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