The potential use of physical resilience to predict healthy aging

Physical resilience is the ability of an organism to respond to stressors that acutely disrupt normal physiological homeostasis. By definition, resilience decreases with increasing age, while frailty, defined as a decline in tissue function, increases with increasing age. Assessment of resilience could therefore be an informative early paradigm to predict healthy aging compared to frailty, which measures late life dysfunction. Parameters for resilience in the laboratory mouse are not yet well defined, and no single standardized stress test exists. Since aging involves multiple genetic pathways, integrative responses involving multiple tissues, organs, and activities need to be measured to reveal the overall resilience status, suggesting a battery of stress tests, rather than a single all-encompassing one, would be most informative. Three simple, reliable, and inexpensive stressors are described in this review that could be used as a panel to determine levels of resilience. Brief cold water immersion allows a recovery time to normothermia as an indicator of resilience to hypothermia, i.e. the quicker the return to normal body temperature, the more robust the resilience. Sleep deprivation (SD) impairs remote memory in aged mice, and has detrimental effects on glucose metabolism. Cyclophosphamide (CYP) targets white blood cells, especially myeloid cells resulting in neutropenia with a rebound neutrophilia in an age-dependent manner. Thus a strong neutrophilic response indicates resilience. In conclusion, resilience promises to be an especially useful measurement of biological age, i.e. how fast a particular organ or tissue ages. The three stressors, cold, SD, and CYP, are applicable to human medicine and aging because they represent clinically relevant stress conditions that have effects in an age-dependent manner. They are thus an attractive perturbation for resilience testing in mice to measure the effectiveness of interventions that target basic aging processes