Current Predictive Resting Metabolic Rate Equations Are Not Sufficient to Determine Proper Resting Energy Expenditure in Olympic Young Adult National Team Athletes

Validation of predictive equations to estimate resting metabolic rate of females and males across different activity levels

OBJECTIVES

Using equations to predict resting metabolic rate (RMR) has yielded different degrees of validity, particularly when sex and different physical activity levels were considered. Therefore, the purpose of the present study was to determine the validity of several different predictive equations to estimate RMR in female and male adults with varying physical activity levels.

METHOD

We measured the RMR of 50 adults (26 females and 24 males) evenly distributed through activity levels varying from sedentary to ultra-endurance. Body composition was measured by dual X-ray absorptiometry and physical activity was monitored by accelerometry. Ten equations to predict RMR were applied (using Body Mass [BM]: Harris & Benedict, 1919; Mifflin et al., 1990 [MifflinBM]; Pontzer et al., 2021 [PontzerBM]; Schofield, 1985; FAO/WHO/UNU, 2004; and using Fat-Free Mass (FFM): Cunningham, 1991; Johnstone et al., 2006; Mifflin et al., 1990 [MifflinFFM]; Nelson et al. 1992; Pontzer et al., 2021 [PontzerFFM]). The accuracy of these equations was analyzed, and the effect of sex and physical activity was evaluated using different accuracy metrics.

RESULTS

Equations using BM were less accurate for females, and their accuracy was influenced by physical activity and body composition. FFM equations were slightly less accurate for males but there was no obvious effect of physical activity or other sample parameters. PontzerFFM provides higher accuracy than other models independent of the magnitude of RMR, sex, activity levels, and sample characteristics.

CONCLUSION

Equations using FFM were more accurate than BM equations in our sample. Future studies are needed to test the accuracy of RMR prediction equations in diverse samples.

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.

Development and validation of novel equation for prediction of resting energy expenditure in active Saudi athletes

Being the most stable component of energy expenditure, resting metabolic rate (RMR) is usually used in the calculation of energy requirements for athletes. An adequate energy prescription is essential in supporting athlete development. This work aims to develop and validate an equation for calculating energy requirements for Arabic Saudi athletes. This cross-sectional study included 171 active athletes aged 18 to 45 years. The sample was divided into a development group (n = 127) and a validation group (n = 44). Anthropometry, indirect calorimetry, and body composition analysis via bioelectric impedance analysis were performed on all participants. The novel predictive equations were created by using stepwise linear regression analyses. The accuracy of the novel equations was compared with 10 equations, and Bland and Altman plots were used to estimate the limits of agreement between measured RMR and novel equations. The first novel equation used a set of basic measures, including weight, gender, and age, was [RMR = 1137.094 + (Wt × 14.560)-(Age × 18.162) + (G × 174.917)] (R = 0.753, and R2 = 0.567, wt = weight, G = gender; for male use 1 and female 0). The second equation used fat-free mass, age, and weight [RMR = 952.828 + (fat-free mass × 10.970)-(Age × 18.648) + (Wt × 10.297)] (R = 0.760 and R2 = 0.577). Validation of the second novel equation increased the prediction of measured RMR to 72.7% and reduced the amount of bias to 138.82 ± 133.18 Kcal. Finally, the new set of equations was designed to fit available resources in clubs and showed up to 72.73% accurate prediction and good agreement with measured RMR by Bland and Altman plots.

Accuracy of Resting Metabolic Rate Prediction Equations in Athletes: A Systematic Review with Meta-analysis

BACKGROUND

Resting metabolic rate (RMR) prediction equations are often used to calculate RMR in athletes; however, their accuracy and precision can vary greatly.

OBJECTIVE

The aim of this systematic review and meta-analysis was to determine which RMR prediction equations are (i) most accurate (average predicted values closest to measured values) and (ii) most precise (number of individuals within 10% of measured value).

DATA SOURCES

A systematic search of PubMed, CINAHL, SPORTDiscus, Embase, and Web of Science up to November 2021 was conducted.

ELIGIBILITY CRITERIA

Randomised controlled trials, cross-sectional observational studies, case studies or any other study wherein RMR, measured by indirect calorimetry, was compared with RMR predicted via prediction equations in adult athletes were included.

ANALYSIS

A narrative synthesis and random-effects meta-analysis (where possible) was conducted. To explore heterogeneity and factors influencing accuracy, subgroup analysis was conducted based on sex, body composition measurement method, athlete characteristics (athlete status, energy availability, body weight), and RMR measurement characteristics (adherence to best practice guidelines, test preparation and prior physical activity).

RESULTS

Twenty-nine studies (mixed sports/disciplines n = 8, endurance n = 5, recreational exercisers n = 5, rugby n = 3, other n = 8), with a total of 1430 participants (822 F, 608 M) and 100 different RMR prediction equations were included. Eleven equations satisfied criteria for meta-analysis for accuracy. Effect sizes for accuracy ranged from 0.04 to - 1.49. Predicted RMR values did not differ significantly from measured values for five equations (Cunningham (1980), Harris-Benedict (1918), Cunningham (1991), De Lorenzo, Ten-Haaf), whereas all others significantly underestimated or overestimated RMR (p < 0.05) (Mifflin-St. Jeor, Owen, FAO/WHO/UNU, Nelson, Koehler). Of the five equations, large heterogeneity was observed for all (p < 0.05, I2 range: 80-93%) except the Ten-Haaf (p = 0.48, I2 = 0%). Significant differences between subgroups were observed for some but not all equations for sex, athlete status, fasting status prior to RMR testing, and RMR measurement methodology. Nine equations satisfied criteria for meta-analysis for precision. Of the nine equations, the Ten-Haaf was found to be the most precise, predicting 80.2% of participants to be within ± 10% of measured values with all others ranging from 40.7 to 63.7%.

CONCLUSION

Many RMR prediction equations have been used in athletes, which can differ widely in accuracy and precision. While no single equation is guaranteed to be superior, the Ten-Haaf (age, weight, height) equation appears to be the most accurate and precise in most situations. Some equations are documented as consistently underperforming and should be avoided. Choosing a prediction equation based on a population of similar characteristics (physical characteristics, sex, sport, athlete status) is preferable. Caution is warranted when interpreting RMR ratio of measured to predicted values as a proxy of energy availability from a single measurement.

PROSPERO REGISTRATION

CRD42020218212.

The accuracy of ten common resting metabolic rate prediction equations in men and women collegiate athletes

Predictive resting metabolic rate (RMR) equations are widely used to determine total daily energy expenditure (TDEE). However, it remains unclear whether these predictive RMR equations accurately predict TDEE in the athletic populations. The purpose of this study was to examine the accuracy of 10 commonly used RMR prediction equations (Cunningham, De Lorenzo, Freire, Harris-Benedict, Mifflin St. Jeor, Nelson, Owen, Tinsley, Watson, Schofield) in collegiate men and women athletes. One-hundred eighty-seven National Collegiate Athletic Association Division III men (n = 97) and women (n = 90) athletes were recruited to participate in one day of metabolic testing. RMR was measured using indirect calorimetry and body composition was analyzed using air displacement plethysmography. A repeated measures ANOVA with Bonferroni post hoc analyses was selected to determine mean differences between measured and predicted RMR. Linear regression analysis was used to assess the accuracy of each RMR prediction method (p<0.05). All prediction equations significantly underestimated RMR (p<0.001), although there was no difference between the De Lorenzo and Watson equations and measured RMR (p = 1.00) for women, only. In men, the Tinsley and Freire equations were the most agreeable formulas with the lowest root-mean-square prediction error value of 404 and 412 kcals, respectively. In women, the De Lorenzo and Watson equations were the most agreeable equations with the lowest root-mean-squared error value of 171 and 211 kcals, respectively. The results demonstrate that such RMR equations may underestimate actual energy requirements of athletes and thus, practitioners should interpret such values with caution.Highlights All prediction equations significantly underestimated RMR in men athletes.All prediction equations, except for the De Lorenzo and Watson equations, significantly underestimated RMR in women athletes.Although a significant underestimation of RMR in men athletes, the Freire and Tinsley equations were the most agreeable prediction equations.In women athletes, the De Lorenzo and Watson equations were the most agreeable prediction equations.

Review of the scientific rationale, development and validation of the International Olympic Committee Relative Energy Deficiency in Sport Clinical Assessment Tool: V.2 (IOC REDs CAT2)-by a subgroup of the IOC consensus on REDs

Relative Energy Deficiency in Sport (REDs) has various different risk factors, numerous signs and symptoms and is heavily influenced by one's environment. Accordingly, there is no singular validated diagnostic test. This 2023 International Olympic Committee's REDs Clinical Assessment Tool-V.2 (IOC REDs CAT2) implements a three-step process of: (1) initial screening; (2) severity/risk stratification based on any identified REDs signs/symptoms (primary and secondary indicators) and (3) a physician-led final diagnosis and treatment plan developed with the athlete, coach and their entire health and performance team. The CAT2 also introduces a more clinically nuanced four-level traffic-light (green, yellow, orange and red) severity/risk stratification with associated sport participation guidelines. Various REDs primary and secondary indicators have been identified and 'weighted' in terms of scientific support, clinical severity/risk and methodological validity and usability, allowing for objective scoring of athletes based on the presence or absence of each indicator. Early draft versions of the CAT2 were developed with associated athlete-testing, feedback and refinement, followed by REDs expert validation via voting statements (ie, online questionnaire to assess agreement on each indicator). Physician and practitioner validity and usability assessments were also implemented. The aim of the IOC REDs CAT2 is to assist qualified clinical professionals in the early and accurate diagnosis of REDs, with an appropriate clinical severity and risk assessment, in order to protect athlete health and prevent prolonged and irreversible outcomes of REDs.

Predicting resting energy expenditure among athletes: a systematic review

Resting energy expenditure (REE) is often estimated in athletes using equations developed from the general population however, the application in athletic-specific populations is questionable. The aim of this systematic review was to compare measured REE and estimations of REE obtained from non-sport participants and athletes. Inclusion criteria met PICO criteria: population - participants involved in organized sport; intervention - resting energy expenditure was obtained by calorimetry; comparator - equations to estimate REE; outcomes - comparisons between measured REE and predicted REE. The search was conducted in Web of Science all databases, PubMed and Scopus. Comparisons between measured REE and predicted REE as well the potential models to estimate REE developed among athletes were summarized. Allowing for variation among studies, equations developed within general populations were not comparable to REE measured by calorimetry in athletes. Equations across athletic samples were obtained but, few studies tested their validity across independent samples of sport participants. Nevertheless, equations developed within athlete populations seem to be widely unused in sports nutrition literature and practice. De Lorenzo and ten Haaf equations appear to present an acceptable agreement with measured REE. Finally, equations used among adults should not be generalised for youth sport participants.

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)%. RMR_ratios (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.

Accuracy of Resting Metabolic Rate Prediction Equations in Sport Climbers

Resting metabolic rate (RMR) represents the energy required to maintain vital body functions. In dietary practice, RMR is determined by predictive equations on the basis of using body weight or fat-free mass. Our study aimed to assess whether predictive equations used to estimate RMR are reliable tools for estimating the energy requirements of sport climbers. The study included 114 sport climbers whose RMR was measured with a Fitmate WM. Anthropometric measurements were performed with X-CONTACT 356. The resting metabolic rate was measured by indirect calorimetry and was compared with the RMR estimated by 14 predictive equations on the basis of using body weight/fat-free mass. All equations underestimated RMR in male and female climbers, except for De Lorenzo's equation in the group of women. The De Lorenzo equation demonstrated the highest correlation with RMR in both groups. The results of the Bland-Altman tests revealed an increasing measurement error with increasing metabolism for most of the predictive equations in male and female climbers. All equations had low measurement reliability according to the intraclass correlation coefficient. Compared with the indirect calorimetry measurement results, none of the studied predictive equations demonstrated high reliability. There is a need to develop a highly reliable predictive equation to estimate RMR in sport climbers.

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.