(45) VALIDITY OF FAT-FREE MASS-BASED PREDICTION EQUATIONS FOR ESTIMATING RESTING METABOLIC RATE IN MUSCULAR RESISTANCE-TRAINED ADULTS

Purpose: The purpose of this study was to determine the validity of three FFM-based prediction equations for estimating RMR in muscular resistance-trained adults. Methods: A sample of 39 resistance-trained adults (16 F, 23 M; [mean ± SD] age 28.6 ± 7.9 y, height 171.8 ± 9.5 cm, weight 77.2 ± 12.9 kg, body fat% 17.3 ± 5.6, fat-free mass index 21.7 ± 2.9 kg/m²) underwent metabolic assessment via a metabolic cart (IC) and body composition assessment via dual-energy X-ray absorptiometry (DXA) in a single visit. For this visit, participants reported to the laboratory after an overnight period of resting and fasting from food and fluid intake. RMR estimates obtained from IC were considered the reference values. Three FFM-based prediction equations were also employed to estimate RMR. For the FFM component in these equations, FFM was calculated by subtracting total fat mass from total body mass estimated by the DXA device. Equivalence testing was used to evaluate whether any prediction equation demonstrated equivalence with IC. Null hypothesis significance testing was also performed, and Bland-Altman analysis was used alongside linear regression to assess the degree of proportional bias. Constant error (CE), root mean square error (RMSE), and 95% limits of agreement (LOA) were also calculated. Results: The mean ± SD RMR estimate for IC was 2186.3 ± 405.4 kcal/d, while the estimates for the FFM-based equations ranged from 1924.0 ± 309.7 to 1960.4 ± 364.6 kcal/d. The CE and RMSE for the FFM-based equations ranged from -262.3 to -225.9 kcal/d and 299.5 to 330.5 kcal/d, respectively. Additionally, proportional bias was observed for two of the three equations, and wide LOA were present for all equations, ranging from 390.6 to 399.1 kcal/d. Lastly, no prediction equation demonstrated equivalence with IC. Conclusions: In the present analysis, the consistent underestimation of RMR by the FFM-based equations indicate that they may not be suitable for application in muscular resistance-trained adults. While noteworthy, FFM contributes to a considerable portion of variance within RMR, and it has been suggested that the relationship between underprediction bias and RMR is likely a result of greater FFM. In other words, as FFM increases, the probability of underestimation also increases. Therefore, though these equations included a FFM input, the weight of this input may not have been sufficient to fully account for the influence of FFM on RMR in this population. Practical Applications: Users should be aware of the potential for underestimation when applying these equations in practice. It is also critical that these equations be applied to populations like the one the equations were derived from. Acknowledgements: This study was supported by an unstructured donation from Renaissance Periodization.