Resting Metabolic Rate Values Research Articles

The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players.

Determining resting metabolic rate (RMR) is an important aspect when calculating energy requirements for professional rugby union players. Prediction equations are often used for convenience to estimate RMR. However, the accuracy of current prediction equations for professional rugby union players remains unclear. The aims of this study were to examine the RMR of professional male rugby union players compared to nine commonly used prediction equations and develop and validate RMR prediction equations specific to professional male rugby union players. One hundred and eight players (body mass (BM) = 102.9 ± 13.3 kg; fat-free mass (FFM) = 84.8 ± 10.2 kg) undertook Dual-energy X-ray Absorptiometry scans to assess body composition and indirect calorimetry to determine RMR. Mean RMR values of 2585 ± 176 kcal∙day-1 were observed among the group with forwards (2706 ± 94 kcal·day-1), demonstrating significantly (p < 0.01; d = 1.93) higher RMR compared to backs (2465 ± 156 kcal·day-1), which appeared to be due to their higher BM and FFM measures. Compared to the measured RMR for the group, seven of the nine commonly used prediction equations significantly (p < 0.05) under-estimated RMR (-104-346 kcal·day-1), and one equation significantly (p < 0.01) over-estimated RMR (192 kcal·day-1). This led to the development of a new prediction equation using stepwise linear regression, which determined that the strongest predictor of RMR for this group was FFM alone (R2 = 0.70; SEE = 96.65), followed by BM alone (R2 = 0.65; SEE = 104.97). Measuring RMR within a group of professional male rugby union players is important, as current prediction equations may under- or over-estimate RMR. If direct measures of RMR cannot be obtained, we propose the newly developed prediction equations be used to estimate RMR within professional male rugby union players. Otherwise, developing team- and/or group-specific prediction equations is encouraged.

Evaluation of Different Equations for Resting Metabolic Rate Prediction in Female Combat Sports Athletes

Only a few studies have produced equations that can estimate resting metabolic rate (RMR) in female athletes, but the accuracy of these equations for combat athletes has not yet been tested. The aim of this study was to evaluate the 12 different equations which are commonly using to determine resting metabolite rate (RMR) in the literature. Twenty-three female combat sport athletes (24.23± 3.39 years; 166.8 ± 5.3 cm; 63.13±6.53 kg; 8.78±3.19 experience years.; 56.40±3.43 VO2 mL/kg/min) were participated this study in voluntarily basis. A cross-validation approach used to compare the accuracy of 12 commonly prediction equations with measured RMR by indirect calorimetry to determine RMR in female combat sports athletes. All the predictive equation was underestimated RMR when compared with the measured RMR (p &lt; 0.05) and the smallest mean difference (92.46 ± 210.38 kcal·d-1) was observed for Altman &amp; Dittmer equation amongst the 12 predictive equations. The Altman &amp; Dittmer equation was accurately predicted 16 out of 30 subjects’ RMR value within the range ±10%. However, based on the Bland–Altman plots, the prediction equations were not accurately nor precisely predicted RMR in the current sample of female combat sport athletes. The results in the present study showed that the Altman &amp; Dittmer equation is most suitable equation to predict RMR amongst 12 equations. Although the Altman &amp; Dittmer equation was resulted with smallest mean difference, it seems that there is need to further research with longitudinal approach to understand the effects of training intensity and body mass changes on RMR in order to develop the formulas already exist used commonly.

Effect of gas exchange data selection methods on resting metabolic rate estimation in young athletes.

This cross-sectional study analysed the effect of the gas exchange data selection methods on the resting metabolic rate (RMR) estimation and proposed a protocol shortening providing a suitable RMR estimation for young athletes. Sixty-six healthy young Brazilian athletes performed a 30-minute RMR assessment. Different methods of gas exchange data selection were applied: short and long-time intervals, steady-state (SSt), and filtering. A mixed one-way ANOVA was used to analyse the mean differences in gas exchange, RMR, respiratory exchange ratio (RER), and coefficients of variation across all methods. Additionally, paired Student's t-test were used to compare the first and best SSt RMR values for each SSt method (3, 4, and 5-min). The 5-min SSt method provided the lowest RMR estimate (1454 kcal.day-1). There was a statistical difference between methods (F = 2.607, p = 0.04), but they presented a clinically irrelevant absolute difference (~36 kcal.day-1). There were no differences in RER among methods. In addition, using the SSt method, 12 minutes of assessment were enough to obtain a valid estimation of RMR. The 5-min SSt method should be employed for assessing the RMR among young athletes, considering the possibility of obtaining a shortened assessment (~12 min) with an acceptable and low coefficient of variation.

Accuracy Of Bioimpendance Analysis For Measuring Body Compositionand Resting Metabolism In Youth.

PURPOSE: This study assessed the accuracy of bioimpedance analysis (BIA) for measuring body composition and resting metabolic rate (RMR) against criterion measures in youth. METHODS: Youth of varying sex, age (child, 7-12 y; teen, 13-17 y), weight status (healthy weight, BMI < 85th %ile; overweight, BMI ≥ 85th %ile; obese, BMI ≥ 95th %ile), and pubertal status (years from peak height velocity) were recruited from Athens and surrounding counties of Southeast Ohio. Using paired t-tests, fat mass (FM; kg), fat free mass (FFM; kg), visceral adipose tissue (VAT; cm2) and RMR (kcal/day) measures from a multi-frequency, tetrapolar, eight-point BIA device were compared to dual-energy X-ray absorptiometry (DXA) and a portable metabolic analyzer, respectively. Relationships among all measures were assessed using correlation analyses. Accuracy was calculated as the root mean squared error (RMSE) and bias between methods. RESULTS: In total, 130 youth (11.9 ± 2.9 y; 68 female, 85 healthy weight, 77 children, 61 pre-pubertal) participated in the study. BIA measures of FM was greater in girls (16.0 ± 1.2 kg), and obese (27.6 ± 2.5 kg) compared to boys (13.2 ± 0.9 kg) and healthy weight youth (10.9 ± 0.4 kg). FFM was higher in boys (41.8 ± 1.8 kg), teens (49.2 ± 1.4 kg), and post-pubertal youth (52.0 ± 9.6 kg) compared to their counterparts (35.3 ± 1.2, 30.9 ± 0.9, and 28.5 ± 5.4 kg, respectively). BIA-estimated RMR was higher in boys (1276 ± 40 kcal/day), teens (1434 ± 29 kcal/day), and post-pubertal youth (1493 ± 207 kcal/day) compared to their counterparts (1133 ± 25, 1038 ± 19, 985 ± 116 kcal/day, respectively). Assessing the entire sample, BIA reported lower FFM (38.4 ± 1.1 kg) and higher FM (11.8 ± 0.8 kg) and VAT (50.4 ± 4.1 cm2) compared to DXA (32.6 ± 1.0 kg, 14.7 ± 0.8 kg, 45.8 ± 2.7 cm2), whereas estimated and measured RMR values were similar (1199 ± 24 vs. 1145 ± 254 kcal/day; p > 0.05). Correlations between methods ranged from r = 0.71 to 1.00 (p < 0.05). BIA overestimated FM by 2.8 kg and underestimated FFM by 5.7 kg. CONCLUSIONS: While measures between methods were statistically different, the magnitude of these differences were physiologically insignificant and strong relationships between BIA and criterion measures were observed. Future research should assess the sensitivity of BIA to body composition and RMR changes across growth trajectories.

Respirometry protocols for avian thermoregulation at high air temperatures: stepped and steady-state profiles yield similar results.

Relationships between air temperature (Tair) and avian body temperature (Tb), resting metabolic rate (RMR) and evaporative water loss (EWL) during acute heat exposure can be quantified through respirometry using several approaches. One involves birds exposed to a stepped series of progressively increasing Tair setpoints for short periods (<20-30 min), whereas a second seeks to achieve steady-state conditions by exposing birds to a single Tair for longer periods (>1-2 h). To compare these two approaches, we measured Tb, RMR and EWL over Tair=28°C to 44°C in the dark-capped bulbul (Pycnonotus tricolor). The two protocols yielded indistinguishable values of Tb, RMR and EWL and related variables at most Tair values, revealing that both are appropriate for quantifying avian thermal physiology during heat exposure over the range of Tair in the present study. The stepped protocol, however, has several ethical and practical advantages.

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

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. 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). 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. 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.

Resting metabolic rate and its association with body composition parameters in 9- to 18-year-old Indian children and adolescents

ObjectivesObesity is a disorder of energy balance with increasing prevalence in Indian children. Objectives were to study 1) resting metabolic rate (RMR), anthropometry, body composition (BC), and their relationships in Indian children and adolescents affected with overweight (OW) or obesity (OB) and in non-overweight (NOW) children and 2) factors influencing RMR, such as age, sex, and BC. MethodsThis cross-sectional, observational, multicentric study included 495 children (260 boys) of age 9–18 y who were assessed from six Indian states for sociodemographic and anthropometric parameters using standard protocols. Body composition was assessed by a body impedance analyzer technique (Tanita-BC-420), and RMR was assessed using indirect calorimetry. Data were analyzed using SPSS (version 26) with appropriate statistical tests. The level of significance was set at P < 0.05. ResultsBoys had higher absolute RMR values than did girls (1212±263 kcal/d versus 1066±204 kcal/d; P < 0.001). Weight-adjusted RMR was significantly lower among children classified as OW or OB than among NOW children of both sexes, respectively (P < 0.001). Linear regression analysis revealed fat-free mass, sex, age, and body mass index (BMI) as significant predictors of RMR (P < 0.05). ConclusionsWe found lower weight-adjusted RMR in children classified as OW or OB as compared with NOW children. Our study underlines the importance of low RMR levels among children with a higher BMI, in girls, and with increasing age.

Predictive Equations Overestimate Resting Metabolic Rate in Survivors of Chronic Stroke

ObjectiveTo measure resting metabolic rate (RMR) in survivors of chronic (>3 months prior) stroke (mean ± SEM age, 61±7.5 years) and to compare to predicted RMR using predictive equations in adults without stroke. DesignCross-sectional study. SettingHospital. ParticipantsSurvivors of stroke (N=71). InterventionNot applicable. Main Outcome MeasuresRMR was measured by indirect calorimetry. Participants underwent a total body dual-energy x-ray absorptiometry scan and treadmill test for peak oxygen consumption (V̇o2peak). RMR was calculated using 9 established equations. ResultsRMR measured (1552±319 kcal/d) was significantly lower than 9 predicted RMR values (all P<.001), with the best being McArdle-Katch (1652±233 kcal/d), Livingston (1677±230 kcal/d), and Mifflin (1707±242 kcal/d). The Institute of Medicine of the National Academies (2437±386 kcal/d) had the largest discrepancy with measured RMR. Predicted RMR determined with 8 of 9 equations was between 9% and 18% greater than measured RMR. Appendicular lean mass (r=0.64, P<.001), total lean mass (r=0.64, P<.001), and V̇o2peak (r=0.41, P<.001) were associated with measured RMR. ConclusionsRMR predictive equations established in adults without stroke are not appropriate for the population with stroke population, indicating the need to measure RMR until a more accurate predictive equation is developed. This could support modifications to nutritional intake guidelines in patients with conditions of muscle atrophy. If measurement of RMR is not feasible, the Katch-McArdle equation should be used to estimate RMR in a patient with stroke because on average it provides the lowest percentage overestimate compared with other equations.

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

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. The RMR of 102 athletes (44 women) was assessed using indirect calorimetry, while 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, 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 regressions models were employed to develop the new predictive equations based on traditional predictors (Equation 1) and the stepwise method (Equation 2). 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 Harris & Benedict's equation showed the worst performance for males (mean difference = 406 kcal.d-1; RMSE = 473 kcal.d-1). Compared to measured RMR, most predictive equations showed heteroscedastic distribution (linear regression's intercept and slope significantly different from zero; p ≤ 0.05), mainly in males. The new proposed equations can estimate the RMR in high-level athletes accurately. Cunningham's equation is a good option from existing equations, and Harris & Benedict's equation should not be used in high-level male athletes.

Reliability of resting metabolic rate between and within day measurements using the Vyntus CPX system and comparison against predictive formulas.

Background: To detect longitudinal changes of resting metabolic rate (RMR) resulting from the effects of energetic stress, reliable RMR measurements are crucial. The Vyntus CPX is a new automated indirect calorimetry system for which RMR reliability has not been determined. Additionally, its agreement with common predictive RMR formulas is unknown. Aim: To determine the within and between-day reliability of RMR measurements using the Vyntus CPX system and its agreement with predictive RMR formulas. Methods: Young (31 ± 7 years) healthy participants (n = 26, 12 females, 14 males) completed three measurements of RMR, two consecutive measures on the same day, one the day before/after, all under standardised conditions. Reliability was assessed with pairwise comparisons of between-day at the same time (BDST), within day consecutive measurements (WDCM) and between-day different time (BDDT), for parameters of reliability (mean change (MC), intraclass correlation (ICC) and typical error of measurement (TEM)). Measured RMR values (kcal/day) were compared against predictive values of 4 common formulas. Results: Parameters of reliability (mean, (95% confidence interval)) were: -BDST: MC, 0.2(-2.3-2.7)% (p = 0.67); ICC, 0.92(0.84-0.97); TEM, 4.5(3.5-6.2)%. -WDCM: MC, -2.5(-6.2-1.3)% (p = 0.21); ICC, 0.88(0.74-0.88); TEM, 7.0(5.4-9.8)%. -BDDT: MC, -1.5(-4.8-1.9)% (p = 0.57); ICC, 0.90(0.76-0.95); TEM, 6.1(4.8-8.5)%. RMRratios (measured/predicted) were: 1.04 ± 0.14 (Nelson, p = 0.13), 1.03 ± 0.10 (Mifflin, p = 0.21), 0.98 ± 0.09 (Harris-benedict, p = 0.30), 0.95 ± 0.11 (Cunningham1980, p = 0.01), 1.00 ± 0.12 (Cunningham1991, p = 0.90) and 0.96 ± 0.13 (DXA, p = 0.03). Conclusions: The Vyntus CPX is reliable and measured RMR values agreed with four predictive formulas but are lower than Cunningham1980 and DXA RMR estimates for this population.

Resting metabolic rate in adults with facioscapulohumeral muscular dystrophy.

This study aimed to determine whether resting metabolic rate (RMR) is altered in adults with facioscapulohumeral muscular dystrophy (FSHD). Eleven people with FSHD (51± 12yrs, 2 females) and 11 controls (48± 14 yrs, 2 females) completed 1 visit, including 30-minutes of indirect calorimetry and dual-energy X-ray absorptiometry (DXA) scanning. RMR was calculated from resting oxygen consumption/carbon dioxide production; regional/whole-body fat mass and lean mass were collected from the DXA scan. Absolute RMR was 15% lower in FSHD (p= 0.04); when normalized to regional/local lean mass, no differences in RMR were observed (p> 0.05). Absolute RMR was correlated with total lean mass for all participants combined (p< 0.01, r= 0.70, males only: p< 0.01, r= 0.81) and when analyzed separately (FSHD males: p= 0.001, r= 0.92 and control males: p= 0.004, r= 0.85). Whole-body lean mass was 16% lower in FSHD and leg, arm and appendicular lean mass were lower in FSHD (p< 0.05 for all), though trunk lean mass was not (p= 0.15). Whole-body fat mass was 45% higher in FSHD, with greater leg fat mass (p= 0.01), but not trunk or arm fat mass (p> 0.05 for both). When RMR was expressed relative to lean body mass, no differences in RMR were found, indicating that the lower levels of lean mass observed in FSHD patients likely contribute to the lower absolute RMR values. Novelty: RMR is lower among people with FSHD, as compared with controls. The reduced RMR among people with FSHD is due to disease-related loss in muscle mass and likely related to lower physical activity and/or exercise levels.

Long-term Resting Metabolic Rate Analysis in Pregnancy and Weight Loss Interventions

In this paper, we first studied the change in resting metabolic rate (RMR) of 4 women during their pregnancy period. We retrospectively analyzed published data, which lacked rigorous statistical analysis. We introduced new data that helps to define RMR baseline variabilities and further compare the RMR fluctuations in steady physiological conditions (no pregnancy, no weight/diet/exercise regime change) to assess “true” RMR changes that can guide healthy weight management in pregnancy and other conditions. For each subject, the change in the RMR values were computed as the difference between the values during the metabolic rate inspection period and the baseline values. This difference was compared against the difference values of a reference subject, using a two-sided paired t-test at the significance level of 5%. Our results indicated that some subjects exhibit a statistically significant increase, some exhibit a decrease while others show no significant statistical variation in RMR values during pregnancy. These are important findings that demystify the old idea that the RMR of a pregnant woman “always” increases since she is generating a new life; rather, individualized physiological processes can produce metabolic changes that cannot be generalized and need individual RMR measurements throughout pregnancy. The insights gained from this study were then applied to retrospectively analyze the RMR of 20 subjects during a 6-month pilot weight loss intervention with 89% efficiency in weight loss. Our analysis revealed that there was no significant decrease in metabolic activities at the end of the program. Although this contradicts the belief that weight loss is associated with a decrease in metabolic activities, our results can be explained by the fact that subjects adhered to a healthy nutritional diet and regular exercise during the pro- gram; thus, the effect of weight loss on decreasing the RMR was counter-balanced by the effect of healthier diet and exercise on increasing the RMR, which helped in maintaining a steady and healthy metabolic rate. Both studies, pregnancy and weight loss interventions indicated that changes in the metabolic rate of pregnant women and individuals undergoing weight loss interventions are unpredictable, therefore there is an urgent need to implement personalized practices of weight management by periodically measuring RMR and adjusting food caloric intakes based on the individual’s metabolic rate.

Resting Oxygen Uptake Value of 1 Metabolic Equivalent of Task in Older Adults: A Systematic Review and Descriptive Analysis.

It is important for sport scientists and health professionals to have estimative methods for energy demand during different physical activities. The metabolic equivalent of task (MET) provides a feasible approach for classifying activity intensity as a multiple of the resting metabolic rate (RMR). RMR is generally assumed to be 3.5mL of oxygen per kilogram of body mass per minute (mLO2kg-1min-1), a value that has been criticized and considered to be overestimated in the older adult population. However, there has been no comprehensive effort to review available RMR estimations, equivalent to 1 MET, obtained in the older adult population. The aim of this review was to examine the existing evidence reporting measured RMR values in the older adult population and to provide descriptive estimates of 1 MET. A systematic review was conducted by searching PubMed, Web of Science, Scopus, CINAHL, SPORTDiscus, and Cochrane Library, from database inception to July 2021. To this end, original research studies assessing RMR in adults ≥ 60years old using indirect calorimetry and reporting results in mLO2kg-1min-1 were sought. Twenty-three eligible studies were identified, including a total of 1091 participants (426 men). All but two studies reported RMR values lower than the conventional 3.5mLO2kg-1min-1. The overall weighted average 1 MET value obtained from all included studies was 2.7 ± 0.6mLO2kg-1min-1; however, when considering best practice studies, this value was 11% lower (2.4 ± 0.3mLO2kg-1min-1). Based on the results of this systematic review, we would advise against the application of the standard value of 1 MET (3.5mLO2kg-1min-1) in people ≥ 60years of age and encourage the direct assessment of RMR using indirect calorimetry while adhering to evidence-based best practice recommendations. When this is not possible, assuming an overall value of 2.7mLO2kg-1min-1 might be reasonable. Systematic review registration: International Prospective Register of Systematic Reviews on 30 September 2020, with registration number CRD42020206440.

Resting metabolic rate: a comparison between different measurement methods used in male university students

Resting metabolic rate (RMR) is a widely used method to determine an individual's nutritional need. There are different methods of obtaining the RMR values to identify these needs. This study aimed to compare the RMR values using two different indirect calorimetry (IC) devices, bioelectrical impedance analysis (BIA), and two predictive equations in a sample of male university students. This cross-sectional study analyzed 65 male university students (aged 23.3 ± 2.8 years, body mass index 25.78 ± 3.98 kg/m2). RMR was obtained using two IC devices (VO2000® and Fitmate PRO®) and BIA (InBody 570®), and two predictive equations by Harris and Benedict (Proc Natl Acad Sci 4:370–373, 1918) and FAO/WHO/UNU (Food Agric Organ 1:103, 2001). The Bland–Altman (B&A) plot demonstrated considerable differences between the two gold standard methods, VO2000® and Fitmate PRO®, and BIA, with a mean difference of 7.8 and 5.7%, respectively. The B&A plot presented the lowest difference between the two IC devices with a mean difference of 2.1%. The B&A plot also showed a low difference between the two IC devices and predictive equations, with values ranging from −1.3 to −3.6%. In turn, the B&A plot between BIA and the predictive equations comprised approximately − 9.2% of the mean difference. The analysis of variance (ANOVA) demonstrated no significant differences between the measurements (p > 0.05). Based on the results, InBody 570® underestimates the reference values for RMR in two different IC devices as well as in the predictive equations. Although no significant differences were observed between ANOVA comparisons, the RMR values obtained using BIA should be analyzed with caution. The absence of sophisticated devices for RMR analysis indicates the need to use the FAO/WHO/UNU (Food Agric Organ 1:103, 2001) equation, which presented a lower difference compared with that of the gold standard measurements in male university students.

Abstract P195: Assessing the Validity of Predicting Resting Metabolic Rate in Chronic Stroke Patients

Chronic stroke patients may have lower resting metabolic rate (RMR) due to disability and ensuing loss of skeletal muscle. Established equations used to estimate RMR based on weight, height, age, or lean body mass in healthy non-stroke individuals may not be accurate for a hemiparetic patient population. The purpose of this study is to determine resting metabolic rate in chronic stroke survivors and compare to RMR calculated with established equations in healthy adults. Adults (n=71; 56 males, 15 females; 40 African American, 27 Caucasian, 4 other / not reported) aged 44-76 years (61 ± 7.5 yrs) who had a history of chronic stroke (&gt; 3 months prior) underwent a 30 minute test after a 12-hour fast to measure RMR by indirect calorimetry, total body DXA scan, and treadmill test (VO2 peak). Estimated RMR was calculated using nine established equations. RMR measured in the total group (1552 ± 319 kcal/day) was significantly different from all nine estimated RMR values (Katch-McArdle 1664 ± 242 kcal/day, P=0.05; Livingston 1671 ± 239 kcal/day, P&lt;0.001; Mifflin 1703 ± 254 kcal/day, P&lt;0.001; Owen 1761 ± 269 kcal/day, P&lt;0.001; Harris Benedict 1782 ± 308 kcal/day, P&lt;0.001; revised Harris-Benedict 1795 ± 306 kcal/day, P&lt;0.001; Cunningham 1818 ± 247 kcal/day, P&lt;0.001; Schofield 2147 ± 301 kcal/day, P&lt;0.001; IMNA 2428 ± 405 kcal/day, P&lt;0.001). Calculated RMR was between 9% and 60% greater than measured RMR, regardless of race. Appendicular lean mass (r=0.65, P&lt;0.001), total lean mass (r=0.65, P&lt;0.001), and VO2 peak (r=0.50, P&lt;0.001) were associated with measured RMR. RMR estimation equations established in healthy adults are not reliable for the chronic stroke population, indicating the need for a more accurate predictive equation to better assist nutritional status in patients with conditions of muscle atrophy.

Predictive equations for resting metabolic rate are not appropriate to use in Brazilian male adolescent football athletes.

High accuracy in estimating energy expenditure is essential for enhancing sports performance. The resting metabolic rate (RMR), as a primary component of total energy expenditure (TEE), is commonly estimated using predictive equations. However, these references may not be applicable to adolescent athletes. The purpose of this cross-sectional study was to analyse the differences between predicted RMR in relation to energy expenditure measured by indirect calorimetry (IC) among 45 Brazilian male adolescent football athletes. Indirect calorimetry (IC) and anthropometric (bioimpedance) measurements were recorded at a single visit to the laboratory after fasting overnight. The mean age was 15.6 ± 1.14 years, body mass was 63.05 ± 7.8 kg, and height was 172 ± 7.5 cm. The RMR values predicted by equations proposed by the Food and Agriculture Organization (FAO) (United Nations), Henry and Rees (HR), Harris Benedict (HB), and Cunningham (CUN) were compared with IC RMR values, by correlation analysis. The FAO and HR predictive equations yielded different values from IC (IC: 1716.26 ± 202.58, HR: 1864.87 ± 147.78, FAO: 1854.28 ± 130.19, p = 0.001). A moderate correlation of 0.504 was found between the results of HB and IC. In the survival-agreement model, the CUN equation showed low disagreement with the IC RMR, with error values between 200 and 300 kcal/day. The results showed that HB and CUN yielded similar values as IC, with the CUN equation showing low disagreement with IC; hence, adolescent athletes should undergo evaluation with precise laboratory methods to ensure that accurate information about RMR is recorded.

Metabolic cost of inflammatory response of ruby-throated hummingbirds (Archilochus colubris)

Animals with a slow pace of life and high mass-specific metabolic rates are expected to invest less in innate immune responses. We measured skin inflammation and the resting metabolic rate (RMR) of ruby-throated hummingbirds ( Archilochus colubris) after their immune system was challenged with phytohemagglutinin (PHA) and compared with the response of birds injected a saline solution. The PHA test measures the inflammatory process, a component of the innate response. Ruby-throated hummingbirds belong to a group that is under-represented in avian immunological studies characterized by a slow pace of life and fast metabolic rate. Hummingbirds developed an inflammatory response that lasted &lt;28 h. PHA injection produced a significant increment of RMR (up to ~13%) with respect to RMR values after the injection of the saline solution indicating that immune response involved a metabolic cost for hummingbirds. This increment lies within the range of values previously reported for birds injected PHA (5%–29%).

Impact of the Method Used to Select Gas Exchange Data for Estimating the Resting Metabolic Rate, as Supplied by Breath-by-Breath Metabolic Carts.

The method used to select representative gas exchange data from large datasets influences the resting metabolic rate (RMR) returned. This study determines which of three methods yields the lowest RMR (as recommended for use in human energy balance studies), and in which method the greatest variance in RMR is explained by classical determinants of this variable. A total of 107 young and 74 middle-aged adults underwent a 30 min RMR examination using a breath-by-breath metabolic cart. Three gas exchange data selection methods were used: (i) steady state (SSt) for 3, 4, 5, or 10 min, (ii) a pre-defined time interval (TI), i.e., 6–10, 11–15, 16–20, 21–25, 26–30, 6–25, or 6–30 min, and (iii) “filtering”, setting thresholds depending on the mean RMR value obtained. In both cohorts, the RMRs yielded by the SSt and filtering methods were significantly lower (p < 0.021) than those yielded by the TI method. No differences in RMR were seen under the different conditions of the SSt method, or of the filtering method. No differences were seen between the methods in terms of the variance in RMR explained by its classical determinants. In conclusion, the SSt and filtering methods return the lowest RMRs and intra-measurement coefficients of variation when using breath-by-breath metabolic carts.

Were the synapsids primitively endotherms? A palaeohistological approach using phylogenetic eigenvector maps.

The acquisition of mammalian endothermy is poorly constrained both phylogenetically and temporally. Here, we inferred the resting metabolic rates (RMRs) and the thermometabolic regimes (endothermy or ectothermy) of a sample of eight extinct synapsids using palaeohistology, phylogenetic eigenvector maps (PEMs), and a sample of 17 extant tetrapods of known RMR (quantified using respirometry). We inferred high RMR values and an endothermic metabolism for the anomodonts (Lystrosaurus sp., Oudenodon bainii) and low RMR values and an ectothermic metabolism for Clepsydrops collettii, Dimetrodon sp., Edaphosaurus boanerges, Mycterosaurus sp., Ophiacodon uniformis and Sphenacodon sp. A maximum-likelihood ancestral states reconstruction of RMRs performed using the values inferred for extinct synapsids, and the values measured using respirometry in extant tetrapods, shows that the nodes Anomodontia and Mammalia were primitively endotherms. Finally, we performed a parsimony optimization of the presence of endothermy using the results obtained in the present study and those obtained in previous studies that used PEMs. For this, we assigned to each extinct taxon a thermometabolic regime (ectothermy or endothermy) depending on whether the inferred values were significantly higher, lower or not significantly different from the RMR value separating ectotherms from endotherms (1.5 ml O2 h-1 g-0.67). According to this optimization, endothermy arose independently in Archosauromorpha, Sauropterygia and Therapsida. This article is part of the theme issue 'Vertebrate palaeophysiology'.

Accuracy of resting metabolic rate prediction equations in female rugby players

AbstractThis study aimed to investigate (1) the accuracy of resting metabolic rate (RMR) prediction equations in female rugby players on a group and individual level; and (2) whether individual differences in the accuracy of prediction equations is associated with muscle damage or energy availability.RMR was assessed in 14 female provincial and club rugby players (Age: 20–34 years, FFM: 47–63 kg, FM: 15–37%) training a minimum of twice per week. Participants attended the laboratory following an overnight fast and having avoided strenuous exercise for 24 hours. RMR was measured over 30 minutes by indirect calorimetry, and taken as the 10 minutes with the lowest variation. Body composition was assessed by air displacement plethysmography, muscle damage indicated by creatine kinase (CK) and risk of low energy availability assessed by the Low Energy Availability in Females Questionnaire. Accuracy of RMR prediction equations relevant to the general population and athletes were assessed including the Harris Benedict (1919), Cunningham (1980) and Ten Haaf FFM (2014) based equations.Measured RMR was 1748 ± 146 kcal/day (range: 1474–2010 kcal/day). Predicted RMR determined by the Harris-Benedict equation (1601 ± 120 kcal/day) was significantly lower than measured RMR (p &lt; 0.001), whereas predicted RMR using the Cunningham (1753 ± 146 kcal/day, p = 0.89) and the Ten Haaf (1781 ± 115 kcal/day, p = 0.33) equations did not differ from measured RMR. On an individual level, 50% (n = 7), 86% (n = 12) and 79% (n = 11) of participants fell within 10% of the measured RMR value when RMR was predicted by Harris-Benedict, Cunningham and Ten Haaf equations respectively. CK values were 182 ± 155U/L (range: 25–490U/L). When correlations of the whole group were studied, the difference between predicted and measured RMR was not associated with CK (r = 0.13). However, in the two individuals who fell outside the 10% range of that predicted by the Cunningham equation, one above and one below, CK values were 428U/L and 166U/L respectively. Muscle damage (as indicated by a high CK value) could therefore be one potential explanation for the higher measured RMR in the individual who was above the Cunningham predicted value.In this cohort of female rugby players, the Cunningham equation showed the best accuracy on a group and individual level, suggesting this may be the most suitable prediction equation for this population. Further studies with larger sample sizes and investigating underlying reasons for why RMR measured values may differ from predicted values are needed.