Predicting Impending Cognitive Decline in Cognitively Normal Individuals

预测认知正常个体即将发生的认知衰退

• 批准号: 10112797
• 负责人: William Rodman Shankle
• 金额: $ 30.43万
• 依托单位: MEDICAL CARE CORPORATION
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10112797
• 关键词: Aging; Alzheimer' s Disease; Alzheimer' s disease patient; American; Automobile Driving; Caring; Clinic; Clinical; Clinical Trials; Cognition; Cognitive; Competence; Data Set; Development; Disease; Early Intervention; Enrollment; Ensure; Epidemic; Future; Goals; Human; Impaired cognition; Individual; Intervention; Literature; Longevity; Measures; Medical; Memory; Methods; Modeling; Neurobehavioral Manifestations; Patients; Phase; Process; Research; Symptoms; Task Performances; Technology; Testing; Time; Validation; Work; care costs; clinical care; cognitive process; drug development; expectation; improved; neuroimaging; neuroinformatics; normal aging; novel; performance tests; predictive modeling; success; therapy development; treatment effect

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.

项目概要/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) 患者在出现标志性症状之前的阶段。随着 AD痴呆症日益流行，是当前阿尔茨海默病科学研究和临床试验的焦点 疾病和相关疾病（ADRD）已转向无症状和早期阶段的干预 疾病的阶段。然而，此类早期试验一直因无法识别和确定而陷入困境。 招募没有认​​知能力下降的外在症状的受试者。此外，一旦治疗获得批准 为了治疗早期ADRD，识别无症状患者将是交付的一个重大障碍 的及时护理。因此，迫切需要一种实用的方法来预测即将到来的下降 认知正常的受试者可以参加 ADRD 临床试验并确定那些可以 可能受益于未来 ADRD 疗法的治疗。 我们的初步研究表明，分层贝叶斯认知处理（HBCP） 词表记忆模型 (WLM) 测试表现可以 (1) 量化非认知过程 通过传统的评估评分（例如 AVLT 或 ADAS-Cog）或最近的综合评分来捕获 ADCOMS 等措施； (2)准确地将认知正常的个体分为两类 群体：那些潜在认知过程表明认知正常老化（稳定）的群体和那些 其潜在认知过程表明进展为 MCI/AD（进展者）。使用 HBCP 模型 分析 WLM 测试，我们将能够对预测的潜在认知过程进行定量估计 ADRD 导致认知能力即将下降。 拟议研究的成功交付将通过加快 ADRD 药物开发的效率来提高 注册和缩短试验持续时间，并通过量化认知过程的变化来 在无症状受试者中表现出有意义的治疗效果。这项技术也将有利于 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。