RT Journal Article
SR Electronic
T1 Probing the network structure of health deficits in human aging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 270876
DO 10.1101/270876
A1 Spencer G. Farrell
A1 Arnold B. Mitnitski
A1 Olga Theou
A1 Kenneth Rockwood
A1 Andrew D. Rutenberg
YR 2018
UL http://biorxiv.org/content/early/2018/02/23/270876.abstract
AB Human aging leads to the stochastic accumulation of damage. We model an aging population using a stochastic network model. Individuals are modeled as a network of interacting nodes, representing health attributes. Nodes in the network stochastically damage and repair, with rates dependent on the state of their neighbors. Damaged nodes represent health deficits. The Frailty Index (FI) assesses age-related damage as the proportion of health deficits an individual has accumulated, from a selection of attributes. Here, we use computational, information-theoretic, and mean-field approaches to show that the degree distribution and degree correlations of the underlying network are important to the model’s ability to recover the behavior of observational health data. We use different measures of damage in the network to probe the structure of the network. We find that the behavior of different classes of observational health deficits (laboratory or clinical) is similar to the behavior of nodes of low or high degree in the model, respectively. This explains how damage can propagate within the network, leading towards individual mortality.