Longitudinal Study of MRI, Clinical, and Genetic Biomarkers of Cognitive Impairment and Alzheimer's Disease in Elderly American Indians

摘要 精准医疗是在特定的基础上为患者提供量身定做的医疗保健的新兴实践 影响疾病风险、预后和治疗反应的因素。精密技术的应用前景 治疗阿尔茨海默病(AD)等神经退行性疾病的药物尤其有希望。遗传 标记物，最显著的载脂蛋白E基因变异，以及血管脑损伤和 经MRI评估的脑萎缩已成为临床前AD和临床风险的有用生物标志物 疾病。超过300万美国印第安人(AIS)、阿拉斯加原住民、黑人、拉丁裔和亚洲人患有 AD，并且比白人更早出现认知障碍和AD。人工智能中AD的少数研究 受到小的单一群落样本的限制。我们将使用来自强心脏研究(SHS)的数据和 美国印第安人辅助脑血管疾病及其后果(CDCAI)研究评价 老年AAI患者的认知功能、AD危险因素和MRI定义的AD生物标志物。SHS收集的数据 来自西南、南部平原和北部平原部落的4549名年龄在45-74岁的AI在3年内 从1988年到2000年的阶段。CDCAI研究评估了认知功能和MRI定义的血管脑 2010-2013年818名幸存的SHS参与者的伤害情况，并正在用相同的 核磁共振和认知测试。我们将使用统计绘图软件重新处理来自原始和 后续的CDCAI检查，并将为较老的人工智能创建三维脑图。我们将量化大脑 易受阿尔茨海默病影响的脑区萎缩，如海马区、海马区、内侧区 颞区和顶区，以及后扣带皮质。我们将比较磁共振成像上的结构模式 在两个CDCAI时间点与来自其他人群的AD患者的标准数据进行比较。我们将定义可能的 通过评估选择性地受AD影响的区域MRI定义的损失的变化，结合 与AD相关的认知测试表现和日常生活活动；并检查可能的AD的关联 有风险因素和功能结果。我们的具体目标是：1)建立人工智能特有的规范性价值观 对选择性地受AD影响的脑区的MRI萎缩进行研究，并评估AD相关的区域萎缩 结合认知和行为变化来计算可能的AD患病率；2)使用遗传， 在其他人群中观察到的AD危险因素的社会人口学和临床数据，以确定相关因素 3)估计可能AD的MRI标志物与以下指标的相关性 认知和身体功能，不受脑血管损伤的影响。广告是最主要的原因 痴呆症和死亡在美国。CDCAI样本是具有MRI数据的唯一以总体为基础的人工智能队列 以及与AD相关的遗传生物标记物。我们将利用这些独特的数据来解决PM的关键要素， 即AIS中AD临床前病理生理过程的风险评估和检测。

ABSTRACT Precision medicine is the emerging practice of delivering healthcare tailored to patients on the basis of specific factors that contribute to disease risk, prognosis, and treatment response. The prospect of applying precision medicine to neurodegenerative disorders such as Alzheimer's disease (AD) is especially promising. Genetic markers, most notably variation in the apolipoprotein E gene, and measures of vascular brain injury and cerebral atrophy assessed by MRI have emerged as useful biomarkers of preclinical AD and risk of clinical disease. More than 3 million American Indians (AIs), Alaska Natives, Blacks, Latinos, and Asians suffer from AD, and experience earlier onset of cognitive impairment and AD than Whites. The few studies of AD in AIs are limited by small, single-community samples. We will use data from the Strong Heart Study (SHS) and ancillary Cerebrovascular Disease and its Consequences in American Indians (CDCAI) study to evaluate cognitive function, AD risk factors, and MRI-defined biomarkers of AD in older AIs. The SHS collected data from 4,549 AIs aged 45-74 years from tribes in the Southwest, Southern Plains, and Northern Plains in 3 phases from 1988 to 2000. The CDCAI study assessed cognitive function and MRI-defined vascular brain injury in 818 surviving SHS participants in 2010-2013 and is reassessing surviving participants with the same MRI and cognitive tests. We will use statistical mapping software to reprocess MRIs from both the original and follow-up CDCAI examinations and will create 3-dimensional brain maps for older AIs. We will quantify cerebral atrophy in brain regions preferentially affected by AD such as the hippocampus, parahippocampal, medial temporal, and parietal regions, and the posterior cingulate cortex. We will compare structural patterns on MRI at both CDCAI time points with normative data on AD patients from other populations. We will define probable AD cases by assessing change in MRI-defined loss in regions selectively affected by AD, in combination with AD-related cognitive test performance and activities of daily living; and examine associations of probable AD with risk factors and functional outcomes. Our Specific Aims are to: 1) establish AI-specific normative values of MRI atrophy in brain regions selectively affected by AD, and evaluate AD-related regional atrophy in combination with cognitive and behavioral changes to calculate prevalence of probable AD; 2) use genetic, sociodemographic, and clinical data on risk factors for AD observed in other populations to identify correlates of probable AD in elderly AIs; and 3) estimate associations of MRI markers of probable AD with measures of cognitive and physical function, independent of the effects of vascular brain injury. AD is the leading cause of dementia and of death in the US. The CDCAI sample is the only population-based cohort of AIs with MRI data and genetic biomarkers relevant to AD. We will leverage these unique data to address key elements of PM, namely, risk assessment and detection of preclinical pathophysiologic processes of AD among AIs.