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: 10096896
• 负责人: William Rodman Shankle
• 金额: $ 83.76万
• 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 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10096896
• 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.

项目总结/摘要 本研究的目的是验证一种实用的方法来预测即将发生的认知功能下降的早期- 阶段阿尔茨海默病（AD）患者，在发展标志性症状之前。与 AD痴呆症的流行日益增加，这是目前阿尔茨海默氏症科学研究和临床试验的重点 疾病和相关疾病（ADRD）已转向无症状和早期干预， 疾病的阶段。然而，这种早期阶段的试验一直在努力与无法识别， 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， 为了治疗早期ADRD，识别无症状患者将是交付过程中的重大障碍 及时的照顾。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治疗。 我们的初步研究表明，层次贝叶斯认知加工（HBCP） 词表记忆模型（WLM）测试性能可以（1）量化认知过程，这不是 通过传统的评估评分（如AVLT或ADAS-Cog）或通过最近的复合 ADCOMS等指标;（2）准确地将认知正常的个体分为两类 组：那些潜在的认知过程表明认知正常老化（稳定）和那些 其潜在的认知过程指示进展为MCI/AD（进展者）。使用HBCP模型 分析WLM测试，我们将能够定量估计潜在的认知过程，预测 ADRD导致的认知能力下降 拟议研究的成功交付将通过加快ADRD药物开发的有效性 招募和缩短试验持续时间，并通过量化认知过程的变化， 在无症状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，对早期ADRD患者进行及时的临床干预。