Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit

Mapping the Evidence for Measuring Energy Expenditure and Indicating Hypermetabolism in Motor Neuron Disease: A Scoping Review

OBJECTIVE

To map the international methods used to measure energy expenditure of adults living with motor neuron disease (MND) and to highlight discrepancies when indicating hypermetabolism in the MND literature.

BACKGROUND

A decline in the nutritional status of patients is associated with exacerbated weight loss and shortened survival. Assessments of energy expenditure, using a variety of methods, are important to ensure an adequate energy intake to prevent malnutrition-associated weight loss. Assessments of energy expenditure are also commonly used to indicate hypermetabolism in MND, although these approaches may not be optimal.

METHODS

A protocol based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for Scoping Reviews Guidelines was developed. Three electronic databases (Medline [Ovid], CINAHL [EBSCO], and Web of Science) were exhaustively searched. Identified publications were systematically screened according to predefined PICOS eligibility criteria. The primary outcome was the identification of methods used to measure energy expenditure in MND. The secondary outcome was the identification of applications of energy expenditure assessments to indicate hypermetabolism in MND.

RESULTS

Thirty-two observational primary research publications were identified. Thirteen (40.6%) were longitudinal in design, with data on repeated measurements of energy expenditure presented in 3 (9.4%). Thirteen (40.6%) were case-control studies, of which 11 use a matched control group. Pulmonary function was used to assess eligibility in 10 publications. Energy expenditure was measured using indirect calorimetry (IC) in 31 studies. Discrepancies in the durations of fasted, measurement, and washout periods were observed. Of all included publications, 50% used assessments of resting energy expenditure to identify hypermetabolism. Bioelectrical impedance analysis was used to assess body composition alongside energy expenditure in 93.8% of publications.

CONCLUSIONS

Resting energy expenditure is most frequently measured using an open-circuit IC system. However, there is a lack of a standardized, validated protocol for the conduct and reporting of IC and metabolic status in patients with MND.

Frailty and Energy Intake Deficiency Reduce the Efficiency of Activities of Daily Living in Patients with Musculoskeletal Disorders: A Retrospective Cohort Study

Background/Objective: This study aimed to investigate the relationship between rehabilitation effectiveness (RE) and pre-admission Clinical Frailty Scale (CFS) scores and energy intake. Methods: This retrospective observational study included 735 patients (81 ± 10 years; male: 27.5%) with musculoskeletal disorders discharged from convalescent rehabilitation wards between April 2018 and April 2024. The patients were classified into four groups based on their CFS scores (non-frail, CFS 1-3; frail, CFS ≥ 4) and rate of energy intake (energy-sufficient vs. energy-deficient). Group comparisons of RE were conducted, and the relationships between the CFS score, energy intake, and RE were analyzed. Results: The RE was significantly lower in the frail/energy-deficient group (53.6 [41.9-78.1]) than in the non-frail/energy-sufficient (78.5 [61.8-90.7]), non-frail/energy-deficient (70.6 [53.4-87.4]), and frail/energy-sufficient (59.9 [41.9-78.1]) groups. Additionally, the frail/energy-sufficient group had significantly lower RE scores than the non-frail/energy-sufficient and non-frail/energy-deficient groups. A multiple linear regression analysis revealed that age, male sex, CFS score, energy intake, handgrip strength, Functional Oral Intake Scale score, Mini Nutritional Assessment-Short Form score, B-type natriuretic peptide, and creatinine were significantly associated with the RE. Conclusions: Both frailty and inadequate energy intake reduce the rate of improvement in activities of daily living in patients with musculoskeletal diseases.

Low variability of resting metabolic rate among early, middle, and late achievers of steady state suggests a shortened indirect calorimetry protocol for young children

OBJECTIVE

Standardized protocols for measuring resting metabolic rate (RMR; kJ/d) in children, using indirect calorimetry (IC), are lacking. In this cross-sectional, observational study we aimed to determine, in a diverse group of children, variability of RMR-associated measurements after achieving steady state (SS) with a shortened IC protocol. RMR of 120 conveniently sampled healthy Southern African children 6 to 9 years (60 black [32 girls], 60 white [37 girls]) was measured via IC (Quark RMR, Cosmed).

METHODS

Measurements, without a prior rest period, continued for 15 to 20 minutes. Participants were categorized according to time when machine-generated SS was achieved: early achievers <5 minutes, middle achievers ≥5 and <10 minutes, late achievers ≥10 minutes. Readings before achieving SS were eliminated. Intra-individual percentage coefficient of variation of the mean RMR (kJ/d), VCO2 (mL/min), VO2 (mL/min), and respiratory quotient (VCO2/VO2) was determined for each SS category for the remaining measurement period.

RESULTS

For 113 (94%) participants, usable data were obtained. Mean (min; max) time to reach SS was 5.7 minutes (0.0; 16.2). Nineteen (17%; 6 black; 10 girls [3 black]) did not achieve SS, with no significant difference between sexes (P = 0.290). Significantly (P = 0.048) more white (12%) than black (5%) participants did not achieve SS. Forty-seven (50%) were early, 29 (31%) middle, and 18 (19%) late achievers of SS. There was no significant difference in the sample distribution of SS categories between sexes (P = 0.955) and race/ethnicity categories (P = 9.758).

CONCLUSIONS

After achieving SS, percentage coefficient of variation for all RMR-related variables remained ≤2.89%, well below the recommended <5% to 10% for adults. A shortened IC protocol can be considered for diverse groups of 6- to 9-year-old children.

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.

A century of exercise physiology: concepts that ignited the study of human thermoregulation. Part 2: physiological measurements

In this, the second of four historical reviews on human thermoregulation during exercise, we examine the research techniques developed by our forebears. We emphasise calorimetry and thermometry, and measurements of vasomotor and sudomotor function. Since its first human use (1899), direct calorimetry has provided the foundation for modern respirometric methods for quantifying metabolic rate, and remains the most precise index of whole-body heat exchange and storage. Its alternative, biophysical modelling, relies upon many, often dubious assumptions. Thermometry, used for >300 y to assess deep-body temperatures, provides only an instantaneous snapshot of the thermal status of tissues in contact with any thermometer. Seemingly unbeknownst to some, thermal time delays at some surrogate sites preclude valid measurements during non-steady state conditions. To assess cutaneous blood flow, immersion plethysmography was introduced (1875), followed by strain-gauge plethysmography (1949) and then laser-Doppler velocimetry (1964). Those techniques allow only local flow measurements, which may not reflect whole-body blood flows. Sudomotor function has been estimated from body-mass losses since the 1600s, but using mass losses to assess evaporation rates requires precise measures of non-evaporated sweat, which are rarely obtained. Hygrometric methods provide data for local sweat rates, but not local evaporation rates, and most local sweat rates cannot be extrapolated to reflect whole-body sweating. The objective of these methodological overviews and critiques is to provide a deeper understanding of how modern measurement techniques were developed, their underlying assumptions, and the strengths and weaknesses of the measurements used for humans exercising and working in thermally challenging conditions.

Low Energy Intake Diagnosed Using the Harris-Benedict Equation Is Associated with Poor Prognosis in Elderly Heart Failure Patients

INTRODUCTION

Insufficient nutrient intake is a strong independent predictor of mortality in elderly patients with heart failure. However, it is unclear to what extent energy intake affects their prognosis. This study investigated the association between patient outcomes and actual measured energy intake in elderly patients (≥65 years) with heart failure.

METHODS

This study enrolled 139 elderly patients who were hospitalized with worsening heart failure at Shingu Municipal Medical Center, Shingu, Japan, between May 2017 and April 2018. Energy intake was evaluated for three days (from three days prior to the day of discharge until the day of discharge). Based on basal energy expenditure calculated using the Harris-Benedict equation, the patients were classified into a low-energy group (n = 38) and a high-energy group (n = 101). We assessed the prognosis in terms of both all-cause mortality and readmission due to worsening heart failure as a primary outcome.

RESULTS

Compared to the patients in the high-energy group, the patients in the low-energy group were predominantly female, less frequently had smoking habits and ischemic heart diseases, and had a higher left ventricular ejection fraction. The low-energy group had higher mortality than the high-energy group (p = 0.028), although the two groups showed equivalent event rates of the primary outcome (p = 0.569).

CONCLUSION

Calculations based on the Harris-Benedict equation revealed no significant difference in the primary outcome between the two groups, with a secondary outcome that showed worse mortality in the low-energy group. Given this result, energy requirement-based assessments using the Harris-Benedict equation might help in the management of elderly heart failure patients in terms of improved life outcomes.

New prediction equations for resting energy expenditure in older hospitalized patients: Development and validation

OBJECTIVES

Accurate resting energy expenditure (REE) prediction is needed to prevent over- or underfeeding in older hospitalized patients. However, few validated REE prediction Equations are known for such patients. Therefore, this study aimed to develop new REE prediction Equations and evaluate their validity.

METHODS

This single-center, cross-sectional study enrolled 134 patients ages ≥70 y. For holdout validation, patients were randomized in a 3:1 ratio; for the development data set, a new Equation was developed according to the measured REE using indirect calorimetry. The new and existing Equations were compared using the validation data set.

RESULTS

Mean patient age was 87.4 ± 6.9 y, and 34.3% were male. Two Equations were developed in multivariable regression models: Equation 1: REE (kcal/day) = 313.582 + Height (cm) × 3.973 + Body weight (kg) × 5.332 - Age (y) × 5.474 - (0 if male; 1 if female) × 20.012 + Calf circumference (cm) × 12.174; and Equation 2: REE (kcal/day) = 594.819 + Height (cm) × 3.760 + Body weight (kg) × 8.888 - Age (y) × 6.298 - (0 if male; 1 if female) × 16.396. The mean relative bias (95% CI) with measured REE as a reference had a small bias for Equations 1 and 2 (-0.1 [-4.1 to 3.9]% and -0.2 [-4.4 to 4.1]%, respectively); however, the Harris-Benedict, Food and Agriculture Organization of the United Nations/World Health Organization/United Nations University, Ganpule, and body weight × 20 Equations had larger biases (-6.2 [-10.3 to -2.0]%; 5.3 [1.3 to 9.3]%; -13.9 [-18.6 to -9.3]%; and -11.6 [-16.1 to -7.1]%, respectively).

CONCLUSIONS

New prediction Equations using height, body weight, age, sex, and calf circumference improve REE prediction accuracy in older hospitalized patients.

Predictive Equation for Basal Metabolic Rate in Normal-Weight Chinese Adults

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland-Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland-Altman plots were -514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

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.

Malnutrition assessment by Global Leadership Initiative on Malnutrition criteria in patients with amyotrophic lateral sclerosis

Malnutrition can play an important prognostic role in terms of survival in patients with amyotrophic lateral sclerosis (ALS). In this clinical context, applying criteria defining malnutrition requires particular attention, especially in the initial stage of the disease. This article discusses the application of the most recent criteria used for the definition of malnutrition when applied to patients with ALS. Currently, the Global Leadership Initiative on Malnutrition (GLIM) criteria, which have received a worldwide consensus, are based on parameters such as unintentional weight loss, low body mass index (BMI), and reduced muscle mass (phenotypic criteria) in combination with reduced food intake and assimilation or inflammation and disease (etiologic criteria). However, as discussed in this review, the initial unintentional weight loss and the consequent BMI reduction could be attributed, at least in part, to muscle atrophy, which also alters the reliability of muscle mass assessment. Moreover, the condition of hypermetabolism, which is observed in up to 50% of these patients, may complicate the calculation of total energy requirements. Finally, it remains to be established if the presence of neuroinflammation can be considered a type of inflammatory process able to induce malnutrition in these patients. In conclusion, the monitoring of BMI, associated with body composition evaluation by bioimpedance measurement or specific formulas, could be a practicable approach to the diagnosis of malnutrition in patients with ALS. In addition, attention should be given to dietary intake (e.g., in patients with dysphagia) and excessive involuntary weight loss. On the other hand, as suggested by GLIM criteria, a single assessment of BMI resulting in <20 kg/m2 or <22 kg/m2 in patients aged <70 y and ≥70 y, respectively, should always be considered a sign of malnutrition.

Nutritional Considerations in Celiac Disease and Non-Celiac Gluten/Wheat Sensitivity

A gluten-free diet (GFD) is the only available treatment for celiac disease (CeD), and it may also improve symptoms in non-celiac gluten/wheat sensitivity (NCGWS). In CeD, gluten triggers an immune reaction leading to enteropathy, malabsorption, and symptoms; in NCGWS, the mechanism leading to symptoms is unknown, and neither wheat nor gluten triggers enteropathy or malabsorption. A strict GFD is, therefore, necessary for CeD, but a gluten-restricted diet (GRD) may suffice to achieve symptom control for NCGWS. Regardless of this distinction, the risk of malnutrition and macro- and micronutrient deficiencies is increased by the adoption of a GFD or GRD. Thus, patients with CeD or NCGWS should undergo nutritional assessment and subsequent monitoring, based on evidence-based tools, under the care of a multidisciplinary team involving physicians and dietitians, for the long-term management of their nutrition. This review gives an overview of available nutrition assessment tools and considerations for the nutritional management of CeD and NCGWS populations.

Energy Guidance Using Indirect Calorimetry for Intestinal Failure Patients with Home Parenteral Nutrition: The Right Bag Right at the Start

Intestinal failure is defined as the inability to absorb the minimum of macro and micronutrients, minerals and vitamins due to a reduction in gut function. In a subpopulation of patients with a dysfunctional gastrointestinal system, treatment with total or supplemental parenteral nutrition is required. The golden standard for the determination of energy expenditure is indirect calorimetry. This method enables an individualized nutritional treatment based on measurements instead of equations or body weight calculations. The possible use and advantages of this technology in a home PN setting need critical evaluation. For this narrative review, a bibliographic search is performed in PubMed and Web of Science using the following terms: 'indirect calorimetry', 'home parenteral nutrition', 'intestinal failure', 'parenteral nutrition', 'resting energy expenditure', 'energy expenditure' and 'science implementation'. The use of IC is widely embedded in the hospital setting but more research is necessary to investigate the role of IC in a home setting and especially in IF patients. It is important that scientific output is generated in order to improve patients' outcome and develop nutritional care paths.

Predicted resting metabolic rate and prognosis in patients with ischemic stroke

PURPOSE

Resting metabolic rate (RMR) could represent metabolic health status. This study aims to examine the association of the predicted RMR with 1-year poor functional outcome and all-cause mortality in patients with ischemic stroke as a proxy of metabolic profile.

METHODS

A total of 15,166 patients with ischemic stroke or transient ischemic attack (TIA) from the Third China National Stroke Registry (CNSR-III) were enrolled in this study. The Harris-Benedict equation based on sex, age, weight, and height was used to predict RMR. The primary endpoints were poor functional outcome defined as ≥3 modified Rankin Scale (mRS) score and all-cause mortality within 1 year. The association between predicted RMR and prognosis was assessed by multivariable regression analysis. Besides that, subgroup analysis of age, sex, and body mass index (BMI) with predicted RMR was also performed.

RESULTS

12.85% (1657) individuals had poor functional outcome and 2.87% (380) died of whatever causes within 1 year. An inverse association was found between predicted RMR with poor functional outcome and all-cause mortality. Compared to the lowest quartile, the highest quartile was significantly associated with lower risk of poor functional outcome (adjusted odds ratio [OR], 0.43 [95% confidence interval (CI) 0.33-0.56]) and all-cause mortality (adjusted hazard ratio [HR], 0.44 [95% CI 0.28-0.71]). No significant interaction was between predicted RMR and specified subgroup.

CONCLUSIONS

Predicted RMR by the Harris-Benedict equation seems to be an independent protective predictor of poor functional outcome and all-cause mortality after ischemic stroke as a metabolic proxy.

Prediction of Prognosis in Geriatric Palliative Care Patients with Diagnosed Malnutrition: A Comparison of Nutritional Assessment Parameters

Objective

Malnutrition is very commonly encountered in palliative care centers (PCC), especially in geriatric patients. It is known that development of malnutrition increases morbidity and mortality. In this study, we aimed to investigate the effectiveness of commonly used nutritional assessment parameters in predicting prognosis in geriatric patients diagnosed in PCC with malnutrition.

Methods

Our study included 1451 patients aged ≥65 years, who were diagnosed with malnutrition in PCC between 2016-2020 and did not yet start receiving nutritional support. Demographic data, comorbidities, The Nutritional Risk Screening 2002 (NRS-2002), body mass index (BMI), albumin, prealbumin and C-reactive protein (CRP) values of the patients were recorded. Prognostic course was evaluated by dividing the patients into 3 groups, namely mortal patients during PCC follow-up, patients transferred from PCC to Intensive Care (ICU) and patients discharged to home from PCC.

Results

Logistic Regression analysis showed that low albumin levels affected transfer to ICU (P<0.05). Elevated NRS-2002 and low albumin and prealbumin levels were found to be factors affecting mortality (P<0.05). Areas under the ROC Curve were calculated to attain patients' differential diagnosis. The area under the ROC Curve of low albumin in patients transferred to ICU was found to be significant (P<0.05). In the differential diagnosis of patients with mortal course, the area under the ROC Curve of low albumin and prealbumin and high CRP was found to be significant (P<0.05).

Conclusion

We found that BMI had no prognostic predictive effects in geriatric PCC patients with malnutrition. We concluded that NRS-2002 and high CRP and low albumin and prealbumin can be used to predict mortality. In addition, we found that low albumin indicates a poor prognosis and predicts patients to be transferred to ICU.

Nutritional management of individuals with obesity and COVID-19: ESPEN expert statements and practical guidance

The COVID-19 pandemics has created unprecedented challenges and threats to patients and healthcare systems worldwide. Acute respiratory complications that require intensive care unit (ICU) management are a major cause of morbidity and mortality in COVID-19 patients. Among other important risk factors for severe COVID-19 outcomes, obesity has emerged along with undernutrition-malnutrition as a strong predictor of disease risk and severity. Obesity-related excessive body fat may lead to respiratory, metabolic and immune derangements potentially favoring the onset of COVID-19 complications. In addition, patients with obesity may be at risk for loss of skeletal muscle mass, reflecting a state of hidden malnutrition with a strong negative health impact in all clinical settings. Also importantly, obesity is commonly associated with micronutrient deficiencies that directly influence immune function and infection risk. Finally, the pandemic-related lockdown, deleterious lifestyle changes and other numerous psychosocial consequences may worsen eating behaviors, sedentarity, body weight regulation, ultimately leading to further increments of obesity-associated metabolic complications with loss of skeletal muscle mass and higher non-communicable disease risk. Therefore, prevention, diagnosis and treatment of malnutrition and micronutrient deficiencies should be routinely included in the management of COVID-19 patients in the presence of obesity; lockdown-induced health risks should also be specifically monitored and prevented in this population. In the current document, the European Society for Clinical Nutrition and Metabolism (ESPEN) aims at providing clinical practice guidance for nutritional management of COVID-19 patients with obesity in various clinical settings.

Evaluation of a Supervised Adapted Physical Activity Program Associated or Not with Oral Supplementation with Arginine and Leucine in Subjects with Obesity and Metabolic Syndrome: A Randomized Controlled Trial

Background: In patients with obesity and metabolic syndrome (MetS), lifestyle interventions combining diet, in particular, and physical exercise are recommended as the first line treatment. Previous studies have suggested that leucine or arginine supplementation may have beneficial effects on the body composition or insulin sensitivity and endothelial function, respectively. We thus conducted a randomized controlled study to evaluate the effects of a supervised adapted physical activity program associated or not with oral supplementation with leucine and arginine in MetS-complicated patients with obesity. Methods: Seventy-nine patients with obesity and MetS were randomized in four groups: patients receiving arginine and leucine supplementation (ALs group, n = 20), patients on a supervised adapted physical activity program (APA group, n = 20), patients combining ALs and APA (ALs+APA group, n = 20), and a control group (n = 19). After the baseline evaluation (m0), patients received ALs and/or followed the APA program for 6 months (m6). Body composition, MetS parameters, lipid and glucose metabolism markers, inflammatory markers, and a cardiopulmonary exercise test (CPET) were assessed at m0, m6, and after a 3-month wash-out period (m9). Results: After 6 months of intervention, we did not observe variable changes in body weight, body composition, lipid and glucose metabolism markers, inflammatory parameters, or quality of life scores between the four groups. However, during the CPET, the maximal power (Pmax and Ppeak), power, and O2 consumption at the ventilatory threshold (P(VT) and O2(VT)) were improved in the APA and ALs+APA groups (p < 0.05), as well as the forced vital capacity (FVC). Between m6 and m9, a gain in fat mass was only observed in patients in the APA and ALs+APA groups. Conclusion: In our randomized controlled trial, arginine and leucine supplementation failed to improve MetS in patients with obesity, as did the supervised adapted physical activity program and the combination of both. Only the cardiorespiratory parameters were improved by exercise training.

Intestinal permeability and appetite regulating peptides-reactive immunoglobulins in severely malnourished women with anorexia nervosa

BACKGROUND & AIMS

In the last decades, the role of microbiota-gut-brain axis has emerged in the regulation of eating behavior and in the pathophysiology of anorexia nervosa (AN) that remains poorly understood. Particularly, a gut-derived dysregulation of immune response has been proposed leading to immunoglobulins directed against appetite-regulating peptides. However, intestinal permeability in patients with anorexia nervosa has been poorly documented.

METHODS

In the present prospective case-control study, we thus compared intestinal permeability, appetite-regulating peptides and their reactive immunoglobulins measured in severely malnourished women with AN (n = 17; 28 [21-35] y; 14.9 [14.1-15.2] kg/m²) to healthy volunteers (HV, n = 34; 26 [23-35] y; 22.3 [20.6-23.6] kg/m²).

RESULTS

Patients with AN exhibited an increased urinary lactulose/mannitol ratio, both in 0-5 h (0.033 [0.013-0.116]) and 5-24 h samples (0.115 [0.029-0.582]), when compared to HV (0.02 [0.008-0.045], p = 0.0074 and 0.083 [0.019-0.290], p = 0.0174, respectively), suggesting an increased intestinal permeability. Urinary excretion of sucralose and plasma zonulin were not different. The levels of plasma total ghrelin and desacyl-ghrelin were increased in patients with AN compared to HV, whereas plasma leptin concentration was decreased. In addition, αMSH remained unchanged compared to HV. Finally, we did not observe any modification of the levels of total or free αMSH, leptin or ghrelin-reactive immunoglobulin G and M, as well as for their affinity properties. Only, a weak decrease of the dissociation constant (kd) for acyl-ghrelin-reactive IgG was observed in patients with AN (p = 0.0411).

CONCLUSIONS

In conclusion, severely malnourished patients with AN show a higher intestinal permeability than HV without evidence of an effect on appetite regulating peptides-reactive immunoglobulins.

A comparison of three preoperative nutritional assessment methods for predicting ovarian cancer patient prognosis: which is better?

BACKGROUND

The study aimed to compare the predictive values of three widely used nutritional assessment methods, body mass index (BMI), Nutritional Risk Screening 2002 (NRS 2002), and the prognostic nutritional index (PNI), for different clinical prognostic indicators of ovarian cancer patients.

METHODS

Patients diagnosed with ovarian cancer treated in our hospital between January 2017 and March 2019 were retrospectively included. The three nutritional assessment methods were assessed, and multivariable analysis was conducted to explore predictive factors for clinical prognoses. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUROC) were generated to evaluate the discriminative abilities of the three nutritional assessment tools.

RESULTS

A total of 442 patients were recruited. Multivariable analysis revealed that the PNI value predicted 1-year death and 1-year recurrence and that both the NRS 2002 score and the PNI value predicted 30-day readmission (P < 0.05). For PNI, AUROCs were 0.834 for predicting 1-year death and 0.719 for 1-year recurrence prediction; for NRS, the AUROC was 0.820 2002 for predicting 30-day readmission. The optimal cutoff values that maximized the prognostic prediction ability were PNI values of 47.75 g/L and 50.40 g/L for 1-year death and 1-year recurrence, respectively, and an NRS 2002 score of 3 points for 30-day readmission following discharge.

CONCLUSION

For ovarian cancer patients, the PNI is better at predicting 1-year death and 30-day readmission after discharge, and the NRS 2002 is superior for predicting 1-year recurrence.

Estimated versus measured energy expenditure in ventilated surgical-trauma critically Ill patients

BACKGROUND

The American and European guidelines recommend measuring resting energy expenditure (REE) using indirect calorimetry (IC). Predictive equations (PEs) are used to estimate REE, but there is limited evidence for their use in critically ill patients. The aim of this study is to evaluate the degree of agreement and accuracy between IC-REE and ten different PEs in mechanically ventilated surgical-trauma critically ill patients who met their estimated caloric requirement.

METHODS

IC-REE was retrospectively compared to PE-REE by 10 PEs. Degree of agreement between REE-PE and REE-IC was analyzed by the Bland-Altman test (BAt) and the Concordance Correlation Coefficient (CCC). The accuracy was calculated by the percentage of patients whose REE-PE values differ by up to ±10% in relation to REE-IC. All analyses were stratified by gender and BMI (< 25 vs ≥ 25).

RESULTS

104 patients were analyzed and the closest estimate to IC-REE was the modified Harris-Benedict equation (mHB) by the BAt with a mean difference of 49.2 overall, 61.6 for males, 28.5 for females, 67.5 for BMI < 25 and 42.5 for BMI ≥ 25. The overall CCC between the IC-REE and mHB was 0.652; 0.560 for males; 0.496 for females; 0.570 for BMI < 25; and 0.598 for BMI ≥ 25. Modified HB equation was the most accurate with overall accuracy of 44.2%.

CONCLUSIONS

Effectiveness of PEs for estimating REE of mechanically ventilated surgical-trauma critically ill patients is limited. Nonetheless, of all the 10 equations examined, the closest to IC measured REE was the modified HB equation. This article is protected by copyright. All rights reserved.

The centenary of the Harris-Benedict equations: How to assess energy requirements best? Recommendations from the ESPEN expert group

BACKGROUND & AIMS

The year 2019 marked the centenary of the publication of the Harris and Benedict equations for estimation of energy expenditure. In October 2019 a Scientific Symposium was organized by the European Society for Clinical Nutrition and Metabolism (ESPEN) in Vienna, Austria, to celebrate this historical landmark, looking at what is currently known about the estimation and measurement of energy expenditure.

METHODS

Current evidence was discussed during the symposium, including the scientific basis and clinical knowledge, and is summarized here to assist with the estimation and measurement of energy requirements that later translate into energy prescription.

RESULTS

In most clinical settings, the majority of predictive equations have low to moderate performance, with the best generally reaching an accuracy of no more than 70%, and often lead to large errors in estimating the true needs of patients. Generally speaking, the addition of body composition measurements did not add to the accuracy of predictive equations. Indirect calorimetry is the most reliable method to measure energy expenditure and guide energy prescription, but carries inherent limitations, greatly restricting its use in real life clinical practice.

CONCLUSIONS

While the limitations of predictive equations are clear, their use is still the mainstay in clinical practice. It is imperative to recognize specific patient populations for whom a specific equation should be preferred. When available, the use of indirect calorimetry is advised in a variety of clinical settings, aiming to avoid under-as well as overfeeding.

Accuracy of the Resting Energy Expenditure Estimation Equations for Healthy Women

Background: There exist several prediction equations for the estimation of resting energy expenditure (REE). However, none of these equations have been validated in the Chilean female population yet. The aims of this study are (1) to determine the accuracy of existing equations for prediction of REE and (2) to develop new equations in a sample of healthy Chilean women. Methods: A cross-sectional descriptive study was carried out on 620 Chilean women. The sample showed an age range between 18 and 73 years, a body mass index average of 28.5 ± 5.2 kg/m², and a prevalence of overweight and obesity of 41% and 33.2%, respectively. REE was measured by indirect calorimetry (REE_IC), which was used as the gold standard to determine the accuracy of twelve available REE prediction equations and to calculate alternative formulas for estimation of REE. Paired t-tests and Bland–Altman plots were used to know the accuracy of the estimation equations with REE_IC. At the same time, multiple linear regressions were performed to propose possible alternative equations. The analyses were carried out by age groups and according to nutritional status. Results: All the equations showed a tendency to overestimate REE, regardless of age or nutritional status. Overall, the Ireton-Jones equation achieved the highest mean percentage difference from REE_IC at 67.1 ± 31%. The alternative new equations, containing variables of body composition, reached a higher percentage of classification within ±10% of REE_IC. Conclusions: The available equations do not adequately estimate REE in this sample of Chilean women. Although they must be validated, the new formulas proposed show better adaptation to this Chilean sample.

A Comparison of the Indirect Calorimetry and Different Energy Equations for the Determination of Resting Energy Expenditure of Patients With Renal Transplantation

OBJECTIVE

We aimed to evaluate the agreement between the resting energy expenditure (REE) obtained by indirect calorimetry and eight prediction equations in adult patients with renal transplantation and a newly developed REE prediction equation for use in patients with renal transplantation in the clinic.

METHODS

A total of 51 patients (30 males and 21 females) were involved in the study. The REE was measured by indirect calorimetry and compared with the previous prediction equations. The agreement was assessed by the interclass correlation coefficient and by Bland-Altman plot analysis.

RESULTS

No significant difference was found in terms of age and body mass index between the genders. Differences between the predicted and measured REEs were maximum in the Bernstein equation (-478 kcal) and minimum in the Cunningham equation (-69 kcal). It was found that underprediction values varied from 27.5% (chronic kidney disease equation) to 98.0% (Bernstein equation). The highest overprediction value was found in the Schofield equation (17.7%). The Cunningham equation and the new equation had the lowest root mean square error (265 kcal/day). In this study, fat-free mass (FFM) was found to be the most significant variable in multiple regression analysis (r²: 0.55). The new specific equation based on FFM was generated as 424.2 + 24.7∗FFM (kg). Besides that, it was found that the new equation and Cunningham equation were distributed randomly according to Bland-Altman analysis. A supplementary new equation based on available anthropometric measurements was developed as -1996.8 + 19.1∗height (cm) + 7.2∗body weight (kg).

CONCLUSION

This study showed that most of the predictive equations significantly underestimated REE. In patients with renal transplantation, if the REE is not measurable by indirect calorimetry, the use of the proposed equations will be more accurate.

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.

Resting Energy Expenditure during Breastfeeding: Body Composition Analysis vs. Predictive Equations Based on Anthropometric Parameters

Accurate estimation of energy expenditure in a breastfeeding woman is crucial for maintaining the proper nutritional status of the woman and healthy development of the infant. The current literature does not contain data regarding resting energy expenditure (REE) in breastfeeding women. Using mathematical equations is the most common method of REE assessment. However, due to changes in metabolism and body composition during pregnancy and lactation, the mathematical equations used among the general population may not apply. The aim of this study was to evaluate the resting energy expenditure of exclusively breastfeeding women by using body composition analysis - estimated REE (eREE) and to provide the most appropriate predictive equations - predicted REE (pREE) based on anthropometric parameters to estimate it. This was a pilot study with 40 exclusively breastfeeding women. Height and weight were measured and body composition analysis was performed. We predicted REE using fourteen self-selected equations, based on anthropometric parameters and/or age, and/or sex. The median eREE was 1515.0 ± 68.4 kcal (95% Cl, 1477-1582 kcal) and the pREE ranged from 1149.7 kcal (95% Cl, 1088.7-1215.0) by Bernstein et al., to 1576.8 kcal (95% Cl, 1479.9-1683.4), by Müller et al. Significant differences between eREE and all pREE were observed (p < 0.001, except Korth et al. equations). The Müller et al. equation was the most accurate with the smallest individual variation. All predictive equations showed low agreement, and in most cases, the results were underestimated. These findings indicate the need for further studies to propose more suitable methods to determine the energy requirements for breastfeeding women.

Indirect Calorimetry in Clinical Practice

Indirect calorimetry (IC) is considered as the gold standard to determine energy expenditure, by measuring pulmonary gas exchanges. It is a non-invasive technique that allows clinicians to personalize the prescription of nutrition support to the metabolic needs and promote a better clinical outcome. Recent technical developments allow accurate and easy IC measurements in spontaneously breathing patients as well as in those on mechanical ventilation. The implementation of IC in clinical routine should be promoted in order to optimize the cost–benefit balance of nutrition therapy. This review aims at summarizing the latest innovations of IC as well as the clinical indications, benefits, and limitations.

Accuracy of Resting Energy Expenditure Predictive Equations in Patients With Cancer

BACKGROUND

Our purpose was to assess the accuracy of resting energy expenditure (REE) equations in patients with newly diagnosed stage I-IV non-small cell lung, rectal, colon, renal, or pancreatic cancer.

METHODS

In this cross-sectional study, REE was measured using indirect calorimetry and compared with 23 equations. Agreement between measured and predicted REE was assessed via paired t-tests, Bland-Altman analysis, and percent of estimations ≤ 10% of measured values. Accuracy was measured among subgroups of body mass index (BMI), stage (I-III vs IV), and cancer type (lung, rectal, and colon) categories. Fat mass (FM) and fat-free mass (FFM) were assessed using dual x-ray absorptiometry.

RESULTS

Among 125 patients, most had lung, colon, or rectal cancer (92%, BMI: 27.5 ± 5.6 kg/m² , age: 61 ± 11 years, REE: 1629 ± 321 kcal/d). Thirteen (56.5%) equations yielded REE values different than measured (P < 0.05). Limits of agreement were wide for all equations, with Mifflin-St. Jeor equation having the smallest limits of agreement, -21.7% to 11.3% (-394 to 203 kcal/d). Equations with FFM were not more accurate except for one equation (Huang with body composition; bias, limits of agreement: -0.3 ± 11.3% vs without body composition: 2.3 ± 10.1%, P < 0.001). Bias in body composition equations was consistently positively correlated with age and frequently negatively correlated with FM. Bias and limits of agreement were similar among subgroups of patients.

CONCLUSION

REE cannot be accurately predicted on an individual level, and bias relates to age and FM.

Are Predictive Energy Expenditure Equations Accurate in Cirrhosis?

Malnutrition is associated with significant morbidity and mortality in cirrhosis. An accurate nutrition prescription is an essential component of care, often estimated using time-efficient predictive equations. Our aim was to compare resting energy expenditure (REE) estimated using predictive equations (predicted REE, pREE) versus REE measured using gold-standard, indirect calorimetry (IC) (measured REE, mREE). We included full-text English language studies in adults with cirrhosis comparing pREE versus mREE. The mean differences across studies were pooled with RevMan 5.3 software. A total of 17 studies (1883 patients) were analyzed. The pooled cohort was comprised of 65% men with a mean age of 53 ± 7 years. Only 45% of predictive equations estimated energy requirements to within 90⁻110% of mREE using IC. Eighty-three percent of predictive equations underestimated and 28% overestimated energy needs by ±10%. When pooled, the mean difference between the mREE and pREE was lowest for the Harris⁻Benedict equation, with an underestimation of 54 (95% CI: 30⁻137) kcal/d. The pooled analysis was associated with significant heterogeneity (I² = 94%). In conclusion, predictive equations calculating REE have limited accuracy in patients with cirrhosis, most commonly underestimating energy requirements and are associated with wide variations in individual comparative data.

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m². Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m². Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

Prediction of prognosis in chronic obstructive pulmonary disease patients with respiratory failure: A comparison of three nutritional assessment methods

OBJECTIVES

Due to their increased energy expenditure, chronic obstructive pulmonary disease (COPD) patients with respiratory failure are susceptible to malnutrition. This study aimed to compare the predictive values of the following three widely used nutritional assessment methods for the clinical prognosis of COPD patients with respiratory failure: body mass index (BMI), Nutritional Risk Screening 2002 (NRS 2002), and serum albumin (ALB) level.

METHODS

COPD patients with respiratory failure treated in our center from June 2013 to June 2016 were retrospectively included. Patient BMI, NRS 2002 and ALB values were measured to assess their nutritional status. A multivariable analysis was conducted, and receiver operating characteristic (ROC) curves were generated to explore the predictive factors for clinical prognoses.

RESULTS

A total of 438 qualified patients were enrolled in our study. Multivariable analysis revealed that the BMI and ALB values independently predicted in-hospital mortality, the BMI and NRS 2002 predicted 1-year mortality, and all three methods (BMI, NRS 2002, and ALB) predicted 30-day readmission after discharge (P < 0.05). Regarding the results of the AUROC analysis, the optimal cutoff values that maximized the ability to predict the prognosis were an ALB level of 30.5 g/L for in-hospital mortality, an NRS 2002 score of 3 points for 1-year mortality, and an ALB level of 30.1 g/L for readmission within 30 days following discharge.

CONCLUSIONS

For COPD patients with respiratory failure, ALB level was superior for predicting in-hospital mortality and 30-day readmission after discharge, and NRS 2002 was superior for long-term prognosis of 1-year mortality.

Severity of pain is associated with insufficient energy coverage in hospitalised patients: A cross-sectional study

BACKGROUND & AIMS

The severity of pain is routinely assessed in hospitalised patients but the impact of pain and pain control on energy coverage has been poorly studied. This One-day cross-sectional observational study assessed the association between severity of pain and coverage of energy needs in hospitalised patients.

METHODS

Foods provided and consumed were assessed on one day by dedicated dieticians for unselected hospitalised patients receiving three meals per day. Severity of pain was evaluated by a visual analogue scale at the mealtimes, averaged over the study day, and categorized as no pain, slight, moderate or severe pain. The coverage of energy needs was expressed in percentage of predicted needs.

RESULTS

Among the 755 included patients, 63% reported having pain. Severe pain was associated with a lower energy coverage than no pain (p = 0.001) or slight pain (p = 0.001). Insufficient energy coverage, defined as ≤70% of predicted needs, occurred in 13% of the patients. In univariate logistic regressions, predictors of insufficient energy coverage were severe pain as compared to no pain (OR 2.38; 95% CI 1.21, 4.64) and treatment with opioid drugs as compared to no pain killer (OR 1.73; 95% CI 1.07, 2.79). When including sex, age, body mass index, treatment with analgesics and severity of pain in a multivariate logistic regression, severe pain more than doubled the risk of insufficient energy coverage (OR 2.32; CI 1.15, 4.66).

CONCLUSIONS

Patients experiencing severe pain have a high risk of insufficient energy coverage. Optimal pain control is probably critical to prevent underfeeding in the hospital.

TRIAL REGISTRATION

Identifier no NCT02463565 on www.ClinicalTrials.gov.

Evaluation of a short protocol for indirect calorimetry in females with eating disorders and healthy controls

BACKGROUND AND AIMS

To enable clinicians to identify the clinical picture and treatment progress and to adjust eating plans according to personal energy needs, it is important to know the patient's correct resting metabolic rate (RMR). Indirect calorimetry (IC) is the preferred method for assessment of RMR, but long duration of measurement increases the load on the patients, and reduces the effectiveness in clinical and scientific settings. Further; not all patients reach a valid RMR according to the suggested best practice protocol, with 5 min of steady state (SS) where respiratory gas volume exchange varies less than 10%. The aim of this study was to evaluate the possibility for an abbreviated RMR protocol and SS criterion.

METHODS

Forty two women diagnosed with bulimia nervosa or binge eating disorder (eating disorder group, ED), originally recruited for an outpatient treatment study, and 26 age and gender matched healthy controls (HC) were studied during a single, prolonged IC measurement. Participants rested for 10 min in supine position wearing a two-way breathing facemask, before a continuous measurement period of 20 min. Results from a standard 5 min SS criterion was compared to an abbreviated 3 min SS criterion. Both SS-criteria were evaluated through three different SS protocols (<10% variation in respiratory gas exchange), being: 1) measurement during the first 3 or 5 min, 2) measurement after discarding the first 5 min, and 3) the lowest identified RMR during the 20 min of measurement.

RESULTS

About 50% of the participants reached an early SS in both the defined SS minute criteria. Participants reaching a valid SS throughout the 20 min of measurement increased from ∼90% to 100% with an abbreviated 3 min SS. With a 5 min SS criterion, the median (range) RMR for the 3 protocols were 1639.9 (1239.2), 1508.8 (1457.6) and 1500.6 (1328.8) respectively for the ED group, and 1702.2 (1239.4), 1608.4 (1076.4) and 1594.8 (1029.2) respectively for the HC group, (p > 0.05 for all between-group analysis). With a 3 min SS criterion, the median (range) RMR were 1533.6 (1298.2), 1461.2 (1406.1), and 1395.8 (1447.3) respectively for the ED group and 1681.7 (1332.4), 1613.7 (1266.0) and 1523.1 (1050.3), respectively for the HC group, (p > 0.05 for all between-group analysis). Lowest measured RMR was different compared to the other two SS protocols in both the ED- and the HC group, and for both the 5 min- and the 3 min SS criteria, respectively (p < 0.04). Furthermore, a SS of 3 min resulted in lower RMR compared to 5 min SS (p < 0.00) and an increased number of participants classified as hypo-metabolic (RMRmeasured/RMRcalculated < 0.9).

CONCLUSIONS

An abbreviated measurement protocol to identify the lowest RMR using IC was not successful. Abbreviating the SS criteria from 5 to 3 min, resulted in a lower RMR, hence encouraging further examination of the validity of shorter SS criterion than practiced today. Registered in Clinical Trials by id-number NCT02079935, and approved by the Norwegian Regional Committee for Medical and Health Research Ethics with id-number 2013/1871. The trial in which control persons were recruited, is approved by the Norwegian Regional Committees for Medical and Health Research Ethics with the id-number 2016/1718, and prospectively registered in Clinical Trials with the id-number NCT03007459.

Validity of Predictive Equations for Resting Energy Expenditure in Greek Adults

AIM

To examine the validity of published resting energy expenditure (REE) equations in Greek adults, and if indicated, develop new cohort-specific predictive REE equations.

METHODS

Indirect calorimetry and anthropometric data were obtained from 226 adult volunteers of diverse age groups and body mass index ranges (18-60 years, 16.6-67.7 kg·m-2). Measured REE was compared to preexisting prediction equations via correlation, regression, and Bland-Altman analysis. Then, cohort-specific REE equations were developed using curve estimation and nonlinear regression. To reduce type I error, presently derived equations were validated by splitting the sample into a training and validation group.

RESULTS

Preexisting equations over-predicted in-cohort REE. Equations by Livigston and Kohlstadt were most accurate at the individual level (63% accuracy), while formulas by Owen and collaborators elicited highest accuracy at the group level (-1.8% bias). Bland-Altman analysis showed proportional bias for most equations. Currently developed equations showed highest overall accuracy with 70% at the individual and group level (1.0% bias), with small differences between measured and predicted REE values (mean, 95% CI 36 [-15 to 88] kcal·day-1).

CONCLUSION

Data indicate currently developed equations to be the most accurate and valid for estimating REE in Greek adults. Further studies should examine the developed equations in an independent sample.

Validity of Predictive Equations for Resting Energy Expenditure Developed for Obese Patients: Impact of Body Composition Method

Predictive equations have been specifically developed for obese patients to estimate resting energy expenditure (REE). Body composition (BC) assessment is needed for some of these equations. We assessed the impact of BC methods on the accuracy of specific predictive equations developed in obese patients. REE was measured (mREE) by indirect calorimetry and BC assessed by bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA). mREE, percentages of prediction accuracy (±10% of mREE) were compared. Predictive equations were studied in 2588 obese patients. Mean mREE was 1788 ± 6.3 kcal/24 h. Only the Müller (BIA) and Harris & Benedict (HB) equations provided REE with no difference from mREE. The Huang, Müller, Horie-Waitzberg, and HB formulas provided a higher accurate prediction (>60% of cases). The use of BIA provided better predictions of REE than DXA for the Huang and Müller equations. Inversely, the Horie-Waitzberg and Lazzer formulas provided a higher accuracy using DXA. Accuracy decreased when applied to patients with BMI ≥ 40, except for the Horie-Waitzberg and Lazzer (DXA) formulas. Müller equations based on BIA provided a marked improvement of REE prediction accuracy than equations not based on BC. The interest of BC to improve REE predictive equations accuracy in obese patients should be confirmed.

An artificial neural network to predict resting energy expenditure in obesity

BACKGROUND & AIMS

The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity.

METHODS

A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m^-2) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation.

RESULTS

Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m^-2. External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects.

CONCLUSIONS

We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at: https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator.

Low validity of predictive equations for calculating resting energy expenditure in overweight and obese women with polycystic ovary syndrome

BACKGROUND

Predictive equations are the main clinical tools for determining resting energy expenditure (REE). However, their adequate use in overweight and obese individuals is unclear. Thus, we investigated the best predictive equations for estimating REE in overweight and obese women with polycystic ovary syndrome (PCOS).

METHODS

Eleven analyses were performed with prediction equations (pREE) based on anthropometric parameters in 30 overweight or obese women with PCOS without other chronic diseases. The measured REE (mREE) was calculated by indirect calorimetry. The validity of the equations was investigated by comparison, accuracy and agreement tests between pREE and mREE at both the individual and group level.

RESULTS

Four analyses were similar to those of mREE, and smallest mean differences were observed for the World Health Organization/Food and Agriculture Organization of the United Nations/United Nations University (WHO/FAO/UNU) considering weight (W) [0.07 (1.13) MJ (16 [270] kcal)]. Individual accuracy was greater than 50% for Harris and Benedict, Müller and Lazzer equations. The percentage of REE underestimation ranged between 16.7% and 73.3%, whereas higher rates of overestimation were observed in the De Luis (66.7%) and Ireton-Jones (43.3%) equations. Mean bias at the group level was lowest in the WHO/FAO/UNU W and WHO/FAO/UNU considering weight and height (WH), Müller and Lazzer equations (-2.8 to 0.5). The WHO/FAO/UNU W and WHO/FAO/UNU WH formulas were optimal in individual agreement (33.3%).

CONCLUSIONS

FAO/WHO/UNU W equations may estimate the REE in overweight and obese women with PCOS. However, the low individual accuracy and agreement in relation to mREE suggest caution regarding when to use the formula to perform an individual nutritional plan.

Which indicators for measuring the daily physical activity? An overview on the challenges and technology limits for Telehealth applications

BACKGROUND

Obesity is one of the biggest drivers of preventable chronic diseases and healthcare costs in Worldwide. Different prevention activities are suggested. By monitoring daily energy expenditure (EE) could be possible make personalized diets and programming physical activity. In this, physical inactivity is one of the most important public health problems. Some studies refer the effort of the international community in promoting physical activities. Physical activity can be promoted only by increasing citizens' empowerment on taking care of their health, and it passes from the improving of individual information. Technology can offer solutions and metrics for monitoring and measuring daily activity by interacting with individuals, sharing information and feedbacks.

OBJECTIVE

In this study we review indicators of total energy expenditure and weaknesses of available devices in assessing these parameters.

METHODS

Literature review and technology testing EuNetHta core model.

RESULTS

For the clinical aspects, it is fundamental to take into account all the factor that can influence the personal energy expenditure as: heart rate, blood pressure and thermoregulation (influenced by the body temperature).

DISCUSSION

In this study we focused the attention on the importance of tools to encourage the physical activity. We made an analysis of the factor that can influence the right analysis of energy expenditure and at the same time the energy regime. A punctual monitoring of the exercise regime could be helpful in Telemedicine application as Telemonitorig. More study are needed to value the impact of physical activity tracker in Telemonitorig protocols.

CONCLUSION

On the assessment of the energy expenditure, critical issues are related to the physiological data acquisition. Sensors connected with mobile devices could be important tools for disease prevention and interventions affecting health behaviors. New devices applications are potential useful for telemedicine assistance, but security of data and the related communication protocol limits should be taking into account.