Probing the network structure of health deficits in human aging

TL;DR

This study models human aging as a scale-free network of health deficits, revealing how network connectivity influences the informativeness of health attributes and their damage progression, with implications for aging prediction.

Contribution

It introduces a disassortative scale-free network model of human aging that aligns with observational data and explains the damage sequence of health deficits.

Findings

Higher-ranked nodes are more informative about mortality.

Laboratory deficits damage earlier than clinical deficits.

Network topology affects the progression and informativeness of health deficits.

Abstract

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.