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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.

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PURPOSE: Patients after metabolic bariatric surgery (MBS) require attention to maintain energy balance and avoid weight regain. Predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) are needed since gold standard methods like calorimetry and doubly labeled water are rarely available in routine clinical practice. This study aimed to determine which predictive equation for REE and TEE has the lowest bias in subjects after MBS. METHODS: MEDLINE, Embase, Web of Science, and CENTRAL searches were performed. Meta-analyses were performed with the data calculated by the predictive equations and measured by the gold standard methods for those equations that had at least two studies with these data. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist. RESULTS: Seven studies were included. The present study found that the Mifflin St. Jeor (1990) equation had the lowest bias (mean difference = - 39.71 kcal [95%CI = - 128.97; 49.55]) for calculating REE in post-BS individuals. The Harris-Benedict (1919) equation also yielded satisfactory results (mean difference = - 54.60 kcal [95%CI = - 87.92; - 21.28]). CONCLUSION: The predictive equation of Mifflin St. Jeor (1990) was the one that showed the lowest bias for calculating the REE of patients following MBS.

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

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There are several equations based on bioelectrical impedance analysis (BIA) to estimate with high precision appendicular skeletal muscle mass (ASM). However, most of the external validation studies have reported that these equations are inaccurate or biased when applied to different populations. Furthermore, none of the published studies has derived correction factors (CFs) in samples of community-dwelling older adults, and none of the published studies have assessed the influence of the dual-energy X-ray absorptiometry (DXA) model on the validation process. This study assessed the agreement between six BIA equations and DXA to estimate ASM in non-Caucasian older adults considering the DXA model and proposed a CF for three of them. This analysis included 547 non-institutionalized subjects over 60 years old from the northwest of Mexico who were physically independent and without cognitive impairment: 192 subjects were measured using DXA Hologic, while 355 were measured by DXA Lunar. The agreement between each of the equations and DXA was tested considering the DXA model used as a reference method for the design of each equation, using the Bland and Altman procedure, a paired t test, and simple linear regression as objective tests. This process was supported by the differences reported in the literature and confirmed in a subsample of 70 subjects measured with both models. Only six published BIA equations were included. The results showed that four equations overestimated ASMDXA, and two underestimated it (p < 0.001, 95% CI for Kim's equation:-5.86--5.45, Toselli's:-0.51--0.15, Kyle's: 1.43-1.84, Rangel-Peniche's: 0.32-0.74, Sergi's: 0.83-1.23, and Yoshida's: 4.16-4.63 kg). However, Toselli's, Kyle's and Rangel-Peniche's equations were the only ones that complied with having a homogeneous bias. This finding allowed the derivation of CFs, which consisted of subtracting or adding the mean of the differences from the original equation. After estimating ASM applying the respective CF, the new ASM estimations showed no significant bias and its distribution remained homogeneously distributed. Therefore, agreement with DXA in the sample of non-Caucasian was achieved. Adding valid CFs to some BIA equations allowed to reduce the bias of some equations, making them valid to estimate the mean values of ASM at group level.

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

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CONTEXT: Energy expenditure predictive equations can generate inaccurate estimates for overweight or obese individuals. OBJECTIVE: The objective of this review was to determine which predictive equations for resting energy expenditure (REE) and total energy expenditure (TEE) have the lowest bias and the highest precision in adults with overweight and obesity. DATA SOURCES: Searches were performed in January 2022 in MEDLINE, Web of Science, Scopus, CENTRAL, and the gray literature databases. DATA EXTRACTION: Meta-analyses were performed with equations included in more than 1 study. The DerSimonian and Laird random-effects model and the I2 statistic were used to quantify heterogeneity in the quantitative analyses. The Egger test was performed to assess potential publication biases, and metaregressions were conducted to explore the heterogeneity. Findings were presented separated by participants' body mass index classification (overweight and obesity). DATA ANALYSIS: Sixty-one studies were included. The FAO/WHO/UNU (1985) equation, which uses only body weight in its formula, showed the lowest bias in estimating REE (mean difference [MD] = 8.97 kcal; 95% CI = -26.99; 44.94). In the subgroup analysis for individuals with obesity, the Lazzer (2007) equation showed the lowest bias (MD = 4.70 kcal; 95% CI = -95.45; 104.86). The Harris-Benedict equation (1919) showed the highest precision values for individuals with overweight (60.65%) and for individuals with obesity (62.54%). Equations with body composition data showed the highest biases. The equation proposed by the Institute of Medicine (2005) showed the lowest bias (MD = -2.52 kcal; 95% CI = -125.94; 120.90) in estimating the TEE. Most analyses showed high heterogeneity (I2 > 90%). There was no evidence of publication bias. CONCLUSION: For individuals with overweight, the FAO/WHO/UNU (1985) and the Harris-Benedict equations (1919) showed the lowest bias and the highest precision in predicting the REE, respectively. For individuals with obesity, the Harris-Benedict equation (1919) showed the highest precision and the Lazzer equation (2007) showed the lowest bias. More studies are needed on predictive equations to estimate the TEE. SYSTEMATIC REVIEW REGISTRATION: PROSPERO registration no. CRD42021262969.

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ABSTRACT Maximal inspiratory and expiratory pressures (MIP and MEP) assess the strength index of the respiratory muscles. These measures are relevant to assess respiratory muscle strength and for clinical monitoring. This study evaluates papers that suggest predictive equations of MIP and MEP for the Brazilian population. We included studies that established prediction equations for MIP and MEP for the healthy Brazilian population, aged from 4 to 90 years old, both men and women and that had the maximum respiratory pressures measured in a sitting position. A search was carried out in March 2020 on MEDLINE, LILACS, Cochrane, SciELO, CINAHL, Web of Science, and SCOPUS databases, without date or language filters. The descriptors used were "muscle strength," "equations," "predictive respiratory muscles" and their respective synonyms. Out of the 3,920 studies found in databases, 963 were duplicates, 2,779 were excluded, 178 had their full texts analyzed, and only 9 met the inclusion criteria. The predictive equations of ventilatory muscle strength analyzed in this review used age, weight, and stature as variables. However, the studies showed methodological weaknesses, such as lack of cross-validation of the equation, exclusion of outliers, and lack of familiarization of MIP and MEP.

RESUMO As pressões respiratórias máximas (PImáx e PEmáx) avaliam o índice de força dos músculos respiratórios. Essas medidas são relevantes para a avaliação da força muscular respiratória e para o monitoramento clínico. O objetivo deste estudo foi avaliar os artigos que sugerem equações preditivas de PImáx e PEmáx para a população brasileira. Foram incluídos estudos que estabeleceram equações de predição para PImáx e PEmáx da população brasileira saudável, com idades entre 4 e 90 anos e de ambos os sexos, que mediam as pressões respiratórias máximas na posição sentada. Uma pesquisa foi realizada, em março de 2020, nas bases de dados MEDLINE, LILACS, Cochrane, SciELO, CINAHL, Web of Science e SCOPUS, sem filtros de tempo ou idioma. Os descritores utilizados foram "força muscular", "equações" e "músculos respiratórios preditivos", com seus respectivos sinônimos. Dos 3.920 estudos encontrados nas bases de dados, 963 eram duplicados e 2.779 foram excluídos, 178 tiveram seus textos analisados integralmente e apenas 9 atendiam aos critérios de inclusão. As variáveis utilizadas nas equações preditivas de força muscular ventilatória analisadas nesta revisão foram: idade, peso e estatura. No entanto, os estudos mostraram fragilidades metodológicas, como falta de validação cruzada da equação, exclusão de outliers e familiarização do PImáx e PEmáx.

RESUMEN Las presiones inspiratoria y espiratoria máximas (PImáx y PEmáx) evalúan el índice de fuerza muscular respiratoria. Estas medidas son importantes en la evaluación de la fuerza muscular respiratoria y el seguimiento clínico. El objetivo de este estudio fue evaluar los artículos proponen ecuaciones predictivas para PImáx y PEmáx a la población brasileña. Se incluyeron estudios que establecieron ecuaciones predictivas para PImáx y PEmáx a la población brasileña sana de ambos sexos, de entre 4 y 90 años de edad, y que miden las presiones respiratorias máximas en posición sentada. Se realizó, en marzo de 2020, una búsqueda en las bases de datos MEDLINE, LILACS, Cochrane, SciELO, CINAHL, Web of Science y SCOPUS, sin año de publicación específico ni idioma. Los descriptores utilizados fueron "fuerza muscular", "ecuaciones" y "músculos respiratorios predictivos" y sus respectivos sinónimos. De los 3.920 estudios encontrados, 963 eran duplicados y se excluyeron 2.779, así se analizaron 178 textos en su totalidad y solo 9 cumplieron con los criterios de inclusión. Las variables edad, peso y talla fueron las que habían sido utilizadas en las ecuaciones predictivas de fuerza muscular respiratoria analizadas por esta revisión. Sin embargo, los estudios apuntaron limitaciones metodológicas, como falta de validación cruzada de la ecuación, exclusión de outliers y familiaridad de la PImáx y PEmáx.

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BACKGROUND: The accuracy of methods to determine resting energy expenditure (REE) contributes toward the adequate provision of nutrition support to hospitalized patients. Indirect calorimetry (IC) is considered the gold-standard method to determine REE. The aim of this study is to evaluate the degree of agreement between the REE measured by IC (REE-IC) and REE estimated by predictive equations in intensive care unit patients. METHODS: The sample is made up of intensive care unit patients aged >18 years, both male and female, undergoing nutrition therapy. The predictive equations to estimate REE were the Harris Benedict (HB), Ireton Jones (IJ), and practical method (PM). Degree of agreement between REE-predictive equations and REE-IC was analyzed by the interclass correlation coefficient (ICC) and the Bland-Altman test. RESULTS: Average energy obtained by IC was significantly different from HB and IJ equations (P < .001). The HB equation significantly underestimated the REE-IC for body mass index (BMI) classification. Significant concordance was observed between the REE-IC and all estimate equations (P < .05). The IJ equation showed the greatest degree of concordance for BMI classification of underweight (ICC = 0.674; P = .011) and presented the least difference between the averages of the energy when compared with REE-IC (107.8 kcal/d; P < .05). CONCLUSION: The IJ equation showed better results with IC, with the greatest degree of concordance for BMI classification of underweight. Further research should develop others equations and validate tools to measure energy expenditure for accurate dietary recommendations for hospitalized patients undergoing nutrition therapy.

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BACKGROUND & AIMS: Cancer is one the principal causes of death, and is considered a health issue worldwide. Cancer patients are at high risk of malnutrition due to the disease and the treatment itself. Nutritional therapy is part of a multi-modal treatment and it is important to be aware of the patient's energy expenditure to aid in decision-making for dietotherapeutic prescription. Indirect Calorimetry (IC) is the gold standard method for measuring energy expenditure (EE); but due to its often high cost in clinical practise, equations that measure energy expenditure are usually used. OBJECTIVES: To perform an integrative systematic review, searching in the literature for how predictive equations of EE behave in relation to IC in cancer patients with solid tumors, considering the overall accuracy for cancer patients, the different tumor types, and the type of anti-cancer therapy applied. METHODS: A review was carried out of systematic integrative type literature. The articles were searched for in three databases (Pubmed, Embase, and Web of Science) using descriptors accompanied by Boolean operators. Inclusion and exclusion criteria were determined, and the articles found went through selection, analysis and extraction of their results. RESULTS: A total of 688 articles were identified that underwent a thorough selection, resulting in 15 studies that included in this review. In five studies, the results showed that predictive equations underestimated the EE of cancer patients; in three studies the EE was overestimated by predictive equations, and in seven studies predictive equations underestimated or overestimated the EE. The low accuracy of predictive EE equations was present regardless of tumor type and type of anti-cancer therapy received by patients. CONCLUSION: The predictive energy expenditure equations available to date are generally not in accordance with IC results for cancer patients with solid tumors, since these individuals present clinical situations or are exposed to factors that alter EE and are not considered in these equations.

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BACKGROUND: The adequate provision of energy for obese patients by estimation without indirect calorimetry (IC) is challenging. The goal was to establish values of kilocalories per kilogram (kcal/kg) for patients in different ranges of body mass index (BMI [kg/m2 ]) comparable to resting energy expenditure (REE [kcal/kg/d]) measurements by IC. METHODS: In 63 overweight (OW) hospitalized patients with BMIs (25.0-29.9) or obesity (OB; ≥30), the REE was measured in fasting and fed states. IC was performed with Deltatrac II on patients with enteral or parenteral nutrition, classified by their BMI, in 3 groups: OW (25-29.9), OB (30-39.9), and morbidly obese (≥40). The actual body weight was measured with a microprocessed bed scale or a precision anthropometric scale, and REE was transformed to kcal/kg. RESULTS: Ninety-seven IC measurements were obtained from 63 patients: in 54 on a ventilator, and of 9 with a canopy; 58 in the fasting state; and 39 after reaching measured caloric requirements. The patients' BMIs ranged from 27.3 to 53.4, average of 33.9 ± 6.2. There were no differences in measured REE (mREE) between the fasting and fed states (P > 0.05) within each BMI group; however, differences were observed when comparing the mREE between the groups in the fed state. CONCLUSION: These results, in kcal/kg/d, suggest using 22.0 for OW, 18.4 for OB, and 16.9 for individuals with BMIs >40. However, we recommend the use of IC to determine REE for the latter subset of patients, since 16.9 kcal/kg/d might be an underestimation.

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