Aging, frailty and complex networks

Abstract

When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of interconnected elements.

Unifying aging and frailty through complex dynamical networks

“…We have used a complex network to model human aging and relate it to frailty ( Figure 1) (Taneja et al, 2016;Farrell et al, 2016;Mitnitski et al, 2017b). Nodes of the network can each be either undamaged or damaged (thereby representing deficits); damaged nodes can also be repaired, reflecting an important source of the observed dynamics of frailty (Mitnitski et al, 2017a), also account for a possibility to recover after being damaged.…”

How Season of Birth Affects Health and Aging

“…Exponential growth of health deficits can be motivated with a microfoundation of aging from reliability theory (Gavrilov and Gavrilova, 1991;Dalgaard et al, 2017) and from network theory (Mitnitski et al, 2017). Individuals born in the other seasons also accumulate health deficits exponentially but the speed of aging may differ.…”

“…Specifically it has been shown that organisms constructed in parallel from non-aging elements age according to the Gompertz-law of mortality when the number of initially nonfunctioning elements follows a Poisson distribution (Gavrilov and Gavrilova, 1991). A recent study uses network theory to explain human aging and provides a microfounded explanation for why an unweighted health deficit index provides an accurate measure of aging and mortality (Mitnitski et al, 2017).…”

Interdependence Theory of Tissue Failure: Bulk and Boundary Effects

“…Organisms do not ultimately die because they run out of cells, but because cellular damage manifests as larger scale problems in tissues and organs, through a cascade of interactions. It remains unclear how failures dynamically propagate and accumulate to lead to frailty, aging related diseases, and ultimately, death [6][7][8][9][10].…”