Integrating Polygenic Risk and Environmental Exposures to Uncover Biological Mechanisms Underlying Dementia in a Diverse Cohort

整合多基因风险和环境暴露来揭示不同人群中痴呆症的生物机制

• 批准号: 10560160
• 负责人: Diane Xue
• 金额: $ 4.83万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-16 至 2025-03-15
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10560160
• 关键词: Address; Aging; Air Pollution; Algorithms; Alzheimer disease prevention; Alzheimer' s Disease; Alzheimer' s disease related dementia; Alzheimer' s disease risk; Ancillary Study; Award; Biological; Biological Markers; Biological Process; Biology; Cause of Death; Cognitive; Computing Methodologies; Data; Data Analyses; Dementia; Developed Countries; Diagnosis; Disease; Dose; Education; Environment; Environmental Exposure; Environmental Risk Factor; Ethnic group; Exposure to; Fellowship; Gene Expression; Genes; Genetic; Genetic Risk; Genetic Variation; Genetic study; Genomics; Goals; Health; Heritability; Impaired cognition; Individual; Intervention; Late Onset Alzheimer Disease; Lead; Location; Longitudinal cohort; Machine Learning; Mediating; Mediator of activation protein; Methods; Modeling; Molecular; Multi-Ethnic Study of Atherosclerosis; Multiomic Data; Neighborhoods; Nerve Degeneration; Neurodegenerative Disorders; Nitrogen Dioxide; Outcome; Parents; Participant; Pathogenesis; Pathway interactions; Pattern; Persons; Pharmacologic Substance; Phase; Policies; Population; Postdoctoral Fellow; Precision therapeutics; Predictive Value; Prevention; Prevention strategy; Proteins; Proteomics; Reporting; Research; Research Personnel; Risk; Risk Factors; Safety; Scoring Method; Socioeconomic Status; Subgroup; Testing; Training; Work; adjudication; ambient air pollution; brain health; career; career development; cohort; data integration; dementia risk; design; differential expression; diverse data; ethnic diversity; fine particles; gene environment interaction; genome wide association study; genomic locus; genomic profiles; hazard; human old age (65+); improved; interest; longitudinal analysis; modifiable risk; multi-ethnic; multiple omics; non-genetic; polygenic risk score; proteomic signature; racial diversity; research and development; resilience; response; risk prediction; social; social cohesion; social health determinants; transcriptomics; treatment strategy

PROJECT SUMMARY Alzheimer’s disease (AD) is a leading cause of death in developed countries. Over 50 genetic loci have been associated with late-onset AD risk, yet we still do not fully understand the disease pathogenesis and have failed to find successful solutions for prevention or treatment of dementia or cognitive decline. AD is influenced by genetic and environmental (social, built, and physical) factors. Understanding the interplay between these genetic and non-genetic factors is crucial to address the underlying biology of the disease. Many studies have focused on identifying either genetic causes or modifiable risk factors associated with AD, and most gene- environment studies of AD have been restricted to single-gene x single-environmental factor studies (primarily focusing on the gene APOE). Few studies have addressed how upstream factors like socioeconomic status and ambient air pollution interact with risk across many genetic locations (polygenic) to influence gene expression and proteomic changes that lead to AD and related dementias. The specific aims of this study are to 1. (F99 phase) determine social, built, and physical environmental variables associated with dementia risk and/or cognitive decline, independent of and modified by polygenic risk and 2. (K00 phase) identify transcriptomic and proteomic signatures that mediate the effect of environmental exposure on dementia/cognitive decline. During my dissertation phase, I will train in polygenic risk score computation, predictive analysis, mixed-effect modeling, and machine learning to characterize the effects of genetic and environmental determinants on AD and related dementias. I will employ polygenic risk score methods that have been designed to improve predictive accuracy in multi-ethnic populations. As a post-doctoral fellow, I will expand my training to multi-omic data integration and analysis to move our understanding of risk factors for AD and related dementias beyond studies of association to an understanding of causal pathways and the biological mediators of environmental exposures. This research will leverage the Multi-Ethnic Study of Atherosclerosis parent and ancillary studies (MESA Neighborhood and Aging, MESA MIND, and MESA Air), a longitudinal cohort unprecedented in its scope of social determinants of health along with dementia adjudication and multi-omic data. This fellowship application aligns with the NIA Strategic Directions for Research Goal D-1 “to determine how genetic, molecular, cellular, and social/environmental factors interact for brain health and neurodegeneration.” As a result of this work, we will have identified upstream (policy-level) and downstream (biological mechanisms) points of intervention and prevention. In addition, the research and career development provided by this award will help me launch my career as an independent investigator of AD prediction and prevention.

项目概要 阿尔茨海默病 (AD) 是发达国家的主要死亡原因。超过 50 个基因位点已被证实。 与迟发性 AD 风险相关，但我们仍然不完全了解该疾病的发病机制，并且 未能找到预防或治疗痴呆症或认知能力下降的成功解决方案。 通过遗传和环境（社会、建筑和物理）因素了解这些因素之间的相互作用。 许多研究表明，遗传和非遗传因素对于解决该疾病的生物学基础至关重要。 重点是确定与 AD 相关的遗传原因或可改变的危险因素，并且大多数基因- AD 的环境研究仅限于单基因 x 单环境因素研究（主要是 很少有研究关注社会经济地位等上游因素。 环境空气污染与许多遗传位置（多基因）的风险相互作用，从而影响基因 导致 AD 和相关痴呆症的表达和蛋白质组变化本研究的具体目的是。 1.（F99阶段）确定与痴呆风险相关的社会、建筑和物理环境变量 和/或认知能力下降，独立于多基因风险并受其影响；2.（K00 阶段）确定 介导环境暴露影响的转录组学和蛋白质组学特征 在我的论文阶段，我将接受多基因风险评分计算方面的培训。 预测分析、混合效应建模和机器学习来表征遗传和基因的影响 我将采用多基因风险评分方法来研究 AD 和相关痴呆症的环境决定因素。 作为一名博士后研究员，我将旨在提高多种族人群的预测准确性。 将我的培训扩展到多组学数据集成和分析，以加深我们对 AD 风险因素的理解 以及相关的痴呆症，超出了对因果路径的理解和相关研究的范围 这项研究将利用多种族研究。 动脉粥样硬化母体和辅助研究（MESA Neighborhood and Aging、MESA MIND 和 MESA Air） 纵向队列的健康社会决定因素以及痴呆症的范围是前所未有的 该奖学金申请符合 NIA 的战略方向。 研究目标 D-1“确定遗传、分子、细胞和社会/环境因素如何相互作用 这项工作的结果是，我们将确定上游（政策层面） 此外，还有下游（生物机制）点的干预和预防。 该奖项提供的职业发展将帮助我开启我作为独立调查员的职业生涯 AD预测和预防。