Network model of human aging: Frailty limits and information measures

Abstract

Aging is associated with the accumulation of damage throughout a persons life. Individual health can be assessed by the Frailty Index (FI). The FI is calculated simply as the proportion f of accumulated age-related deficits relative to the total, leading to a theoretical maximum of f≤1. Observational studies have generally reported a much more stringent bound, with f≤f_{max}<1. The value of f_{max} in observational studies appears to be nonuniversal, but f_{max}≈0.7 is often reported. A previously developed network model of individual aging was unable to recover f_{max}<1 while retaining the other observed phenomenology of increasing f and mortality rates with age. We have developed a computationally accelerated network model that also allows us to tune the scale-free network exponent α. The network exponent α significantly affects the growth of mortality rates with age. However, we are only able to recover f_{max} by also introducing a deficit sensitivity parameter 1-q, which is equivalent to a false-negative rate q. Our value of q=0.3 is comparable to finite sensitivities of age-related deficits with respect to mortality that are often reported in the literature. In light of nonzero q, we use mutual information I to provide a nonparametric measure of the predictive value of the FI with respect to individual mortality. We find that I is only modestly degraded by q<1, and this degradation is mitigated when increasing number of deficits are included in the FI. We also find that the information spectrum, i.e., the mutual information of individual deficits versus connectivity, has an approximately power-law dependence that depends on the network exponent α. Mutual information I is therefore a useful tool for characterizing the network topology of aging populations.

Probing the network structure of health deficits in human aging

“…Nevertheless, we can reliably reconstruct the relative connectivity of high degree nodes in both model and in large-cohort observational data by measuring mutual dependence between pairs of nodes. This reconstruction allows us to qualitatively confirm the relationship between the connectivity of nodes and how informative they were about mortality [13].…”

“…By including a falsenegative attribution error (i.e. a finite sensitivity) [13], we can also explain an empirical maximum of observed FI values -typically between 0.6 − 0.8 [6,[16][17][18][19][20]. This shows that age-dependent "programming" of either mortality or damage rates are not necessary to explain these features [1].…”

“…Our model was previously presented [13]. Individuals are represented as a network consisting of N nodes, where each node i ∈ {1, 2, .…”

“…In previous work we developed a stochastic network model of aging with damage accumulation [12,13]. Each individual is modeled as a network of interacting nodes that represent health attributes.…”

“…Specifically, we aim to determine what qualitative network features are necessary to explain age-structure. Our working hypothesis is that low-degree nodes should correspond to F lab , just as high-degree nodes correspond to F clin [12,13].…”