Evidence map›Paper›PMID 38383858›Full record

ArticleNature aging2024

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 and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
57citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

57 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Women's musculoskeletal health: insights from the 2024 National Academies of Sciences, Engineering, and Medicine report on chronic conditions.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2026
    Review
  3. Review
  4. A strategic pathway for the ethical development of AI tools in dementia care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  5. Observational
  6. Article
  7. Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Alzheimer's Disease Risk Factor APOE4 Exerts Dimorphic Effects on Female Bone.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  15. System changes to empower primary care in Alzheimer's disease detection and care.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026
    Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. PreoperativeFrontiers in bioinformatics · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Alice S TangBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. alice.tang@ucsf.edu.ORCID http://orcid.org/0000-0003-4745-0714
Katherine P RankinBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Gabriel CeronoWeill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Silvia MiramontesBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Hunter MillsBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Jacquelyn RogerBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-1823-0421
Billy ZengBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Charlotte NelsonWeill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Karthik SomanWeill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Sarah WoldemariamBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-1873-8575
Yaqiao LiBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Albert LeeBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Riley BoveWeill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0002-2034-8800
Maria GlymourDepartment of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA.
Nima AghaeepourDepartment of Anesthesiology, Pain, and Perioperative Medicine, Stanford University, Palo Alto, CA, USA.ORCID http://orcid.org/0000-0002-6117-8764
Tomiko T OskotskyBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0001-7393-5120
Zachary MillerMemory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Isabel E AllenDepartment of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA.
Stephan J SandersBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
Sergio BaranziniWeill Institute for Neuroscience. Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.ORCID http://orcid.org/0000-0003-0067-194X
Marina SirotaBakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA. marina.sirota@ucsf.edu.ORCID http://orcid.org/0000-0002-7246-6083

Funding

Research Education ComponentP30AG062422 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Gil Dan Rabinovici · 2019 to 2026
$36.9M
MEDICAL SCIENTIST TRAINING PROGRAMT32GM007618 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI ANDERSON, MARK S · 1985 to 2020
$29.6M
An Integrative Multi-Omics Approach to Elucidate Sex-Specific Differences in Alzheimers DiseaseR01AG060393 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SIROTA, MARINA · 2018 to 2022
$4.1M
Machine Learning for Integrative Modeling of the Immune System in Clinical SettingsR35GM138353 · NIGMS · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA · 2020 to 2024
$2.2M
Neuropathology of synapses in AD and ADRDRF1AG077443 · NIA · STANFORD UNIVERSITY · PI AGHAEEPOUR, NIMA, MONTINE, THOMAS J · 2023 to 2023
$2.2M
Neuropathology of synapses in AD and ADRDR01AG077443 · NIA · STANFORD UNIVERSITY · PI Nima Aghaeepour, Thomas J Montine · 2026 to 2026
$707k
Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s DiseaseF30AG079504 · NIA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI TANG, ALICE SUMMER · 2022 to 2025
$187k
NIA NIH HHS F30 AG079504NIA NIH HHS P30 AG062422NIA NIH HHS R01 AG060393NIA NIH HHS R01 AG077443NIA NIH HHS RF1 AG077443NIGMS NIH HHS R35 GM138353NIGMS NIH HHS T32 GM007618
6 · The paper itself

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.

Indexed as

Alzheimer DiseaseApolipoproteins EElectronic Health RecordsFemaleHumansMaleSan FranciscoApolipoproteins E

Identifiers

PMID38383858
PMCPMC10950787

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.