Longitudinal interactive vascular exposure and Alzheimer's Disease

描述（由申请人提供）：许多血管疾病和血管风险因素，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病（AD）的风险因素，但并不一致。此外，使用这些风险因素的组合来预测AD风险的研究仅报告了适度的准确性。目前AD的预测模型通常通过在基线的单个时间点评估血管标志物来表征风险暴露。这样的表征未能捕获在AD症状发作之前相对长的潜伏期内的潜在变化或可变性。这些静态预测模型也忽略了个体纵向血管标志物随时间的巨大异质性。我们提出了一个二次数据分析开发动态模型，使用纵向收集的血管标记物来预测AD风险。我们将合并参加印第安纳波利斯队列的纵向社区为基础的印第安纳波利斯-伊巴丹痴呆症项目（IIDP）的参与者的电子病历与IIDP中收集的研究数据。IIDP共招募了4，105名年龄在65岁或以上的非洲裔美国人，并对参与者进行了长达19年的随访，每2至3年定期进行认知评估，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志物，包括血压，血脂，血红蛋白A1 C和空腹血糖水平从电子病历中获得的AD的风险。在目标1中，我们将比较AD患者和认知正常者之间的纵向血管危险因素特征，并确定纵向血管特征的差异是否由药物使用的差异引起。在目标2中，我们将使用纵向血管标记物开发AD的动态风险评估算法，并将该新算法的性能与现有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）参与者向痴呆转化相关的纵向血管特征。在目标4中，我们将使用函数回归模型检查纵向血管标记物轨迹与纵向认知功能之间的关联，以确定血管标记物的变化如何与认知功能的变化相关。

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.