Dataset Information

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

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.

SUBMITTER: Tang AS

PROVIDER: S-EPMC10950787 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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

Tang Alice S AS Rankin Katherine P KP Cerono Gabriel G Miramontes Silvia S Mills Hunter H Roger Jacquelyn J Zeng Billy B Nelson Charlotte C Soman Karthik K Woldemariam Sarah S Li Yaqiao Y Lee Albert A Bove Riley R Glymour Maria M Aghaeepour Nima N Oskotsky Tomiko T TT Miller Zachary Z Allen Isabel E IE Sanders Stephan J SJ Baranzini Sergio S Sirota Marina M

Nature aging 20240221 3

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 ...[more]

PMID: 38383858

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Dataset Information

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

Publications

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

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