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Dynamic Multi-System Resilience in Human Aging/Physical resilience is a predictor of healthy aging in mice

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November 13, 2018
1:20 pm - 2:00 pm


Warren C. Ladiges (Univ. Washington)


Physical resilience is the ability of an organism to respond to physical stress, and can be measured with various types of stress tests. The loss of resilience occurs earlier than the development of frailty. Thus, loss of resilience may result in age-related frailty. When measuring overall resilience, integrative responses involving multiple tissues, organs, and activities are desirable, so as to inform about the overall health status of the animal. Therefore, it is more likely that a battery of stress tests, rather than a single all-encompassing one, will be more informative. An ideal battery of tests should have enough dynamic range in the response to allow characterization of an individual in easily distinguishable groups as being resilient or non-resilient. Based on features of duplication as well as translational relevance, we have selected a number of stressors to investigate including the chemotherapeutic drug cyclophosphamide, sleep deprivation, wheel running, high fat diet, and pneumococcal vaccine. All stressors have quantifiable readouts, and we are showing that an age-dependent response of each individual stressor aligns with systemic physiological and geropathological  measurements. For example, the neutrophil rebound response to cyclophosphamide decreases with increasing age, and young high-responder mice have better physiological performance and less disease at middle age than young low-responder mice. We are finding similar profiles for the other stressors, and will soon begin panel testing to determine if a battery approach provides a more robust prediction of resilience to aging in mice.  We also are investigating the ability of individual stressors to measure resilience as an endpoint to anti-aging drugs. These preclinical mouse studies are aimed at development of resilience as a translational aging signature to not only predict healthy aging, but validate drug responses in middle age and geriatric populations.

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Post-meeting Reflection

Warren C. Ladiges (Univ. Washington) Link to the source page

The talks today brought out insight into the theory of resilience, based on historical concepts of aging. The focus was on connecting these concepts to human clinical conditions, and how to measure resilience based on response to artificial as well ass naturl sressors

Several specific questions are of interest. The concept of protective factors was presented but how these protctive factorws would actually be measured is of great interest. A second question is the challenge of how to define the variation that would separate out resilience snd nonresilience. A third question is hwo to address the epigencitc impact that environemtn might have on resilience, or lack of resilience.

The multiscale modeling concept is of interest to apply to mouse studies, since it could enable preliminary study data with a more structured format that would provide more translational impact. It would be especially of interest to pursue multiscale sublevel interactions in relation to data already generated to see if future effort would be productive.

One of the points of the second day was the global view of social networks and how interactions and connections could be viewed as resilience models. In addition, clinical and preclinical presentations were made that showed ways of aligning more naturally occurring stressors with stress situations at the population or ecosystem levels. An indepth discussion on how to develop markers of resilience in humans was very productive, but uncertain what the next steps will be. More discussion after my talk on mouse modeling was informative as to the potential of applying specific stressors to predict resilience to aging in mice to clinical situations. Dual tasks assessments in people are currently being done to determine risk for such things as Alzheimers dementia, and other age related conditions. A focus should be to to connect these with other healthy aging paramters to determine variation and risk for developing age-related conditions.

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