Early Onset AD Consortium - the LEAD Study (LEADS)

早发性 AD 联盟 - LEAD 研究 (LEADS)

基本信息

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
项目类别：
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&apos s Disease Alzheimer&apos 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

项目摘要

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.

项目概要虽然患阿尔茨海默病 (AD) 的风险随着年龄的增长而增加，但大约 5% 的 AD 患者 65 岁之前出现症状（约 280,000 名美国人）绝大多数 (90%-95%) EOAD 患者不会出现症状。 APP 或 PSEN1/2 存在已知突变，并且只有约 50% 是 APOE4 携带者，与迟发性 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。 EOAD 患者有积极性且几乎没有合并症，因此通常被排除在大规模观察之外由于年龄小、非记忆删除，因此进行生物标志物研究（例如 ADNI 和 DIAN）和治疗试验此外，研究表明 EOAD 具有高遗传力。不存在已知突变或 APOE4，表明该人群可能因新的遗传风险而丰富淀粉样蛋白和 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 的进展，主要涉及非记忆认知域和后皮质为了填补 AD 研究的这一空白，我们计划招募并纵向跟踪 400 名淀粉样蛋白 PET- 阳性 EOAD 受试者符合因 AD 或可能的 AD 痴呆（包括原发性痴呆）导致的 MCI NIA-AA 标准记忆删除、执行障碍、语言和视觉空间表现）和 100 名年龄匹配的对照。纵向早发性阿尔茨海默病研究 (LEADS) 的参与者将接受临床研究评估、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)。将在三个时间点对患者进行评估——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 之外的所有测量）。方法将与 ADNI 和 DIAN 相协调，我们将全面描述认知、成像和诊断的特征。 EOAD 中的生物流体随时间变化，并与 ADNI 中确定的 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 的敏感临床和影像测量探索性目标进展将应用下一代测序来评估新的遗传风险因素该研究还将建立一个 EOAD 研究地点网络，并为启动 EOAD 奠定基础。对该人群进行的临床试验。