PROJECT SUMMARY
Alzheimer’s disease (AD) is a debilitating neurodegenerative condition, and its pathogenesis and progression
remain poorly understood. With few significant disease-modifying treatments available for affected patients and
rising AD prevalence worldwide, there is a high need for new research studying AD pathogenesis and risk
factors outside of canonical protein pathophysiological mechanisms. AD has a strong immune component, as
many genetic risk variants and protein pathways involved in both AD and the immune system have been
studied at the molecular level. However, there has been little research at the clinical population level to
determine if significant immune dysfunction plays a role in AD risk and development over time. In this
proposal, I will use real-world clinical data from electronic health record (EHR) databases to determine
whether patients with autoimmune diseases exhibit increased AD risk, as autoimmune diseases are
conditions involving substantial immune dysfunction and are often diagnosed in individuals earlier in the
lifespan, prior to AD symptomatology. Autoimmune diseases share several characteristics with AD, including
higher prevalence in women compared to men and prevalence inequity across different racial and ethnic
groups, potentially suggesting shared disease mechanisms or social factors and interaction between the two
conditions. To understand whether autoimmune diseases exacerbate or attenuate the risk of AD across diverse
human populations, I will quantify the clinical risk relationship between autoimmune diseases and AD overall
across all patients, across sexes, and in several self-reported race and ethnicity groups in the UCSF EHR
system. I will also assess whether certain autoimmune subtypes or specific autoimmune diseases are
associated with higher AD risk and whether AD risk is influenced by treatments for autoimmune disease. I
hypothesize that there will be significantly increased AD risk in autoimmune patients compared to controls
across several population and disease stratifications in my EHR study groups, with few to no autoimmune
diseases conferring protection from AD. To evaluate the robustness of my findings in the UCSF EHR system, I
will assess the similarity of risk association results across two retrospective observational study designs, a
case-control and cohort study. To validate externally I will additionally perform the same analyses in the larger
University of California Health Data Warehouse (UCHDW) system. The key innovations of this proposed work
will be extensively characterizing the effect of immune dysfunction on AD risk at the individual phenotypic level
in diverse human populations. This could indicate potential neuroimmune interactions that may play a role in
AD pathogenesis and inspire drug repurposing and treatment development in the future.