UCSF

Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder that is difficult to study and treat despite decades of progress. This is due to disease heterogeneity, lack of precise phenotyping, and limited understanding of molecular mechanisms underlying clinical manifestations. Electronic medical records (EMR) are emerging as a real-world dataset with abundance of longitudinal human data across diagnoses, medications, and measurements with opportunity to derive insights without predefined selection criteria or limitations in scope. Recent developments of integrative heterogeneous graph databases that combine knowledge across omics relationships provide a means to further identify molecular hypotheses underlying complex clinical phenotypes. We performed deep phenotyping to characterize AD and sex differences in the EMR against a control cohort, and identified sex and AD associated comorbidities, medication use, and lab values. Extending this work to apply machine learning, we utilize clinical information to predict AD onset and identify prioritized genes via knowledge networks (e.g., APOE, ACTB, IL6) and genetic colocalization analysis (e.g., MS4A6A with osteoporosis). Our findings suggest that AD onset risk can be predicted based on clinical data and that there are sex-specific relationships in AD including musculoskeletal disorders among females with AD and neurological or sensory disorders among males with AD. Extensions to knowledge networks and molecular datasets further prioritize genes depending on an individual’s comorbid conditions. By leveraging clinical data to identify hypotheses for complex disease, we can further make steps towards better understanding molecular mechanisms and advance personalized treatment approaches in AD.