Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

Alice S Tang; Katherine P Rankin; Gabriel Cerono; Silvia Miramontes; Hunter Mills; Jacquelyn Roger; Billy Zeng; Charlotte Nelson; Karthik Soman; Sarah Woldemariam; Yaqiao Li; Albert Lee; Riley Bove; Maria Glymour; Nima Aghaeepour; Tomiko T Oskotsky; Zachary Miller; Isabel E Allen; Stephan J Sanders; Sergio Baranzini; Marina Sirota

doi:10.1038/s43587-024-00573-8

Leveraging electronic health records and knowledge networks for Alzheimer's disease prediction and sex-specific biological insights

Nat Aging. 2024 Mar;4(3):379-395. doi: 10.1038/s43587-024-00573-8. Epub 2024 Feb 21.

Authors

Alice S Tang^{1

2}, Katherine P Rankin^{3

4}, Gabriel Cerono⁵, Silvia Miramontes³, Hunter Mills³, Jacquelyn Roger³, Billy Zeng³, Charlotte Nelson⁵, Karthik Soman⁵, Sarah Woldemariam³, Yaqiao Li³, Albert Lee³, Riley Bove⁵, Maria Glymour⁶, Nima Aghaeepour^{6

7

8}, Tomiko T Oskotsky³, Zachary Miller⁴, Isabel E Allen⁹, Stephan J Sanders^{3

10

11}, Sergio Baranzini⁵, Marina Sirota^{12

13}

Affiliations

¹ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. [email protected].
² Graduate Program in Bioengineering, University of California, San Francisco and University of California, Berkeley, San Francisco and Berkeley, CA, USA. [email protected].
³ Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
⁴ Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
⁵ Weill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
⁶ Department of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA.
⁷ Department of Pediatrics, Stanford University, Palo Alto, CA, USA.
⁸ Department of Biomedical Data Science, Stanford University, Palo Alto, CA, USA.
⁹ Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
¹⁰ Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, UK.
¹¹ Department of Psychiatry and Behavioral Sciences, Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
¹² Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. [email protected].
¹³ Department of Pediatrics, University of California, San Francisco, CA, USA. [email protected].

Abstract

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

MeSH terms

Alzheimer Disease* / diagnosis
Apolipoproteins E / genetics
Electronic Health Records
Female
Humans
Male
San Francisco

Substances

Apolipoproteins E

Abstract

MeSH terms

Substances

Grants and funding