Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models
A machine learning-based protein score discriminates 5-year incident AF risk favorably compared with clinical and genetic risk factors. Large-scale proteomic analysis may assist in the prioritization of individuals at risk for AF for screening and related preventive interventions.
Authors
- Lu‐Chen Weng (ORCID: https://orcid.org/0000-0003-1475-4930)
- Leonoor F. J. M. Wijdeveld (ORCID: https://orcid.org/0000-0003-2601-5801)
- Shinwan Kany (ORCID: https://orcid.org/0000-0001-8113-733X)
- Shaan Khurshid (ORCID: https://orcid.org/0000-0002-2840-4539)
- Joel Rämö (ORCID: https://orcid.org/0000-0002-6429-5149)
- Min Seo Kim (ORCID: https://orcid.org/0000-0003-2115-7835)
- Sarah Urbut (ORCID: https://orcid.org/0000-0002-1135-9647)
- Patrick T. Ellinor (ORCID: https://orcid.org/0000-0002-2067-0533)
- Akl C. Fahed (ORCID: https://orcid.org/0000-0002-4849-6389)
- Sean J. Jurgens (ORCID: https://orcid.org/0000-0002-1605-9782)
- Carolina Roselli (ORCID: https://orcid.org/0000-0001-5267-6756)
Institutions
- Broad Institute (US)
- Boston Foundation (US)
- Massachusetts General Hospital (US)
- University Medical Center Hamburg-Eppendorf (DE)
- Institute for Molecular Medicine Finland (FI)
Publication Details
- Journal
- Circulation Genomic and Precision Medicine
- Published
- 2025-06-17
- DOI
- https://doi.org/10.1161/circgen.124.004943
- Citations
- 5
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 6.94