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，吸烟和肥胖症，但并未始终如一地确定为阿尔茨海默氏病（AD）的风险因素。此外，结合这些风险因素预测AD风险的研究仅报告了适度的准确性。当前的AD预测模型通常通过在基线时单点评估血管标记来表征风险暴露。这种表征无法在AD症状发作之前的相对较长的潜伏期内捕获潜在的变化或可变性。这些静态预测模型还忽略了随着时间的流逝，个体纵向血管标记的巨大异质性。我们建议使用纵向收集的血管标记来预测AD风险的二级数据分析，开发动态模型。我们将融合参加基于纵向社区的印第安纳波利斯 - 伊巴丹痴呆症项目（IIDP）的印第安纳波利斯队的参与者的电子病历与IIDP收集的研究数据。 IIDP总共有4,105名65岁或65岁以上的非洲裔美国人，并以每2至3岁的时间间隔定期进行认知评估，临床诊断和危险因素信息进行19年的认知评估，临床诊断和风险因素信息。我们的分析将集中在纵向测量的血管标记上，包括血压，脂质，血红蛋白A1C和从电子病历中获得的空腹葡萄糖水平，以便有AD的风险。在AIM 1中，我们将比较AD参与者与具有正常认知的参与者之间的纵向血管风险因素特征，并确定纵向血管谱的差异是否由药物使用差异解释。在AIM 2中，我们将使用纵向血管标记为AD开发动态风险评估算法，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AIM 3中，我们将确定患有轻度认知障碍参与者转化为痴呆症（MCI）的纵向血管特征。在AIM 4中，我们将使用功能回归模型来研究纵向血管标记轨迹与纵向认知函数之间的关联，以确定血管标记中的变化与认知功能的变化如何相关。