Longitudinal interactive vascular exposure and Alzheimer's Disease

纵向交互血管暴露与阿尔茨海默病

• 批准号: 9041471
• 负责人: SUJUAN GAO
• 金额: $ 31.1万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9041471
• 关键词: Acceleration; Accounting; Affect; African American; Aging; Algorithms; Alzheimer' s Disease; Alzheimer' s disease risk; American; Blood Pressure; Blood Vessels; Characteristics; Clinical Data; Cognition; Cognitive; Collection; Communities; Computerized Medical Record; Data; Data Analyses; Databases; Dementia; Diabetes Mellitus; Diagnostic tests; Disease; Disease Marker; Distant; Elderly; Enrollment; Evaluation; Health; Hemoglobin; Heterogeneity; Hyperlipidemia; Hypertension; Individual; Intervention; Lead; Lipids; Measures; Medical; Methods; Modeling; National Institute on Aging; Obesity; Participant; Performance; Pharmaceutical Preparations; Population; Prevention approach; Prevention strategy; Procedures; Public Health; Reporting; Research; Risk; Risk Assessment; Risk Factors; Schedule; Smoking; Stroke; Symptoms; Time; Vascular Diseases; Visit; aged; base; clinical Diagnosis; clinical practice; cognitive function; cohort; design; fasting glucose; mild cognitive impairment; modifiable risk; novel strategies; population based; predictive modeling; prevent; rate of change; research clinical testing; vascular risk factor

DESCRIPTION (provided by applicant): A number of vascular diseases and vascular risk factors including diabetes, hypertension, hyperlipidemia, smoking, and obesity have been implicated but not consistently established as risk factors for Alzheimer's disease (AD). In addition, studies using a combination of these risk factors to predict AD risk have reported only modest accuracy. Current predictive models for AD have typically characterized risk exposure by assessing vascular markers at a single point in time at the baseline. Such characterization fails to capture potential changes or variability over the relatively long latency period prior to he onset of AD symptoms. These static predictive models also ignore the vast heterogeneity in individuals' longitudinal vascular markers over time. We propose a secondary data analysis developing dynamic models using longitudinally collected vascular markers to predict AD risk. We will merge electronic medical records of participants enrolled in the Indianapolis cohort of the longitudinal community-based Indianapolis-Ibadan Dementia Project (IIDP) with research data collected in the IIDP. The IIDP has enrolled a total of 4,105 African Americans aged 65 or older and followed the participants for up to 19 years with cognitive evaluation, clinical diagnosi and risk factor information at regularly scheduled intervals every 2 to 3 years. Our analyses will focus on longitudinally measured vascular markers including blood pressure, lipids, hemoglobin A1C and fasting glucose levels obtained from electronic medical records for the risk of AD. In Aim 1, we will compare longitudinal vascular risk factor profiles between participants with AD and those with normal cognition and determine whether differences in longitudinal vascular profiles are accounted for by differences in medication use. In Aim 2, we will develop a dynamic risk assessment algorithm for AD using longitudinal vascular markers and compare the performance of this new algorithm with existing AD assessment risk scores. In Aim 3 we will identify longitudinal vascular characteristics associated with conversion to dementia in participants with mild cognitive impairment (MCI). In Aim 4, we will examine the association between longitudinal vascular marker trajectories and longitudinal cognitive function using functional regression models to determine how changes in the vascular markers are related to changes in cognitive function.

描述（由申请人提供）：许多血管疾病和血管危险因素，包括糖尿病、高血压、高脂血症、吸烟和肥胖，已被认为是阿尔茨海默氏病（AD）的危险因素，但尚未一致确定。此外，结合这些风险因素来预测 AD 风险的研究报告的准确性有限。目前的 AD 预测模型通常通过评估基线单个时间点的血管标志物来表征风险暴露。这种表征未能捕获 AD 症状出现之前相对较长的潜伏期内的潜在变化或变异性。这些静态预测模型还忽略了个体纵向血管标记随时间的巨大异质性。我们提出二次数据分析，使用纵向收集的血管标志物开发动态模型来预测 AD 风险。我们将把参加纵向社区印第安纳波利斯-伊巴丹痴呆项目 (IIDP) 印第安纳波利斯队列的参与者的电子病历与 IIDP 中收集的研究数据合并起来。 IIDP 共招募了 4,105 名 65 岁或以上的非裔美国人，并对参与者进行长达 19 年的跟踪，每 2 至 3 年定期进行一次认知评估、临床诊断和危险因素信息。我们的分析将重点关注从电子病历中获得的 AD 风险的纵向测量血管标志物，包括血压、血脂、糖化血红蛋白和空腹血糖水平。在目标 1 中，我们将比较 AD 参与者和认知正常参与者之间的纵向血管危险因素概况，并确定纵向血管概况的差异是否是由药物使用的差异造成的。在目标 2 中，我们将使用纵向血管标记物开发一种 AD 动态风险评估算法，并将这种新算法的性能与现有的 AD 评估风险评分进行比较。在目标 3 中，我们将确定与轻度认知障碍 (MCI) 参与者转变为痴呆相关的纵向血管特征。在目标 4 中，我们将使用功能回归模型检查纵向血管标记轨迹与纵向认知功能之间的关联，以确定血管标记的变化如何与认知功能的变化相关。