  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.
