Early Onset AD Consortium - the LEAD Study (LEADS)

早发性 AD 联盟 - LEAD 研究 (LEADS)

• 批准号: 9912388
• 负责人: LIANA G APOSTOLOVA
• 金额: $ 247.33万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-30 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9912388
• 关键词: Age; Alzheimer' s Disease; Alzheimer' s disease risk; American; Amyloid; Amyloid beta-Protein; Apolipoprotein E; Atrophic; Biological Markers; Blood; Brain; Cerebrospinal Fluid; Characteristics; Clinical; Clinical Research; Clinical Trials; Clinical assessments; Cognitive; Collection; Comorbidity; DNA; Data; Dementia; Diagnosis; Disease; Disease Progression; Elderly; Episodic memory; Future; Genes; Genetic; Genotype; Goals; Heritability; Image; Impairment; Individual; Inflammation; Inherited; Language; Late Onset Alzheimer Disease; Lead; Lipids; Liquid substance; Magnetic Resonance Imaging; Measures; Medial; Memory; Methods; Mutation; Nerve Degeneration; Observational Study; Outcome; Outcome Measure; Participant; Pathogenicity; Pathway interactions; Patients; Performance; Peripheral Blood Mononuclear Cell; Phenotype; Plasma; Population; Positron-Emission Tomography; Presenile Alzheimer Dementia; Primary Progressive Aphasia; Procedures; Psychometrics; RNA; Research; Sampling; Signal Transduction; Site; Symptoms; Syndrome; Testing; Therapeutic Trials; Thinking; Time; Variant; Visuospatial; Work; age related; biomarker development; cerebral atrophy; clinical biomarkers; clinical phenotype; cognitive function; cohort; design; disorder control; genetic risk factor; gray matter; imaging biomarker; innovation; machine learning algorithm; meetings; neuroimaging; next generation sequencing; novel; patient population; presenilin-1; recruit; risk variant; serial imaging; tau Proteins; treatment trial

Project Summary While the risk of Alzheimer’s disease (AD) increases with advancing age, approximately 5% of AD patients develop symptoms before age 65 (~280,000 Americans). The vast majority (90%-95%) of EOAD patients do not have a known mutation in APP or PSEN1/2, and only ~50% are APOE4 carriers. Unlike late-onset AD (LOAD), 30-64% of EOAD have non-amnestic presentations, leading to missed or delayed diagnosis. Despite being highly motivated and having few comorbidities, EOAD patients are commonly excluded from large scale observational biomarker studies (e.g. ADNI and DIAN) and therapeutic trials due to their young age, non- amnestic deficits, or absence of known pathogenic mutations. Furthermore, studies suggest high heritability in EOAD in the absence of known mutations or APOE4, signifying that this population may be enriched for novel genetic risk factors. Emerging biomarkers of amyloid and tau have not been systematically characterized in this population. Clinical and neuroimaging measures employed in LOAD may be insensitive to baseline deficits and disease progression in EOAD, which predominantly involve non-memory cognitive domains and posterior cortical neurodegeneration. To fill this gap in AD research, we plan to recruit and longitudinally follow 400 amyloid PET- positive EOAD subjects meeting NIA-AA criteria for MCI due to AD or probable AD dementia (including primary amnestic, dysexecutive, language and visuospatial presentations) and 100 age-matched controls. Participants in the Longitudinal Early-onset Alzheimer’s Disease Study (LEADS) will undergo clinical assessments, psychometric testing, MRI, amyloid ([18F]Florbetaben) and tau ([18F]AV1451) PET, CSF and blood draw for collection of DNA, RNA, plasma, serum and peripheral blood mononuclear cells (PBMC). Patients will be assessed at three time points – baseline (both EOAD and controls), 12 months (EOAD all measures; controls – clinical and cognitive measures only) and 24 months (EOAD, all measures except PET). Methods will be harmonized with ADNI and DIAN. We will comprehensively characterize cognitive, imaging and biofluid changes over time in EOAD, and compare to a matched sample of LOAD participants identified in ADNI. We will employ machine learning algorithms to develop sensitive clinical and imaging measures of EOAD progression. An exploratory aim will apply next generation sequencing to assess for novel genetic risk factors for disease. The study will also establish a network of EOAD research sites and set the stage for the launch of clinical trials in this population.

项目摘要 虽然阿尔茨海默病（AD）的风险随着年龄的增长而增加，但大约5%的AD患者 在65岁之前出现症状（约28万美国人）。绝大多数（90%-95%）EOAD患者没有 在APP或PSEN 1/2中有已知的突变，只有约50%是APOE 4携带者。与晚发型AD（LOAD）不同， 30-64%的EOAD具有非遗忘性表现，导致漏诊或延迟诊断。尽管高度 有动机且合并症很少，EOAD患者通常被排除在大规模观察性研究之外。 生物标志物研究（例如ADNI和DIAN）和治疗试验，由于其年龄较小，非遗忘 缺陷或不存在已知的致病性突变。此外，研究表明EOAD的高遗传性， 不存在已知的突变或APOE 4，这意味着该人群可能富含新的遗传风险 因素淀粉样蛋白和tau蛋白的新兴生物标志物尚未在该人群中进行系统表征。 LOAD中采用的临床和神经影像学测量可能对基线缺陷和疾病不敏感 EOAD进展，主要涉及非记忆认知领域和后皮质 神经变性为了填补AD研究中的这一空白，我们计划招募并纵向跟踪400名淀粉样蛋白PET- 符合NIA-AA MCI标准的阳性EOAD受试者，由于AD或可能的AD痴呆（包括原发性 健忘症，执行障碍，语言和视觉空间呈现）和100名年龄匹配的对照。 早发性阿尔茨海默病纵向研究（LEADS）的参与者将接受临床检查。 评估、心理测试、MRI、淀粉样蛋白（[18 F]Florbetaben）和tau（[18 F] AV 1451）PET、CSF和 抽血以收集DNA、RNA、血浆、血清和外周血单核细胞（PBMC）。 患者将在三个时间点进行评估-基线（EOAD和对照）、12个月（EOAD所有 测量;对照-仅临床和认知测量）和24个月（EOAD，除PET外的所有测量）。 方法将与ADNI和DIAN协调。我们将全面描述认知，成像和 EOAD中生物流体随时间的变化，并与ADNI中识别的LOAD参与者的匹配样本进行比较。 我们将采用机器学习算法来开发EOAD的敏感临床和成像措施 进展一个探索性的目标将应用下一代测序来评估新的遗传风险因素 疾病。这项研究还将建立一个EOAD研究网站网络，并为推出 在这个人群中进行临床试验。