Racial/Ethnic Disparities in the Alzheimer's Disease Link with Heart Disease and Stroke

摘要： 阿尔茨海默病（Alzheimer's disease，AD）是痴呆的主要亚型，占美国痴呆病例的60-80%。 美国（US）。随着发达国家预期寿命的增加，AD正在成为一个主要的 在生命的后期阶段。心脏病和中风事件是另两个日益严重的问题， 老年人口。越来越多的文献将心脏病和中风作为危险因素 对于AD，突出了细胞水平上的重要途径。然而，还需要进一步研究 心脏、中风和AD发展之间的联系，以提高我们对流行病学的理解。 这一联系的特征，这可能使我们确定高风险亚组谁可能处于不利地位， 这样的依赖。夏威夷的人口是美国最多样化的种族人口之一， 在这个多民族人口中存在着显著的种族/民族健康差异。尤其是夏威夷原住民， 太平洋岛民（NHPI）是许多疾病的高风险群体，包括 心血管疾病和中风。心脏功能障碍和中风之间的联系使发展中的AD NHPI可能是AD的高危人群。我们提出了一项研究，以探讨老年患者的AD风险65岁， 根据夏威夷医疗保险数据库，拟议 工作将在一个时间到事件的多状态模型框架下进行，同时考虑竞争风险 用于获得无偏估计和推断。从2009年到2017年的九年夏威夷医疗保险数据 将被用来收集重要的时间到事件的细节，为拟议的工作，这使我们能够进行 对以下几个方面进行全面的至事件发生时间分析：从心脏病或卒中进展至AD; AD 死亡率;心脏病和中风风险。拟议研究的一个主要特点是调查 AD、心脏病和中风相关的种族/民族依赖性。我们还建议开发机器 学习驱动的预测模型，用于基于受试者概况预测过渡和死亡率。

Abstract: Alzheimer’s disease (AD) is the major subtype of dementia, which accounts for 60-80% of dementia cases in the United States (US). With the increased life expectancy in developed countries, AD is becoming a major issue at the late stage of life. Heart disease and stroke events are another two growing issues among the elderly population. There is an expanding body of literature implicating heart disease and stroke as risk factors for AD, highlighting significant pathways at the cellular level. However, further research is needed on the association between heart, stroke, and development of AD to improve our understanding of epidemiological features of this link, which could lead us to identify high-risk subgroups who potentially disadvantaged from such dependencies. Hawaii's population is one of the most diverse ethnic populations in the US and there exist significant racial/ethnic health disparities in this multiethnic population. Especially, Native Hawaiians and Pacific Islanders (NHPI) are a well-known high-risk group for many disease conditions, including cardiovascular conditions and stroke. Links between heart dysfunction and stroke on developing AD makes NHPI potentially a high-risk group for AD. We propose a study to explore the AD risk for patients age 65 and above those who had major heart events and stroke, based on the Hawaii Medicare database. The proposed work will be conducted under a time-to-event multistate model framework while accounting for competing risks for acquiring unbiassed estimations and inferencing. Nine years of Hawaii Medicare data from 2009 to 2017 will be utilized to gather important time-to-event details for the proposed work, which allows us to conduct comprehensive time-to-event analyses on several aspects: progression from heart disease or stroke to AD; AD mortality; heart disease and stroke risks for AD. A key feature of the proposed study is the investigation of racial/ethnic dependency on AD, heart disease, and stroke links. We also propose to develop machine learning-driven predictive models for predicting transitions and mortality based on subject profiles.