Department of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada.
Exp Gerontol. 2012 Dec;47(12):893-9. doi: 10.1016/j.exger.2012.06.015. Epub 2012 Jul 9.
Aging in a given individual can be characterized by the number of deficits (symptoms, signs, laboratory abnormalities, disabilities) that they accumulate. The number of accumulated deficits, more than their nature, well characterizes health status in individuals - the proportion of deficits present in an individual to deficits considered is known as a frailty index. While on average deficits accumulate with age, individual trajectories in the number of deficits is highly dynamic. Transitions in the number of deficits over a fixed time interval can be represented by the Poisson law, with the Poisson mean dependent on the deficit numbers at baseline. Here we present an extension of the model to make possible predictions for any given time period. Using data from the Canadian National Population Health Survey of people aged 55 and over (n=4330), followed during 7cycles being the baseline and 6cycles of follow-up every 2years, we found that the transition in the number of deficits during any time period can be approximated using a time dependent Poisson distribution with the Poisson mean tending to decelerate over time, according to square-root-of-time kinetics characteristic for stochastic processes (e.g. diffusion, Brownian motion ) while the probability of death shows a pattern of time acceleration with a high degree of precision, "explaining" over 98% of variance. The model predicts a variety of changes in health status including the possibility of health improvement indicating the repair/remodeling abilities of the organism. The model is valuable for estimating how changes in health can influence mortality across the life course from late middle age.
在给定的个体中,衰老可以通过他们积累的缺陷数量(症状、体征、实验室异常、残疾)来描述。积累的缺陷数量,而不是其性质,很好地描述了个体的健康状况——个体中存在的缺陷比例与被认为的缺陷比例被称为虚弱指数。虽然平均而言,缺陷随着年龄的增长而积累,但个体在缺陷数量上的轨迹是高度动态的。在固定时间间隔内缺陷数量的变化可以用泊松定律来表示,泊松均值取决于基线时的缺陷数量。在这里,我们扩展了该模型,使其能够对任何给定的时间段进行预测。使用来自加拿大国家人口健康调查(年龄在 55 岁及以上的人群,n=4330)的数据,在 7 个周期的基线和 6 个每 2 年一次的随访周期中进行随访,我们发现任何时间段内缺陷数量的变化可以使用依赖于时间的泊松分布来近似,泊松均值随着时间的推移趋于减速,符合随机过程(例如扩散、布朗运动)的平方根时间动力学特征,而死亡率则表现出时间加速的模式,具有高度的精度,“解释”了超过 98%的方差。该模型预测了各种健康状况的变化,包括健康改善的可能性,表明了生物体的修复/重塑能力。该模型对于估计健康变化如何影响从中年后期到生命全程的死亡率非常有价值。