher-quality methodological designs are used. Furthermore, AT seems to be attenuated, or non-existent, after periods of weight stabilisation/neutral EB. More high-quality studies are warranted not only to disclose the existence of AT but also to understand its clinical implications on weight management outcomes.

Characteristics of healthy German children and adolescents across tertiles of calcaneal stiffness index

Aim

Identifying risk factors for low bone stiffness index (SI) might be one crucial strategy for osteoporosis prevention. Purpose was to characterize healthy schoolchildren across tertiles of SI.

Subject and methods

In 248 girls (13.4 ± 1.9 years, BMI: 20.2 ± 4.8 kg/m2) and 231 boys (13.6 ± 1.7 years, BMI: 19.3 ± 3.3 kg/m2), the following parameters were assessed: calcaneal SI (quantitative ultrasound), body composition (bioelectrical impedance analysis), Bone Healthy Eating Index (BoneHEI; food frequency questionnaire), and physical activity level (PAL; activity questionnaire). Participants were classified according to age- and sex-specific SI tertiles (low, medium, and high). Between-group comparisons were achieved by Kruskal–Wallis-H-tests (α = 0.05).

Results

Girls with low SI had significantly lower body mass (49.2 ± 16.7 vs 54.8 ± 12.2 kg; p < 0.01), BMI (19.6 ± 5.4 vs 21.3 ± 3.9 kg/m2; p < 0.0001), fat-free mass (36.3 ± 8.3 vs 39.5 ± 6.0 kg; p < 0.01), and fat mass (23.7 ± 9.1 vs 26.8 ± 7.2%; p < 0.05) compared to those with high SI. In boys, significant differences between low and high SI were obtained for PAL (1.49 ± 0.12 vs 1.56 ± 0.14; p < 0.01). BoneHEI was not significantly different between tertiles in both sexes.

Conclusion

Girls with low body mass and boys with low PAL have a higher risk for low SI. Schoolchildren should strive for normal body mass and perform regular physical activity.

Effects of an individualised nutritional intervention to tackle malnutrition in nursing homes: a pre-post study

PURPOSE

Individualised interventions are recommended to tackle malnutrition in older adults, but approaches for nursing home (NH) residents are scarce. This study investigated the effects of an individualised nutritional intervention in NH residents with (risk of) malnutrition.

METHODS

In a pre-post study, 6 weeks (w) of usual care were followed by 6w of intervention. The intervention consisted of up to three supplement modules (sweet and savoury protein creams and protein-energy drink, single or combined) and, if required, reshaped texture-modified meals (RTMM).

RESULTS

Fifty residents completed the study (84 ± 8 years, 74% female). One-third (32%) received RTMM. Additional 258 ± 167 kcal/day and 23 ± 15 g protein/day were offered. Mean daily energy intake increased by 207 (95%CI 47-368, p = 0.005) kcal and protein intake by 14 (7-21, p < 0.001) g (w12 vs w1). Quality of life (QoL) increased in the subscale "care relationship" (+ 9 (3-15) points, p = 0.002, w12 vs w6). Body weight, handgrip strength, and other QoL subscales did not change.

CONCLUSION

Our intervention improved dietary intake and one QoL subscale in NH residents with (risk of) malnutrition. As a next step, randomized controlled trials are needed to investigate the impact of individualised interventions more comprehensively.

Modeling Energy Expenditure Estimation in Occupational Context by Actigraphy: A Multi Regression Mixed-Effects Model

The accurate prediction of energy requirements for healthy individuals has many useful applications. The occupational perspective has also been proven to be of great utility for improving workers' ergonomics, safety, and health. This work proposes a statistical regression model based on actigraphy and personal characteristics to estimate energy expenditure and cross-validate the results with reference standardized methods. The model was developed by hierarchical mixed-effects regression modeling based on the multitask protocol data. Measurements combined actigraphy, indirect calorimetry, and other personal and lifestyle information from healthy individuals (n = 50) within the age of 29.8 ± 5 years old. Results showed a significant influence of the variables related to movements, heart rate and anthropometric variables of body composition for energy expenditure estimation. Overall, the proposed model showed good agreement with energy expenditure measured by indirect calorimetry and evidenced a better performance than the methods presented in the international guidelines for metabolic rate assessment proving to be a reliable alternative to normative guidelines. Furthermore, a statistically significant relationship was found between daily activity and energy expenditure, which raised the possibility of further studies including other variables, namely those related to the subject's lifestyle.

Peripheral impairments of oxidative metabolism after a 10-day bed rest are upstream of mitochondrial respiration

Abstract

In order to identify peripheral biomarkers of impaired oxidative metabolism during exercise following a 10‐day bed rest, 10 males performed an incremental exercise (to determine peak pulmonary V̇O₂ (V̇O₂p)) and moderate‐intensity exercises, before (PRE) and after (POST) bed rest. Blood flow response was evaluated in the common femoral artery by Eco‐Doppler during 1 min of passive leg movements (PLM). The intramuscular matching between O₂ delivery and O₂ utilization was evaluated by near‐infrared spectroscopy (NIRS). Mitochondrial respiration was evaluated ex vivo by high‐resolution respirometry in isolated muscle fibres, and in vivo by NIRS by the evaluation of skeletal muscle V̇O₂ (V̇O₂m) recovery kinetics. Resting V̇O₂m was estimated by NIRS. Peak V̇O₂p was lower in POST vs. PRE. The area under the blood flow vs. time curve during PLM was smaller (P = 0.03) in POST (274 ± 233 mL) vs. PRE (427 ± 291). An increased (P = 0.03) overshoot of muscle deoxygenation during a metabolic transition was identified in POST. Skeletal muscle citrate synthase activity was not different (P = 0.11) in POST (131 ± 16 nmol min^–1 mg^–1) vs. PRE (138 ± 19). Maximal ADP‐stimulated mitochondrial respiration (66 ± 18 pmol s^–1 mg^–1 (POST) vs. 72 ± 14 (PRE), P = 0.41) was not affected by bed rest. Apparent K_m for ADP sensitivity of mitochondrial respiration was reduced in POST vs. PRE (P = 0.04). The V̇O₂m recovery time constant was not different (P = 0.79) in POST (22 ± 6 s) vs. PRE (22 ± 6). Resting V̇O₂m was reduced by 25% in POST vs. PRE (P = 0.006). Microvascular‐endothelial function was impaired following a 10‐day bed rest, whereas mitochondrial mass and function (both in vivo and ex vivo) were unaffected or slightly enhanced.

Key points

Ten days of horizontal bed rest impaired in vivo oxidative function during exercise.

Microvascular impairments were identified by different methods.

Mitochondrial mass and mitochondrial function (evaluated both in vivo and ex vivo) were unchanged or even improved (i.e. enhanced mitochondrial sensitivity to submaximal [ADP]).

Resting muscle oxygen uptake was significantly lower following bed rest, suggesting that muscle catabolic processes induced by bed rest/inactivity are less energy‐consuming than anabolic ones.

Challenges in tackling energy expenditure as obesity therapy: From preclinical models to clinical application

BACKGROUND

A chronic imbalance of energy intake and energy expenditure results in excess fat storage. The obesity often caused by this overweight is detrimental to the health of millions of people. Understanding both sides of the energy balance equation and their counter-regulatory mechanisms is critical to the development of effective therapies to treat this epidemic.

SCOPE OF REVIEW

Behaviors surrounding ingestion have been reviewed extensively. This review focuses more specifically on energy expenditure regarding bodyweight control, with a particular emphasis on the organs and attractive metabolic processes known to reduce bodyweight. Moreover, previous and current attempts at anti-obesity strategies focusing on energy expenditure are highlighted. Precise measurements of energy expenditure, which consist of cellular, animal, and human models, as well as measurements of their translatability, are required to provide the most effective therapies.

MAJOR CONCLUSIONS

A precise understanding of the components surrounding energy expenditure, including tailored approaches based on genetic, biomarker, or physical characteristics, must be integrated into future anti-obesity treatments. Further comprehensive investigations are required to define suitable treatments, especially because the complex nature of the human perspective remains poorly understood.