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.