The Neuropathologic Landscape of Alzheimer's Disease in Hispanic Decedents

摘要：拉美裔是美国人口增长最快的群体，流行病学数据显示他们是 罹患阿尔茨海默病的风险更高，脑血管疾病的发生率也更高 (CVD)与非西班牙裔白人(NHW)相比。AD在神经病理学上的定义是 神经原纤维缠结(NFT)和淀粉样斑块。除了这些特征外，许多AD患者还患有混合型 神经病理学检查，包括路易小体(Lbs)和脑血管病变的病理指标。研究，主要涉及 已显示这些神经病理与临床、遗传和人口统计学变量有关。 提供对疾病机制和预防策略的见解。然而，有一个相对稀缺的 拉美裔的神经病理学研究。进行神经病理学研究是当务之急。 不同的特征，因为这最大化了变异性，从而允许更好地识别疾病风险因素 在制定预防措施方面更加精准。这项研究将检查神经病理 拉美裔美国人的景观(NFT、斑块、CVD和LBS的存在、位置和密度)与NHW相比 并确定这种情况是否被临床、遗传和人口统计学变量所改变。提高效率 在时间和进一步的测量精度上，我们将增强和调整创新的机器学习算法，以 缩小病理部位，提供更多的定量分析。根据之前的调查结果，我们的中心 假设是拉美裔美国人会有不同的神经病理图景，当与卫生保健工作者和这些 差异受到潜在风险因素的影响，尤其是心血管风险因素。我们的具体目标 目的：1)描述NFT、斑块、LBS和病理性病变的存在、位置和半定量密度 拉美裔阿尔茨海默病患者的心血管血管密度指标与健康状况的比较，2)侧写和确定是否病理 目标1的测量因临床、遗传和人口统计学变量(载脂蛋白E状态、西班牙裔血统、 死亡年龄、脑血管事件(如中风)、高脂血症、糖尿病、高血压、性别、 和/或教育)以及3)加强和调整用于量化AD病理的深度学习管道。我们会 充分利用加州大学三个阿尔茨海默病中心现有的特点良好的资源 加州-戴维斯，加州大学圣地亚哥分校，和哥伦比亚大学，包含了不同的尸检证实 广告西班牙裔和nhw队列。此外，我们还将使用加州大学旧金山分校的机器学习资源 弗朗西斯科。这项提案将是第一个描述100多年来神经病理学的大规模倡议 墨西哥人、波多黎各人、古巴人和多米尼加人的西班牙裔美国人与200多名健康工人进行了比较，并确定了 风险因素并存的影响。描绘神经病理图景并了解潜在因素 有可能阐明疾病发展过程中导致更好诊断的机制差异， 治疗和预防策略。结果将直接促进对脑健康(病理)的了解 到目前为止，这是老年人口中一个重要且不断增长的部分，只有极少的神经病理信息。

Abstract: Hispanics are the fastest growing population in the US and epidemiological data suggests they are at a higher risk of developing Alzheimer’s disease and have a higher occurrence of cerebrovascular disease (CVD) when compared to non-Hispanic Whites (NHWs). AD is defined neuropathologically by the deposition of neurofibrillary tangles (NFTs) and amyloid plaques. In addition to these hallmarks, many AD patients have mixed neuropathologies, including Lewy bodies (LBs) and pathological indices of CVD. Studies, involving mainly NHWs, have shown associations of these neuropathologies with clinical, genetic, and demographic variables providing insights into disease mechanisms and prevention strategies. However, there is a relative dearth of neuropathology studies on Hispanics. It is imperative to have neuropathology studies with individuals having diverse characteristics as this maximizes variability allowing for better identification of disease risk factors for more precision in development of preventative measures. This study will examine the neuropathologic landscape (the presence, location, and density of NFTs, plaques, CVD and LBs) of Hispanics compared to NHWs and determine if this landscape is altered by clinical, genetic, and demographic variables. To achieve efficiencies in time and further measurement precision we will enhance and adapt innovative machine learning algorithms to narrow in on pathology location and provide more quantitative analyses. Based on previous findings our central hypothesis is that Hispanics will have different neuropathologic landscapes when compared to NHWs and these differences are influenced by underlying risk factors, especially cardiovascular risk factors. Our specific aims are to: 1) profile the presence, location, and semi-quantitative densities of NFTs, plaques, LBs, and pathological indices of CVD in the setting of AD in Hispanics compared to NHWs, 2) profile and determine if pathologic measures from Aim 1 are altered by clinical, genetic, and demographic variables (APOE status, Hispanic origin, age at death, clinical history of cerebrovascular events (i.e. stroke), hyperlipidemia, diabetes, hypertension, sex, and/or education) and 3) strengthen and adapt a deep learning pipeline for quantifying AD pathologies. We will capitalize on existing well-characterized resources within three Alzheimer’s disease centers at University of California- Davis, the University of California- San Diego, and Columbia that contain a diverse autopsy confirmed AD Hispanic and NHW cohort. Also, we will use machine learning resources at the University of California San Francisco. This proposal will be the first largescale initiative to delineate the neuropathology in over 100 Hispanics of Mexican, Puerto Rican, Cuban, and Dominican origins compare to over 200 NHWs and determine the impact of comorbid risk factors. Profiling neuropathologic landscapes and understanding underlying factors has potential to shed light on mechanistic differences in disease development leading to better diagnosis, treatment, and prevention strategies. Results will directly advance understanding of brain health (pathology) in an important and growing part of the elderly population that to date there is miniscule neuropathology information.