Re-visiting Methods for MCI Diagnosis to Improve Biomarker and Trial Findings

重新审视 MCI 诊断方法以改进生物标志物和试验结果

• 批准号: 9236145
• 负责人: Mark W Bondi
• 金额: $ 74.77万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN DIEGO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9236145
• 关键词: Address; Age; Aging; Alzheimer disease prevention; Alzheimer' s disease model; Biological Markers; Clinical; Clinical Trials; Cognition; Cognitive; Communities; Comorbidity; Data; Data Set; Decision Making; Dementia; Detection; Diagnosis; Diagnostic; Diagnostic Errors; Episodic memory; Excision; Functional disorder; Future; Genetic Markers; Genetic study; Impaired cognition; Individual; Judgment; Measures; Methods; Modeling; Nerve Degeneration; Neuropsychological Tests; Neuropsychology; Outcome; Participant; Pathology; Patients; Phenotype; Prevention; Prospective Studies; Reporting; Research; Research Design; Risk; Role; Sample Size; Semantic memory; Severities; Specific qualifier value; Specificity; Statistical Methods; Subgroup; Techniques; Visit; Visuospatial; accurate diagnosis; aging brain; base; cognitive testing; cohort; computerized tools; design; donepezil; executive function; hazard; high risk; improved; mild cognitive impairment; normal aging; novel; open source; personalized diagnostics; power analysis; pre-clinical; prospective; public health relevance; response; screening; secondary analysis; treatment effect; trial design

 DESCRIPTION (provided by applicant): In Response to PA-13-168 (Secondary Analyses of Existing Data Sets and Stored Biospecimens To Address Clinical Aging Research Questions (R01)). Despite increasing sophistication in the application of biomarkers to the study of mild forms of cognitive impairment (MCI), sophistication in profiling cognition has not been commensurate. Criteria for MCI diagnosis in many large-scale studies rely on a single cognitive test score, screening measures, rating scales and clinical judgment, resulting in coarse characterizations of the types or severity of MCI being studied despite the availability of rich neuropsychological data from such studies. We propose to apply our novel actuarial neuropsychological and statistical methods to more accurately diagnose MCI and predict its progression. Applying these methods to large-scale existing open source (ADCS donepezil trial, ADNI, NACC/UDS) and institutional (FHS, MCSA, WHICAP) datasets will uncover stronger relationships between biomarkers, cognition, pathology, and progression rates, and will result in stronger treatment effects in clinical trials aimed at MCI. Our methods will improve effect sizes that inform power analyses for clinical trials and reduce the number of patients needed for such trials. Finally, our methods will be implemented to improve the NIA-AA operational definition of 'subtle cognitive decline' in Preclinical AD. These improvements will have important impacts on prospective design of future biomarker and clinical trial studies. Specific aims: Aim 1. Actuarial neuropsychological criteria for MCI diagnosis will better specify cognitive phenotypes as well as identify possible diagnostic errors from conventional criteria; removal of the resultant false positive (i.e., cognitively normal via neuropsychological criteria) cases and addition of false negative (i.e., `missed') cases will strengthen biomarker and trial findings from several large-scale studies. Aim 2. Empirically derived MCI diagnostic criteria will result in more efficient tril and study designs (i.e., studies that need fewer subjects) compared to conventional MCI criteria. Aim 3. An operational definition of subtle cognitive decline based on extensions of the above neuropsychological MCI criteria will improve characterization of NIA-AA criteria for "Preclinical" AD. Aim 4. In exploratory analyses, we will use novel computational tools to harmonize and combine 1) cognitive and 2) multi-marker profiles predictive of progression/pathology across multiple datasets. Demonstrations of improvement in diagnostic precision in MCI and Preclinical AD will have an important impact on prospective design of future studies of genetics, biomarkers, treatments and ultimately prevention. If successful, we will be able to more clearly model effects of biomarkers changes and neurodegeneration, together with factors such as age and comorbidities, on specific profiles and trajectories of cognitive decline.

 描述（应用程序提供）：响应PA-13-168（对现有数据集和存储生物测量的次要分析以解决临床老化研究问题（R01））。尽管在生物标志物应用于轻度认知障碍（MCI）的研究中越来越复杂，但复杂的认知尚未相称。在许多大规模研究中，MCI诊断的标准依赖于单个认知测试评分，筛查措施，评分量表和临床法官，尽管从此类研究中获得了丰富的神经心理学数据，但研究了MCI的类型或严重性的粗糙特征。我们建议将新型的实际神经心理学和统计方法应用于更准确地诊断MCI并预测其进展。将这些方法应用于大规模现有开源（ADC踢球试验，ADNI，NACC/UDS）和机构（FHS，MCSA，WHICAP）数据集将发现生物标志物，认知，病理学和进展速率之间的牢固关系，并将在MCI AIM AIM AIM中产生强大的治疗效果。我们的方法将改善效应尺寸，该大小为临床试验的电力分析提供了信息，并减少了此类试验所需的患者数量。最后，我们的方法将被实施，以改善临床前AD中“微妙认知下降”的NIA-AA操作定义。这些改进将对未来生物标志物和临床试验研究的前瞻性设计产生重要影响。具体目的：目标1。MCI诊断的精算神经心理学标准将更好地指定认知表型，并确定传统标准的可能诊断错误；删除由此产生的假阳性（即通过神经心理学标准的认知正常）病例的案例，并添加假阴性（即“错过”）病例将增强生物标志物和一些大规模研究的试验结果。 AIM 2。与常规MCI标准相比，具有更有效的TRIL的MCI诊断标准将导致更有效的TRIL和研究设计（即需要较少受试者的研究）。目标3。基于上述神经心理学MCI标准的扩展，对微妙认知下降的操作定义将改善“临床前” AD的NIA-AA标准的表征。目的4。在探索性分析中，我们将使用新颖的计算工具来协调和结合1）认知和2）多个数据集的进展/病理学的多标记谱。 MCI和临床前AD诊断精度改善的演示将对未来遗传学，生物标志物，治疗方法和最终预防研究的预期设计产生重要影响。如果成功的话，我们将能够更清楚地模拟生物标志物变化和神经变性的影响，以及年龄和合并症等因素，对认知能力下降的特定概况和轨迹。