Integrating large scale genetic and clinical information to predict cases of heart failure
We demonstrate the additive power of integrating GWAS- and EHR-derived risk scores to predict HF cases prior to diagnosis. This standardizable and scalable risk predictor may enable physicians to provide earlier interventions to improve patient outcomes.
Authors
- Cristen J. Willer
- Michael R. Mathis (ORCID: https://orcid.org/0000-0002-9697-2212)
- Ida Surakka (ORCID: https://orcid.org/0000-0002-6669-5541)
- Jiang Bian (ORCID: https://orcid.org/0000-0002-2238-5429)
- Sarah E. Graham (ORCID: https://orcid.org/0000-0003-1271-2489)
- Kuan-Han H. Wu (ORCID: https://orcid.org/0000-0003-4286-4299)
- Xu Shi (ORCID: https://orcid.org/0000-0001-8566-9552)
- Xianshi Yu (ORCID: https://orcid.org/0000-0003-2687-5246)
- Jiacong Du (ORCID: https://orcid.org/0000-0002-7768-9083)
- Nicholas J. Douville (ORCID: https://orcid.org/0000-0003-0997-6272)
- Brooke N. Wolford (ORCID: https://orcid.org/0000-0003-3153-1552)
- Whitney Hornsby (ORCID: https://orcid.org/0000-0002-0672-0338)
- Lili Zhao (ORCID: https://orcid.org/0000-0001-5735-7666)
Institutions
- Norwegian University of Science and Technology (NO)
- University of Michigan (US)
- Beaumont Health (US)
- University of Florida (US)
- Michigan Medicine (US)
Publication Details
- Journal
- Communications Medicine
- Published
- 2025-11-21
- DOI
- https://doi.org/10.1038/s43856-025-01198-7
- Primary Topic
- Genetic Associations and Epidemiology
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Commission
- National Institutes of Health
- HORIZON EUROPE Framework Programme
- National Heart, Lung, and Blood Institute
- National Institute of General Medical Sciences
- National Institute of Diabetes and Digestive and Kidney Diseases