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（Neuropsychiatric symptoms，NMPs）是阿尔茨海默病（Alzheimer's disease，AD）的核心特征， 痴呆与日常功能和生活质量的主要不良影响有关，并加速 时间到了制度化。在所有的症状中，抑郁症是患有抑郁症的人最常见的症状。 轻度认知障碍和早期AD。随着病情的发展，焦虑，妄想和幻觉 变得更加普遍，而冷漠是所有阶段中最持久和最频繁的焦虑。 AD. AD-NPS与严重精神疾病（SMIs）有一些共同的临床特征，例如精神分裂症、躁郁症 精神障碍和重度抑郁症，但这些条件是否共享类似的aethiopathies尚不清楚。 考虑到在AD和其他痴呆的背景下不存在可靠的治疗方法， 了解AD和其他神经精神疾病中的神经元凋亡的分子机制和途径 疾病是确定可靠的生物标志物的关键下一步，这些生物标志物可能导致新的治疗方法。 这项建议有两个首要目标。首先，我们将确定分子机制 以及与AD患者中存在AD相关的神经病理学变化。二是 将检查AD和SMI之间是否共享或不同的与AD相关的病理机制。 更具体地说，我们建议使用表型组学和基因组学数据建立多尺度综合模型， 1,264个尸检案例来自一个脑库。该银行包括详细的表型组学数据，如 充分表征的阿尔茨海默病、临床诊断（AD和其他神经退行性或神经精神病学特征）、严重程度 认知能力下降和神经病理学。从每一个案例中，我们将应用创新的 减少与传统方法相关的成本和技术偏差的方法， 表达标签和表观遗传调控元件在单细胞水平。新的深度学习方法 将应用于神经病理变化与遗传标记和功能的多尺度整合 特定细胞类型内的基因组变化（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基因型;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 在单细胞水平上，AD和其他神经精神特征中的特异性标志物，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 使用独立的大规模生物库数据集预测AD-100，其中基因型和电子健康 记录可用。成功完成拟议的研究将产生直接的效用， 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造一个治疗的蓝图， 策略和干预措施来治疗阿尔茨海默病。