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心血管症状和处理速度的纵向下降可预测老年人的脑白质病变。

Cardiovascular symptoms and longitudinal declines in processing speed differentially predict cerebral white matter lesions in older adults.

机构信息

Faculty of Psychology and Educational Sciences, University of Geneva, Switzerland; Swiss National Center of Competence in Research LIVES-Overcoming vulnerability: Life course perspectives, Universities of Lausanne and of Geneva, Switzerland.

Department of Experimental Psychology, University of Oxford, UK.

出版信息

Arch Gerontol Geriatr. 2018 Sep-Oct;78:139-149. doi: 10.1016/j.archger.2018.06.010. Epub 2018 Jun 23.

Abstract

It is well established that cerebral white matter lesions (WML), present in the majority of older adults, are associated with cardiovascular and cerebrovascular diseases and also with cognitive decline. However, much less is known about how WML are related to other important individual characteristics and about the generality vs. brain region-specificity of WML. In a longitudinal study of 112 community-dwelling adults (age 50-71 years at study entry), we used a machine learning approach to evaluate the relative strength of 52 variables in association with WML burden. Variables included socio-demographic, lifestyle, and health indices-as well as multiple cognitive abilities (modeled as latent constructs using factor analysis)-repeatedly measured at three- to six-year intervals. Greater chronological age, symptoms of cardiovascular disease, and processing speed declines were most strongly linked to elevated WML burden (accounting for ∼49% of variability in WML). Whereas frontal lobe WML burden was associated both with elevated cardiovascular symptoms and declines in processing speed, temporal lobe WML burden was only significantly associated with declines in processing speed. These latter outcomes suggest that age-related WML-cognition associations may be etiologically heterogeneous across fronto-temporal cerebral regions.

摘要

众所周知,脑白质病变(WML)存在于大多数老年人中,与心血管和脑血管疾病以及认知能力下降有关。然而,人们对 WML 与其他重要个体特征的关系以及 WML 的普遍性与大脑区域特异性知之甚少。在一项对 112 名居住在社区的成年人(研究开始时年龄为 50-71 岁)的纵向研究中,我们使用机器学习方法来评估 52 个变量与 WML 负担的关联强度。这些变量包括社会人口统计学、生活方式和健康指数,以及多次在三到六年的时间间隔内测量的多种认知能力(使用因子分析建模为潜在结构)。较高的年龄、心血管疾病的症状和处理速度的下降与 WML 负担的增加最密切相关(占 WML 变异性的约 49%)。虽然额叶 WML 负担与心血管症状升高和处理速度下降有关,但颞叶 WML 负担仅与处理速度下降显著相关。这些结果表明,与年龄相关的 WML-认知关联可能在额颞大脑区域具有不同的病因。

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