Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, Canada.
Division of Geriatric Medicine, Dalhousie University, Halifax, NS, Canada.
Sci Rep. 2020 Nov 16;10(1):19833. doi: 10.1038/s41598-020-76827-3.
We develop a computational model of human aging that generates individual health trajectories with a set of observed health attributes. Our model consists of a network of interacting health attributes that stochastically damage with age to form health deficits, leading to eventual mortality. We train and test the model for two different cross-sectional observational aging studies that include simple binarized clinical indicators of health. In both studies, we find that cohorts of simulated individuals generated from the model resemble the observed cross-sectional data in both health characteristics and mortality. We can generate large numbers of synthetic individual aging trajectories with our weighted network model. Predicted average health trajectories and survival probabilities agree well with the observed data.
我们开发了一种人类衰老的计算模型，该模型可以根据一组观察到的健康属性生成个体健康轨迹。我们的模型由一个相互作用的健康属性网络组成，这些属性会随着年龄的增长而随机受损，形成健康缺陷，最终导致死亡。我们对两个不同的横截面观察性衰老研究进行了模型训练和测试，这些研究包括健康的简单二进制临床指标。在这两项研究中，我们发现，从模型中生成的模拟个体队列在健康特征和死亡率方面与观察到的横截面数据相似。我们可以使用加权网络模型生成大量的合成个体衰老轨迹。预测的平均健康轨迹和生存概率与观察数据吻合良好。
