Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.
Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Sci Rep. 2024 Jan 12;14(1):1196. doi: 10.1038/s41598-024-51669-5.
Understanding and facilitating healthy aging has become a major goal in medical research and it is becoming increasingly acknowledged that there is a need for understanding the aging phenotype as a whole rather than focusing on individual factors. Here, we provide a universal explanation for the emergence of Gompertzian mortality patterns using a systems approach to describe aging in complex organisms that consist of many inter-dependent subsystems. Our model relates to the Sufficient-Component Cause Model, widely used within the field of epidemiology, and we show that including inter-dependencies between subsystems and modeling the temporal evolution of subsystem failure results in Gompertizan mortality on the population level. Our model also provides temporal trajectories of mortality-risk for the individual. These results may give insight into understanding how biological age evolves stochastically within the individual, and how this in turn leads to a natural heterogeneity of biological age in a population.
理解和促进健康老龄化已成为医学研究的主要目标,人们越来越认识到,需要全面了解衰老表型,而不是专注于个别因素。在这里,我们使用系统方法为复杂生物体的衰老提供了一个普遍的解释,这些生物体由许多相互依赖的子系统组成。我们的模型与广泛应用于流行病学领域的充分组成因模型有关,我们表明,包括子系统之间的相互依赖关系,并对子系统故障的时间演变进行建模,会导致群体水平上出现戈梅茨死亡率。我们的模型还为个体的死亡率风险提供了时间轨迹。这些结果可能有助于理解个体内部的生物年龄如何随机进化,以及这反过来如何导致群体中生物年龄的自然异质性。