An Investigation Into Atypical Alzheimer's Disease

描述(申请人提供)：阿尔茨海默病(AD)是一种与年龄相关的进行性疾病，在美国有500万人受到影响。它的典型特征是脑组织中存在特定的异常蛋白，这些蛋白以一种刻板的方式通过大脑的不同区域发展，从海马体开始，扩散到颞顶皮质。然而，在一些患者中，异常蛋白质的进展并不符合典型的模式。有些病例显示海马区受累很少(海马区AD，HPSP AD)，有些病例中蛋白质没有扩散到海马区外(边缘占优势的AD)。人们对这些非典型变异知之甚少，但它们可能在所有AD病例中占相对较大的比例(27%)，并可能影响AD的临床研究结果。本研究项目的目的是调查非典型AD患者与典型AD患者的区别。目标1将评估临床特征，如临床诊断和认知功能测试中的表现，如记忆和语言，跨组。还将评估是否存在特定的遗传风险因素。AIM 2将使用复杂的自动化技术分析大脑磁共振成像(MRI)扫描，评估脑组织丢失的模式，即萎缩，以及大脑如何随着时间的推移而萎缩。我们预计，不同组的脑组织丢失模式将有所不同，并将反映在尸检中发现异常蛋白质的大脑区域。此外，AIM 3将使用来自AIM 1和AIM 2的数据以及新的统计技术来确定这些数据是否可以用来预测个体患者一生中是否存在非典型AD。这一分析也将有助于验证非典型AD与典型AD的区别。这笔赠款将研究236名已死亡并在尸检中被诊断为阿尔茨海默病的患者。所有患者都将在一生中接受年度临床评估和核磁共振扫描。这些患者将根据尸检时大脑中异常蛋白的分布情况分为三组(HPSP AD、边缘AD和典型AD)。有关特定临床测试的表现和遗传生物标记物存在的数据将从医疗记录中提取，临床诊断将由行为神经学家确定。所有MRI扫描将使用三种不同类型的分析进行处理：将使用一种名为基于体素的形态测量的技术来评估脑组织丢失的模式；将使用图像配准和边界移位积分来评估大脑体积的变化率；以及将使用Freesurfer软件来评估大脑中特定结构的变化率。将进行统计分析，以比较AIMS 1和AIMS 2的分组结果，并对AIM 3进行聚类分析。该项目的结果将提供数据，帮助临床医生识别可能患有非典型AD的患者，并可能在未来改善对这些患者的治疗。由于被诊断为AD的患者中有很高比例可能患有非典型AD，这可能会显着改善许多患者的健康状况。 与公共卫生相关：这项研究将描述阿尔茨海默病(AD)重要但未被认识的非典型变种的临床、遗传和影像特征，这将有助于临床医生识别这些患者，并将导致改善对这些患者的治疗。非典型AD并不少见，估计有100多万美国人受到影响，因此这项研究将对公共健康产生重大影响。此外，识别这些非典型变异可能会对旨在研究典型AD患者的临床治疗试验的结果产生积极影响。

DESCRIPTION (provided by applicant): Alzheimer's disease (AD) is an age-related progressive disorder that affects 5 million people in the US. It is typically characterized by the presence of specific abnormal proteins in brain tissue that progress in a stereotypic fashion through different regions of the brain, starting in the hippocampus and spreading to the temporoparietal cortex. However, in some patients the progression of abnormal proteins does not conform to the typical pattern. There are cases which show very little involvement of the hippocampus (Hippocampal Sparing AD, HpSp AD), and those in which the proteins do not spread outside the hippocampus (limbic predominant AD). Little is known about these atypical variants yet they may account for a relatively large proportion of all AD cases (27%) and could influence the results of clinical studies on AD. The goal of this research project is to investigate how patients with atypical AD differ from those with typical AD. Aim 1 will assess clinical features, such as clinical diagnosis and performance on tests of cognitive functions such as memory and language, across the groups. The presence of specific genetic risk factors will also be assessed. Aim 2 will assess the patterns of brain tissue loss, i.e. atrophy, and how the brain shrinks over time across the groups using sophisticated automated techniques that analyze brain magnetic resonance imaging (MRI) scans. We expect that the patterns of brain tissue loss will differ across the groups, and will reflect the regions of the brain that the abnormal proteins are found at autopsy. In addition, aim 3 will use data from aim 1 and aim 2 and novel statistical techniques to determine whether this data could be used to predict the presence of atypical AD during life in an individual patient. This analysis will also serve to validate the distinction of atypical AD from typical AD. The grant will study 236 patients that have died and had a diagnosis of AD at autopsy. All patients will have been followed during life with yearly clinical assessments and MRI scans. The patients will be divided into three groups (HpSp AD, limbic AD and typical AD) based on the distribution of abnormal proteins in their brains at autopsy. Data concerning performance on specific clinical tests and presence of genetic biomarkers will be abstracted from the medical records and a clinical diagnosis will be determined by a behavioral neurologist. All MRI scans will be processed using three different types of analysis: patterns of brain tissue loss will be assessed using a technique called voxel-based morphometry; rates of change in brain volume will be assessed using image registration and the boundary-shift integral; and rates of change of particular structures in the brain will be assessed using Freesurfer software. Statistical analysis will be performed to compare results across groups for aims 1 and 2 and to perform cluster analysis for aim 3. The results from this project will provide data that will help clinicians to identify patients that may have atypical AD and may in the future lead to improved treatments for these patients. Since a high proportion of patients diagnosed with AD may have atypical AD this could lead to significant health improvements for many patients. PUBLIC HEALTH RELEVANCE: This study will characterize the clinical, genetic and imaging features of important, yet under recognized, atypical variants of Alzheimer's disease (AD) which will help clinicians identify these patients and will lead to improved treatments for such patients. Atypical AD is not uncommon, estimated to affect over 1 million Americans, and hence this study will have a significant impact on public health. Furthermore, identification of these atypical variants will likely have a positive effect on the outcome of clinical treatment trials which are designed to study typical AD patients.