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

Abstract

Predictive resting metabolic rate (RMR) equations are widely used to determine athletes’ resting energy expenditure (REE). However, it remains unclear whether these predictive RMR equations accurately predict REE in the athletic populations. The purpose of the study was to compare 12 prediction equations (Harris-Benedict, Mifflin, Schofield, Cunningham, Owen, Liu’s, De Lorenzo) with measured RMR in Turkish national team athletes and sedentary controls. A total of 97 participants, 49 athletes (24 females, 25 males), and 48 sedentary (28 females, 20 males), were recruited from Turkey National Olympic Teams at the Ministry of Youth and Sports. RMR was measured using a Fitmate GS (Cosmed, Italy). The results of each 12 prediction formulas were compared with the measured RMR using paired t-test. The Bland-Altman plot was performed to determine the mean bias and limits of agreement between measured and predicted RMRs. Stratification according to sex, the measured RMR was greater in athletes compared to controls. The closest equation to the RMR measured by Fitmate GS was the Harris-Benedict equation in male athletes (mean difference -8.9 (SD 257.5) kcal/day), and Liu’s equation [mean difference -16.7 (SD 195.0) kcal/day] in female athletes. However, the intra-class coefficient (ICC) results indicated that all equations, including Harris-Benedict for male athletes (ICC = 0.524) and Liu’s for female athletes (ICC = 0.575), had a moderate reliability compared to the measured RMR. In sedentary subjects, the closest equation to the measured RMR is the Nelson equation in males, with the lowest RMSE value of 118 kcal/day [mean difference: 10.1 (SD 117.2) kJ/day], whereas, in females, all equations differ significantly from the measured RMR. While Nelson (ICC = 0.790) had good and Owen (ICC = 0.722) and Mifflin (calculated using fat-free mass) (ICC = 0.700) had moderate reliability in males, all predictive equations showed poor reliability in females. The results indicate that the predictive RMR equations failed to accurately predict RMR levels in the participants. Therefore, it may not suitable to use them in determining total energy expenditure.

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

“…), as previously discussed. Balci et al (35) tested the accuracy of most used predictive equations in athletes and sedentary people, and Cunningham's equation did not present the best performance to predict the RMR on this sample. In the present study, Cunningham2 showed the best performance among all predictive equations for group and individual levels (Tables 2, 3).…”

“…Moreover, it was calculated the percentage of subjects that presented the estimated RMR value within ±5% and ±10% of the measured RMR. The literature proposed these thresholds as an acceptable error comparing the measured and estimated RMR (35)(36)(37). To be considered as a "good equation," all the three following criteria must be accomplished: a) no statistical difference (post hoc test, P ≥ 0.05) between measured and predicted RMR values, b) %MD ≤ ±10%, and c) %RMSE ≤ 10%.…”

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

“…A brief overview of relevant research shows that Cunningham 1980 equation tends to result in higher predictive values than the measured values (Balci et al, 2021; Flack et al, 2016; Staal et al, 2018; Strock et al, 2019), Cunningham 1991 closely matching the measured values (Strock et al, 2019), Harris-Benedict shows typically to be closer to the measured (Balci et al, 2021; Flack et al, 2016; Staal et al, 2018; Strock et al, 2019) and Nelson tends to be lower (Balci et al, 2021; Flack et al, 2016). Different to us, prior comparisons using DXA-derived RMR showed close predictions (Koehler et al, 2016; Strock et al, 2019) but these studies were on ovulatory exercising women but did not assess RMR in men.…”

“…Moreover, comparisons of RMR against predictive RMR formulas represent potential markers of RMR suppression when RMR measured /RMR predicted (RMR ratio ) is under a certain threshold value (Staal et al, 2018; Strock et al, 2019). Prediction equations have utility in clinical settings, as allow estimation of resting energy needs of individuals in normal conditions, but the accuracy of these equations is variable and some equations are more suited to different populations (Balci et al, 2021; Flack et al, 2016; Schofield et al, 2019), which, in turn, can impact RMR ratio . Moreover, the accuracy of metabolic carts to measure RMR can be variable (Cooper et al, 2009; Kennedy et al, 2014), ensuring RMR ratio may also be impacted by the selection of equipment.…”

Exploring the Influence of Age-sex Difference and Nutritional Status on Blood Lipid Variables among Nigerian Athletes by Bioelectrical Impedance Analysis

“…(4) Athletes are often considered to be at lower risk for CVD due to their high levels of physical activity and tness. (5,6) Athletes are a unique population that requires special attention when it comes to their health and wellbeing. They engage in intense physical activity, which can have both positive and negative effects on their health.…”