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

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.