TWO PRECLINICAL LATENT SCORES TO PREDICT OCCURRENCE OF DAT

描述（由申请人提供）：两个临床前潜在评分预测DAT的发生与阿尔茨海默病（AD）相关的认知能力下降发生在临床诊断前几年。然而，临床医生对最早的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然知之甚少。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）发病前的认知和功能变化。尽管标准认知和功能电池的传统评分可以成功区分完全表达的DAT和正常衰老，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个项目可能预测AD的症状性发作。利用来自华盛顿大学（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）、爱因斯坦衰老研究（EAS）和爱因斯坦医学院（AECOM）布朗克斯衰老研究（BAS）的丰富高质量纵向数据，本项目将首先进行纵向项目分析，以确定4个认知和功能单元测试的单个项目得分是否以及在多大程度上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（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脑容量标志物，PIB的淀粉样神经成像以及神经病理诊断。其次，通过项目反应理论（IRT）对信息性项目进行优化整合，以估计临床前潜在的认知和功能构念，并评估这些构念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构念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病协调中心（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认知和功能结构，以跟踪阿尔茨海默病的前期纵向变化。我们还将为阿尔茨海默病合作研究（ADCS）提供最佳估计设计参数（例如样本量），以便在估计的临床前潜在认知和功能结构作为主要疗效终点时，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的跨学科研究团队将展示使用最先进的纵向统计方法，现代心理测量理论（即IRT）和尖端生物信息学技术改善阿尔茨海默病临床前纵向认知和功能变化检测的程度。

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. PUBLIC HEALTH RELEVANCE: This project will focus on detecting the earliest possible signs of preclinical cognitive and functional changes of Alzheimer's disease. This project is significant because understanding very early cognitive and functional changes antecedent to the onset of DAT will allow therapeutic interventions to be administered well before dementia symptoms are fully developed and a clinical diagnosis is rendered. The knowledge obtained will greatly help develop early therapeutic treatments or preventions of the disease before it is too late.