Department of Psychology, University of Alberta, Edmonton, AB, Canada.
Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
J Alzheimers Dis. 2019;70(s1):S101-S118. doi: 10.3233/JAD-180571.
Non-demented cognitive aging trajectories are characterized by vast level and slope differences and a spectrum of outcomes, including dementia.
The goal of AD risk management (and its corollary, promoting healthy brain aging) is aided by two converging objectives: 1) classifying dynamic distributions of non-demented cognitive trajectories, and 2) identifying modifiable risk-elevating and risk-reducing factors that discriminate stable or normal trajectory patterns from declining or pre-impairment patterns.
Using latent class growth analysis we classified three episodic memory aging trajectories for n = 882 older adults (baseline Mage=71.6, SD=8.9, range = 53-95, female=66%): Stable (SMA; above average level, sustained slope), Normal (NMA; average level, moderately declining slope), and Declining (DMA; below average level, substantially declining slope). Using random forest analyses, we simultaneously assessed 17 risk/protective factors from non-modifiable demographic, functional, psychological, and lifestyle domains. Within two age strata (Young-Old, Old-Old), three pairwise prediction analyses identified important discriminating factors.
Prediction analyses revealed that different modifiable risk predictors, both shared and unique across age strata, discriminated SMA (i.e., education, depressive symptoms, living status, body mass index, heart rate, social activity) and DMA (i.e., lifestyle activities [cognitive, self-maintenance, social], grip strength, heart rate, gait) groups.
Memory trajectory analyses produced empirical classes varying in level and slope. Prediction analyses revealed different predictors of SMA and DMA that also varied by age strata. Precision approaches for promoting healthier memory aging-and delaying memory impairment-may identify modifiable factors that constitute specific targets for intervention in the differential context of age and non-demented trajectory patterns.
非痴呆认知老化轨迹的特点是水平和斜率差异很大,结果也多种多样,包括痴呆。
AD 风险管理(及其推论,促进健康的大脑老化)的目标是通过两个趋同的目标来辅助:1)分类非痴呆认知轨迹的动态分布,2)确定可改变的风险升高和降低因素,这些因素可区分稳定或正常轨迹模式与下降或早期损伤模式。
使用潜在类别增长分析,我们对 882 名老年人(基线平均年龄=71.6,标准差=8.9,范围=53-95,女性=66%)的三种情景记忆老化轨迹进行分类:稳定(SMA;高于平均水平,稳定斜率)、正常(NMA;平均水平,适度下降斜率)和下降(DMA;低于平均水平,显著下降斜率)。使用随机森林分析,我们同时评估了 17 个来自不可改变的人口统计学、功能、心理和生活方式领域的风险/保护因素。在两个年龄层(年轻老年人,老年老年人)中,三项两两预测分析确定了重要的区分因素。
预测分析显示,不同的可改变风险预测因素,无论是在年龄层之间共享还是独特的,都可以区分 SMA(即教育、抑郁症状、生活状况、体重指数、心率、社会活动)和 DMA(即生活方式活动[认知、自我维护、社交]、握力、心率、步态)组。
记忆轨迹分析产生了在水平和斜率上不同的经验类。预测分析显示,SMA 和 DMA 的不同预测因素也因年龄层而异。促进更健康的记忆老化和延缓记忆损伤的精准方法可能会确定可改变的因素,这些因素构成了在年龄和非痴呆轨迹模式的不同背景下干预的具体目标。