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

 描述(由申请人提供)：回答PA-13-168(对现有数据集和存储的生物样本进行二次分析以解决临床衰老研究问题(R01))。尽管生物标记物在轻度认知障碍(MCI)研究中的应用越来越复杂，但在描述认知方面的复杂程度并不相称。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分数、筛查措施、评定量表和临床判断，导致对正在研究的MCI的类型或严重程度的粗略描述，尽管此类研究提供了丰富的神经心理学数据。我们建议应用我们的新的精算、神经心理学和统计学方法来更准确地诊断MCI并预测其进展。将这些方法应用于现有的大规模开源(ADCS donpezil Trial，ADNI，NACC/UDS)和机构(FHS，MCSA，WHICAP)数据集，将揭示生物标记物、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。我们的方法将改进为临床试验提供能量分析的效果大小，并减少此类试验所需的患者数量。最后，我们的方法将被用来改进NIA-AA对临床前AD患者“微妙认知功能下降”的操作定义。这些改进将对未来生物标记物和临床试验研究的前瞻性设计产生重要影响。具体目的：目的1.MCI诊断的精算神经心理学标准将更好地明确认知表型，并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)病例和增加假阴性(即，“遗漏”)病例将加强生物标志物和几项大规模研究的试验结果。目的2.与传统的MCI诊断标准相比，经验性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)。目的3.基于上述神经心理学MCI标准的扩展，对细微认知功能减退的可操作性定义将改善NIA-AA标准对“临床前”AD的定性。目的4.在探索性分析中，我们将使用新的计算工具来协调和结合1)认知和2)多标记轮廓预测多个数据集的进展/病理。MCI和临床前AD诊断精度的提高将对未来遗传学、生物标记物、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记物的变化和神经退行性变，以及年龄和合并症等因素对认知下降的特定轮廓和轨迹的影响。

 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.