Department of Physics and Atmospheric Science, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
Department of Medicine, Dalhousie University, Halifax, Nova Scotia B3H 2Y9, Canada.
Exp Gerontol. 2018 Jul 1;107:126-129. doi: 10.1016/j.exger.2017.08.027. Epub 2017 Aug 25.
To explore the mechanistic relationships between aging, frailty and mortality, we developed a computational model in which possible health attributes are represented by the nodes of a complex network, with the connections showing a scale-free distribution. Each node can be either damaged (i.e. a deficit) or undamaged. Damage of connected nodes facilitates local damage and makes local recovery more difficult. Our model demonstrates the known patterns of frailty and mortality without any assumption of programmed aging. It helps us to understand how the observed maximum of the frailty index (FI) might arise. The model facilitates an initial understanding of how local damage caused by random perturbations propagates through a dynamic network of interconnected nodes. Very large model populations (here, 10 million individuals followed continuously) allow us to exploit new analytic tools, including information theory, showing, for example that highly connected nodes are more informative than less connected nodes. This model permits a better understanding of factors that influence the health trajectories of individuals.
为了探究衰老、虚弱和死亡之间的机制关系，我们开发了一个计算模型，其中可能的健康属性由复杂网络的节点表示，连接显示出无标度分布。每个节点可以是受损的（即有缺陷）或未受损的。连接节点的损伤会促进局部损伤，并使局部恢复更加困难。我们的模型展示了虚弱和死亡的已知模式，而无需任何程序性衰老的假设。它帮助我们了解观察到的虚弱指数 (FI) 最大值是如何出现的。该模型有助于初步了解由随机扰动引起的局部损伤如何通过相互连接的节点的动态网络传播。非常大的模型群体（这里是连续跟踪的 1000 万人）允许我们利用新的分析工具，包括信息论，例如，表明高度连接的节点比连接较少的节点更具信息量。该模型可以更好地理解影响个体健康轨迹的因素。
