Integrating Genetic, Neuroimaging, Transcriptomic, and Clinical Risk Factors as Multivariate Predictors of Cognitive Deterioration in Alzheimer's Disease.

整合遗传、神经影像、转录组和临床风险因素作为阿尔茨海默病认知恶化的多变量预测因子。

• 批准号: 10673857
• 负责人: Jonathan Hess
• 金额: $ 37.6万
• 依托单位: UPSTATE MEDICAL UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10673857
• 关键词: Acceleration; Accounting; Address; Aging; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease risk; Applied Genetics; Automobile Driving; Bioinformatics; Biological; Biology; Blood; Brain; Brain Diseases; Brain imaging; Cardiac; Cardiovascular system; Catalogs; Clinical; Cognition; Cognitive; Complex; Data; Dementia; Deterioration; Diagnosis; Diagnostic; Dimensions; Disease; Disease Progression; Elderly; Functional disorder; Gene Expression; Genes; Genetic; Genetic Risk; Genomics; Genotype; Genotype-Tissue Expression Project; Heart; Human; Impaired cognition; Individual; Institution; Intervention; Investigation; Knowledge; Measurement; Measures; Mediating; Medical Research; Memory; Metabolic; Methods; Modeling; Molecular; Myocardial; National Institute of Neurological Disorders and Stroke; Nerve Degeneration; Neural Network Simulation; Neurobiology; Neuropsychology; Ontology; Outcome; Participant; Pathway interactions; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Pilot Projects; Recovery; Research Institute; Risk; Risk Factors; Scientist; Signal Transduction; Source; Strategic Planning; Structure; Symptoms; System; Testing; Time; Tissue-Specific Gene Expression; Tissues; Training; Transcript; brain based; cardiovascular disorder risk; clinical risk; cognitive performance; cohort; deep neural network; demographics; differential expression; experience; functional disability; gene interaction; gene network; genetic architecture; genome wide association study; improved; indexing; insight; interest; mental state; mild cognitive impairment; multiple data types; nervous system disorder; neural network; neurobiological mechanism; neuroimaging; novel; novel marker; peripheral blood; polygenic risk score; prevent; religious order study; resilience; risk variant; tool; transcriptome; transcriptomics

Over the past decade, scientists have accelerated efforts to better understand Alzheimer’s disease (AD). Much progress has been made in revealing the genetic architecture of AD and its common antecedent, mild cognitive impairment (MCI). Yet, some people who incur excessive AD risk remain cognitively normal. Identifying risk factors for cognitive deterioration in dementia can guide novel investigations into mechanisms underlying resilience to AD. The best-available polygenic risk score for AD explains 1.7% of overall liability independent from the leading risk gene, APOE (accounts for 17.4% of the variance in AD), indicating that a massive portion of genetic liability remains unresolved. Genetic risk for cardiovascular disease contributes additional risk for AD, thus a systems-level investigation into how cardiovascular dysfunction interacts with neurobiological mechanisms of cognitive decline is warranted. Toward this end, we developed a transcriptome-imputation method—the Brain Gene Expression and Network Imputation Engine (BrainGENIE)—to measure the brain transcriptome in living individuals using blood-based gene-expression profiles. BrainGENIE is fundamentally different from other transcriptome-imputation methods, and captures a much larger proportion of the variance in the brain transcriptome. BrainGENIE can predict 9–57% of the brain transcriptome, yielding an approximate 1.8-fold increase in coverage relative to the prior “gold standard” method PrediXcan, and which greatly improves our statistical power to detect genes and pathways associated with disease. We have also generalized our BrainGENIE framework to impute cardiac-specific transcriptome profiles (HeartGENIE), thereby allowing us to investigate brain- and cardiac-specific transcriptome signatures associated with cognitive deterioration in dementia. Our proposal contains three Specific Aims to improve our transcriptome-imputation methods, reveal gene networks and biological pathways in brain and cardiac tissue underlying cognitive impairment in dementia, and accurately predict an individual’s longitudinal cognitive decline pave the way to precisely define individuals who are at risk for or resilient to AD. Aim 1: Optimize our BrainGENIE and HeartGENIE algorithms to improve the accuracy of predicted gene-expression levels for transcripts in the brain and cardiac tissue that are not currently well predicted. Aim 2: Identify transcriptomic signatures of cognitive impairment in dementia with BrainGENIE and HeartGENIE. Aim 3: Develop an neural network to accurately predict cognitive decline longitudinally. This project will identify reveal multivariate risk factors potentially driving cognitive decline, a critical step toward improving diagnosis, intervention, and prevention of AD.

在过去的十年里，科学家们加快了更好地了解阿尔茨海默氏症的努力 疾病（AD）。在揭示AD的遗传结构及其与AD发病的关系方面取得了很大进展， 共同前因，轻度认知障碍（MCI）。然而，有些人谁招致过度 AD风险保持认知正常。识别痴呆症认知恶化的风险因素 可以指导新的调查机制的基础弹性AD。最好的可用 AD的多基因风险评分解释了独立于主要风险的总体责任的1.7% 基因，APOE（占AD变异的17.4%），表明大部分的 遗传责任尚未解决。心血管疾病的遗传风险 AD的额外风险，因此，对心血管功能障碍如何 与认知能力下降的神经生物学机制相互作用。为此， 我们开发了一种转录组填补方法--脑基因表达和网络 插补引擎（BrainGENIE）-使用 基于血液的基因表达谱BrainGENIE从根本上不同于其他 转录组插补方法，并捕捉了更大比例的方差在 脑转录组。BrainGENIE可以预测9-57%的大脑转录组， 相对于先前的“金标准”方法，覆盖率增加约1.8倍 PrediXcan，它大大提高了我们检测基因和途径的统计能力 与疾病有关。我们还概括了我们的BrainGENIE框架， 心脏特异性转录组谱（HeartGENIE），从而使我们能够研究大脑- 和与认知功能减退相关的心脏特异性转录组特征 痴呆我们的建议包含三个具体目标，以改善我们的转录组插补 方法，揭示基因网络和生物途径，在大脑和心脏组织的基础上， 认知功能障碍的痴呆症，并准确预测个人的纵向认知 下降铺平了道路，以精确定义谁是在风险或适应AD的个人。目标1： 优化我们的BrainGENIE和HeartGENIE算法，以提高预测的准确性 基因表达水平的转录本在大脑和心脏组织，目前还不好 预测了目的2：确定痴呆症中认知障碍的转录组学特征， BrainGenie和HeartGenie。目标3：开发一个神经网络来准确预测认知 纵向下降。该项目将识别和揭示潜在的多变量风险因素， 认知能力下降，这是改善AD诊断、干预和预防的关键一步。