A hallmark of aging is progressive decline in the capacity of an organism to withstand stress. The ability to maintain or recover healthy functioning in the aftermath of a stressor is known as resilience (Varadhan et al., 2018; Whitson et al., 2018). Although decreasing resilience is a hallmark of aging, individuals of the same age likely have underlying biological features that make them more of less resilient in the face of stressors.

As an example, consider the case of two 80 year old women who undergo knee replacement surgery.  They both have a similar medical history of hypertension and osteoarthritis, are cognitively intact, but have decreasing function related to chronic knee pain. After the surgery, one of the patients does very well, leaves the hospital quickly, and recovers all of her function with the help of physical therapy within a few months.  The second patient has the same surgery, but develops delirium in the hospital, is unable to participate in physical therapy for almost a week, and develops worsening hypertension.  Although she gradually recovers to near her pre-operative baseline, knee pain and some confusion continues almost a year after the surgery.  The first patient represents a resilient outcome to the surgical stressor, while the second had a much less resilient response.

Resilience and physical frailty are distinct but related concepts that describe the effects of aging on homeostasis, the main mechanism by which organisms compensate for and respond to stressors. Homeostasis allows for the maintenance of a system within narrow limits – for example the ability of the kidneys to rapidly change the amount of water and salt being absorbed or excreted  in order  to maintain the serum volume and electrolytes at a constant level. Such systems are stable because a stressor that pushes them away from the ideal is met by a counter-reaction that keeps them close to the original state.

Buchner and Wagner (1992) viewed frailty as a precursor state in which the loss of physiologic capacity is either not severe enough to interfere with major activities of daily living or is compensated for by alternative strategies, but which nonetheless creates a significant risk to the individual. They hypothesized that this risk would be unmasked by physiologic challenges, like a very hot summer day or a lung infection. The loss of physiologic reserves in older adults deprives them of a “margin of safety” in meeting the challenges of aging and disease. Preserving this margin of safety is the goal of research on the mechanisms of resiliency.

Loss of resilience occurs when the mechanisms which maintain homeostasis are no longer fully functional. For example, as renal failure develops, the amount of water that can be excreted in a given amount of time declines. To the extent that there are no stressors and other compensatory mechanisms, like thirst, are not impaired, the system appears to function properly. However, when a large volume of water is consumed, and resilience mechanisms are not optimally functioning, excess water backs up, causing edema and electrolyte abnormalities. Physical frailty has often been used to describe someone who is not resilient and vulnerable to stressors.  A robust state describes someone who is able to maintain function in the face of stress, even if that requires alternative systems to accomplish. Resilience remains as long as function can be recovered after a perturbation. Indeed, we and others have hypothesized that resilience and frailty both reflect the state of the dynamic physiological systems network as described below.

Dynamic testing to unmask resiliency or frailty:

Varadhan and colleagues (2008) proposed that the loss of resilience and the development of frailty could be quantified using a mathematical model of a physiologic system’s response to stress. Some of the key features of this approach include time to peak response, time to recovery, and amplitude of response. Varadhan and colleagues exemplified these concepts using a simplified model of a hypothetical physiologic system involving two signaling components “neurotransmitter A” and “hormone B.” In the model system, hormone B can not only inhibit its own production locally, but also may inhibit the production of neurotransmitter A via a negative feedback mechanism. The neurotransmitter and hormone concentration dynamics will vary as the parameters modeling the strength of the interactions varies. In particular, the negative feedback parameters of the hormone on itself and the neurotransmitter has a large impact on the time to recover of the system following a stressor. This suggests that loss of resilience results from aging-related changes in the ability of feedback mechanisms to rapidly dampen perturbations.

In practice, this schema implies that resiliency is a dynamic construct that can best be measured by dynamic stimulation tests of systems, like a glucose tolerance test. The Women’s Health and Aging Study performed a series of such tests in women. For example, oral glucose tolerance testing showed that the frail older adults had exaggerated responses to stimulation and/or slower recovery to baseline when compared to non-frail older adults (Kalyani et al., 2012). Resilience also may be studied through reaction to acute disease. The slowing down of recovery, following a stimulus, has been shown to predict impaired resilience (Hadley et al., 2017; Gijzel et al., 2017).

Olde Rikkert and Melis (2019) proposed that resilience research will be best served by focusing on time series of responses of key physiologic systems to perturbations.  These novel investigations would complement traditional approaches based on static measures of frailty and other syndromes of geriatrics research and ultimately lead to better prognosis of impending critical transitions in the health of older adults.

Building on this concept and on our prior work, a research team at Johns Hopkins University has designed and implemented the Study of Physical Resilience and AgING (SPRING).  This NIA-funded study measures physiological systems governing energy metabolism (via glucose tolerance testing) and stress response (via in-vivo adrenocortical axis testing and ex-vivo immune system cell response) in subjects who are about to undergo medical or surgical procedures which serve as major stressors.  We hypothesize that dynamic responses in the stimulation tests we are applying will provide insight into the function of the physiological systems potentiating resilience and allow us to develop surrogate and baseline biological measures that help to predict vulnerability.  Physical frailty measurements have also been built into this study, as this may serve as a static surrogate measure for resilience (or lack of resilience) in older adults.

Understanding determinants of resilience will position researchers to develop interventions to fortify resilience preceding clinical stressors. Developing measures of resilience will provide means to identify vulnerable individuals before they undergo clinical stressors, so that these interventions can be targeted effectively.