Approved research

Understanding Alzheimer's Disease in the Context of the Aging

Lay summary

Alzheimer's disease (AD) is a complex health disorder, with many biological pathways involved in it. Some of these pathways can be shared between AD and physiological aging. For example, decline in brain resilience (ability to recover, e.g., after damage) is typical of both normal aging and AD; however, in AD such decline is more severe and contributes to AD pathology. The objective of this project is to significantly improve our understanding of the shared genetic mechanisms between aging and AD, and as result suggest new genetic targets for AD prevention/treatment. To fulfil this objective, we will analyze data that have been collected in several large human studies of aging and health, including the UK Biobank containing rich genetic and phenotypic information on thousands of individuals with AD and related traits. The following Aims will be addressed: In Aim 1, we will select genetic variants associated with both AD and aging-related traits and estimate their collective effects on AD risk and survival. We will also describe biological pathways enriched for respective genes and suggest mechanisms connecting these pathways with AD. In Aim 2, we will focus on candidate genes representing the aging-related pathways involved in resilience, and estimate their joint influence on AD risk and survival, to find combinations of the genes that show protective effects against AD, and may also oppose the aging-related decline in brain resilience to damage. In Aim 3, we will further validate the findings of Aims 1-2 using biomarkers of AD pathology available in data, and explore causal relationships between selected genes and AD traits, using Mendelian Randomization and related approaches, to get deeper insights into biological mechanisms of the observed genetic associations. Results of this study will significantly improve our understanding of the etiology of AD and common genetic factors involved in AD and physiological aging, and will help find new genetic targets for personalized prevention of AD and interventions into the brain aging.

Scope extension:

This project objective is to significantly improve our understanding of the heterogeneity of Alzheimer's disease (AD) and  shared genetic mechanisms between normal aging and AD; identify genetic factors that may protect against the age-associated declines in brain resilience and resistance to AD, and validate the candidate genetic targets for AD prevention using preclinical biomarkers of AD pathology. To address this objective, we will analyze genetic and phenotypic information collected in several large human studies, including the UK Biobank. Specific Aims: Aim 1. Identify new pleiotropic genetic variants that individually influence both aging- and AD-related traits, and evaluate their joint impacts on the AD risk and survival/longevity. Aim 2. Explore shared mechanisms between aging and AD, and evaluate the collective effects of genes from major aging-related pathways on AD and longevity.  Aim 3.  Preclinical validation of candidate genetic targets selected in Aims 1, 2, using biomarkers of AD pathology, and further exploration of mechanisms of genetic associations. To validate sets of genetic variants that influenced AD risk and/or survival in Aims 1 and 2, we will estimate their joint effects on preclinical markers of AD pathology, such as hippocampal volume, CSF and metabolic biomarkers, considering other covariates.

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As requested, herewith we submit a Scope Extension for this application, to cover additional analyses of gene x environment interactions (GxE) for neurodegenerative diseases and relevant biomarkers and phenotypes.  Examples include  analyses of interactions between indicators of exposure to air pollution (as well as other health-related exposures  and phenotypes) and candidate genetic variants, and their associations with AD and related traits, such as preclinical biomarkers of AD pathology (e.g., hippocampal volume), among others.

Note that in the approved application, we listed many of the abovementioned factors and phenotypes as covariates (such as gender, race, education,  exposure to air pollution, and smoking, among others). Examples of the other covariates that may  also be used in the analyses  include comorbidities, treatments, and other risk factors relevant to Alzheimer's disease. We  have Tier3 data approved for this project.