Understanding the molecular mechanisms that contribute to neuropsychiatric symptoms in Alzheimer Disease

了解导致阿尔茨海默病神经精神症状的分子机制

• 批准号: 10651757
• 负责人: STEVEN M FINKBEINER
• 金额: $ 183.32万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10651757
• 关键词: Acceleration; Adverse effects; Agitation; Alzheimer' s Disease; Alzheimer' s disease patient; Alzheimer' s disease related dementia; Autopsy; Biological; Biological Assay; Biological Markers; Biological Models; Bipolar Disorder; Brain; Caregiver Burden; Cell Nucleus; Cells; Clinical; Clinical Trials; Data; Data Set; Delusions; Dementia; Development; Diagnosis; Disease; Disease Progression; Electronic Health Record; Elements; Enhancers; Epigenetic Process; Epitopes; Factor Analysis; Gene Expression; Gene Expression Profile; Gene Expression Profiling; Genes; Genetic Enhancer Element; Genetic Markers; Genetic Variation; Genomics; Genotype; Goals; Hallucinations; Human; Impaired cognition; Institutionalization; Intervention; Link; Machine Learning; Major Depressive Disorder; Measurement; Medicine; Mental Depression; Methods; Modeling; Molecular; Nerve Degeneration; Nuclear; Outcome; Output; Pathology; Pathway interactions; Patients; Pattern; Persons; Phenotype; Prefrontal Cortex; Prognosis; Quality of life; Regulatory Element; Research; Resolution; Sampling; Schizophrenia; Severities; Slide; Structure; Symptoms; Testing; Therapeutic; Time; Tissues; Variant; Weight; associated symptom; biobank; brain tissue; caregiving; cell type; clinical application; clinical diagnosis; clinical predictors; cohort; cost; daily functioning; deep learning; epigenome; epigenomic profiling; epigenomics; functional genomics; genetic risk factor; genetic variant; genomic data; high dimensionality; indexing; innovation; interest; learning strategy; mild cognitive impairment; molecular marker; neuropathology; neuropsychiatric symptom; neuropsychiatry; novel; novel therapeutics; patient stratification; phenomics; phenotypic biomarker; potential biomarker; predictive modeling; prognostic model; severe mental illness; therapeutically effective; trait; transcriptome; transcriptomics; translational potential

PROJECT SUMMARY Neuropsychiatric symptoms (NPS) are core features of Alzheimer's disease (AD) and related dementias that are associated with major adverse effects on daily function and quality of life, and accelerate time to institutionalization. Of all the NPS, depression is the most frequently observed symptom in people with mild cognitive impairment and early AD. As the disease progresses, agitation, delusions and hallucinations become more common, whereas apathy is the most persistent and frequent NPS throughout all the stages of AD. AD-NPS share some clinical features with serious mental illnesses (SMIs), such as schizophrenia, bipolar disorder and major depressive disorder, but whether these conditions share similar aethiopathies is unclear. Given that reliable treatments for NPS in the context of AD and other dementias do not exist, a better understanding of the molecular mechanisms and pathways underlying NPS in AD and other neuropsychiatric illnesses is a critical next step to identify reliable biomarkers that could lead to novel therapeutics. There are two overarching goals of this proposal. First, we will identify the molecular mechanisms and neuropathological changes that are associated with the presence of NPS in patients with AD. Second, we will examine if the mechanisms of pathology associated with NPS are shared or distinct among AD and SMIs. More specifically, we propose to build multi-scale integrative models using phenomics and genomics data from 1,264 autopsy cases derived from a single brain bank. The bank includes detailed phenomics data such as well characterized NPS, clinical diagnosis (AD and other neurodegenerative or neuropsychiatric traits), severity of cognitive decline and neuropathology for each patient sample. From each case, we will apply innovative approaches that reduce the cost and technical biases associated with conventional methods, and capture gene expression signatures and epigenetic regulatory elements at the single-cell level. Novel deep-learning methods will be applied for the multi-scale integration of neuropathologic changes with genetic markers and functional genomic changes (such as changes in gene expression and enhancer sequences) within specific cell types, to predict various NPS in AD and other neuropsychiatric traits; we refer to these integrative models as genotype- marker-phenotype models. We expect that these models will enable us to assign genotypes and molecular markers to specific NPS within AD and other neuropsychiatric traits at the single-cell level, an unprecedented level of resolution. In addition, we will test the translational potential of the genotype-marker-phenotype models to predict AD-NPS using independent large-scale biobank datasets, in which genotypes and electronic health records are available. Successful completion of the proposed studies will have immediate utility by generating potential biomarkers for NPS diagnosis and prognosis and by providing predictive models for patient stratification in clinical trials. In the longer term, our models will help us create a blueprint for therapeutic strategies and interventions to treat NPS in AD.

项目摘要 神经精神症状（NP）是阿尔茨海默氏病（AD）和相关的核心特征 痴呆与对日常功能和生活质量的重大不利影响相关的痴呆症，并加速 制度化的时间。在所有NP中，抑郁症是患有患者的最常观察到的症状 轻度认知障碍和早期广告。随着疾病的发展，躁动，妄想和幻觉 变得更加普遍，而冷漠是在所有阶段中最持久和频繁的NP 广告。 AD-NP与严重的精神疾病（SMI）共享一些临床特征，例如精神分裂症，双极 疾病和重度抑郁症，但是这些疾病是否具有类似的嗜心性疾病尚不清楚。 鉴于不存在在AD和其他痴呆症的背景下对NP的可靠治疗 了解AD和其他神经精神病学中NP的分子机制和途径 疾病是识别可靠的生物标志物，可能导致新型治疗剂的关键下一步。 该提案有两个总体目标。首先，我们将确定分子机制 与AD患者中NPS存在相关的神经病理学变化。第二，我们 将检查与NP相关的病理机制是否在AD和SMI中共享或独特。 更具体地说，我们建议使用现象学和基因组学数据构建多尺度的集成模型 1,264例尸检病例来自单个大脑库。该银行包括详细的现象学数据，例如 良好的NP，临床诊断（AD和其他神经退行性或神经精神特征），严重程度 每个患者样本的认知能力下降和神经病理学。从每种情况下，我们都将应用创新 减少与常规方法相关的成本和技术偏见并捕获基因的方法 单细胞水平的表达特征和表观遗传调节元件。新颖的深度学习方法 将应用于神经病理学变化与遗传标记和功能的多尺度整合 特定细胞类型中的基因组变化（例如基因表达和增强子序列的变化） 预测AD和其他神经精神特征的各种NP；我们将这些综合模型称为基因型 标记 - 表型模型。我们希望这些模型将使我们能够分配基因型和分子 在单细胞级别的AD和其他神经精神特征中的特定NP的标记，这是前所未有的 分辨率水平。此外，我们将测试基因型标记 - 表型模型的翻译潜力 使用独立的大型生物库预测AD-NP，其中基因型和电子健康 记录可用。成功完成拟议的研究将通过产生立即实用 NPS诊断和预后的潜在生物标志物，并通过为患者提供预测模型 临床试验中的分层。从长远来看，我们的模型将帮助我们创建用于治疗的蓝图 在AD中处理NP的策略和干预措施。