Predicting Impending Cognitive Decline in Cognitively Normal Individuals

项目摘要/摘要 本研究的目的是验证一种实用的方法来预测早期认知能力下降。 阿尔茨海默病(AD)患者分期，在发展出标志性症状之前。与 阿尔茨海默病的科学研究和临床试验的当前焦点--阿尔茨海默病的日益流行 疾病及相关障碍(ADRD)已转向在无症状和早期- 疾病的各个阶段。然而，这样的早期试验一直在努力识别和识别 招募没有认知衰退外在症状的受试者。此外，一旦治疗获得批准， 对于早期ADRD的治疗，识别无症状患者将是分娩过程中的一个重大障碍 及时的护理。因此，迫切需要一种实用的方法来预测 认知正常的受试者，可以登记参加ADRD临床试验，并确定哪些人可以 潜在受益于未来ADRD疗法的治疗。 我们的初步研究表明，分层贝叶斯认知加工(HBCP) 词表记忆模型(WLM)测试成绩可以(1)量化认知过程 通过传统的评估评分，如AVLT或ADAS-Cog，或通过最近的组合 ADCOMS等指标；以及(2)准确地将认知正常的人分为两类 群体：潜在认知过程表明认知正常衰老(稳定)的群体和 其潜在的认知过程表明进展到MCI/AD(进展者)。使用HBCP模型 分析WLM测试，我们将启用对预测的潜在认知过程的定量估计 ADRD导致的认知功能下降。 拟议研究的成功交付将通过加快ADRD药物开发的速度 登记和缩短试验持续时间，并通过量化认知过程的变化来 在无症状的受试者中显示有意义的治疗效果。这项技术还将促进 当新的治疗方法获得批准时，及时对早期ADRD患者进行临床干预。

PROJECT SUMMARY / ABSTRACT The goal of this study is to validate a pragmatic method to predict impending cognitive decline in early- stage Alzheimer’s disease (AD) patients, prior to the development of hallmark symptoms. With the growing epidemic of AD dementia, the current focus of scientific research and clinical trials in Alzheimer’s disease and related disorders (ADRD) has shifted toward intervention during the asymptomatic and early- stages of the disease. However, such early-stage trials have struggled with an inability to identify and enroll subjects with no outward symptoms of cognitive decline. In addition, once treatments are approved to treat early-stage ADRD, identifying asymptomatic patients will be a significant obstacle in the delivery of timely care. Therefore, there is an urgent need for a pragmatic method to predict impending decline in cognitively normal subjects who could enroll in ADRD clinical trials and identify those who could potentially benefit from treatment with future ADRD therapies. Our preliminary studies have demonstrated that a Hierarchical Bayesian Cognitive Processing (HBCP) model of wordlist memory (WLM) test performance can (1) quantify cognitive processes which are not captured by traditional scoring of assessments such as the AVLT or ADAS-Cog, or by recent composite measures such as the ADCOMS; and (2) accurately classify cognitively normal individuals into two groups: those whose latent cognitive processes indicate cognitively normal aging (stable) and those whose latent cognitive processes indicate progression to MCI/AD (progressor). Using HBCP models to analyze WLM tests, we will enable quantitative estimations of latent cognitive processes that predict impending cognitive decline due to ADRD. Successful delivery of the proposed study will improve efficacy of ADRD drug development by expediting enrollment and shortening trial duration and by quantifying changes in cognitive processes to demonstrate meaningful treatment effects in asymptomatic subjects. This technology will also facilitate timely clinical intervention for early-stage ADRD patients when new treatments are approved.