Leveraging Electronic Medical Records and Knowledge Networks to Predict Disease Onset and Gain Biological Insight Into Alzheimer’s Disease

Early identification of Alzheimer’s Disease (AD) risk can aid in interventions before disease progression. We demonstrate that electronic health records (EHRs) combined with heterogeneous knowledge networks (e.g., SPOKE) allow for (1) prediction of AD onset and (2) generation of biological hypotheses linking phenotypes with AD. We trained random forest models that predict AD onset with mean AUROC of 0.72 (-7 years) to .81 (-1 day). Top identified conditions from matched cohort trained models include phenotypes with importance across time, early in time, or closer to AD onset. SPOKE networks highlight shared genes between top predictors and AD (e.g., APOE, IL6, TNF, and INS). Survival analysis of top predictors (hyperlipidemia and osteoporosis) in external EHRs validates an increased risk of AD. Genetic colocalization confirms hyperlipidemia and AD association at the APOE locus, and AD with osteoporosis colocalize at a locus close to MS4A6A with a stronger female association. ### Competing Interest Statement Dr. Bove has received research support for F Hoffman LaRoche, Novartis and Biogen. She has received personal support for consulting and/or scientific advisory boards from Alexion, EMD Serono, Horizon, Jansen, and TG Therapeutics. ### Funding Statement Primary support was provided by grant numbers NIA R01AG060393 (AT, SM, SW, TTO, MS). Additional support was provided by the Medical Scientist Training Program T32GM007618 and F30 Fellowship 1F30AG079504-01 (AT) and NSF GRFP 2038436 (JR). SEB holds the Heidrich Family and Friends Endowed Chair of Neurology at UCSF. SEB holds the Distinguished Professorship in Neurology I at UCSF. Dr. Bove is the recipient of a National Multiple Sclerosis Society Harry Weaver Award. She is supported by the NIH, NMSS, NSF, DOD, UCSF Weill Institute for Neurosciences, and by various foundations. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of University of California San Francisco gave ethical approval for this work (IRB #20-32422). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes EHR concepts and identification approaches are described in Methods, and concepts are provided in Supplemental Tables 1 and 2. Phecodes can be downloaded at phewascatalog.org/phecodes_icd10 or phewascatalog.org/phecodes, and mappings between ICD-10 codes and SNOMED can be accessed at [www.nlm.nih.gov/healthit/snomedct/us_edition.html][1]. Data for UK Biobank phenotype GWAS can be found at [www.nealelab.is/uk-biobank/][2], and eQTL data can be downloaded from [www.eqtlgen.org/][3]. The UCSF EHR database can be accessed to UCSF-affiliated. The SPOKE knowledge network can be accessed at spoke.rbvi.ucsf.edu/, and more details about the network can be found in Morris et al. and mappings to EHR concepts can be found in Nelson et al. [1]: https://www.nlm.nih.gov/healthit/snomedct/us_edition.html [2]: https://www.nealelab.is/uk-biobank/ [3]: https://www.eqtlgen.org/