arXiv subjects

Arnold B. Mitnitski

Publications and source records attributed to Arnold B. Mitnitski.

3 recordsLinked to original sources

arXiv2018

Probing the network structure of health deficits in human aging

We confront a network model of human aging and mortality in which nodes represent health attributes that interact within a scale-free network topology, with observational data that uses both clinical and laboratory (pre-clinical) health deficits as network nodes. We find that individual health attributes exhibit a wide range of mutual information with mortality and that, with a re- construction of their relative connectivity, higher-ranked nodes are more informative. Surprisingly, we find a broad and overlapping range of mutual information of laboratory measures as compared with clinical measures. We confirm similar behavior between most-connected and least-connected model nodes, controlled by the nearest-neighbor connectivity. Furthermore, in both model and observational data, we find that the least-connected (laboratory) nodes damage earlier than the most-connected (clinical) deficits. A mean-field theory of our network model captures and explains this phenomenon, which results from the connectivity of nodes and of their connected neighbors. We find that other network topologies, including random, small-world, and assortative scale-free net- works, exhibit qualitatively different behavior. Our disassortative scale-free network model behaves consistently with our expanded phenomenology observed in human aging, and so is a useful tool to explore mechanisms of and to develop new predictive measures for human aging and mortality.

q-bio.PE↗

arXiv2017

Unifying ageing and frailty through complex dynamical networks

To explore the mechanistic relationships between ageing, frailty and mortality, we developed a computational model in which possible health attributes are represented by the nodes of a complex network. Each node can be either damaged (i.e. a deficit) or undamaged. Damage of connected nodes facilitates further local damage. Our model recovers the known patterns of frailty and mortality without any programmed ageing. It helps us to understand how the observed maximum of the frailty index (FI) might arise, and allows us to start to understand how health deficits accumulate. Large model populations allow us to exploit new analytic tools, including information theory. This will allow us to systematically characterize the effects of sudden changes in the health trajectories of individuals and serve as a way to evaluate large clinical and population databases.

q-bio.PE↗

arXiv2016

A dynamical network model for age-related health deficits and mortality

How long people live depends on their health, and how it changes with age. Individual health can be tracked by the accumulation of age-related health deficits. The fraction of age-related deficits is a simple quantitative measure of human aging. This quantitative frailty index (F) is as good as chronological age in predicting mortality. In this paper, we use a dynamical network model of deficits to explore the effects of interactions between deficits, deficit damage and repair processes, and the connection between the F and mortality. With our model, we qualitatively reproduce Gompertz's law of increasing human mortality with age, the broadening of the F distribution with age, the characteristic non-linear increase of the F with age, and the increased mortality of high-frailty individuals. No explicit time-dependence in damage or repair rates is needed in our model. Instead, implicit time-dependence arises through deficit interactions -- so that the average deficit damage rates increases, and deficit repair rates decreases, with age . We use a simple mortality criterion, where mortality occurs when the most connected node is damaged.

q-bio.QM↗