Urban Air Pollution and Pathological Brain Aging: A Nationwide Twin Study in Men

大量证据表明，暴露于室外空气污染物可能会加速认知老化。 来自动物模型的新数据也指出了阿尔茨海默病（AD）风险的可能增加 与交通相关的空气污染物（TRAP）暴露相关的痴呆症。TRAP动物模型显示， 神经毒性的有力证据，但现有的研究神经毒性作用的陷阱对人脑老化 重要的知识差距：1）很少有长期的地址历史数据; 2）很少有关于晚期前接触影响的数据 生活; 3）认知数据有限，特别是轻度认知障碍（MCI）或AD; 4）神经影像学有限 措施; 5）没有检查潜在的混淆早期生活因素：6）老年人的研究主要是关于 女性; 7）需要更好地了解基因与环境的相互作用。项目2，建立在NIA- 资助的纵向越南时代老化双胞胎研究（VETSA; R01 AG018386 & AG022381），是理想的， 弥补这些差距。包括VETSA 1组51 - 60岁（n = 1291）和VETSA 2组55 - 66岁的男性双胞胎 （n = 1205）。VETSA是一个来自49个州的地理上多样化的队列，在TRAP中提供了很大的差异性 exposure.受试者在每个时间点有10 - 12小时的神经心理学、心理社会学和健康/医学数据 时间点; 545和447分别在VETSA 1和2进行结构磁共振成像（MRI）。我们 将通过收集1993年以来的居住历史数据并对其进行地理编码， 环境暴露核心C，创建TRAP累积指数。我们就能直接解决 间隙2 - 7。TRAP的特征在于NO2（一种气体替代物）的估计环境水平， PM2.5的元素碳（EC）组分（柴油机排气颗粒物的标志物），以及预测的源分布 PM2.5。VETSA受试者的年龄是研究晚期生活前TRAP效应的理想年龄。我们研究的 目标：目标1。评估TRAP对大脑结构/功能的影响：主要影响。我们预测陷阱 暴露将与更高的AD相关的脑特征评分、脑灌注不足（平行 小鼠项目4）和白色物质高信号。TRAP暴露将与认知能力较差相关 随着时间的推移，功能和更大的认知能力下降。目标2.评估TRAP对认知和 大脑老化：调解。我们将研究TRAP和认知的具体措施之间的关联 重点是情景记忆、执行功能和处理速度，以及特定的 大脑测量脑微结构的扩散张量成像（DTI）指数尚未在 以前的研究。我们将检查内侧颞叶和额叶的灰质和白色物质的平均扩散率 地区目标3。基因-环境（GE）相互作用3a）我们假设不良TRAP 对大脑和认知能力下降的影响将因APOE-ε 4和AD的多基因风险评分而异， 炎性加工和tau代谢/加工。3b）我们将使用MZ配对内差异分析 作为另一种方法来阐明遗传和环境影响如何协同工作。

Substantial evidence indicates that exposure to outdoor air pollutants may accelerate cognitive aging. Emerging data from animal models also point to a possible increase in the risk of Alzheimer's disease (AD) and related dementias with exposure to traffic-related air pollutants (TRAP). Animal models of TRAP show strong evidence of neurotoxicity, but existing studies of neurotoxic effects of TRAP on human brain aging have important knowledge gaps: 1) little long-term address history data; 2) little data on exposure effects before late life; 3) limited data on cognition, particularly mild cognitive impairment (MCI) or AD; 4) limited neuroimaging measures; 5) no examination of potential confounding early-life factors: 6) studies of older adults mostly on women; and 7) need for better understanding of gene-environment interactions. Project 2, built on the NIA- funded longitudinal Vietnam Era Twin Study of Aging (VETSA; R01 AG018386 & AG022381), is ideal for addressing these gaps. It includes male twins ages 51-60 at VETSA 1 (n=1291) and 55-66 at VETSA 2 (n=1205). VETSA is a geographically-diverse cohort from 49 states, offering great variability in TRAP exposure. Subjects have 10-12 hours of neuropsychological, psychosocial, and health/medical data at each timepoint; 545 and 447 have structural magnetic resonance imaging (MRI) at VETSA 1 and 2, respectively. We will address gap #1 by collecting and geocoding residential history data back to 1993, and in conjunction with Environmental Exposures Core C, create cumulative indices of TRAP. We will then be able to directly address gaps 2-7. TRAP will be characterized by the estimated ambient levels of NO2 (a gaseous surrogate), elemental carbon (EC) component of PM2.5 (a marker of diesel exhaust particle), and predicted source profiles of PM2.5. The age of VETSA subjects is ideal for examining TRAP effects before late life. We examine the following aims: Aim 1. Assess TRAP effects on brain structure/function: Main effects. We predict TRAP exposure will be associated with higher AD-related brain signature scores, cerebral hypoperfusion (paralleling Mouse Project 4), and white matter hyperintensities. TRAP exposure will be associated with poorer cognitive function and greater cognitive decline over time. Aim 2. Assess the impact of TRAP on cognitive and brain aging: Mediation. We will examine associations between TRAP and specific measures of cognition with a focus on episodic memory, executive function, and processing speed, as well its mediation by specific brain measures. Diffusion tensor imaging (DTI) indices of brain microstructure have not been examined in prior studies. We will examine mean diffusivity in both grey and white matter in medial temporal and frontal regions. Aim 3. Examine gene-environment (GE) interaction. 3a) We hypothesize that adverse TRAP effects on brain and cognitive decline will differ as a function of APOE-ε4, and polygenic risk scores for AD, inflammatory processing, and tau metabolism/processing. 3b) We will use MZ within-pair difference analysis as another approach to shed light on how genetic and environmental influences work in tandem.