Genomic, physiological, and environmental predictors of AD risk, resilience and resistance

由于阿尔茨海默病(AD)缺乏有效的治疗方法，导致人们呼吁早些时候发现这种疾病 这是当然的。然而，阿尔茨海默病的潜伏性发病可能跨越多年，增加了早期 诊断。人们普遍认为，即使在有充分记录的AD风险因素的个人中(例如，年龄， 性别、低教育程度、载脂蛋白ε4、高心血管风险、高血浆Aβ40/42比率、tau病理)，诊断不是 无可避免。通过对认知老化和痴呆症/AD的长期研究，弗雷明翰心脏 研究(FHS)积累了可以说是从基于社区的队列中获得的最丰富的数据库之一。横穿 它的几代人，参与者已经接受了长达70年的定期健康检查， 记录许多与未来老年认知衰退和痴呆症风险相关的共病特征。因为AD- 相关过程可能在疾病症状出现前许多年就开始了，这是 这个项目是为了更好地阐明与AD风险相关的中年血管和炎症特征。 该项目的其他独特目标是利用这一前所未有的资源来确定相关因素 有认知衰退的纵向轨迹，有测量的神经变性的纵向轨迹 通过核磁共振，并具有对发展中的认知衰退的韧性。为了实现这些目标，我们将首先应用预测 识别与阿尔茨海默病和相关内表型相关的测量和派生特征的建模方法。 从人口统计、生活方式、血管/代谢、血浆和组学指标(包括 全基因组、转录组和甲基组)已经作为FHS的一部分捕获，我们将使用传统模型 构建(由先验确定的AD路径引导)和数据驱动的方法，以识别相关特征 (A)轻度认知障碍、痴呆症和阿尔茨海默病，(B)认知衰退的纵向轨迹，(C) 结构MRI指标；(D)AD相关神经病理指标。我们将执行多效性GWAS来识别 在初始分析中共享显著相关性状的遗传基础，并测试是否使用数字 神经心理表型强化了这一发现。接下来，使用先前测量的相同数据库 特征，我们将应用预测建模方法来识别与以下相关的测量和派生特征 认知抵抗，定义为不能转化为痴呆症。最后，我们将识别血管和 通过执行孟德尔随机化来评估病因的遗传影响的炎症调节剂 血管危险因素(如血糖、血脂、血压、BMI、香烟)之间的关系 吸烟)和炎症标志物(如C反应蛋白、IL-β、肿瘤坏死因子α、IL-6)和AD 统计数据。对于有显著因果影响的血管和炎症危险因素，我们将评估基因ˣ 环境与先前与阿尔茨海默病相关的靶基因中的变异的相互作用。确定的新因素 该项目将为AD预测提供信息，并提供对疾病机制和新目标的洞察 在预防和治疗方面，预示着AD的个性化医学方法。

The lack of an effective treatment for Alzheimer's disease (AD) has led to a call to detect the disease earlier in its course. However, AD's insidious onset that can span many years, adds complexity to making an early diagnosis. It is widely accepted that even among individuals with well-documented AD risk factors (e.g., age, sex, low education, APOE ε4, high cardiovascular risk, high plasma Aβ40/42 ratio, tau pathology), diagnosis is not inevitable. By way of its longstanding investigation of cognitive aging and dementia/AD, the Framingham Heart Study (FHS) has amassed arguably one of richest databases acquired from a community-based cohort. Across its multi- generational cohorts, participants have undergone up to 7 decades of regular health examinations that document many co-morbid features linked to future risk of late life cognitive decline and dementia. Because AD- related processes are likely initiated many years before onset of disease symptoms, one primary objective of this project is to better elucidate mid-life vascular and inflammatory traits that are associated with AD risk. Additional unique goals of this project are to leverage this unprecedented resource to identify factors associated with longitudinal trajectories of cognitive decline, with longitudinal trajectories of neurodegeneration as measured by MRI, and with resilience to developing cognitive decline. To achieve these goals, we will first apply prediction modeling approaches to identify measured and derived traits associated with AD and related endophenotypes. From the extensive list of demographic, lifestyle, vascular/metabolic, plasma and omics measures (including whole genome, transcriptome, and methylome) already captured as part of the FHS, we will use traditional model building (guided by a priori determined AD pathways) and data driven approaches to identify traits associated with (a) MCI, dementia and AD, (b) longitudinal trajectories of cognitive decline, (c) longitudinal trajectories of structural MRI indices, and (d) AD-related neuropathological indices. We will perform pleiotropy GWAS to identify shared genetic underpinnings of significantly correlated traits in initial analyses and test whether using digital neuropsychological phenotypes strengthen findings. Next, using the same database of previously measured traits, we will apply prediction modeling approaches to identify measured and derived traits associated with cognitive resistance, as defined by lack of conversion to dementia. Finally, we will identify vascular and inflammatory moderators of genetic influences by performing Mendelian randomization to assess the causal relationship between vascular risk factors (e.g., blood glucose, lipid fractions, blood pressure, BMI, cigarette smoking) and inflammatory markers (e.g., CRP, IL-β, TNFα, IL6) and AD using existing GWAS summary statistics. For vascular and inflammatory risk factors with significant causal effects, we will assess gene ˣ environment interactions with variants in targeted genes previously implicated in AD. The novel factors identified in this project will inform AD prognostication as well as provide insight into disease mechanisms and new targets for prevention and therapy, heralding a personalized medicine approach to AD.