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

TL;DR

This paper introduces a dynamical network model that captures how interactions between health deficits influence aging and mortality, reproducing key empirical patterns without explicit time-dependent damage or repair rates.

Contribution

The model demonstrates how deficit interactions lead to age-related mortality patterns and frailty progression, providing a new mechanistic understanding of aging dynamics.

Findings

Reproduces Gompertz's law of mortality increase with age

Explains broadening of frailty index distribution over time

Shows increased mortality risk for highly frail individuals

Abstract

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.