Use of a Novel Whole-Body Imaging Approach to Predict Resting Metabolic Rates in Athletes

Authors

Nicole Vencil, University of Nebraska - LincolnFollow

First Advisor

Heather Rasmussen

Second Advisor

Karsten Koehler

Date of this Version

Spring 4-2020

Document Type

Article

Citation

Vencil, N., Koehler, K., Rasmussen, H., and Boeckner, L. (2020). Use of a Novel Whole-Body Imaging Approach to Predict Resting Metabolic Rates in Athletes.

Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Science, Major: Nutrition and Health Sciences, Under the Supervision of Professors Heather Rasmussen and Karsten Koehler. Lincoln, Nebraska: April, 2020

Copyright 2020 Nicole M. Vencil

Abstract

Prediction of energy expenditure allows for calculation of appropriate energy requirements, which is especially important for athletes. Resting metabolic rate (RMR) is the greatest contributor to total daily energy expenditure (TDEE) and is typically measured via indirect calorimetry. Indirect calorimetry is not always available, which results in the need for predictive equations. Most predictive equations have been developed with participants resembling the general population and have not been found to be appropriate for athletes, as they may incorrectly predict RMR due to the unique differences of body composition between athletes and the general population. The purpose of the present study was to test whether advanced segmental body composition, as measured by dual energy x-ray absorptiometry (DXA), can be utilized to more accurately predict RMR in athletes compared to previously established predictive equations. Male participants were recruited from three different sites and categorized based on body composition and energy status: sedentary controls (SED; n=33), non-weight-sensitive athletes (NWS; n=13), and weight-sensitive athletes (WS; n=55). RMR was assessed via indirect calorimetry and segmental body composition was assessed via DXA. Expanded (DXA_E) and condensed (DXA_C) DXA equations were used, in addition to three simple predictive equations (Harris-Benedict, Mifflin-St. Jeor, and Cunningham). In SED, mean bias was found to be the lowest in DXA_E (2 kcal/d) and Cunningham (33 kcal/d) and agreement was also best (R²=0.58) for DXA_E and Cunningham predictive equations. In athletes, mean bias was lowest in Mifflin-St. Jeor (14 kcal/d) and agreement was highest for DXA_E (R²=0.60) and Cunningham (R²=0.59) predictive equations. DXA_C resulted in the greatest discrimination between NWS and WS (1.00 vs. 0.92, p=0.059). Results of this study demonstrate that DXA_E is the most accurate predictive equation for SED, and DXA_E and Cunningham equations both reliably predict RMR in athletes. There is a need for future research to validate DXA_E in athletic populations, especially those experiencing a chronic state of energy deficiency.

Advisors: Heather Rasmussen and Karsten Koehler