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.
This nonfunded project is the same as a subsequently funded project (reimbursable agreement #60-1235-0-0168). Through this collaboration, we are developing advanced mathematical models to predict the water and metabolic fuel requirements of physically active humans. In a cohort of older women, we measured instantaneous substrate oxidation and compared the impaired glucose tolerance state and the euglycemic state. Using a room-size calorimeter during a 48 hour period, the subjects performed three bouts of postprandial exercise on the second day of measurement. Instantaneous gas exchange rates were estimated along with the instantaneous respiratory quotient (RQ) for the whole 48-hour experiment. The relative dynamics of oxygen 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 oxygen consumption and peaks in RQ, was found to be higher in the euglycemic state. 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.