Identifying best methods for the detection of subtle cognitive decline

确定检测微妙认知能力下降的最佳方法

基本信息

批准号：
10328956
负责人：
Kelsey R Thomas
金额：
$ 15.8万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN DIEGO
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-15 至 2023-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10328956
关键词：
Alzheimer&apos s Disease Alzheimer&apos s disease model Alzheimer&apos s disease pathology Alzheimer&apos s disease related dementia Alzheimer’s disease biomarker Amyloid Applications Grants Archives Area Attention Biological Markers Classification Clinical Clinical Trials Cognitive Cost Savings Data Data Set Dementia Diagnosis Disease Early Diagnosis Early Intervention Early identification Family Future Gold Hippocampus (Brain)Impaired cognition Individual Long-Term Care Measures Medical Memory Methods Nerve Degeneration Neuropsychology Outcome Measure Participant Pathogenesis Pathologic Pathologic Processes Patient Self-Report Patients Performance Persons Phase Positron-Emission Tomography Predictive Value Prevention Questionnaires Reporting Research Research Personnel Research Project Grants Risk Savings Scoring Method Testing White Matter Hyperintensity Work accurate diagnosis base care costs classification algorithm clinical care cognitive change cognitive performance cost cost efficient depressive symptoms design detection method experience functional decline functional disability improved informant mild cognitive impairment neuroimaging next generation open source pre-clinical prognostic value programs progression marker prospective recruit rural area screening statistics tau Proteins tool

项目摘要

PROJECT SUMMARY / ABSTRACT New submission for PAS-19-391 (Small Research Grant Program for the Next Generation of Researchers in AD/ADRD Research: Area of Focus Archiving and Leveraging Existing Data Sets for Analyses [R03]). Recent statistics show that if everyone alive in 2018 who will develop Alzheimer’s disease (AD) had early and accurate diagnosis, there would be a savings of $7.9 trillion in medical and long-term care costs. Although the advent of Alzheimer’s disease (AD) biomarkers has revolutionized our understanding of AD pathogenesis during the preclinical phase, these approaches are often expensive, invasive, inaccessible due to rural location, cost, or medical contraindications. Additionally, beyond a focus on AD biomarkers alone, it is essential to emphasize that cognitive difficulties and subsequent functional impairment are the features of the disease that negatively impact the lives of patients and their families. Emerging evidence suggests that subtle cognitive changes may develop much earlier than originally described in models of AD and these subtle cognitive changes add meaningful prognostic value, above and beyond AD biomarkers, in predicting progression to mild cognitive impairment (MCI) and dementia. The gold standard approach for identifying subtle cognitive decline in preclinical AD, however, remains unclear. Therefore, we propose to apply four different classification algorithms for subtle cognitive decline in the open source Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The four classifications methods include two subjective approaches: self-reported subjective cognitive decline (Self-SCD) and informant-reported subjective cognitive decline (Inform-SCD); and two objective approaches: a sensitive neuropsychological individual test-based approach called objectively-defined subtle cognitive decline (Obj-SCD) and a composite score-based approach using the preclinical Alzheimer’s cognitive composite to identify subtle cognitive decline (PACC-SCD). The specific aims of this proposal include: 1) Compare AD biomarkers across 4 subtle cognitive decline definitions (Self-SCD, Inform-SCD, Obj-SCD, and PACC-SCD); 2) Determine which subtle cognitive decline definitions best capture objective cognitive decline in the year preceding the subtle cognitive decline classification; and 3) Examine longitudinal a) clinical and b) biomarker progression across subtle cognitive decline definitions to determine the definitions with the best predictive utility. Results of the proposed aims will likely impact the design of future studies, as having simple, yet reliable, and highly cost- efficient methods for narrowing the initial pool of participants so that only those at greatest risk require a PET scan for screening purposes would result in significant cost-savings. Additionally, these results will serve as critical preliminary data for future grant applications, and be a key step toward improving clinically meaningful early detection methods of those at risk for future decline, particularly those with limited access to AD biomarker testing.

项目概要/摘要新提交的 PAS-19-391（针对下一代研究人员的小型研究资助计划） AD/ADRD 研究：重点领域归档和利用现有数据集进行分析 [R03]）。最近的统计数据显示，如果 2018 年活着的每个人都会患上阿尔茨海默氏病 (AD)，准确的诊断，将节省 7.9 万亿美元的医疗和长期护理费用。阿尔茨海默病 (AD) 生物标志物的出现彻底改变了我们对 AD 发病机制的理解在临床前阶段，这些方法往往昂贵、具有侵入性，并且由于位于农村而难以获得，此外，除了单独关注 AD 生物标志物之外，还必须考虑成本或医疗禁忌症。强调认知困难和随后的功能障碍是该疾病的特征新出现的证据表明，微妙的认知会对患者及其家人的生活产生负面影响。变化可能比 AD 模型中最初描述的要早得多，并且这些微妙的变化在预测轻度认知进展方面，除了 AD 生物标志物之外，还增加了有意义的预后价值识别临床前轻微认知能力下降的黄金标准方法。然而，AD 仍不清楚，因此，我们建议对微妙的情况应用四种不同的分类算法。开源阿尔茨海默病神经影像倡议 (ADNI) 数据集中的认知能力下降。分类方法包括两种主观方法：自我报告的主观认知下降（Self-SCD）和知情者报告的主观认知下降（Inform-SCD）和两种客观方法：敏感的方法基于神经心理学个体测试的方法，称为客观定义的微妙认知衰退（Obj-SCD）以及基于综合评分的方法，使用临床前阿尔茨海默氏症认知综合来识别微妙的认知能力下降 (PACC-SCD) 该提案的具体目标包括： 1) 比较 4 个 AD 生物标志物。微妙的认知下降定义（Self-SCD、Inform-SCD、Obj-SCD 和 PACC-SCD）； 2) 确定哪一个微妙认知下降的定义最好地反映了微妙认知下降之前一年的客观认知下降认知能力下降分类；以及 3) 检查纵向 a) 临床和 b) 生物标志物进展微妙的认知下降定义，以确定具有最佳预测效用的定义。拟议的目标将影响未来研究的设计，因为具有简单但可靠的可能性，并且成本高昂缩小初始参与者范围的有效方法，以便只有那些风险最大的人才需要 PET 此外，用于筛选目的的扫描将节省大量成本。为未来拨款申请提供关键的初步数据，并成为改善临床意义的关键一步为那些面临未来衰退风险的人（特别是那些无法获得 AD 生物标志物的人）提供早期检测方法测试。