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)。在揭示阿尔茨海默病及其相关基因结构方面取得了很大进展。 常见的前驱轻度认知障碍(MCI)。然而，有些人招致了过度的 广告风险在认知上仍然是正常的。确定痴呆症认知恶化的危险因素 可以指导对阿尔茨海默病潜在恢复机制的新研究。最好的-可用 AD的多基因风险分数解释了1.7%的总体负债，独立于主要风险 基因，载脂蛋白E(占AD变异的17.4%)，表明很大一部分 遗传责任仍未解决。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的系统水平调查 与认知衰退的神经生物学机制相互作用是有根据的。为此， 我们开发了一种转录组定位方法--脑基因表达和网络 归罪引擎(BrainGENIE)-用来测量活体个体的大脑转录组 基于血液的基因表达谱。BrainGenie从根本上不同于其他 转录组-归因法，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，产生 与以前的“黄金标准”方法相比，覆盖率增加了约1.8倍 PrediXcan，它极大地提高了我们检测基因和通路的统计能力 与疾病相关的。我们还将我们的BrainGENIE框架概括为 心脏特异转录组图谱(HeartGENIE)，从而使我们能够研究大脑- 和心脏特异转录组特征与认知功能减退有关 痴呆症。我们的提案包含三个具体目标，以改善我们的转录-归因于 方法，揭示大脑和心脏组织中潜在的基因网络和生物通路 痴呆症中的认知障碍，并准确预测个体的纵向认知 下降为准确定义有AD风险或有恢复能力的个人铺平了道路。目标1： 优化我们的BrainGENIE和HeartGENIE算法以提高预测的准确性 大脑和心脏组织转录本的基因表达水平目前还不是很好 预测到了。目的2：确定痴呆患者认知功能障碍的转录特征 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。该项目将确定揭示潜在驱动因素的多元风险因素 认知能力下降，这是改善AD诊断、干预和预防的关键一步。