Matthew Chadwick

A multi-disciplinary approach to understanding the mechanisms that limit exercise tolerance

Background

Exercise tolerance is the strongest predictor of mortality and an important determinant of quality of life across the lifecourse. However, the mechanisms that limit exercise tolerance, and how these are altered with training are poorly understood.

We have recently developed an exercise testing protocol (BB/100162X/1) that provides insight into the underpinning mechanisms that limit exercise tolerance. Furthermore, we have shown that during exercise we can systematically alter the stress on the different physiological systems that support exercise to different degrees (BB/100162X/1), and understand the systems interaction using a computational model of oxygen uptake and circulatory dynamics (BB/100162X/1).

Objectives

To determine the influence of different physiological stresses on improvements in exercise tolerance and the mechanisms that underpin this improvement in function.

Novelty

This will combine novel experimental and computational techniques to investigate for the first time how the mechanisms that limit exercise tolerance are differentially altered by divergent physiological stresses.

Timeliness

New insight into the mechanisms that limit exercise tolerance will provide therapeutic targets that can be exploited in the future to promote healthy ageing, and reduce health care costs associated with poor exercise tolerance. These costs represent a current and growing burden on health care resources.

Experimental approach: Using novel techniques developed in our labs, exercise tests will be used to measure exercise tolerance, and identify the underpinning mechanisms of the exercise limitation. Other key markers of physiological function will also be measured. These data will be used to explore the complex systems integration that limit exercise tolerance across the lifespan. Training studies will then be used to explore how the mechanisms that limit exercise tolerance respond to different physiological stresses. Again, these data will be used in combination with our computational model to understand the physiological drivers that promote specific adaptations with training.

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