Molecular studies of Alzheimer's disease (AD) implicate potential links between autoimmunity and AD, but the underlying clinical relationships between these conditions remain poorly understood. Electronic health records (EHRs) provide an opportunity to determine the clinical risk relationship between autoimmune disorders and AD and understand whether specific disorders and disorder subtypes affect AD risk at the phenotypic level in human populations. We evaluated relationships between 26 autoimmune disorders and AD across retrospective observational case-control and cohort study designs in the EHR systems at UCSF and Stanford. We quantified overall and sex-specific AD risk effects that these autoimmune disorders confer. We identified significantly increased AD risk in autoimmune disorder patients in both study designs at UCSF and at Stanford. This pattern was driven by specific autoimmunity subtypes including endocrine, gastrointestinal, dermatologic, and musculoskeletal disorders. We also observed increased AD risk from autoimmunity in both women and men, but women with autoimmune disorders continued to have a higher AD prevalence than men, indicating persistent sex-specificity. This study identifies autoimmune disorders as strong risk factors for AD that validate across several study designs and EHR databases. It sets the foundation for exploring how underlying autoimmune mechanisms increase AD risk and contribute to AD pathogenesis.

The authors have declared no competing interest.

This research was funded by NIA R01AG060393, NIAMS P30 AR070155, F30 Fellowship 1F30AG079504-01, and the UCSF Discovery Fellows. Through the use of the UCSF Information Commons and UCSF Research Analysis Environment computational research platforms, the project was supported by the National Center for Advancing Translational Sciences, National Institutes of Health, through UCSF-CTSI Grant Number UL1 TR001872. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the NIH.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All analysis of University of California, San Francisco and Stanford University electronic health record data was performed under the approval of the Institutional Review Boards from University of California, San Francisco and Stanford University, respectively. All clinical data were de-identified and written informed consent was waived by the institutions.

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Individual patient data is not publicly available due to patient data sharing privacy. Code not limited by patient data sharing permissions can be found at https://github.com/gramey02/AD_AID_Project. All patient and demographic data curation from the UCSF and Stanford EHR systems was performed using Microsoft SQL server and the DBI (v1.1.3) and odbc (1.3.4) packages in R. Discovery data was last curated from the UCSF OMOP database on August 4th, 2023, and validation data was last curated from the Stanford OMOP database on December 12th, 2023. Data cleaning, matching, and analysis steps were conducted using R version 4.1.3, and plots were created with the ggplot2 package (v3.4.2). Values for all data points in graphs are reported in the Supporting Data Values file.

https://github.com/gramey02/AD_AID_Project