Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

年龄是流行疾病和死亡的主要危险因素。然而，人们对年龄相关的生理变化和寿命之间的关系知之甚少。我们结合了分析和机器学习工具来描述大量纵向测量中的老化过程。假设衰老是由生物体状态的动态不稳定性引起的，我们设计了一个深度人工神经网络，包括自动编码器和自回归（AR）组件。 AR 模型将生理状态的动态与单个变量“动态脆弱指标”（dFI）的随机演化联系起来。在小鼠表型组数据库的血液测试子集中，dFI 呈指数增长并预测剩余寿命。限制 dFI 的观察结果与晚年死亡率下降一致。 dFI 随衰老标志一起变化，包括虚弱指数、炎症分子标记、衰老细胞积累，并对缩短寿命（高脂肪饮食）和延长寿命（雷帕霉素）治疗有反应。