1a.Objectives (from AD-416)
Through a joint USARIEM/MRMC and BHNRC/USDA biomedical research initiative, develop practical biomedical models of human energy metabolism.
1b.Approach (from AD-416)
USDA’s BHNRC will host an applied mathematician supported as a MRMC TATRC-funded IPA from the University of Tennessee. As a visiting scientist at BHNRC/USDA to jointly develop advanced mathematical models to predict the water and metabolic fuel requirements of physically active humans.
Through this agreement, we are working to develop advanced mathematical models to predict the water and metabolic fuel requirements of physically active humans. We examined the case of instantaneous substrate oxidation in older women comparing the impaired glucose tolerance state and the euglycemic state. Subjects stayed in the room-size calorimeter for 48 hours and performed three bouts of postprandial exercise on the second day. Instantaneous gas exchange rates were estimated along with the instantaneous respiratory quotient (RQ) for the whole 48-hour experiment. The relative dynamics of O2 consumption and RQ showed a greater reliance on the carbohydrate as energy source in the dysglycemic state compared to the euglycemic state. Also, the rate of glucose mobilization, quantified as the time lag between peaks in O2 consumption and peaks in RQ, was found to be higher in the euglycemic state. For the first time, results relating the “instantaneous” metabolic flexibility and impaired glucose tolerance are presented. We are also developing a model for real-time monitoring of heat flux. We used the Equivital physiologic monitoring system (Hidalgo Limited, UK) to collect core temperature and heart rate (HR) data in order to develop and test our new algorithm. The core temperature data were collected using a telemetry pill (VitalSense, Respironics, USA). The algorithm uses the antecedent samples of core temperature along with the current readings of the heart rate to predict the core temperature signal 20 minutes ahead in real time. We compared the performance of the new algorithm with the previously developed auto regressive algorithm and found that the prediction time lag is reduced by 50 % from 10 to 5 minutes. Activities under this agreement are monitored through email, phone calls, and site visits.