[Characteristics of resting energy expenditure and evaluation of prediction formulas in young men with different body mass indexes].

OBJECTIVE: To compare the resting energy expenditure (REE) characteristics among young men with different body mass indexes (BMI). METHODS: Thirty young men [average age was (26.93±4.16) years] were enrolled in this study. They underwent resting metabolism tests in the Department of Sports Medicine of Peking University Third Hospital from December 2017 to June 2021. The resting metabolic rate (RMR) was measured by indirect calorimetry, the body composition was measured by bioresistance antibody component analyzer. The REE characteristics were analyzed, and 11 predictive equations were used to estimate RMR and compared with the measured value. The differences were analyzed by paired t-test and intra-class correlation coefficient (ICC). RESULTS: The RMR of the overall 30 young men was (1 960.17±463.11) kcal/d (1 kcal=4.186 8 kJ). Including (1 744.33±249.62) kcal/d in those with normal BMI, which was significantly lower than that in those who were overweight or obese [(2 104.06± 520.32) kcal/d, P < 0.01], but the weight-corrected RMR in those with normal BMI was significantly higher than that in those who were overweight or obese [(24.02±2.61) kcal/(kg·d) vs. (19.98±4.38) kcal/(kg·d), P < 0.01]. The RMR was significantly and positively correlated with body weight, adiposity, lean body mass, body surface area, and extracellular fluid in the subjects with diffe-rent BMI (all P < 0.05). The predicted values of the 11 prediction equations were not in good agreement with the measured values (all ICC < 0.75), with relatively high agreement between the predicted and measured values of the World Health Organization (WHO) equation in overweight obese young men (ICC=0.547, P < 0.01). CONCLUSION: There were significant differences in RMR among young men with different BMI, and the RMR after weight correction should be considered for those who were overweight or obese. The consistency between the predicted values of different prediction equations and the actual measured values of RMR was relatively poor, and it is recommended to accurately measure RMR by indirect calorimetry. For overweight or obese young men, the WHO prediction equation can be considered to calculate RMR, but it is necessary to establish an RMR prediction equation applicable to different BMI populations.

Development and validation of age-specific predictive equations for total energy expenditure and physical activity levels for older adults.

BACKGROUND: Predicting energy requirements for older adults is compromised by the underpinning data being extrapolated from younger adults. OBJECTIVES: To generate and validate new total energy expenditure (TEE) predictive equations specifically for older adults using readily available measures (age, weight, height) and to generate and test new physical activity level (PAL) values derived from 1) reference method of indirect calorimetry and 2) predictive equations in adults aged ≥65 y. METHODS: TEE derived from "gold standard" methods from n = 1657 (n = 1019 females, age range 65-90 y), was used to generate PAL values. PAL ranged 1.28-2.05 for males and 1.26-2.06 for females. Physical activity (PA) coefficients were also estimated and categorized (inactive to very active) from population means. Nonlinear regression was used to develop prediction equations for estimating TEE. Double cross-validation in a randomized, sex-stratified, age-matched 50:50 split, and leave one out cross-validation were performed. Comparisons were made with existing equations. RESULTS: Equations predicting TEE using the Institute of Medicine method are as follows: For males, TEE = -5680.17 - 17.50 × age (years) + PA coefficient × (6.96 × weight [kilograms] + 44.21 × height [centimeters]) + 1.13 × resting metabolic rate (RMR) (kilojoule/day). For females, TEE = -5290.72 - 8.38 × age (years) + PA coefficient × (9.77 × weight [kilograms] + 41.51 × height [centimeters]) + 1.05 × RMR (kilojoule/day), where PA coefficient values range from 1 (inactive) to 1.51 (highly active) in males and 1 to 1.44 in females respectively. Predictive performance for TEE from anthropometric variables and population mean PA was moderate with limits of agreement approximately ±30%. This improved to ±20% if PA was adjusted for activity category (inactive, low active, active, and very active). Where RMR was included as a predictor variable, the performance improved further to ±10% with a median absolute prediction error of approximately 4%. CONCLUSIONS: These new TEE prediction equations require only simple anthropometric data and are accurate and reproducible at a group level while performing better than existing equations. Substantial individual variability in PAL in older adults is the major source of variation when applied at an individual level.

Energy expenditure and dietary intake in professional female football players in the Dutch Women's League: Implications for nutritional counselling.

In contrast to male football players, research on the nutritional requirements of female football players is limited. This study aimed to assess total daily energy expenditure (TDEE) in professional female football players, along with body composition, physical activity and dietary intake. This observational study included 15 professional football players playing in the highest Dutch Women's League. TDEE was assessed by doubly labelled water over 14 days, along with resting metabolic rate (RMR; ventilated hood), fat-free mass (FFM; dual-energy x-ray absorptiometry), and dietary intake (24-h recalls). Physical activity energy expenditure (PAEE) was derived from subtracting RMR and estimated diet-induced thermogenesis (10%) from TDEE. TDEE was 2882 ± 278 kcal/day (58 ± 5 kcal/kg FFM) and significantly (p < 0.05) correlated with FFM (r = 0.62). PAEE was 1207 ± 213 kcal/d. Weighted energy intake was 2344 kcal [2023-2589]. Carbohydrate intakes were 3.2 ± 0.7, 4.4 ± 1.1 and 5.3 ± 1.9 g/kg body mass for rest, training and match days, respectively, while weighted mean protein intake was 1.9 ± 0.4 g/kg body mass. In conclusion, the energy requirements of professional female football players are moderate to high and can be explained by the substantial PAEE. To fuel these requirements, sports nutritionists should consider shifting the players' focus towards prioritizing adequate carbohydrate intakes, rather than emphasizing high protein consumption.

Estimates of resting energy expenditure using predictive equations in adults with severe burns: A systematic review and meta-analysis.

BACKGROUND: Many equations to estimate the resting energy expenditure (REE) of patients with burns are currently available, but which of them provides the best guide to optimize nutrition support is controversial. This review examined the bias and precision of commonly used equations in patients with severe burns. METHODS: A systematic search of the PubMed, Web of Science, Embase, and Cochrane Library databases was undertaken on June 1, 2023, to identify studies comparing predicted REE (using equations) with measured REE (by indirect calorimetry [IC]) in adults with severe burns. Meta-analyses of bias and calculations of precisions were performed in each predictive equation, respectively. RESULTS: Nine eligible studies and 12 eligible equations were included. Among the equations, the Toronto equation had the lowest bias (26.1 kcal/day; 95% CI, -417.0 to 469.2), followed by the Harris-Benedict equation × 1.5 (1.5HB) and the Milner equation. The Ireton-Jones equation (303.4 kcal/day; 95% CI, 224.5-382.3) acceptably overestimated the REE. The accuracy of all of the equations was <50%. The Ireton-Jones equation had the relatively highest precision (41.2%), followed by the 1.5HB equation (37.0%) and the Toronto equation (34.7%). CONCLUSION: For adult patients with severe burns, all of the commonly used equations for the prediction of REE are inaccurate. It is recommended to use IC for accurate REE measurements and to use the Toronto equation, 1.5HB equation, or Ireton-Jones equation as a reference when IC is not available. Further studies are needed to propose more accurate REE predictive models.

Trajectories of resting energy expenditure and performance of predictive equations in children hospitalized with an acute illness and malnutrition: a longitudinal study.

There is scarce data on energy expenditure in ill children with different degrees of malnutrition. This study aimed to determine resting energy expenditure (REE) trajectories in hospitalized malnourished children during and after hospitalization. We followed a cohort of children in Bangladesh and Malawi (2-23 months) with: no wasting (NW); moderate wasting (MW), severe wasting (SW), or edematous malnutrition (EM). REE was measured by indirect calorimetry at admission, discharge, 14-and-45-days post-discharge. 125 children (NW, n = 23; MW, n = 29; SW, n = 51; EM, n = 22), median age 9 (IQR 6, 14) months, provided 401 REE measurements. At admission, the REE of children with NW and MW was 67 (95% CI [58, 75]) and 70 (95% CI [63, 76]) kcal/kg/day, respectively, while REE in children with SW was higher, 79 kcal/kg/day (95% CI [74, 84], p = 0.018), than NW. REE in these groups was stable over time. In children with EM, REE increased from admission to discharge (65 kcal/kg/day, 95% CI [56, 73]) to 79 (95% CI [72, 86], p = 0.0014) and was stable hereafter. Predictive equations underestimated REE in 92% of participants at all time points. Recommended feeding targets during the acute phase of illness in severely malnourished children exceeded REE. Acutely ill malnourished children are at risk of being overfed when implementing current international guidelines.

Energy Expenditure of Elite Male and Female Professional Tennis Players During Habitual Training.

Understanding the daily energy expenditure of athletes during training is important to support recovery, adaptation, and the maintenance of performance. The aim of the current research was to assess the total daily energy expenditure (TDEE) and the acute energy expenditure (EE) of tennis training sessions during habitual training of elite tennis players. Using a cohort study design, 27 (n = 10, male; age; 22.3 ± 3.2 years and n = 17, female; age: 23.8 ± 3.5 years) elite singles tennis players were assessed for TDEE and tennis training EE. Using Actiheart activity monitors during a 2- to 5-day training period, male players were analyzed for 26 days and 33 (1.3 ± 0.5 sessions/day) tennis training sessions, and female players for 43 days and 58 (1.2 ± 0.4 sessions/day) tennis training sessions. Male TDEE (4,708 ± 583 kcal/day) was significantly higher than female (3,639 ± 305 kcal/day). Male absolute and relative tennis training EEs (10.2 ± 2.3 kcal/min and 7.9 ± 1.4 kcal·hr-1·kg-1) were significantly higher than those of females (7.6 ± 1.0 kcal/min and 6.8 ± 0.9 kcal·hr-1·kg-1). The resting metabolic rate was assessed via indirect calorimetry. The physical activity level for both groups was 2.3 AU. The TDEE of male and female players during habitual training now highlights the continual cycle of high energy demands experienced by the elite tennis player. The broad ranges of TDEE and EE reported here suggest individual assessment and nutritional planning be prioritized, with a particular focus on carbohydrate requirements.

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

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

Estimating Total Energy Expenditure to Determine Energy Requirements in Free-Living Children With Stage 3 Chronic Kidney Disease: Can a Structured Approach Help Improve Clinical Care?

OBJECTIVE: Malnutrition and obesity are complex burdensome challenges in pediatric chronic kidney disease (CKD) management that can adversely affect growth, disease progression, wellbeing, and response to treatment. Total energy expenditure (TEE) and energy requirements in children are essential for growth outcomes but are poorly defined, leaving clinical practice varied and insecure. The aims of this study were to explore a practical approach to guide prescribed nutritional interventions, using measurements of TEE, physical activity energy expenditure (PAEE), and their relationship to kidney function. DESIGN AND METHODS: In a cross-sectional prospective age-matched and sex-matched controlled study, 18 children with CKD (6-17 years, mean stage 3) and 20 healthy, age-matched, and gender-matched controls were studied. TEE and PAEE were measured using basal metabolic rate (BMR), activity diaries and doubly labeled water (healthy subjects). Results were related to estimated glomerular filtration rate (eGFR). The main outcome measure was TEE measured by different methods (factorial, doubly labeled water, and a novel device). RESULTS: Total energy expenditure and PAEE with or without adjustments for age, gender, weight, and height did not differ between the groups and was not related to eGFR. TEE ranged from 1927 ± 91 to 2330 ± 73 kcal/d; 95 ± 5 to 109 ± 5% estimated average requirement (EAR), physical activity level (PAL) 1.52 ± 0.01 to 1.71 ± 0.17, and PAEE 24 to 34% EAR. Comparisons between DLW and alternative methods in healthy children did not differ significantly, except for 2 (factorial methods and a fixed PAL; and the novel device). CONCLUSION: In clinical practice, structured approaches using supportive evidence (weight, height, BMI sds), predictive BMR or TEE values and simple questions on activity, are sufficient for most children with CKD as a starting energy prescription.

Association of resting energy expenditure with phase angle in hospitalized older patients: a cross-sectional analysis.

BACKGROUND/OBJECTIVES: Resting energy expenditure (REE) constitutes the largest component of total energy expenditure and undergoes an age-related decline that is unexplained by decreased fat-free mass. Phase angle (PhA) is a cellular health indicator that is possibly associated with REE. We investigated the association of REE and PhA in hospitalized older adults. SUBJECTS/METHODS: This single-center, cross-sectional analysis utilized the baseline data from a prospective longitudinal study and included 131 eligible patients aged ≥70 years. The REE was measured using indirect calorimetry, and PhA and body composition were assessed using bioelectrical impedance. The association between REE, PhA, and body composition was examined, and REE was compared using previously reported PhA cutoff values. RESULTS: In this cohort with a mean (±standard deviation) age of 87.4 (±7.0) years, 34.4% of the participants were men. REE and PhA correlated strongly (r: 0.562, p < 0.001) and significantly after adjusting for age and sex (r: 0.433, p < 0.001). Multivariate analysis showed a significant independent association between REE and PhA and skeletal muscle mass (standardized ß [95% CI]; 28.072 [2.188-53.956], p = 0.035) without any significant interaction between PhA and age on REE. The low PhA group had a significantly lower REE (kcal/day; 890 [856-925] vs. 1077 [1033-1122], p < 0.001), and this remained significant after adjusting for age, sex, and skeletal muscle mass index. CONCLUSIONS: PhA is associated with REE in older adults. Adjusting REE calculation algorithms based on PhA values and correcting predicted REE according to PhA may aid in determining more accurate energy requirements.

Body composition and resting energy expenditure in a group of children with achondroplasia.

BACKGROUND: Persons with achondroplasia develop early obesity, which is a comorbidity associated with other complications. Currently, there are no validated specific predictive equations to estimate resting energy expenditure in achondroplasia. METHODS: We analyzed the influence of body composition on this parameter and determined whether predictive models used for children with standard height are adjusted to achondroplasia. In this cross-sectional study, we measured anthropometric parameters in children with achondroplasia. Fat mass was obtained using the Slaughter skinfold-thickness equation and resting energy expenditure was determined with a Fitmate-Cosmed calorimeter and with predictive models validated for children with average height (Schofield, Institute of Medicine, and Tverskaya). RESULTS: All of the equations yielded a lower mean value than resting energy expenditure with indirect calorimetry (1256±200 kcal/day [mean±SD]) but the closest was the Tverskaya equation (1017 ± 64 kcal/day), although the difference remained statistically significant. We conclude that weight and height have the greatest influence on resting energy expenditure. CONCLUSION: We recommend studying the relationship between body composition and energy expenditure in achondroplasia in more depth. In the absence of valid predictive models suitable for clinical use to estimate body composition and resting energy expenditure in achondroplasia, it is recommended to use the gold standard methods by taking into account certain anthropometric parameters.

Longitudinal estimates of resting energy expenditure using predictive equations in individuals with excess weight after weight loss: A systematic review with meta-analysis.

BACKGROUND & AIMS: To determine which resting energy expenditure (REE) predictive equation has the lowest bias in the aggregate level in individuals with excess weight during weight loss interventions. METHODS: Searches were performed in MEDLINE, Web of Science, Scopus, CENTRAL and gray literature databases. Longitudinal studies on weight loss interventions which evaluated REE by predictive equations compared to that measured by indirect calorimetry in adults with excess weight at different follow-up times were included. Meta-analyses were performed with the differences between biases of predictive equations of the REE at the different follow-up times of weight loss. RESULTS: Of the total of 2178 occurrences found in the databases, only eight studies were included. The Harris-Benedict (1919) equation showed the smallest differences between bias up to the third month (MD = 103.33 kcal; 95%CI = -39.01; 245.67), in the sixth month (MD = 59.16 kcal; 95%CI = 8.74; 109.57) and at the 12th month (MD = -71.41 kcal; 95%CI = -150.38; 7.55) of weight loss follow-up. Weight loss does not seem to have an effect on bias at different follow-up times. CONCLUSION: Harris-Benedict (1919) equation seems to be the most adequate to assess the REE of individuals with excess weight during weight loss. However, the finding of large estimated predictive intervals may indicate that predictive equations may not be handy tools for individuals losing and regaining weight due to changes other than body weight.

Comparison of energy expenditure measurements by a new basic respiratory room vs. classical ventilated hood.

BACKGROUND: Nutritional support is often based on predicted resting energy expenditure (REE). In patients, predictions seem invalid. Indirect calorimetry is the gold standard for measuring EE. For assessments over longer periods (up to days), room calorimeters are used. Their design makes their use cumbersome, and warrants improvements to increase utility. Current study aims to compare data on momentary EE, obtained by a basic respiration room vs. classical ventilated hood. The objective is to compare results of the basic room and to determine its 1)reliability for measuring EE and 2)sensitivity for minute changes in activity. METHODS: Two protocols (P1; P2)(n = 62; 25 men/37 women) were applied. When measured by hood, participants in both protocols were in complete rest (supine position). When assessed by room, participants in P1 were instructed to stay half-seated while performing light desk work; in P2 participants were in complete rest mimicking hood conditions. The Omnical calorimeter operated both modalities. Following data were collected/calculated: Oxygen uptake ([Formula: see text] O2(ml/min)), carbon dioxide production ([Formula: see text] CO2ml/min), 24h_EE (kcal/min), and respiratory exchange ratio (RER). Statistical analyses were done between modalities and between protocols. The agreement between 24h_EE, [Formula: see text] O2 and [Formula: see text] CO2 obtained by both modalities was investigated by linear regression. Reliability analysis on 24h_EE determined ICC. RESULTS: No significant differences were found for 24h_EE and [Formula: see text] O2. [Formula: see text] CO2 significantly differed in P1 + P2, and P2 (hood > room). RER was significantly different (hood > room) for P1 + P2 and both protocols individually. Reliability of 24h_EE between modalities was high. Modality-specific results were not different between protocols. DISCUSSION/CONCLUSION: The room is valid for assessing momentary EE. Minute changes in activity lead to a non-significant increase in EE and significant increase in RER. The significant difference in [Formula: see text] CO2 for hood might be related to perceived comfort. More research is necessary on determinants of RER, type (intensity) of activity, and restlessness. The design of the room facilitates metabolic measurements in research, with promising results for future clinical use.

Resting energy expenditure changes after antineoplastic treatment in gynecological cancer: a prospective pilot study.

Introduction: Introduction: energy metabolism in cancer patients is influenced by different factors. However, the effect of antineoplastic treatment is not clear, especially in women. Objective: to evaluate resting energy expenditure (REE) by indirect calorimetry (IC) before (T0) and after (T1) first cycle period of antineoplastic therapy: radiotherapy (RT), chemotherapy (CT), and concomitant chemoradiation therapy (CRT), quality of life (QoL) and accuracy of REE were compared with international guidelines recommendations per kilogram (European Society for Clinical Nutrition and Metabolism [ESPEN]). Methods: an observational, longitudinal study was conducted in women with gynecological cancer diagnosis undergoing antineoplastic treatment: RT, CT and CRT. Weight loss, actual body weight and height were measured. REE was evaluated in T0-T1 and compared with ESPEN recommendations. Kruskal-Wallis test and Bland-Alman analysis were used to determine the agreement (± 10 % of energy predicted) of REE adjusted by physical activity (TEE) compared with ESPEN recommendations, respectively. Results: fifty-four women with cancer were included: 31.5 % (n = 17) for RT group, 31.5 % (n = 17) for CT group and 37 % (n = 20) for CRT group. REE showed statistical differences between T0 and T1 in the total population (p = 0.018), but these were not associated with anticancer therapy groups (p > 0.05). QoL had no significant changes after treatment (p > 0.05). Accuracy of 25 and 30 kcal/kg compared to TEE was less than 30 %. Conclusion: REE in women with gynecological cancer decreased after antineoplastic treatments but this is not associated with a particular antineoplastic therapy. It is needed to develop research to determine the accuracy of ESPEN recommendations with TEE estimated by IC and clinical factors in women with cancer.

Introducción: Antecedentes: el metabolismo energético en pacientes con cáncer está influenciado por diferentes factores. Sin embargo, el efecto sobre el tratamiento antineoplásico no es claro, especialmente en mujeres. Objetivo: evaluar el gasto energético en reposo (GER) mediante calorimetría indirecta (CI) antes (T0) y después (T1) del primer ciclo del tratamiento antineoplásico: radioterapia (RT), quimioterapia (QT) y quimio-radioterapia concomitante (QRT), calidad de vida (CdV) y precisión del GER con las con las recomendaciones internacionales por kilogramo de peso (European Society for Clinical Nutrition and Metabolism [ESPEN]). Métodos: se realizó un estudio longitudinal, observacional en mujeres con diagnóstico de cáncer ginecológico en tratamiento antineoplásico. Se evaluó el GER en T0 y T1. Se midieron la pérdida de peso, el peso corporal y la talla. Se usaron las pruebas de Kruskal-Wallis y el análisis Bland-Altman para determinar la concordancia (± 10 % de GER) del REE ajustado por actividad física (TEE) en comparación con las recomendaciones de ESPEN. Resultados: se incluyeron 54 mujeres con cáncer; 31,5 % (n = 17) en el grupo RT, 31,5 % (n = 17) en el de QT y 37 % (n = 20) en el de QRT. GER mostró diferencias estadísticas entre T0 y T1 en la población total (p = 0,018); no se asoció con la terapia contra el cáncer (p > 0,05). La calidad de vida no tuvo cambios significativos después del tratamiento (p > 0,05). La precisión de 25 y 30 kcal/kg en comparación con TEE fue inferior al 30 %. Conclusión: el GER en mujeres con cáncer ginecológico disminuyó después del tratamiento antineoplásico, pero no se asoció a una terapia antineoplásica en particular. Es fundamental desarrollar más investigaciones que compare las recomendaciones de ESPEN y con los valores de la CI comparando más factores clínicos para ofrecer una intervención nutricional precisa.

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.

Total Energy Expenditure and Nutritional Intake in Continuous Multiday Ultramarathon Events.

Continuous multiday ultramarathon competitions are increasingly popular and impose extreme energetic and nutritional demands on competitors. However, few data have been published on energy expenditure during these events. Here, we report doubly labeled water-derived measures of total energy expenditure (in kilocalories per day) and estimated physical activity level (PAL: total energy expenditure/basal metabolic rate) collected from five elite and subelite finishers (four males and one female, age 34.6 ± 4.9 years)-and nutritional intake data from the winner-of the Cocodona 250, a ∼402-km race in Arizona, and from a fastest-known-time record (one male, age 30 years) on the ∼1,315-km Arizona Trail. PAL during these events exceeded four times basal metabolic rate (Cocodona range: 4.34-6.94; Arizona Trail: 5.63). Combining the results with other doubly labeled water-derived total energy expenditure data from ultraendurance events show a strong inverse relationship between event duration and PAL (r2 = .68, p < .0001). Cocodona race duration was inversely, though not significantly, associated with PAL (r2 = .70, p = .08). Water turnover varied widely between athletes and was not explained by PAL or body mass. The Cocodona race winner met ∼53% of energy demand via dietary intake, 85.6% of which was carbohydrate, while ∼47% of energy demand was met via catabolism of body energy stores. Together, these results illustrate the energetic deficits incurred during competitive continuous multiday ultramarathon efforts and implicate macronutrient absorption and/or storage as key factors in ultramarathon performance.

Measured vs estimated resting energy expenditure in children and adolescents with obesity.

Pediatric obesity requires early targeted interventions consisting mainly of a low-calorie diet prescribed based on resting energy expenditure (REE), often estimated through predictive equations. The aim of this study was to define the prevalence of "hypo-", "normo-" and "hypermetabolic" in a large cohort of children and adolescents with obesity by comparing measured and estimated REE and to evaluate the characteristics related to these metabolic statuses in both males and females. The study population was divided into the three subgroups by comparing REE measured using indirect calorimetry and estimated using the Molnar equation, and subsequently analyzed. The majority of the participants (60.6%) were normometabolic, 25.5% hypermetabolic and 13.9% hypometabolic. No significant differences in age, Tanner stage, systolic blood pressure, or the presence of metabolic syndrome were found. However, the hypermetabolic subgroup was significantly lighter, shorter, with lower hip and waist circumferences, had a greater amount of fat-free mass and lower fat mass, significantly lower diastolic blood pressure, and a significantly higher frequency of non-alcoholic liver steatosis. Pediatric obesity is more associated with normal or increased REE than with a hypometabolic condition, suggesting that estimation of energy expenditure with predictive equations is still inadequate for prescribing the appropriate diet plan.

Validation of predictive equations for resting energy expenditure in children and adolescents with different body mass indexes.

BACKGROUND: Accurate estimation of resting energy expenditure (REE) in children and adolescents is important to establish estimated energy requirements. The objective of this study was to assess the validity of existing equations in literature and a newly developed equation in estimating REE in children and adolescents. METHODS: 275 participants (148 boys, 127 girls) aged 6-18 years included in the study were classified as normal-weighted, overweight, obese based on BMI z-scores for age according to WHO-2007 growth curves for 5-19 years of age. REEs were measured using an indirect calorimeter, with various equations, and a newly established equation [REE = 505.412+(24.383*FFM);Adjusted R2 = 0.649] were compared with REE measured using Bland-Altman and further validation parameters. RESULTS: When the predicted REEs were compared with the measured REEs, the highest prediction accuracy was achieved using the new Eq. (64.8%) and IOM (63.8%) for normal-weight participants, Müller FFM and new Eq. (59.6%) for overweight participants and Lazzer (44.9%) for obese participants. In normal and overweight participants, lowest root mean squared error (RMSE) values were acquired from Schmelzle's equation (respectively 136.2;159.9 kcal/d), and the highest values were found in Kim's Eq. (315.2; 295.2 kcal/d respectively). RMSE value of the new equation was 174.7 kcal/d for normal-weight children and adolescents, and 201.9 kcal/d for overweight ones. In obese participants, the lowest RMSE value was obtained from Schmelzle's Eq. (305.4 kcal/d) and the new Eq. (317.4 kcal/d), while the highest value was obtained from IOM Eq. (439.9 kcal/d). RMSE was higher in obese groups compared to the other BMI groups. CONCLUSION: Indirect-calorimeter is the most suitable method for REE measurement in especially obese children and adolescents. The new equation and Schmelzle's equation appear to be most accurate equations for normal and overweight children and adolescents.

Quantifying physical activity energy expenditure based on doubly labelled water and basal metabolism calorimetry: what are we actually measuring?

PURPOSE OF REVIEW: Physical activity impacts energy balance because of its contribution to total energy expenditure. Measuring physical activity energy expenditure (PAEE) is often performed by subtracting the estimated 24 h expenditure on basal metabolism (called basal energy expenditure or BEE) from the total energy expenditure (TEE) measured by doubly labelled water minus an estimate of the thermic effect of food (TEF). Alternatively it can be measured as the ratio of TEE/BEE, which is commonly called the physical activity level (PAL). RECENT FINDINGS: PAEE and PAL are widely used in the literature but their shortcomings are seldom addressed. In this review, we outline some of the issues with their use. SUMMARY: TEE and BEE are both measured with error. The estimate of PAEE by difference magnifies these errors and consequently the precision of estimated PAEE is about 3× worse than TEE and 25-35× worse than BEE. A second problem is that the component called PAEE is actually any component of TEE that is not BEE. We highlight how the diurnal variation of BEE, thermoregulatory expenditure and elevations of RMR because of stress will all be part of what is called PAEE and will contribute to a disconnect between what is measured and what energy expenditure is a consequence of physical activity. We emphasize caution should be exerted when interpreting these measurements of PAEE and PAL.

Using energy expenditure to estimate the minute ventilation and inhaled load of air pollutants: a pilot survey in young Chinese adults.

Existing equations to estimate ventilation (VE) may not represent the Chinese population. The objective is to develop regression equations to predict the basal metabolic rate (BMR) for ventilation estimation. 80 participants underwent the incremental tests on a bicycle ergometer, wearing a fitted facial mask with an airflow sensor connected to the cardiopulmonary gas analyzer, where the energy expenditure, metabolic factors and VE were monitored simultaneously. Linear regression models were established between BMR and body weight, which were used to estimate energy expenditure and VE. Extrapolation of the regression model was evaluated by the five-fold cross-validation. And we also assessed the inhaled load of air pollutants in subgroups at the same exposure levels. Regression models for males and females were BMR (kJ/d) = 107.58 × weight (kg)-172.61 and BMR (kJ/d) = 105.61 × weight (kg)-26.94, respectively. The model showed good fitness between the measured and predicted VE. Differences between the measured and predicted VE of this model are smaller than that of other models. There were significant differences in inhaled load participants in the same exposure concentrations. The regression model showed that weight and BMR are highly correlated and can be used to estimate individual VE.

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.