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 use both clinical and laboratory (preclinical) health deficits as network nodes. We find that individual health attributes exhibit a wide range of mutual information with mortality and that, with a reconstruction 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 are damaged 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 networks, 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 predictive measures for human aging and mortality.