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 尽管随着年龄的增长，阿尔茨海默氏病（AD）的风险增加了 在65岁之前开发符号（约28万美国人）。绝大多数（90％-95％）的EOAD患者没有 在APP或PSEN1/2中具有已知的突变，APOE4载体只有〜50％。与晚发广告（负载）不同， 30-64％的EOAD具有非静态演示，导致诊断遗漏或延迟。尽管很高 EOAD患者的动机和几乎没有合并症，通常被排除在大规模观察中 生物标志物研究（例如ADNI和DIAN）和由于年轻，非敏感性而进行的治疗试验 缺乏或缺乏已知的致病突变。此外，研究表明在EOAD中具有很高的遗传力 缺乏已知突变或APOE4，这表明该人群可能会富含新的遗传风险 因素。淀粉样蛋白和TAU的新兴生物标志物在该人群中尚未系统地表征。 在负载中采取的临床和神经影像措施可能对基线防御和疾病不敏感 EOAD的进展主要涉及非记忆认知域和后皮质 神经变性。为了填补广告研究中的这一空白，我们计划招募并纵向遵循400个淀粉样蛋白宠物 - 正面EOAD受试者符合由于AD或有问题的AD痴呆而符合MCI的NIA-AA标准（包括主要 敏感性，二十二次，语言和视觉演示）和100个年龄匹配的控件。 纵向早期发作的阿尔茨海默氏病研究（Leads）的参与者将接受临床 评估，心理测试，MRI，淀粉样蛋白（[18F] Florbetaben）和Tau（[18F] AV1451）PET，CSF和 抽血，用于收集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和对照组），12个月（EOAD全部 措施；对照 - 仅临床和认知测量值）和24个月（EOAD，除PET以外的所有措施）。 方法将与ADNI和DIAN协调。我们将全面地描述认知，成像和 在EOAD中，生物流体随时间变化，并与ADNI中确定的匹配负载参与者的样本进行比较。 我们将使用机器学习算法来开发EOAD敏感的临床和成像度量 进展。探索目的将应用下一代测序以评估新的遗传危险因素 疾病。该研究还将建立一个EOAD研究站点网络，并为启动 该人群的临床试验。