Identifying Groups of Alzheimer's Disease Patients with Slower Disease Progression

Project Summary

We aim to identify Alzheimer's disease subtypes with different health trajectories. First, we will use machine learning to cluster Alzheimer's disease patients based on the results of common lab tests such as blood draws and MRIs. We will use several datasets to ensure that the identified clusters are broadly applicable. Next, we will link genetic variants to each cluster so that we can predict the odds of a person belonging to a given cluster based solely on their DNA. We will assess the genetic basis of each cluster to evaluate the original classifications that were made based on common lab tests. After we are confident that we have identified Alzheimer's disease clusters with unique genetic signatures, we will determine the rate at which Alzheimer's disease progresses in people belonging to each cluster. We will also evaluate the age at onset and other health markers that may precede the development of Alzheimer's disease within each cluster. Currently, most Alzheimer's disease research focuses on Alzheimer's disease as a single disease with many different causes, although some distinct clusters have recently emerged that indicate Alzheimer's disease may be more complex than previously thought. Our research takes an innovative statistical approach to determine if the different causes of Alzheimer's disease result in different disease trajectories that may require a different mode of treatment. We anticipate that this research will improve our understanding of Alzheimer's disease, provide patients and caregivers with more information about the trajectory of their specific Alzheimer's disease subtype, and facilitate the future development of subtype-specific treatments.