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

 描述(申请人提供)：K01候选人的职业目标是成为一名独立的调查员，通过阐明疾病机制并得出疾病临床前阶段的个人风险概况，进行有助于预防阿尔茨海默病(AD)痴呆症的研究。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。在此次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，并获得基因分析方面的专业知识，以追求一条新的研究道路。本文提出的研究计划考察了已知的遗传危险因素，特别是与临床AD相关的APOE4基因和21个非APOE基因座在大型GWAS分析中对临床正常老年人(CN)中AD生物标志物与认知功能下降的关系的影响。考虑到使用生物标记物选择CN的能力，对CN内的遗传风险因素的理解尤其相关 Aβ升高的证据(临床前AD)。尽管纵向研究表明，临床前AD患者的CN具有较高的临床损害风险，但在这一组中存在异质性下降，因此这些个体中的一些人在较长时间内保持临床正常。识别影响A-β初始积聚和临床症状出现之间的时间的遗传危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大的个体的能力。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Aβ介导的，我们假设这些因素也通过与Aβ相互作用而影响AD的轨迹，从而加速新皮质Tau的积累、神经变性和认知功能下降等下游效应。因此，对于同等水平的Aβ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。这一系列研究应该能够深入了解为什么一些Aβ患者能够在较长时间内保持临床正常，而另一些Aβ患者则迅速发展为临床损害。我们将在哈佛老龄化大脑研究(N=277)、无症状AD抗淀粉样蛋白研究(A4，N=1000)和淀粉样蛋白风险纵向评估和神经变性研究(LINE，N=500)中结合1777个CN的大样本来研究这些假设。所有CN将完成淀粉样蛋白PET、结构磁共振成像、基因分型数据和纵向神经心理测试，还有一大部分将完成Tau PET成像(N=827)。由于疾病调整策略在临床前阶段最有可能取得成功，阐明AD的发病机制并在临床前阶段得出个体风险分布将对AD的预防具有巨大的影响。

 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.