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

由于缺乏有效的治疗阿尔茨海默病（AD）的方法，人们呼吁在早期发现这种疾病。 其自然吧.然而，AD的潜伏发作可持续多年，增加了早期诊断的复杂性。 诊断.人们普遍认为，即使在具有充分记录的AD风险因素的个体中（例如，年龄， 性别、低教育程度、APOE ε 4、高心血管风险、高血浆A β 40/42比值、tau蛋白病理学），诊断不是 不可避免的通过对认知老化和痴呆/AD的长期研究，心脏脆弱性研究中心（Fraudiac Heart） 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。跨 它的多代队列，参与者经历了长达70年的定期健康检查， 记录了许多与晚年认知能力下降和痴呆症的未来风险相关的共病特征。因为AD- 相关的过程可能在疾病症状出现之前许多年就开始了， 该项目旨在更好地阐明与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 认知能力下降的纵向轨迹，神经退行性变的纵向轨迹， 通过核磁共振成像，并具有发展认知能力下降的弹性。为了实现这些目标，我们将首先应用预测 建模方法来鉴定与AD和相关内表型相关的测量和衍生性状。 从人口统计学、生活方式、血管/代谢、血浆和组学指标（包括 全基因组、转录组和甲基化组）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途径指导）和数据驱动的方法，以识别与AD相关的特征。 （a）MCI、痴呆和AD，（B）认知衰退的纵向轨迹，（c）认知衰退的纵向轨迹， 结构MRI指数，和（d）AD相关的神经病理学指数。我们将进行多效性GWAS， 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 神经心理学表型强化了研究结果。接下来，使用先前测量的相同数据库 性状，我们将应用预测建模方法来确定测量和衍生的性状与 认知抵抗力，定义为未转化为痴呆。最后，我们将识别血管和 遗传影响的炎症调节因子，通过进行孟德尔随机化来评估因果关系， 血管危险因素之间的关系（例如，血糖、脂质组分、血压、BMI、香烟 吸烟）和炎症标记物（例如，CRP、IL-β、TNF α、IL 6）和AD，使用现有GWAS总结 统计对于具有显著因果效应的血管和炎症危险因素，我们将评估基因表达， 环境与先前与AD有关的靶基因中的变体的相互作用。确定的新因素 在这个项目中，将告知AD诊断，并提供对疾病机制和新靶点的深入了解 用于预防和治疗，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.