Influence of genetic risk factors on biomarkers and cognitive decline in preclinical AD

 描述（由申请人提供）：K01候选人的职业目标是成为一名独立的研究人员，通过阐明疾病机制和在疾病的临床前阶段获得个人风险特征，为预防阿尔茨海默病（AD）痴呆症做出贡献。马萨诸塞州总医院和哈佛医学院的环境非常适合候选人的培训和拟议的研究，提供最先进的成像设施和世界知名的AD研究人员和临床医生社区。在K奖期间，候选人将通过接受Tau PET成像培训来扩展她在多模式神经成像方面的专业知识，并获得遗传分析方面的专业知识，以追求新的研究路径。本文提出的研究计划将在大型GWAS分析中研究已知遗传风险因素的影响，特别是APOE 4基因型和21个与临床AD相关的非APOE基因座，在临床正常的老年人（CN）中，AD生物标志物与认知能力下降之间的关系。考虑到选择具有生物标志物的CN的能力，对CN内遗传风险因素的理解尤其相关 A β升高的证据（临床前AD）。虽然纵向研究已经收敛到显示CN与临床前AD是在随后的临床损害的风险增加，在这个组中存在下降的异质性，使得这些人中的一些人保持临床正常的一段时间。识别影响初始A β蓄积和临床症状发作之间时间的遗传风险因素将极大地增强我们对临床前AD的理解，并提高我们识别认知能力下降风险最高的个体的能力。尽管已知的遗传风险的一些影响 AD痴呆的因素无疑是由升高的A β介导的，我们假设这些因素也通过与A β相互作用加速下游效应如新皮质Tau的积累、神经变性和认知下降来影响AD轨迹。因此，对于同等水平的A β负荷，具有高遗传风险的个体可能比具有低遗传风险的个体表现出更大的神经变性。这一系列的研究应该能够深入了解为什么一些A β +个体能够在较长时间内保持临床正常，而其他A β +个体则迅速进展为临床损害。我们将在哈佛衰老脑研究（N = 277）、无症状AD抗淀粉样蛋白研究（A4，N = 1000）和淀粉样蛋白风险和神经退行性变纵向评价研究（LEARN，N = 500）的1777个CN的大样本中研究这些假设。所有CN将完成淀粉样蛋白PET、结构MRI、基因分型数据和纵向神经心理学测试，并且一个大子集将额外完成Tau PET成像（N = 827）。由于疾病修饰策略在临床前阶段最有可能成功，因此阐明AD疾病机制并在临床前阶段获得个体风险特征将对AD的预防产生巨大影响。

 DESCRIPTION (provided by applicant): The career goal of the K01 candidate is to become an independent investigator conducting research that contributes to the prevention of Alzheimer's disease (AD) dementia through elucidating disease mechanisms and deriving individual risk profiles during the preclinical stage of the disease. The environment at Massachusetts General Hospital and Harvard Medical School is ideal for the candidate's training and proposed research, providing state-of-the-art imaging facilities and a world-renowned community of AD researchers and clinicians. During this K award, the candidate will extend her expertise in multimodal neuroimaging by receiving training in Tau PET imaging, and acquire expertise in genetic analyses to pursue a novel research path. The research plan proposed herein examines the influence of known genetic risk factors, specifically the APOE4 genotype and 21 non-APOE loci associated with clinical AD in large GWAS analyses, on the relationship between AD biomarkers and cognitive decline in clinically normal older individuals (CN). An understanding of genetic risk factors within CN is especially relevant given the ability to select CN with biomarker evidence of elevated Aβ (preclinical AD). Although longitudinal studies have converged to reveal that CN with preclinical AD are at heightened risk of subsequent clinical impairment, heterogeneity in decline exists within this group, such that some of these individuals remain clinically normal for an extended period of time. Identification of genetic risk factors that influence the time between initial Aβ accumulation and the onset of clinical symptoms will greatly enhance our understanding of preclinical AD and improve our ability to identify individuals most at- risk for cognitive decline. Although some of the effects of known genetic risk factors for AD dementia are undoubtedly mediated by elevated Aβ, we hypothesize that these factors also influence the AD trajectory by interacting with Aβ to accelerate downstream effects such as the accumulation of neocortical Tau, neurodegeneration, and cognitive decline. Thus, for an equivalent level of Aβ burden, an individual with high genetic risk may show greater neurodegeneration than an individual with low genetic risk. This line of research should provide insight into why some Aβ+ individuals are able to remain clinically normal for longer periods of time whereas other Aβ+ individuals rapidly progress to clinical impairment. We will investigate these hypotheses in a large sample of 1777 CN combined across the Harvard Aging Brain Study (N=277), the Anti- Amyloid in Asymptomatic AD Study (A4, N=1000) and Longitudinal Evaluation of Amyloid Risk and Neurodegeneration Study (LEARN, N=500). All CN will complete amyloid PET, structural MRI, genotyping data, and longitudinal neuropsychological testing, and a large subset will additionally complete Tau PET imaging (N=827). Because disease-modifying strategies are most likely to be successful during the preclinical stage, elucidating AD disease mechanisms and deriving individual risk profiles during the preclinical stage will have tremendous impact on the prevention of AD.