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

AD 风险、复原力和抵抗力的基因组、生理学和环境预测因素

• 批准号: 10047358
• 负责人: Lindsay A. Farrer
• 金额: $ 37.05万
• 依托单位: BOSTON UNIVERSITY MEDICAL CAMPUS
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-15 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10047358
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s Disease Pathway; Alzheimer' s disease risk; Amyloid beta-Protein; Blood Glucose; Blood Pressure; Blood Vessels; Brain; Chronic; Clinical; Cognition; Cognitive; Cognitive aging; Communities; Data; Data Set; Databases; Dementia; Diagnosis; Disease; Early Diagnosis; Education; Elderly; Environment; Framingham Heart Study; Future; Generations; Genes; Genetic; Genetic Markers; Genomics; Genotype; Goals; Health; IL6 gene; Impaired cognition; Individual; Inflammation; Inflammatory; Investigation; Life Style; Link; Lipids; MRI Scans; Magnetic Resonance Imaging; Measures; Metabolic; Modeling; Monitor; Nerve Degeneration; Neuropsychology; Onset of illness; Outcome; Participant; Pathology; Phenotype; Physiological; Plasma; Prevention therapy; Process; Randomized; Resistance; Resources; Risk; Risk Factors; Structure; Symptoms; TNF gene; Testing; Time; Variant; apolipoprotein E-4; base; cardiovascular risk factor; cigarette smoking; cognitive ability; cohort; comorbidity; digital; effective therapy; endophenotype; genome wide association study; high risk; in vivo; indexing; inflammatory marker; insight; methylome; middle age; model building; neuropathology; novel; personalized medicine; pleiotropism; predictive modeling; prognostic; prospective; resilience; response; secondary analysis; sex; statistics; tau Proteins; trait; transcriptome; vascular risk factor; whole genome

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.

由于缺乏有效治疗阿尔茨海默病（AD）的方法，人们呼吁尽早发现这种疾病。 然而，AD 的发病过程可能会持续数年，这增加了早期诊断的复杂性。 人们普遍认为，即使是有详细记录的 AD 危险因素（例如年龄、 性别、低教育程度、APOE ε4、高心血管风险、高血浆 Aβ40/42 比率、tau 病理学），诊断不正确 通过对认知衰老和痴呆/AD 的长期研究，弗雷明汉心脏中心不可避免地出现了这种情况。 研究（FHS）积累了从社区群体中获得的最丰富的数据库之一，这是有争议的。 其多代群体中，参与者接受了长达 7 年的定期健康检查 记录了与未来晚年认知能力下降和痴呆症风险相关的许多共病特征。 相关过程在疾病症状出现前许多年就开始了，这是可能的主要目标之一 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 具有认知能力下降的纵向轨迹，具有测量的神经退行性变的纵向轨迹 为了实现这些目标，我们首先应用预测。 建模方法来识别与 AD 和相关内表型相关的测量和衍生特征。 从人口统计、生活方式、血管/代谢、血浆和组学测量的广泛列表中（包括 全基因组、转录组和甲基化组）已作为 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）和数据驱动的方法来识别相关特征 (a) MCI、痴呆和 AD，(b) 认知能力下降的纵向轨迹，(c) 认知能力下降的纵向轨迹 结构 MRI 指数，以及 (d) AD 相关神经病理学指数，我们将进行多效性 GWAS 来识别。 在初步分析中显着相关性状的共同遗传基础，并测试是否使用数字 接下来，使用先前测量的相同数据库来加强神经心理学表型的发现。 特征，我们将应用预测建模方法来识别与相关的测量和派生特征 认知抵抗，定义为未转化为痴呆。最后，我们将识别血管性和痴呆症。 通过孟德尔随机化评估因果关系来调节遗传影响的炎症 血管危险因素（例如血糖、血脂分数、血压、BMI、吸烟）之间的关系 吸烟）和炎症标志物（例如 CRP、IL-β、TNFα、IL6）和 AD（使用现有 GWAS 总结） 对于具有显着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AD有关的目标基因变异的相互作用。 该项目将为 AD 预测提供信息，并深入了解疾病机制和新目标 用于预防和治疗，预示着 AD 的个性化医疗方法。