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.

在过去的十年中，科学家们加快了努力，以更好地了解阿尔茨海默病 疾病（AD）。在揭示 AD 的遗传结构及其相关疾病方面已经取得了很大进展 共同前因，轻度认知障碍（MCI）。然而，有些人却因过度的 AD 风险在认知上仍然正常。识别痴呆症认知恶化的危险因素 可以指导对 AD 恢复机制的新研究。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%），表明很大一部分 遗传责任仍未得到解决。心血管疾病的遗传风险 AD 的额外风险，因此需要对心血管功能障碍如何进行系统级研究 与认知能力下降的神经生物学机制相互作用是有道理的。为了这个目的， 我们开发了一种转录组插补方法——大脑基因表达和网络 插补引擎 (BrainGENIE) — 使用以下方法测量活体个体的大脑转录组 基于血液的基因表达谱。 BrainGENIE 与其他产品有着根本的不同 转录组插补方法，并捕获更大比例的方差 脑转录组。 BrainGENIE 可以预测 9-57% 的大脑转录组，从而产生 相对于之前的“黄金标准”方法，覆盖范围增加约 1.8 倍 PrediXcan，极大地提高了我们检测基因和通路的统计能力 与疾病有关。我们还推广了 BrainGENIE 框架来估算 心脏特异性转录组图谱（HeartGENIE），从而使我们能够研究大脑 和与认知恶化相关的心脏特异性转录组特征 失智。我们的提案包含三个具体目标来改进我们的转录组插补 方法，揭示大脑和心脏组织中的基因网络和生物途径 痴呆症的认知障碍，并准确预测个体的纵向认知 下降为准确定义有 AD 风险或具有抵抗力的个体铺平了道路。目标 1： 优化我们的 BrainGENIE 和 HeartGENIE 算法，以提高预测的准确性 目前大脑和心脏组织中转录物的基因表达水平不佳 预测。目标 2：识别痴呆症认知障碍的转录组特征 BrainGENIE 和 HeartGENIE。目标 3：开发神经网络来准确预测认知 纵向下降。该项目将识别揭示潜在驱动因素的多元风险因素 认知能力下降是改善 AD 诊断、干预和预防的关键一步。