Sum of Skinfold-Corrected Girths Correlates with Resting Energy Expenditure: Development of the NRGCO Equation.

Our study aimed to validate existing equations and develop the new NRGCO equation to estimate resting energy expenditure (REE) in the Colombian population with moderate-to-high physical activity levels. Upon satisfying the inclusion criteria, a total of 86 (43F, 43M) healthy adults (mean [SD]: 27.5 [7.7] years; 67.0 [13.8] kg) were evaluated for anthropometric variables and REE by indirect calorimetry using wearable gas analyzers (COSMED K4 and K5). Significant positive correlations with REE were found for body mass (r = 0.65), body mass-to-waist (r = 0.58), arm flexed and tensed girth (r = 0.66), corrected thigh girth (r = 0.56), corrected calf girth (r = 0.61), and sum of breadths (∑3D, r = 0.59). As a novelty, this is the first time a significant correlation between REE and the sum of corrected girths (∑3CG, r = 0.63) is reported. Although existing equations such as Harris-Benedict (r = 0.63), Mifflin-St. Jeor (r = 0.67), and WHO (r = 0.64) showed moderate-to-high correlations with REE, the Bland-Altman analysis revealed significant bias (p < 0.05), indicating that these equations may not be valid for the Colombian population. Thus, participants were randomly distributed into either the equation development group (EDG, n = 71) or the validation group (VG, n = 15). A new model was created using body mass, sum of skinfolds (∑8S), corrected thigh, corrected calf, and age as predictors (r = 0.755, R2 = 0.570, RMSE = 268.41 kcal). The new NRGCO equation to estimate REE (kcal) is: 386.256 + (24.309 × BM) - (2.402 × ∑8S) - (21.346 × Corrected Thigh) + (38.629 × Corrected Calf) - (7.417 × Age). Additionally, a simpler model was identified through Bayesian analysis, including only body mass and ∑8S (r = 0.724, R2 = 0.525, RMSE = 282.16 kcal). Although external validation is needed, our validation resulted in a moderate correlation and concordance (bias = 91.5 kcal) between measured and estimated REE values using the new NRGCO equation.

Comparison between Measured and Predicted Resting Metabolic Rate Equations in Cross-Training Practitioners.

This study aimed to investigate the resting metabolic rate (RMR) in cross-training practitioners (advanced and novice) using indirect calorimetry (IC) and compare it with predictive equations proposed in the scientific literature. METHODS: A cross-sectional and comparative study analyzed 65 volunteers, both sexes, practicing cross-training (CT). Anthropometry and body composition were assessed, and RMR was measured by IC (FitMate PRO®), bioimpedance (BIA-InBody 570®), and six predictive equations. Data normality was tested by the Kolgomorov-Smirnov test and expressed as mean ± standard deviation with 95% confidence intervals (CI), chi-square test was performed to verify ergogenic resources, and a Bland-Altman plot (B&A) was made to quantify the agreement between two quantitative measurements. One-way ANOVA was applied to body composition parameters, two-way ANOVA with Bonferroni post hoc was used to compare the RMR between groups, and two-way ANCOVA was used to analyze the adjusted RMR for body and skeletal muscle mass. The effect size was determined using Cohen's d considering the values adjusted by ANCOVA. If a statistical difference was found, post hoc Bonferroni was applied. The significance level was p < 0.05 for all tests. RESULTS: The main results indicated that men showed a higher RMR than women, and the most discrepant equations were Cunningham, Tinsley (b), and Johnstone compared to IC. Tinsley's (a) equation indicated greater precision in measuring the RMR in CM overestimated it by only 1.9%, and BIA and the Harris-Benedict in CW overestimated RMR by only 0.1% and 3.4%, respectively. CONCLUSIONS: The BIA and Harris-Benedict equation could be used reliably to measure the RMR of females, while Tinsley (a) is the most reliable method to measure the RMR of males when measuring with IC is unavailable. By knowing which RMR equations are closest to the gold standard, these professionals can prescribe a more assertive diet, training, or ergogenic resources. An assertive prescription increases performance and can reduce possible deleterious effects, maximizing physical sports performance.

Energy Expenditure in Humans: Principles, Methods, and Changes Throughout the Life Course.

Humans require energy to sustain their daily activities throughout their lives. This narrative review aims to (a) summarize principles and methods for studying human energy expenditure, (b) discuss the main determinants of energy expenditure, and (c) discuss the changes in energy expenditure throughout the human life course. Total daily energy expenditure is mainly composed of resting energy expenditure, physical activity energy expenditure, and the thermic effect of food. Total daily energy expenditure and its components are estimated using variations of the indirect calorimetry method. The relative contributions of organs and tissues determine the energy expenditure under different physiological conditions. Evidence shows that energy expenditure varies along the human life course, at least in part due to changes in body composition, the mass and specific metabolic rates of organs and tissues, and levels of physical activity. This information is crucial to estimate human energy requirements for maintaining health throughout the life course.

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.

Comparison of pre- and posttransplant energy expenditure in patients undergoing hematopoietic stem cell transplantation and evaluation of associated factors.

OBJECTIVES: Patients undergoing hematopoietic stem cell transplantation may present with metabolic alterations that can have an effect on their energy expenditure and nutritional status. This project aimed to compare the pre- and posttransplant energy expenditures of patients undergoing hematopoietic stem cell transplantation as well as related factors. METHODS: This prospective study was conducted at a single center. Patients, undergoing autograft or allograft, were evaluated before transplantation and on the 10th and 17th d posttransplantation. Energy expenditure was measured by indirect calorimetry. Diet intake was assessed by a 24-h dietary recall. Infectious and noninfectious complications were analyzed between days 1 to 10 after transplantation and days 11 to 17 after transplantation. Paired model analyses were carried out to identify the pretransplantation and posttransplantation periods. RESULTS: Twenty patients were evaluated with a mean age of 45.6 ± 17.2 y; a majority were male sex (65%), and the most frequent diagnoses were chronic myeloid leukemia (25%) and multiple myeloma (25%). Energy expenditure increased by 15% posttransplantation, and the energy requirement per kilogram of weight was 23 kcal/kg at day 10 after transplantation. Throughout the posttransplantation period, 45% of the patients required nutritional therapy. Negative energy and negative protein balance were observed at all analyzed times. Phase angle (P = 0.018), fever (P = 0.014), mucositis grades I to II (P = 0.018), and the total number of infectious and noninfectious events (P = 0.043) were associated with an increase in energy expenditure at day 10 after transplantation. CONCLUSIONS: Energy expenditure increased after transplantation compared with pretransplantation in 50% of patients. Phase angle, fever, grades I to II mucositis, and infectious and noninfectious events were associated with increased energy expenditure at day 10 after transplantation.

[Correlation of equations for energy expenditure with indirect calorimetry in critically ill patients]. / Correlación de fórmulas para gasto energético con calorimetría indirecta en pacientes críticos.

Background: Nutrition in the Intensive Care Unit (ICU) is a cornerstone; however, energy requirements are a controversial issue that has not yet been resolved. Calorimetry is the gold standard for calculating energy expenditure, but it is expensive and not available in all ICU areas. Formulas have been developed to calculate basal energy expenditure (BAE) and make the process easier. Objective: To validate the predictive formulas of BAE compared to that obtained with ventilatory indirect calorimetry (IC) within the nutritional assessment in ICU patients. Material and methods: Analytical cross-sectional retrolective study. We performed BAE measurement on patients in the ICU of a third level hospital with ventilatory indirect calorimetry and compared the results obtained with those of the Harris Benedict, Muffin-St. Jeor, Institute of Medicine, and Faisy equations. Results: A total of 49 patients were included; a moderate correlation with statistical significance was found between the BAE measurements obtained by indirect calorimetry, with those obtained by four predictive equations that were studied. The Faisy equation obtained the strongest correction with r = 0.461 (p = 0.001). Conclusion: The correlation between the BAE obtained by predictive equations and by IC goes from mild to moderate, due to the heterogeneity of critical patients and their changing nature throughout their disease.

Introducción: la nutrición en la unidad de cuidados intensivos (UCI) es una piedra angular; sin embargo, los requerimientos energéticos son un tema controversial aún no resuelto. La calorimetría es el estándar de oro para calcular el gasto energético, pero es costosa y no está disponible en todas las áreas de las UCI. Se han desarrollado fórmulas para calcular el gasto energético basal (GEB) y hacer el proceso más sencillo. Objetivo: validar las fórmulas predictivas de GEB comparado con el obtenido con calorimetría indirecta (CI) ventilatoria dentro de la valoración nutricia en los pacientes de UCI. Material y métodos: estudio transversal analítico retrolectivo. Realizamos medición de GEB a los pacientes de la UCI de un hospital de tercer nivel con calorimetría indirecta ventilatoria y se compararon los resultados obtenidos con los de las fórmulas de Harris Benedict, Muffin-St. Jeor, Institute of Medicine y Faisy. Resultados: se incluyeron un total de 49 pacientes; se encontró correlación moderada con significación estadística entre las medidas de GEB obtenidas por calorimetría indirecta, con las obtenidas por cuatro fórmulas predictivas que se estudiaron. La fórmula de Faisy obtuvo la corrección más fuerte con una r = 0.461 (p = 0.001). Conclusión: la correlación entre el GEB obtenido por fórmulas predictivas y por CI es de ligera a moderada, debido a la heterogeneidad del paciente crítico y su naturaleza cambiante a lo largo de su enfermedad.

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

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

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

BACKGROUND: Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population. METHODS: An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations. RESULTS: Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2 = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942. CONCLUSION: Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

Resting energy expenditure depends on energy intake during weight loss in people with obesity: a retrospective cohort study.

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.

Energy Expenditure in Free-Living Physical Activities and Sedentary Behaviors in Mexican Schoolchildren.

Purpose: Metabolic equivalents for youth (METy) are derived using the estimated basal metabolic rate (BMR). However, is unknown whether this METy can be different when measured resting energy expenditure (REE) is used. The purposes of this article are to: (a) To determine whether there is equivalence between METy values calculated using measured REE (METy-mea) and METy using predicted BMR (METy-est). (b) To determine whether METy values of different activities are dependent on age, sex, and body composition. Methods: A cross-sectional study with a sample of 122 Mexican children (5-11 years old) was conducted. With indirect calorimetry, energy expenditure was measured at rest and during 16 sedentary- to vigorous-intensity physical activities. METy were obtained in two forms: METy-mea and METy-est. Equivalence testing was used to compare METy-mea and METy-est. To examine the individual-level agreement, Bland-Altman plots were graphed, and intra-class correlation coefficients (ICC) were calculated. Linear regression models were estimated having METy as the outcome. Results: For 15 activities, there was equivalence between METy-mea and METy-est (t > 2.05, p < 0.050). However, at the individual-level, for 7 activities the agreement between METy-eREE and METy-mREE was inadequate (ICC<0.75). In the Bland-Altman plots was evident that in 8 activities METy-est was biased compared to METy-mea, METy-est had more correlations with body mass index and body fat. Conclusions: In conclusion, at group-level, for most activities METy-mea and METy-est were equivalent. However, at the individual level, METy-est of many activities is not a valid estimate of METy-mea. METy-est of many physical activities were dependent on adiposity, which could be an artifact introduced when BMR is predicted.

Gasto energético de repouso em idosos longevos saudáveis: fatores associados e uso de fórmulas preditivas / Resting energy expenditure in healthy long-lived elderly individuals: associated factors and use of predictive formulas

Introdução: O processo de envelhecimento está associado a uma redução progressiva do gasto energético de repouso (GER). Embora a calorimetria indireta (CI) seja considerada padrão ouro para avaliar o GER, equações matemáticas preditivas são mais comuns na prática clínica. Objetivo: Avaliar a acurácia e a concordância entre o gasto energético de repouso (GER) medido (mGER) e o estimado (pGER), bem como suas associações com composição corporal, força e prática de atividade ou exercício físico, em pessoas idosas longevas saudáveis. Métodos: Estudo transversal com 74 pessoas idosas (45 mulheres e 29 homens), com idade ≥ 80 anos, saudáveis. O GER foi medido por CI (após jejum noturno de 12 horas) e estimado por 11 equações de predição. A composição corporal foi avaliada por impedância bioelétrica (BIA). Avaliou-se a normalidade das variáveis pelo teste Shapiro-Wilk. Os testes t Student e Mann Whitney foram utilizados para comparação das médias e medianas, respectivamente, entre os sexos. A comparação de proporções foi efetuada com auxílio do teste Qui-quadrado. A comparação entre os valores de mGER e pGER foi realizada por meio do teste Wilcoxon. O teste de correlação de Spearman e Pearson foi realizado para comparar associações. Variações de 10% do mGER foram usadas como medida de acurácia. A concordância individual dos GER's foi examinada por tercis e pelo Bland-Altman. Resultados: A mediana de idade foi de 85 anos (82,00 ­ 85,25). O mGER apresentou correlação moderada com todos os pGER (0,30 ≤ | r | < 0,60). Ao considerar os sexos, as correlações foram significativas apenas entre os homens. Todas as equações superestimaram os valores de GER. A menor diferença total entre mGER e pGER foi alcançada pela equação de Mifflin et al. (1990) (237,16 kcal/d), para as mulheres por Porter et al. (2023) (247,43 kcal/d) e para os homens por Anjos et al. (2014) (326,59 kcal/d). A maior acurácia entre as mulheres foi identificada pela equação de Porter et al. (2023) (26,7%), enquanto Anjos et al. (2014) propiciou maior acurácia total e nos homens (23,0% e 20,7%, respectivamente). Essa equação também apresentou melhor concordância individual na classificação por tercis (40,5%). Identificou-se baixa concordância em todas as fórmulas pelos gráficos de Bland-Altman. Houve correlação forte entre o mGER e a massa livre de gordura (r=0,329, p=0,008), a massa gorda em homens (r=607, p=<0,001), e o perímetro da panturrilha para o total (r=0,322, p=0,001) e para os homens (r=0,419, p=0,009). Conclusão: Identificamos a necessidade de cautela ao utilizar equações de estimativa do GER em pessoas idosas longevas, com a equação de Anjos et al. (2014) sendo a menos imprecisa, embora a acurácia geral e a concordância individual sejam limitadas. A composição corporal, em particular PP, MLG e MG, foram influenciadores do GER em pessoas idosas

Introduction: The aging process is associated with a progressive reduction in resting energy expenditure (REE). Although indirect calorimetry (IC) is considered the gold standard for assessing REE, predictive mathematical equations are more commonly used in clinical practice. Objective: To evaluate the accuracy and agreement between measured resting energy expenditure (mREE) and estimated resting energy expenditure (pREE), as well as their associations with body composition, strength, and engagement in physical activity or exercise in healthy long-lived elderly individuals. Methods: Cross-sectional study with 74 elderly individuals (45 women and 29 men) aged ≥ 80 years, who were healthy. REE was measured by IC (after a 12-hour overnight fast) and estimated by 11 prediction equations. Body composition was assessed by bioelectrical impedance analysis. The normality was assessed by the Shapiro-Wilk test. Student's t-tests and Mann-Whitney tests were used for comparing means and medians, respectively, between sexes. Proportion comparisons were made using the chi-square test. Comparison between mREE and pREE values was performed using the Wilcoxon test. Spearman and Pearson correlation was conducted to compare associations. Variations of 10% from mREE were used as an accuracy measure. Individual REE agreement was examined by tertiles and Bland-Altman analysis. Results: The median age was 85 years (82.00 ­ 85.25). The mREE showed moderate correlation with all pREE (0.30 ≤ | r | < 0.60). When considering genders, correlations were significant only among men. All equations overestimated REE values. The smallest total difference between mREE and pREE was achieved by the equation by Mifflin et al. (1990) (237.16 kcal/d), for women by Porter et al. (2023) (247.43 kcal/d), and for men by Anjos et al. (2014) (326.59 kcal/d). The highest accuracy among women was identified by the equation Porter et al. (2023) (26.7%), while Anjos et al. (2014) provided higher accuracy in the total group and men (23.0% and 20.7%, respectively). This equation also showed better individual agreement in tertile classification (40.5%). Low agreement was identified in all formulas by Bland-Altman plots. There was a strong correlation between mREE and lean body mass (r=0.329, p=0.008), fat mass in men (r=0.607, p=<0.001), and calf circumference for the total (r=0.322, p=0.001) and for men (r=0.419, p=0.009). Conclusion: We identified the need for caution when using REE estimation equations in long-lived elderly individuals, with the Anjos et al. (2014) equation being the least inaccurate, although overall accuracy and individual agreement are limited. Body composition, particularly fat-free mass, lean body mass, and fat mass, influenced REE in elderly individuals.

Resting energy expenditure in preterm newborns with bronchopulmonary dysplasia.

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.

Predictive Equation to Estimate Resting Metabolic Rate in Older Chilean Women.

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

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

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

Lack of validity of free-living physical activity energy expenditure estimated from ActiGraph equations in severely obese women: A doubly labelled water study.

The current study aimed to investigate the validity of three ActiGraph predictive equations that are available to estimate free-living physical activity energy expenditure (PAEE) in women with severe obesity. The study included 20 women with class III obesity (age: 22-38 years). During 14 days of free-living conditions, total energy expenditure was measured using the doubly labelled water method; in addition, participants wore a triaxial accelerometer (model GT3X+) on the hip. The resting metabolic rate was measured by indirect calorimetry. At group level, the Freedson VM3 Combination was found to be more precise (bias = -61 kcal/day) than the Williams Work-Energy (bias = -283 kcal/day) and the Freedson Combination equations (bias = -186 kcal/day) for estimating PAEE. However, the three predictive equations had a wider limit of agreement (Williams Work-Energy [258, -824 kcal/day], Freedson Combination equations [324, -697 kcal/day] and Freedson VM3 Combination [424, -546 kcal/day]), indicating great uncertainty of the estimate. In conclusion, a wide variation was observed in the performance of different ActiGraph equations in estimating free-living PAEE among women with class III obesity. Therefore, our data do not support the use of these equations, and more studies are needed to improve predictive performance in free-living conditions.

Estimates of resting energy expenditure and total energy expenditure using predictive equations in adults with overweight and obesity: a systematic review with meta-analysis.

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.

New Predictive Resting Metabolic Rate Equations for High-Level Athletes: A Cross-Validation Study.

PURPOSE: The present study aims a) to assess the agreement between the measured resting metabolic rate (RMR) using indirect calorimetry and different predictive equations (predicted RMR), and b) to propose and cross-validate two new predictive equations for estimating the RMR in high-level athletes. METHODS: The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, whereas the body composition was assessed using skinfolds. Comparisons between measured and predicted RMR values were performed using one-way ANOVA. Mean difference, root mean square error (RMSE), simple linear regression, and Bland-Altman plots were used to evaluate the agreement between measured and predicted RMR. The accuracy of predictive equations was analyzed using narrower and wider accuracy limits (±5% and ±10%, respectively) of measured RMR. Multiple linear regression models were employed to develop the new predictive equations based on traditional predictors (equation 1) and the stepwise method (equation 2). RESULTS: The new equations 1 and 2 presented good agreement based on the mean difference (3 and -15 kcal·d -1 ), RMSE (200 and 192 kcal·d -1 ), and R2 (0.71 and 0.74), respectively, and accuracy (61% of subjects between the limit of ±10% of measured RMR). Cunningham's equation provided the best performance for males and females among the existing equations, whereas Jagim's equation showed the worst performance for males (mean difference = -335 kcal·d -1 ; RMSE = 386 kcal·d -1 ). Compared with measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; P ≤ 0.05), mainly in males. CONCLUSIONS: The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Jagim's equation should not be used in high-level male athletes.

Time to achieve steady state for an accurate assessment of resting energy expenditure in adolescents with healthy weight and obesity: A cross-sectional study.

Objective: The present study investigated the time needed to achieve a steady state for an accurate assessment of resting energy expenditure (REE) in adolescents with healthy weight and obesity. Methods: Thirty adolescents aged 12-17 years were assigned to a group with healthy weight (GHW; n = 12, body mass index [BMI] 22.5 ± 3.6 kg/m2) and another group with obesity (GO; n = 18, BMI 34.1 ± 5.2 kg/m2). Participants underwent test-retest reliability of REE assessment as follows: a) 24 h of abstention from physical exercise, soft drinks, or caffeine; b) fasting for ~12 h; c) acclimation period of 10 min; d) 30-min assessment in a supine position. Results: A significant change occurred during the 30 min in REE. Significant differences existed between consecutive means until the 20th and 25th min for the GHW and GO, respectively. Although significant differences between trials 1 and 2 were detected during the first 5-10 min of assessment, the REE for each 5-min time point exhibited high test-retest reliability across trials in both groups (intraclass correlation coefficients range 0.79-0.99). Conclusion: The following recommendations are provided to promote accurate assessment of REE among adolescents: a) initiate the REE assessment with 10 min of acclimation to decrease restlessness; b) determine REE for a minimum of 20 min if healthy weight and 25 min if obesity; c) determine REE for a further 5 min, with the average of this last 5 min of REE data being regarded as the REE.

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

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

Exercise Fat Oxidation Is Positively Associated with Body Fatness in Men with Obesity: Defying the Metabolic Flexibility Paradigm.

Obesity is thought to be associated with a reduced capacity to increase fat oxidation in response to physical exercise; however, scientific evidence supporting this paradigm remains scarce. This study aimed to determine the interrelationship of different submaximal exercise metabolic flexibility (Metflex) markers and define its association with body fatness on subjects with obesity. Twenty-one male subjects with obesity performed a graded-intensity exercise protocol (Test 1) during which cardiorespiratory fitness (CRF), maximal fat oxidation (MFO) and its corresponding exercise intensity (FATmax) were recorded. A week afterward, each subject performed a 60-min walk (treadmill) at FATmax (Test 2), and the resulting fat oxidation area under the curve (TFO) and maximum respiratory exchange ratio (RERpeak) were recorded. Blood lactate (LAb) levels was measured during both exercise protocols. Linear regression analysis was used to study the interrelationship of exercise Metflex markers. Pearson's correlation was used to evaluate all possible linear relationships between Metflex and anthropometric measurement, controlling for CRF). The MFO explained 38% and 46% of RERpeak and TFO's associated variance (p < 0.01) while TFO and RERpeak were inversely related (R2 = 0.54, p < 0.01). Body fatness positively correlated with MFO (r = 0.64, p < 0.01) and TFO (r = 0.63, p < 0.01) but inversely related with RERpeak (r = -0.67, p < 0.01). This study shows that MFO and RERpeak are valid indicators of TFO during steady-state exercise at FATmax. The fat oxidation capacity is directly associated with body fatness in males with obesity.