Yu Lei, Boyle Patricia A, Segawa Eisuke, Leurgans Sue, Schneider Julie A, Wilson Robert S, Bennett David A
Rush Alzheimer's Disease Center.
Department of Medical Social Sciences, Feinberg School of Medicine, Northwestern University.
Neuropsychology. 2015 May;29(3):335-43. doi: 10.1037/neu0000159. Epub 2014 Dec 15.
Neurodegenerative and cerebrovascular conditions are common in old age and are associated with cognitive decline. However, considerable heterogeneity remains in residual decline (i.e., person-specific trajectories of cognitive decline adjusted for these common neuropathologic conditions). The present study aimed to characterize profiles of residual decline in late life cognition.
Up to 19 waves of longitudinal cognitive data were collected from 876 autopsied participants from 2 ongoing clinical-pathologic cohort studies of aging. Uniform neuropathologic examinations quantified measures of Alzheimer's disease, cerebral infarcts, Lewy body disease, and hippocampal sclerosis. Random effects mixture models characterized latent profiles of residual decline in global cognition.
We identified 4 latent groups, and each group demonstrated distinct residual decline profiles. On average, 44% of the participants had little or no decline, 35% showed moderate decline, 13% showed severe decline and the rest (8%) had substantial within-subject fluctuation of longitudinal cognitive measures. These latent groups differed in psychological, experiential and neurobiologic factors that have been previously shown to be associated with cognitive decline. Specifically, compared with nondecliners, decliners had more depressive symptoms, were more socially isolated; were less engaged in cognitive or physical activities; and had lower density of noradrenergic neurons in locus ceruleus.
After controlling for common dementia related pathologies, considerable residual variability remains in cognitive aging trajectories and this variability is not random but rather is related to markers of cognitive and neural reserve. The mixture modeling approach provides a powerful tool to identify latent groups with distinct cognitive trajectories.
神经退行性疾病和脑血管疾病在老年人群中很常见,且与认知能力下降有关。然而,在残余下降方面(即针对这些常见神经病理状况调整后的个体特异性认知下降轨迹)仍存在相当大的异质性。本研究旨在描述晚年认知中残余下降的特征。
从两项正在进行的衰老临床病理队列研究中的876名接受尸检的参与者那里收集了多达19波的纵向认知数据。统一的神经病理学检查对阿尔茨海默病、脑梗死、路易体病和海马硬化的指标进行了量化。随机效应混合模型描述了整体认知中残余下降的潜在特征。
我们识别出4个潜在组,每组都表现出不同的残余下降特征。平均而言,44%的参与者几乎没有下降或没有下降,35%表现为中度下降,13%表现为严重下降,其余(8%)的纵向认知测量在个体内部有显著波动。这些潜在组在先前已证明与认知下降相关的心理、经验和神经生物学因素方面存在差异。具体而言,与未下降者相比,下降者有更多的抑郁症状,社交隔离程度更高;较少参与认知或体育活动;蓝斑中去甲肾上腺素能神经元的密度较低。
在控制了常见的与痴呆相关的病理状况后,认知衰老轨迹中仍存在相当大的残余变异性,且这种变异性并非随机,而是与认知和神经储备的标志物有关。混合建模方法提供了一个强大的工具来识别具有不同认知轨迹的潜在组。