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.

缺乏针对阿尔茨海默氏病（AD）的有效治疗 它的课程。但是，广告的阴险发作可能跨越多年，增加了复杂性 诊断。人们普遍认为，即使在有据可查的AD风险因素的个体中（例如，年龄， 性别，低教育，APOEε4，高心血管风险，高血浆Aβ40/42的比例，tau病理学），诊断不是 不可避免的。通过长期调查认知衰老和痴呆/广告，弗雷明汉心脏 研究（FHS）可以说，可以说是从社区的同伙中获得的最富有的数据库之一。穿过 它的多代人队列，参与者经历了多达7年的定期健康检查， 记录许多与后期认知能力下降和痴呆症未来风险相关的合并功能。因为广告 - 相关过程可能是在疾病症状发作之前多年开始的，这是 该项目是为了更好地阐明与AD风险相关的中年血管和炎症性状。 该项目的其他独特目标是利用这种前所未有的资源来识别相关的因素 随着认知能力下降的纵向轨迹，如测量的神经退行性的纵向轨迹 通过MRI，并具有韧性，能够培养认知能力下降。为了实现这些目标，我们将首先应用预测 建模方法来识别与AD和相关内表型相关的测量和衍生性状。 从人口，生活方式，血管/代谢，等离子体和OMICS的广泛清单（包括） 整个基因组，转录组和甲基组）已经被捕获为FHS的一部分，我们将使用传统模型 建筑物（以先验确定的AD途径为指导）和数据驱动方法以识别相关的性状 与（a）MCI，痴呆和AD，（b）认知下降的纵向轨迹，（c） 结构MRI指数和（D）与广告相关的神经病理学指数。我们将执行多效性GWAS以识别 在初始分析中具有显着相关性状的共享遗传基础，并测试是否使用数字 神经心理学表型更强的发现。接下来，使用先前测量的相同数据库 特征，我们将采用预测建模方法来识别与 认知抗性，这是由于缺乏转化为痴呆的定义。最后，我们将确定血管和 通过进行孟德尔随机化来评估因果关系，遗传影响的炎症调节剂 血管危险因素之间的关系（例如，血糖，脂质分数，血压，BMI，香烟 吸烟）和炎症标记（例如CRP，IL-β，TNFα，IL6）和AD使用现有的GWAS摘要 统计数据。对于具有重大因果影响的血管和炎症危险因素，我们将评估基因ˣ 与先前在AD中实现的靶向基因中的变体相互作用。确定的新因素 在这个项目中，将为广告提示提供信息，并提供有关疾病机制和新目标的见解 为了预防和治疗，请预示着一种个性化的AD医学方法。