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: 9027591
• 负责人: Mark W Bondi
• 金额: $ 82.21万
• 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/9027591
• 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; Impaired cognition; Individual; Judgment; Measures; Methods; Modeling; Nerve Degeneration; Neuropsychological Tests; 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; aging brain; base; clinical Diagnosis; cognitive testing; cohort; computerized tools; design; donepezil; executive function; hazard; high risk; improved; mild cognitive impairment; neuropsychological; normal aging; novel; open source; personalized diagnostics; pre-clinical; prospective; public health relevance; response; screening; 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（由申请方提供）：回复PA-13-168（现有数据集和储存生物样本的二次分析，以解决临床老化研究问题（R 01））。尽管生物标志物在轻度认知障碍（MCI）研究中的应用越来越复杂，但认知分析的复杂性并不相称。在许多大规模研究中，MCI的诊断标准依赖于单一的认知测试评分、筛查措施、评定量表和临床判断，导致尽管从这些研究中获得了丰富的神经心理学数据，但对所研究的MCI的类型或严重程度的粗略表征。我们建议应用我们新的精算神经心理学和统计方法，更准确地诊断MCI和预测其进展。将这些方法应用于大规模现有的开源（ADCS多奈哌齐试验，ADNI，NACC/UDS）和机构（FHS，MCSA，WHICAP）数据集将揭示生物标志物，认知，病理和进展率之间更强的关系，并将在针对MCI的临床试验中产生更强的治疗效果。我们的方法将改善效应量，为临床试验的功效分析提供信息，并减少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NIA-AA的操作定义的'微妙的认知下降'在临床前AD。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。具体目标：目标1。用于MCI诊断的精算神经心理学标准将更好地指定认知表型以及从常规标准中识别可能的诊断错误;去除由此产生的假阳性（即，通过神经心理学标准认知正常）病例和添加假阴性（即，“遗漏”的病例将加强几项大规模研究的生物标志物和试验结果。目标二。根据经验得出的MCI诊断标准将产生更有效的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标准的扩展的轻微认知下降的操作定义将改善“临床前”AD的NIA-AA标准的表征。目标4。在探索性分析中，我们将使用新的计算工具协调和联合收割机1）认知和2）多个数据集中预测进展/病理的多标志物特征。MCI和临床前AD诊断精度的提高将对未来遗传学、生物标志物、治疗和最终预防研究的前瞻性设计产生重要影响。如果成功，我们将能够更清楚地模拟生物标志物变化和神经退行性疾病以及年龄和合并症等因素对认知能力下降的特定特征和轨迹的影响。