Comparative assessment of the basal metabolic rate in athletes with different level of physical activity based on prediction equations

• Radzhabkadiev Radzhabkadi M.
• Kseniya V. Vybornaya
• Aleksandr I. Sokolov
• Dmitriy B. Nikityuk

Abstract

The use of laboratory methods for assessing energy expenditure in athletes requires the availability of appropriate equipment and trained personnel, which is very difficult in the context of everyday sports activities. Therefore, the use of predictive equations that most accurately reflect energy expenditure is of paramount importance for developing dietary and recovery recommendations for athletes.

The purpose of this research was to compare the basal metabolic rate (BMR) of highly skilled athletes obtained using predictive equations.

Material and methods. The results of the examination of 180 elite athletes, members of the Russian national teams in four sports (shooting, biathlon, bobsleigh, snowboarding), of both sexes (107 men and 73 women aged 18 to 30 years), conducted in the morning, on an empty stomach, 10–12 hours after training, were analyzed during the pre-competition period of sports training. BMR was assessed using the InBody 720 bioimpedance analyzer (Katch–McArdle formula) and calculated using Mifflin–St Jeor, Cunningham, De Lorenzo and Harris–Benedict predictive equations. Lean body mass (LBM) was determined using an InBody 720 bioimpedance analyzer and calculated using Boer, Hume and James predictive equations.

Results. When assessing the BMR in athletes, the lowest values were obtained using the Katch–McArdle equation which is built into the InBody 720 analyzer. The highest values for men were obtained using the De Lorenzo equation, they exceeded the calculated values obtained using the Harris–Benedict, Mifflin–St Jeor and Katch–McArdle equations by 3.9–15.5% (p<0.05). In the female groups, the highest BMR values were obtained using the Mifflin–St Jeor equation; they exceeded the data calculated according to the Katch–McArdle, Cunningham and Harris–Benedict equations by 13.8–30.8% (p<0.05). The Cunningham formula, which is used to calculate the BMR based on the LBM, showed significantly higher values compared to the Katch–McArdle formula (p<0.05), the differences were about 180 kcal for the male groups and about 160 kcal for the female groups. In male athletes, the lowest LBM values were obtained using the Hume equation. These values were significantly lower (р<0.05) than the results of LBM calculation using the Boer and James equations (by 5.4–8.3%), as well as when assessing LBM using the InBody 720 analyzer (by 7.1–7.7%). In female sports groups, the lowest LBM values were obtained using the hardware method, while calculations using predictive equations showed higher values (the maximum LBM values using the Boer equation), but the differences were not statistically significant.

Conclusion. When using prediction equations to assess the BMR in athletes of different specializations, it should be taken into account that the results may differ by 3.9–15.5% when assessed in male groups and by 13.8–30.8% in female groups. Since the BMR is the starting point for calculating an athlete’s needs for nutrients and energy, it is recommended to use equations that take into account body composition, namely the content of LBM, or use a bioimpedance analyzer. BMT can also be calculated using prediction equations if a body composition analyzer is not available, but it should be taken into account that there are differences between the measured and calculated values of this indicator.

Keywords: athletes; calorimetry; metabolic rate; energy expenditure; resting metabolism; basal metabolic rate; lean body mass; predictive equations

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