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

Area of Science:

• Sports Science
• Human Physiology
• Metabolic Research

Background:

• Accurate estimation of resting metabolic rate (RMR) is crucial for optimizing energy intake in athletes.
• Existing predictive equations for RMR may not perform optimally in high-level athletic populations.
• Indirect calorimetry provides a gold standard for RMR measurement but is not always accessible.

Purpose of the Study:

• To evaluate the agreement between measured RMR and RMR predicted by various equations in high-level athletes.
• To develop and cross-validate two novel predictive equations for estimating RMR specifically in this population.
• To compare the performance of new and existing equations for RMR prediction in athletes.

Main Methods:

• Measured RMR using indirect calorimetry in 102 high-level athletes.
• Assessed body composition via skinfolds.
• Developed new predictive equations using multiple linear regression and validated them against measured RMR and existing equations.

Main Results:

• The two newly developed equations demonstrated good agreement and accuracy in predicting RMR (R² of 0.71 and 0.74).
• Cunningham's equation showed the best performance among existing equations for both male and female athletes.
• Jagim's equation exhibited the poorest performance, particularly for male athletes, with significant deviations from measured RMR.

Conclusions:

• The proposed equations offer accurate RMR estimation for high-level athletes.
• Cunningham's equation is a reliable choice among currently available predictive models.
• Jagim's equation is not recommended for use in high-level male athletes due to its inaccuracy.