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Introduction: In areas with limited access to healthcare systems, Resting Energy Expenditure (REE) estimation is performed using predictive equations to calculate an individual's caloric requirement. One problem is that these equations were validated in populations with different characteristics from those in Latin America, such as race, height, or body mass, leading to potential errors in the prediction of this parameter. Objective: To determine the REE using predictive formulas compared with bioimpedance in Peruvians. Materials and methods: A comparative analytical cross-sectional study with secondary database analysis of the CRONICAS cohort. Results: we worked with a total of 666 subjects. The Mjeor equation was the one with the highest rating of 0.95, a lower mean absolute percentage error (MAPE) of 4.69%, and equivalence was found with the REE values. In the multiple regression, it was observed that the Mjeor equation was the one that least overestimated the REE, increasing 0.77 Kcal/day (95% CI: 0.769-0.814; p<0.001) for each point that increased the REE determined by bioimpedance. The strength of association between Mjeor and bioimpedance was 0.9037. Furthermore, in the regression of the data (weight, height, age) in the Mjeor equation it was observed that the coefficients obtained were the same as those used in the original equation. Conclusions: The Mjeor equation seems to be the most adequate to estimate the REE in the Peruvian population. Future prospective studies should confirm the usefulness of this formula with potential utility in primary health care(AU)

Introducción: En zonas con acceso limitado a sistemas de salud, la estimación del Gasto Energético en Reposo (GER) se realiza utilizando ecuaciones predictivas para calcular el requerimiento calórico de un individuo. Uno de los problemas es que estas ecuaciones fueron validadas en poblaciones con características diferentes a las latinoamericanas, como raza, talla o masa corporal, lo que conlleva a potenciales errores en la predicción de este parámetro. Objetivo: Determinar el GER mediante fórmulas predictivas comparadas con la bioimpedancia en peruanos. Materiales y métodos: Estudio transversal analítico comparativo con análisis secundario de base de datos de la cohorte CRONICAS. Resultados: Se trabajó con un total de 666 sujetos. La ecuación de Mjeor fue la que obtuvo la puntuación más alta de 0,95, un error medio porcentual absoluto (MAPE) inferior de 4,69%, y se encontró equivalencia con los valores del GER. En la regresión múltiple, se observó que la ecuación de Mjeor fue la que menos sobreestimó el GER, aumentando 0,77 Kcal/día (IC 95%: 0,769-0,814; p<0,001) por cada punto que aumentaba el GER determinado por bioimpedancia. La fuerza de asociación entre Mjeor y bioimpedancia fue de 0,9037. Además, en la regresión de los datos (peso, talla, edad) de la ecuación de Mjeor se observó que los coeficientes obtenidos eran los mismos que los utilizados en la ecuación original. Conclusiones: La ecuación de Mjeor parece ser la más adecuada para estimar el GER en la población peruana. Futuros estudios prospectivos deberán confirmar la utilidad de esta fórmula para su potencial utilidad en la atención primaria de salud(AU)

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Introduction: Migraine is a common and disabling primary headache, and its pathophysiology is not fully understood. Previous studies have suggested that pain can increase humans' Resting Energy Expenditure (REE). However, no previous study has investigated whether the REE of individuals with migraine differs from the general population. Therefore, this study aims to assess whether the REE of women with migraine differs from that of women without headaches. We also tested the accuracy of REE predictive formulas in the migraine patients. Methods: This cross-sectional study involves 131 adult women aged between 18 and 65 years, 83 with migraine and 48 without (controls). We collected clinical, demographic, and anthropometric data. Migraine severity was measured using the Migraine Disability Test and Headache Impact Test, version 6. The REE was measured by indirect calorimetry, and it was compared with the predicted REE calculated by formulas. Results: Patients with migraine had higher REE when compared to controls (p < 0.01). There was a positive correlation between REE and the patient-reported number of migraine attacks per month (Rho = 0.226; p = 0.044). Mifflin-St Jeor and Henry and Rees were the predictive formulas that have more accuracy in predicting REE in women with migraine. Discussion: Considering the benefits of nutritional interventions on treating migraines, accurately measuring REE can positively impact migraine patient care. This study enhances our understanding of the relationship between pain and energy expenditure. Our results also provide valuable insights for healthcare professionals in selecting the most effective predictive formula to calculate energy expenditure in patients with migraine.

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INTRODUCTION: After Roux-en-Y gastric bypass (RYGB), the basal metabolic rate (BMR) falls. However, it is important to know how BMR per kilogram of body weight (BMR/kg) varies in the postoperative period. The present study evaluated the changes in the BMR/kg and its correlates over 30 months after RYGB. METHODS: Eighty adult patients of both genders who underwent RYGB agreed to participate in the study. The following evaluations were performed before surgery (n=48) and 6 (n=27), 12 (n=28), 24 (n=40), and 30 months (n=29) after surgery: anthropometry, body composition (bioelectrical impedance), metabolic analysis (indirect calorimetry), and diet (food recall). Statistical analysis was performed (p = 0.05). RESULTS: Although BMR decreased after surgery, BMR/kg increased significantly as compared to baseline from 12 months onward, peaking at 24 months and not significantly dipping at 30 months, suggesting stabilization of BMR/kg 2 years after surgery (pre, 10.68 ± 2.33 kcal/kg; 12 months, 12.46 ± 2.85 kcal/kg; 24 months, 18.78 ± 4.81 kcal/kg; 30 months, 18.12 ± 3.69 kcal/kg; p <0.001). Regarding the variables that influenced the BMR/kg, at 12 months, they were %LBM and intake of calcium-source foods (34%); at 24 months, it was protein intake (16%); and at 30 months, it was the intake of calcium-source foods (26.7%). CONCLUSION: RYGB is associated with a significant increase in BMR when it is adjusted to body weight from 12 to 24 months postoperatively. Among the factors involved in the increase in BMR/kg are body composition and intake of protein-rich foods.

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Background and aims: Many improvements have been made in the treatment of human immunodeficiency virus (HIV) in pediatric patients; however, challenges remain in terms of achieving normal growth, body composition, and metabolism during treatment, etc. Current nutritional recommendations are based on studies performed in adults, with limited data on the HIV-infected pediatric population. Therefore, this study aimed to compare the resting energy expenditure (REE) of asymptomatic HIV-infected pediatric patients with healthy counterparts and to compare body composition, dietary intake, and physical activity between the two groups. Methods: This was a cross-sectional study of asymptomatic HIV-infected children who were receiving antiretroviral therapy; the infected group was compared with the uninfected group, matched by age (± 6 months), sex, and body mass index (± 0.5 z-score). Participants were recruited between 2021 and 2022, as outpatients. In both groups, REE was determined by indirect calorimetry and body composition by bioelectrical impedance analysis and hand strength, measured using a hydraulic hand dynamometer. Results: Seventy-eight participants were enrolled, where n = 39 HIV-infected children and n = 39 controls, with a mean age of 11.6 ± 3.4 years old. REE was significantly higher in the HIV group (1254.4 ± 334.7 kcal/day vs. 1124.7 ± 321 kcal/day, p = 0.013) than in the control group. Fat-free mass (FFM) was lower in the HIV group (28.2 ± 10.5 kg vs. 32 ± 11.2 kg, p = 0.001); this trend continued when the index skeletal muscle was evaluated (7.2 ± 1.2 vs. 7.6 ± 1.5, p = 0.04). The strength of the dominant hand was also lower in the HIV group (12 (8-18) kg vs. 20 (10.5-26) kg, p < 0.0001). Conclusions: Children with asymptomatic HIV infection have higher REE than their uninfected peers. They also present decreased FFM, skeletal muscle mass index, and muscle strength. These parameters should be considered during nutritional assessment in this population to have a favorable impact on nutritional status and growth.

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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.

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Patients who undergo Roux-en-Y gastric bypass (RYGB) exhibit a reduction in total basal metabolic rate (BMR) after surgery, which seems to be intimately related to the amount of postoperative weight loss. The objective was to perform a systematic review and meta-analysis of the literature to determine and evaluate BMR changes after RYGB. The search was performed in certified databases, and the strategy was structured according to the PRISMA ScR. The quality evaluation of the articles included in this review was assessed with two different bias risk tools (ROBINS-I and NIH) according to each study design. Two meta-analyses were elaborated based on the results. 163 articles were selected (from 2016 to 2020), and 9 articles met the inclusion criteria. All of the selected studies evaluated only adult patients, mostly women. Postoperative BMR diminished in all of the included studies after surgery compared to preoperative values. The follow-up periods were 6, 12, 24 and 36 months. Eight articles were used for the meta-analysis after the quality assessment, a total of 434 participants. Compared to baseline values, mean postoperative reductions of 356.66 kcal/d after 6 months (p < 0.001) and 432.89 kcal/d (p < 0.001) after 1 year were observed. The BMR decreases during the first years after Roux-en-Y gastric bypass, especially during the first year postsurgery.

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Objective: Resting energy expenditure (REE) decreases if there is reduced energy intake and body weight (BW). The decrease in REE could make it difficult for patients with obesity to maintain decreased BW. This study aimed to investigate the correlation among changes in REE, energy intake, and BW during the weight loss process in patients with obesity. Materials and methods: We conducted a retrospective cohort study of patients hospitalized for the treatment of obesity in Japan. Patients received fully controlled diet during hospitalization and performed exercises if able. REE was measured once a week using a hand-held indirect calorimetry. Energy intake was determined by actual dietary intake. Results: Of 44 inpatients with obesity, 17 were included in the analysis. Their BW decreased significantly after 1 week (-4.7 ± 2.0 kg, P < 0.001) and 2 weeks (-5.7 ± 2.2 kg, P < 0.001). The change in REE after 1 and 2 weeks was positively correlated with the energy intake/energy expenditure ratio (r = 0.66, P = 0.004 at 1 week, r = 0.71, P = 0.002 at 2 weeks). Using a regression equation (y = 0.5257x - 43.579), if the energy intake/energy expenditure ratio within the second week was 82.9%, the REE after 2 weeks was similar to the baseline level. There was no significant correlation between the change in REE and BW. Conclusion: Our data suggest that changes in REE depend on energy intake/energy expenditure ratio and that the decrease in REE can be minimized by matching energy intake to energy expenditure, even during the weight loss process.

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Abstract Objective: Resting energy expenditure (REE) decreases if there is reduced energy intake and body weight (BW). The decrease in REE could make it difficult for patients with obesity to maintain decreased BW. This study aimed to investigate the correlation among changes in REE, energy intake, and BW during the weight loss process in patients with obesity. Materials and methods: We conducted a retrospective cohort study of patients hospitalized for the treatment of obesity in Japan. Patients received fully controlled diet during hospitalization and performed exercises if able. REE was measured once a week using a hand-held indirect calorimetry. Energy intake was determined by actual dietary intake. Results: Of 44 inpatients with obesity, 17 were included in the analysis. Their BW decreased significantly after 1 week (−4.7 ± 2.0 kg, P < 0.001) and 2 weeks (−5.7 ± 2.2 kg, P < 0.001). The change in REE after 1 and 2 weeks was positively correlated with the energy intake/energy expenditure ratio (r = 0.66, P = 0.004 at 1 week, r = 0.71, P = 0.002 at 2 weeks). Using a regression equation (y = 0.5257x - 43.579), if the energy intake/energy expenditure ratio within the second week was 82.9%, the REE after 2 weeks was similar to the baseline level. There was no significant correlation between the change in REE and BW. Conclusions: Our data suggest that changes in REE depend on energy intake/energy expenditure ratio and that the decrease in REE can be minimized by matching energy intake to energy expenditure, even during the weight loss process.

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BACKGROUND & AIMS: Greater energy expenditure is reported in newborns with bronchopulmonary dysplasia (BPD). This study assessed resting energy expenditure (REE) in newborns with BPD. METHODS: BPD was classified as mild and moderate/severe. REE was assessed using indirect calorimetry between the time points of the discontinuation of oxygen (O2) (T1) and at term-equivalent age (T2) in preterm newborns with BPD. RESULTS: The moderate group (10 newborns) presented with higher REE (kcal/kg/day) after discontinuation of mechanical ventilation and a decrease of 18% between the two time points; 72.7 and 59.6 kcal/kg/day at T1 and T2 respectively (p value 0.08). No differences were observed in REE in the mild BPD group between timepoints; 50.9-56.4 kcal/kg/day at T1 and T2 respectively (p value 0.73). CONCLUSION: Newborns with BPD presented different metabolic behaviors depending on the classification criteria: those classified as having moderate BPD showed a decrease in REE toward term-equivalent age.

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In this cross-sectional study, conducted in a cohort of infants with a gestational age of < 32 weeks, we aimed to evaluate and compare resting energy expenditure (REE) and body composition between infants who developed bronchopulmonary dysplasia (BPD) and those who did not. REE and body composition were assessed at term equivalent age using indirect calorimetry and air displacement plethysmography. Anthropometric measurements (weight, head circumference, and length) were obtained and transformed into Z-scores per the Fenton (2013) growth curve, at birth and at term equivalent age. Forty-two infants were included in this study, of which 26.2% developed BPD. Infants with BPD had significantly higher energy expenditure at term equivalent age, with no difference in body composition between the two groups. CONCLUSION: Despite expending more energy, infants with BPD maintained a similar body composition distribution to those without BPD, and this is likely due to the recommended nutritional approach. WHAT IS KNOWN: • Greater resting energy expenditure impairs growth of preterm infants with bronchopulmonary dysplasia. WHAT IS NEW: • Although preterm infants with bronchopulmonary dysplasia had a higher resting energy expenditure at the corrected term age, this did not affect their body composition and growth.

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Background: Estimating energy requirements (ER) is crucial for nutritional attention to chronic kidney disease (CKD) patients. Current guidelines recommend measuring ER with indirect calorimetry (IC) when possible. Due to clinical settings, the use of simple formulas is preferred. Few studies have modeled equations for estimating ER for CKD. Nevertheless, variables of interest such as nutritional status and strength have not been explored in these models. This study aimed to develop and validate a model for estimating REE in patients with CKD stages 3-5, who were not receiving renal replacement therapy (RTT), using clinical variables and comparing it with indirect calorimetry as the gold standard. Methods: In this study 80 patients with CKD participated. Indirect calorimetry (IC) was performed in all patients. The calorimeter analyzed metabolic measurements every minute for 15 min after autocalibration with barometric pressure, temperature, and humidity. Bioelectrical Impedance Analysis (BIA) was performed. Fat-free mass (FFM) was registered among other bioelectrical components. Handgrip strength (HGS) was evaluated and an average of 3 repetitions was recorded. Nutritional status was assessed with the subjective global assessment (SGA). Patients categorized as B or C were then considered as having malnutrition. Results: We analyzed 71 patients and 3 models were generated. Model 1a included FFM; Model 2a included weight; Model 3c included handgrip strength (HGS). All other variables were stepwise, computer-selected with a p < 0.01 significance level; Malnutrition was consistently associated with ER among other clinical variables in all models (p < 0.05). The model that included BIA-FFM had R 2 adjusted = 0.46, while the model that included weight (Kg) had an adjusted R 2 adjusted = 0.44. The models had moderate concordance, LC = 0.60-0.65 with the gold standard, whereas other energy expenditure estimation equations had LC = 0.36 and 0.55 with indirect calorimetry. Using these previously validated equations as a reference, our models had concordance values ranging from 0.66 to 0.80 with them. Conclusion: Models incorporating nutritional status and other clinical variables such as weight, FFM, comorbidities, gender, and age have a moderate agreement with REE. The agreement between our models and others previously validated for the CKD patient is good; however, the agreement between the latter and IC measurements is moderate. The KDOQI lowest recommendation (25 Kcals/kg body weight) considering the 22% difference with respect to the IC for total energy expenditure rather than for REE.

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Nuts are high-energy density foods and are associated with beneficial effects on health, including weight control. Effects on resting energy expenditure, respiratory quotient, and diet-induced thermogenesis are suggested mechanisms behind the effects of nuts consumption on weight control. Thus, we revised the randomised clinical trials that assessed acute and chronic nuts consumption effects on energy metabolism. Walnuts (22.1 g to 56 g) consumption appears to modulate energy metabolism markers differently depending on the dose and profile of the evaluated subject. In its turn, 56 g of high-oleic peanuts increased postprandial energy expenditure and thermic effect of food after three hours postprandial compared to consumption of conventional peanuts. Almonds, hazelnuts, peanuts, and a mix of nuts were the nuts studies in the chronic studies, which does not seem to influence energy metabolism markers. Further studies are needed to elucidate the effects of other types of nuts consumption on energy metabolism.

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ABSTRACT BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is currently considered a global public health problem, with changes in lifestyle being the effective way to treat the disease. To date, there is no recommended standard of assessment to determine the resting energy expenditure (REE) of patients with NAFLD, so that dietary therapy can be properly guided. OBJECTIVE: To evaluate the REE of patients with NAFLD through indirect calorimetry and compare with different predictive formulas of REE and with REE by electrical bioimpedance analysis (BIA). Assess body composition through BIA, with NAFLD staging and the presence of comorbidities. METHODS: They were evaluated in patients with NAFLD over 18 years of age treated at the Gastroenterology outpatient clinic of a tertiary level hospital in southern Brazil. NAFLD staging was performed using liver biopsy or a non-invasive method. Weight, height and body mass index (BMI) were determined in all patients. The short version of the International Physical Activity Questionnaire was used to assess physical activity. Comorbidities as arterial hypertension, diabetes mellitus and dyslipidemia were evaluated. To estimate energy expenditure at rest, Harris-Benedict, Jeor Mifflin-St, World Health Organization and Schofield formulas were used. BIA was used to assess resting metabolic rate (RMR) and body mass, and to measure RMR, indirect calorimetry was also used. Associations between categorical variables were tested with Pearson's χ2 test and between groups with McNemar's test. The level of significance assumed was 5%. The degree of agreement between the REE measurement methods was assessed using the Blan-Altman test. RESULTS: A total of 67 patients were evaluated, 70.5% male, with a mean age of 59 years and a mean BMI of 33.08 kg/m2 ±5.13. The average RMR per CI was 1,753 kcal ±614.58. When comparing the RMR estimate by different formulas with indirect calorimetry, only the Jeor Mifflin-St formula showed a statistically significant difference (P=0.0001), with a difference of +318.49 kcal. BIA and Harris Benedict's formula presented values closer to CI, 1,658 and 1,845 kcal respectively. CONCLUSION: We suggest that the Jeor Mifflin-St formula should not be used to estimate the RMR in patients with NAFLD. In the absence of indirect calorimetry, some alternatives can be used safely in this population, such as BIA and the predictive formulas of Harris Benedict, Schofield and the World Health Organization.

RESUMO CONTEXTO: A doença hepática gordurosa não alcoólica (DHGNA) é considerada, atualmente, um problema de saúde pública global, sendo a mudança no estilo de vida a forma efetiva de tratar a doença. Até o momento não há um padrão de avaliação recomendado para determinar o gasto energético de repouso (GER) de pacientes com DHGNA, para que se possa nortear adequadamente a conduta dietoterápica. OBJETIVO: Avaliar o GER de pacientes com DHGNA através da calorimetria indireta (CI) e comparar com diferentes fórmulas preditivas do GER e com GER através da bioimpedância elétrica (BIA). Avaliar a composição corporal através da BIA, com o estadiamento da DHGNA e com a presença de comorbidades. MÉTODOS: Foram avaliados em pacientes com DHGNA maiores de 18 anos de idade atendidos no ambulatório de Gastroenterologia de um Hospital de nível terciário do Sul do Brasil. O estadiamento da DHGNA foi realizado através de biópsia hepática ou método não invasivo. Peso, altura e índice de massa corporal (IMC) foram determinados em todos os pacientes. Para avaliação da atividade física foi utilizada a versão curta do International Physical Activity Questionnaire. Foram avaliadas as comorbidades hipertensão arterial, diabetes mellitus e dislipidemia. Para a estimativa do gasto energético de repouso utilizou-se as fórmulas de Harris-Benedict, de Jeor Mifflin-St, da Organização Mundial de Saúde e de Schofield. A BIA foi utilizada para avaliação do GER e da massa corporal, e para aferição do GER também se utilizou a CI. Associações entre variáveis categóricas foram testadas com teste χ2 de Pearson e entre grupos com teste de McNemar. O nível de significância assumido foi de 5%. O grau de concordância entre os métodos de mensuração do GER foi aferido pelo teste de Blan-Altman. RESULTADOS: Foram avaliados 67 pacientes, sendo 70,5% do sexo masculino, com média de idade de 59 anos e média de IMC 33,08 kg/m2 ±5,13. O GER médio por CI foi de 1.753 kcal ±614,58. Ao comparar a estimativa do GER por diferentes fórmulas com a calorimetria indireta, apenas a fórmula de Jeor Mifflin-St apresentou diferença estatisticamente significativa (P=0,0001), com uma diferença de +318,49 kcal. A BIA e a fórmula de Harris Benedict apresentaram valores mais próximos à CI, 1.658 e 1.845 kcal respectivamente. CONCLUSÃO: Sugerimos que a fórmula de Jeor Mifflin-St não deva ser utilizada para estimativa do GER em pacientes com DHGNA. Na ausência da CI algumas alternativas podem ser utilizadas com segurança nesta população, como a BIA e as fórmulas preditivas de Harris Benedict, de Schofield e da Organização Mundial de Saúde.

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BACKGROUND & AIMS: there is no consensus in the literature about the best method to estimate the RMR in a high-level athlete's cohort. Additionally, a shortening protocol may allow researchers, nutritionists, and clinicians to follow the RMR across the season and to propose better nutritional interventions, but this kind of protocol was not proposed in this cohort yet. Thus, this study aims to analyze the effect of the method of gas exchange data selection upon the RMR estimate and, possibly propose a shortening protocol with a valid and accurate RMR value. METHODS: Eighty-three healthy high-level athletes underwent a 30-minute RMR measurement with no rest period before the test. Different methods of gas exchange data selection were used: short and long time intervals (TI) [6-10, 11-15, 16-20, 21-25, 26-30, 6-25, or 6-30], Steady State (SS) with 3, 4, 5, or 10 min period length, and Filtering (low, medium, and high). Single and multiple linear regressions were used to examine the variance in the RMR provided by each method of gas exchange data selection. RESULTS: The High Filter method provided the lowest RMR estimate (1854 kcal.day-1), and most methods presented a mean absolute difference of ~43 kcal.day-1 from the High Filter method. There were no differences in RER among methods (F = 2.01, p = 0.10). Besides, twenty minutes of gas exchange measurement was necessary to obtain a valid and accurate RMR with no rest period before the test. The linear regression model that included sex, lean body mass, and fat mass better explained the variance in the RMR using the high filter method (~88%). CONCLUSIONS: The High Filter provided the lowest RMR value. Furthermore, a 20-minute protocol estimated a valid and accurate RMR value with no acclimation period before the measurement in high-level athletes.

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BACKGROUND: Nutrition therapy plays a major role in the perioperative management of surgical patients. Understanding energy metabolism and correctly establishing the adequate energy needs is a crucial step to provide optimal nutrition care. The aim of this study was to assess the resting energy expenditure (REE) after major abdominal procedures and its associated factors. METHODS: This was a prospective observational study conducted at a single center. REEs of patients admitted for gastrointestinal surgical procedures were measured by indirect calorimetry 24 hours prior to the procedure and reassessed at least once within the fifth postoperative day. Substrate oxidation was calculated according to the Frayn equation. Nutrition status was evaluated using subjective global assessment. RESULTS: There were no significant changes in the REEs throughout the study period; however, there was a decrease in the respiratory quotient during the postoperative period, as well as a decrease in carbohydrate oxidation and an increase in lipid oxidation. Only 33.3% of the patients presented a postoperative increase in REE > 10%. Those patients presented higher blood-monocyte levels. CONCLUSION: Postoperative REE is not increased in most of the patients. In patients who had increased REE, associated factors included higher levels of monocytes.

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BACKGROUND: The objective of this study was to develop a new predictive equation of resting energy expenditure (REE) for acute kidney injury patients (AKI) on dialysis. MATERIALS AND METHODS: A cross-sectional descriptive study was carried out of 114 AKI patients, consecutively selected, on dialysis and mechanical ventilation, aged between 19 and 95 years. For construction of the predictive model, 80% of cases were randomly separated to training and 20% of unused cases to validation. Several machine learning models were tested in the training data: linear regression with stepwise, rpart, support vector machine with radial kernel, generalised boosting machine and random forest. The models were selected by ten-fold cross-validation and the performances evaluated by the root mean square error. RESULTS: There were 364 indirect calorimetry measurements in 114 patients, mean age of 60.65 ± 16.9 years and 68.4% were males. The average REE was 2081 ± 645 kcal. REE was positively correlated with C-reactive protein, minute volume (MV), expiratory positive airway pressure, serum urea, body mass index and inversely with age. The principal variables included in the selected model were age, body mass index, use of vasopressors, expiratory positive airway pressure, MV, C-reactive protein, temperature and serum urea. The final r-value in the validation set was 0.69. CONCLUSION: We propose a new predictive equation for estimating the REE of AKI patients on dialysis that uses a non-linear approach with better performance than actual models.

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BACKGROUND: Accurate measurements of resting energy expenditure (REE) are important for determining nutritional needs in HIV patients. Indirect calorimetry (IC) is a noninvasive method that reflects REE but can be costly and is frequently calculated with predictive equations. Research suggests that REE obtained by predictive equations in people living with HIV/AIDS (PLWH) is inaccurate. The aim of the study is to develop and validate a new predictive equation of REE based on a population of PLWH. METHODS: Cross-sectional study including 164 PLWH (82 to develop and 82 to validate the equation). Multiple linear regression was used to determine the relationship between variables and to develop the new predictive equation. Intraclass correlation coefficient (ICC) and Bland-Altman methods were used to evaluate agreement between the new predictive equation and indirect calorimetry. RESULTS: A new predictive equation with an accuracy of 67% when compared with IC was developed. This equation included as covariates: fat free mass, antiretroviral therapy status and age. CONCLUSION: A new equation to predict energy expenditure in PLWH was developed and validated. This formula can be used to estimate REE if IC is not available.

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BACKGROUND: Evaluation of the resting energy expenditure (REE) is essential to ensure an appropriate dietary prescription for patients with type 2 diabetes. The aim of this record was to evaluate the accuracy of predictive equations for REE estimation in patients with type 2 diabetes, considering indirect calorimetry (IC) as the reference method. METHODS: A cross-sectional study was performed in outpatients with type 2 diabetes. Clinical, body composition by electrical bioimpedance and laboratory variables were evaluated. The REE was measured by IC (QUARK RMR, Cosmed, Rome, Italy) and estimated by eleven predictive equations. Data were analyzed using Bland-Altman plots, paired t-tests, and Pearson's correlation coefficients. RESULTS: Sixty-two patients were evaluated [50% female; mean age 63.1 ± 5.2 years; diabetes duration of 11 (1-36) years, and mean A1C of 7.6 ± 1.2%]. There was a wide variation in the accuracy of REE values predicted by equations when compared to IC REE measurement. In all patients, Ikeda and Mifflin St-Jeor equations were that most underestimated REE. And, the equations that overestimated the REE were proposed by Dietary Reference Intakes and Huang. The most accurate equations were FAO/WHO/UNO in women (- 1.8% difference) and Oxford in men (- 1.3% difference). CONCLUSION: In patients with type 2 diabetes, in the absence of IC, FAO/WHO/UNO and Oxford equations provide the best REE prediction in comparison to measured REE for women and men, respectively.