Exploring Common Biological Pathways Underlying Insulin Resistance and Alzheimer Disease using Genetic and Omic Tools

项目概要/摘要 胰岛素抵抗（IR）是阿尔茨海默病（AD）的主要危险因素，但IR的机制 易患AD的原因尚不清楚，特别是IR是否与AD有因果关系，以及哪些调节机制 是IR的基础，也是AD的原因。该提案旨在解决科学领域的这些关键差距， 利用组学方法评价IR与AD的因果关系，揭示IR与AD的新的调控机制， 参与IR和AD的机制。主要研究者（PI）Sarnowski博士是一位统计遗传学家， 早期职业调查员，研究重点是识别遗传和环境风险因素 复杂的特征。该项目的长期目标是确定将从治疗中受益的个体 改善胰岛素敏感性，以更好地预防、延缓或阻止AD的进展。总体目标是 更好地表征IR导致AD的机制，并评估它们与已知的 参与AD发病机制。核心假设是，组学将有助于更好地理解 并描述了IR和AD之间的关系。其基本原理是，组学将有助于解开 IR对AD的作用方式并确定预防和治疗干预措施的目标。以强为导 在心脏病研究的初步结果中，该假设将通过三个具体目标进行测试：1） 在孟德尔随机化（MR）框架中确定IR是否与AD有因果关系， 仪器变量（IV）; 2）使用脑和血液表征与AD相关的IR的分子特征 组学数据;和3）开发联合测试以评估与AD相关的IR特征的遗传贡献。 在目标1中，遗传IV，包括标准和途径特异性遗传风险评分和预测因子， 机器学习，将被构造为使用各种MR评估IR和AD之间的因果关系 方法.在目标2中，将进行关联分析以识别IR的脑和血液组学特征 与AD有关在目标3中，将开发利用注释的新的综合统计方法， IR和AD相关基因座对组学的遗传贡献。职业发展活动将包括培训 在AD病理生理学，多组学分析和机器学习技术通过课程，研讨会， 指导，并与领先的专家科学家团队合作。这种方法是创新的， 以组学为重点，研究IR和AD的调控机制。所提出的研究是有意义的 因为预期结果将有助于更好地理解如何改善胰岛素敏感性， 更好地预防、延迟或阻止AD的进展，减少认知能力下降和痴呆症的患病率， AD，促进晚年大脑健康。在实现这一赠款目标方面取得的经验将有助于 PI的职业生涯，一个独立的统计遗传学家与生物信息学专业知识，剖析如何血管风险， 使用大规模组学，与基因组学相互作用的因素影响AD和痴呆症易感性。

Project Summary / Abstract Insulin resistance (IR) is a major risk factor for Alzheimer’s disease (AD) but the mechanisms by which IR predisposes to AD is unknown, notably, if IR is causally related to AD and which regulatory mechanisms underlie IR and contribute to AD. The proposal is designed to address these critical gaps in scientific knowledge by using omics to evaluate the causal relationship of IR on AD and to reveal new regulatory mechanisms involved in IR and AD. The Principal Investigator (PI), Dr. Sarnowski, is a statistical geneticist and an early career investigator with a research focus on the identification of genetic and environmental risk factors of complex traits. The long-term goal of this project is to identify individuals who will benefit from treatments improving insulin sensitivity to better prevent, delay or stop the progression of AD. The overall objective is to better characterize mechanisms by which IR contributes to AD and evaluate how they may differ from known mechanisms involved in AD pathogenesis. The central hypothesis is that omics will help to better understand and characterize the relationships between IR and AD. The rationale is that omics will help to disentangle the mode of action of IR on AD and identify targets for preventive and therapeutic interventions. Guided by strong preliminary results in the Framingham Heart Study, the hypothesis will be tested through three specific aims: 1) Determine if IR is causally related to AD in a Mendelian Randomization (MR) framework with various genetic instrument variables (IVs); 2) Characterize molecular signatures of IR associated with AD using brain and blood omic data; and 3) Develop a joint test to evaluate the genetic contribution at IR signatures associated with AD. In Aim 1, genetic IVs, including standard and pathway-specific genetic risk scores and predictors identified by machine learning, will be constructed to evaluate the causal relationships between IR and AD using various MR methods. In Aim 2, association analyses will be performed to identify brain and blood omic signatures of IR related to AD. In Aim 3, new integrative statistical methods leveraging annotations will be developed to evaluate the genetic contribution on omics at loci involved in IR and AD. Career development activities will include training in AD pathophysiology, multi-omic analysis and machine learning techniques through coursework, seminars, mentorship, and collaborations with a team of leading expert scientists. The approach is innovative by shifting focus to omics to study the regulatory mechanisms involved in IR and AD. The proposed research is significant as the expected outcomes will contribute to a better understanding of how insulin sensitivity can be improved to better prevent, delay or stop the progression of AD, reduce cognitive decline and prevalence of dementia due to AD, and promote brain health in late life. The experience acquired in achieving the aims of this grant will advance the PI’s career to an independent statistical geneticist with bioinformatics expertise, to dissect how vascular risk factors interact with genomics to influence AD and dementia susceptibility using large-scale omics.