The estimation of the resting metabolic rate is affected by the method of gas exchange data selection in high-level athletes

Impact of Equation Choice on Resting Metabolic Rate Ratio in High-Level Men and Women Athletes

OBJECTIVE

To examine the impact of the RMR ratio cutoff point selected on the categorization of prevalence/absence of low energy availability among predictive equations in high-level athletes (n = 241 [99 women]; 52% competed at the World Championship and Olympic Games), and whether this categorization is influenced by sex and the predictive equation used.

METHODS

We assessed RMR using indirect calorimetry, predicted the RMR using the equations proposed by Harris-Benedict, FAO/WHO/UNU, de Lorenzo, ten Haaf and Wejis, Wong, Jagim, Cunningham, and Freire, and computed the RMR ratio for each equation.

RESULTS

We observed that the cumulative percentage of RMR ratio values increased at a faster rate using Jagim, ten Haaf and Wejis, and Cunningham equations compared to the other equations. At the 0.90 value (the most used cutoff point in literature), the Jagim equation categorized ≥ 50% of the athletes into "low energy availability". No Sex × Equation × Sport interaction effect was observed (F = 0.10, p = 1.0). There was a significant main effect to Sex (F = 11.7, p < 0.001, ES = 0.05), Sport (F = 16.4, p < 0.001, ES = 0.01), and Equation (F = 64.1, p < 0.001, ES = 0.19). Wong and FAO/WHO/UNU equations yielded the largest errors (assessed vs. predicted RMR) in men and women, respectively.

CONCLUSION

The selected RMR ratio cutoff point influences the prevalence/absence of low energy availability characterization in high-level athletes and suggests that certain equations could bias its assessment.

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.

Impact of methods for data selection on the day-to-day reproducibility of resting metabolic rate assessed with four different metabolic carts

BACKGROUND AND AIMS

Accomplishing a high day-to-day reproducibility is important to detect changes in resting metabolic rate (RMR) and respiratory exchange ratio (RER) that may be produced after an intervention or for monitoring patients' metabolism over time. We aimed to analyze: (i) the influence of different methods for selecting indirect calorimetry data on RMR and RER assessments; and, (ii) whether these methods influence RMR and RER day-to-day reproducibility.

METHODS AND RESULTS

Twenty-eight young adults accomplished 4 consecutive RMR assessments (30-min each), using the Q-NRG (Cosmed, Rome, Italy), the Vyntus CPX (Jaeger-CareFusion, Höchberg, Germany), the Omnical (Maastricht Instruments, Maastricht, The Netherlands), and the Ultima CardiO2 (Medgraphics Corporation, St. Paul, Minnesota, USA) carts, on 2 consecutive mornings. Three types of methods were used: (i) short (periods of 5 consecutive minutes; 6-10, 11-15, 16-20, 21-25, and 26-30 min) and long time intervals (TI) methods (6-25 and 6-30 min); (ii) steady state (SSt methods); and, (iii) methods filtering the data by thresholding from the mean RMR (filtering methods). RMR and RER were similar when using different methods (except RMR for the Vyntus and RER for the Q-NRG). Conversely, using different methods impacted RMR (all P ≤ 0.037) and/or RER (P ≤ 0.009) day-to-day reproducibility in all carts. The 6-25 min and the 6-30 min long TI methods yielded more reproducible measurements for all metabolic carts.

CONCLUSION

The 6-25 min and 6-30 min should be the preferred methods for selecting data, as they result in the highest day-to-day reproducibility of RMR and RER assessments.

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.

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.