Public Health.

Area of Science:

• Neuroscience
• Data Science
• Pharmacology

Background:

• Longitudinal cohort studies are vital for Alzheimer's disease and related disorders (ADRD) research, offering insights into disease mechanisms and drug repurposing.
• Identifying and integrating suitable cohort data for specific research questions remains a significant challenge in ADRD studies.

Purpose of the Study:

• To demonstrate the utility of the Global Alzheimer's Association Interactive Network (GAAIN) platform for discovering and analyzing longitudinal cohort data in ADRD research.
• To showcase how GAAIN facilitates the identification of appropriately powered samples for testing hypotheses related to ADRD prevention and treatment.

Main Methods:

• Utilized the GAAIN federated data sharing platform, the first of its kind for Alzheimer's disease.
• Employed the GAAIN Interrogator tool for remote data aggregation and exploratory analysis across diverse datasets (biomarker, clinical, neuroimaging).
• Demonstrated an end-to-end workflow for data discovery, interrogation, and analysis using GAAIN and associated resources.

Main Results:

• Successfully identified and analyzed data from multiple longitudinal cohort studies relevant to ADRD.
• Showcased the process of leveraging GAAIN's data resources and analytic tools to pinpoint suitable samples for hypothesis testing.
• Presented a practical example of data interrogation and analysis for advancing ADRD research.

Conclusions:

• GAAIN provides a powerful, federated platform for ADRD researchers to access and analyze diverse longitudinal data.
• The demonstrated workflow highlights the potential of GAAIN to accelerate ADRD research by facilitating data utilization.
• Encourages ADRD investigators to adopt GAAIN-facilitated workflows to enhance research efficiency and discovery.