TWO PRECLINICAL LATENT SCORES TO PREDICT OCCURRENCE OF DAT

描述（由申请人提供）：预测DAT发生的两个临床前潜伏评分与阿尔茨海默病（AD）相关的认知下降发生在临床诊断前数年。然而，临床医生对最早期认知和功能障碍的出现以及临床前进展的确切持续时间仍知之甚少。因此，迫切需要更好的方法来可靠地检测阿尔茨海默型痴呆（DAT）发作前的认知和功能变化。尽管标准认知和功能组合的常规评分成功地将完全表达的DAT与正常衰老区分开来，但它们追踪微妙的临床前疾病进展的能力是不确定的，尽管它们中的许多单独项目可能预测AD的症状发作。使用来自华盛顿大学（WU）阿尔茨海默病研究中心（ADRC）、拉什大学（RU）阿尔茨海默病中心（ADC）以及阿尔伯特爱因斯坦医学院（AECOM）的爱因斯坦衰老研究（EAS）和布朗克斯衰老研究（BAS）的丰富和高质量的纵向数据，本研究将首先进行纵向项目分析，以确定是否以及在何种程度上，个别项目得分从测试的4个认知和功能电池对纵向临床前变化敏感并提供信息，并预测DAT的发展，以及这些项目变化如何与认知储备代理相关（例如，教育和认知活动）、ApoE基因型、生物标志物的临床前测量，包括脑脊液（CSF）分子生物标志物、MRI脑体积标志物、具有PIB的淀粉样蛋白神经成像以及神经病理学诊断。其次，信息项将通过项目反应理论（IRT）进行最佳整合，以估计临床前潜在的认知和功能结构，并评估这些结构的纵向增长模式以及临床前AD的精确持续时间。第三，我们将比较DAT在估计的临床前潜在认知和功能结构与常规测试分数之间的预测能力。最后，我们将为国家阿尔茨海默氏症协调中心（NACC）统一数据集（UDS）开发临床有用的评分报告，以总结最佳估计的临床前潜在的认知和功能结构，用于跟踪AD的先行纵向变化。我们还将提供最佳估计的设计参数（例如，样本量）用于阿尔茨海默病合作研究（ADCS），以在估计的临床前潜在认知和功能结构用作主要疗效终点时，对轻度认知障碍（MCI）进行未来的预防和治疗试验。我们在WU ADRC，RU ADC和AECOM的跨学科研究人员团队将使用最先进的纵向统计方法，现代心理测量理论（即，IRT）和尖端的生物信息学技术。

DESCRIPTION (provided by applicant): Two Preclinical Latent Scores to Predict Occurrence of DAT The cognitive decline associated with Alzheimer's disease (AD) occurs years prior to the clinical diagnosis. However, the emergence of the earliest cognitive and functional impairment and the precise duration of the preclinical progression remain poorly understood by clinicians. Better methods are therefore urgently needed to reliably detect the antecedent cognitive and functional changes before the onset of the dementia of Alzheimer type (DAT). Whereas the conventional scores of the standard cognitive and functional batteries are successful in discriminating fully expressed DAT from normal aging, their ability to track subtle preclinical disease progression is uncertain, although it is possible that many individual items from them may predict the symptomatic onset of AD. Using rich and high quality longitudinal data from Washington University (WU) Alzheimer's Disease Research Center (ADRC), Rush University (RU) Alzheimer's Disease Center (ADC), and the Einstein Aging Study (EAS) and the Bronx Aging Study (BAS) at Albert Einstein College of Medicine (AECOM), this project will first conduct longitudinal item analyses to determine whether and to what degree individual item scores from tests of the 4 cognitive and functional batteries are sensitive and informative to longitudinal preclinical changes and predictive to the development of DAT, and how these item changes are correlated with cognitive reserve proxies (e.g., education and cognitive activities), ApoE genotype, preclinical measures of biomarkers including cerebrospinal fluid (CSF) molecular biomarkers, MRI brain volumetric markers, amyloid neuroimaging with PIB, as well as neuropathological diagnoses. Second, informative items will be optimally integrated through Item Response Theory (IRT) to estimate the preclinical latent cognitive and functional constructs and assess the longitudinal growth pattern of these constructs as well as the precise duration of the preclinical AD. Third, we will compare the predictive power of DAT between the estimated preclinical latent cognitive and functional constructs and the conventional test scores. Finally, we will develop clinically useful score reports for the National Alzheimer's Coordinating Center (NACC) Uniform Data Set (UDS) to summarize the optimally estimated preclinical latent cognitive and functional constructs for tracking the antecedent longitudinal changes of AD. We will also provide optimally estimated design parameters (e.g., sample sizes) for the Alzheimer's Disease Cooperative Study (ADCS) to conduct future preventive and therapeutic trials on Mild Cognitive Impairment (MCI) when the estimated preclinical latent cognitive and functional constructs are used as primary efficacy endpoints. Our interdisciplinary team of investigators at the WU ADRC, RU ADC, and AECOM will demonstrate the degree of improved detection of preclinical longitudinal cognitive and functional changes of AD using the state-of-the-art longitudinal statistical methods, modern psychometric theory (i.e., IRT), and cutting-edge bioinformatics techniques.