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Unbiased Characterization of Atrial Fibrillation Phenotypic Architecture Provides Insight Into Genetic Liability and Clinically Relevant Outcomes

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Τίτλος: Unbiased Characterization of Atrial Fibrillation Phenotypic Architecture Provides Insight Into Genetic Liability and Clinically Relevant Outcomes
Συγγραφείς: Davogustto, Giovanni, Zhao, Shilin, Li, Yajing, Farber-Eger, Eric, Lowery, Brandon D., Shaffer, Lauren Lee, Mosley, Jonathan D., Shoemaker, M. Benjamin, Xu, Yaomin, Roden, Dan M., Wells, Quinn S.
Πηγή: Circulation: Genomic and Precision Medicine ; volume 19, issue 1 ; ISSN 2574-8300
Στοιχεία εκδότη: Ovid Technologies (Wolters Kluwer Health)
Έτος έκδοσης: 2026
Περιγραφή: BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease subtypes in an unbiased fashion, but whether these AF subgroups align with underlying mechanisms, such as polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. METHODS: We identified individuals with AF in a large biobank linked to electronic health records and genome-wide genotyping. We applied an unsupervised coclustering machine learning algorithm to 35 curated and uncorrelated clinical features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention. Polygenic risk scores for AF and for cytokine levels were used to assess the genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from electronic health records up to the last medical contact. RESULTS: The analysis included 23 271 subjects with AF, of which 6023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal disease and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbidity cluster. There was no difference between groups in genetically predicted levels of inflammatory biomarkers. Cluster assignment was associated with mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. CONCLUSIONS: Patients within AF subgroups identified by clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes, polygenic liability to AF, and clinical inflammation, but not with genetically predicted inflammatory cytokine levels. Our ...
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
DOI: 10.1161/circgen.124.004853
DOI: 10.1161/CIRCGEN.124.004853
Διαθεσιμότητα: https://doi.org/10.1161/circgen.124.004853
https://www.ahajournals.org/doi/full/10.1161/CIRCGEN.124.004853
Rights: https://creativecommons.org/licenses/by-nc-nd/4.0/
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  – Url: https://doi.org/10.1161/circgen.124.004853#
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  Data: Circulation: Genomic and Precision Medicine ; volume 19, issue 1 ; ISSN 2574-8300
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  Data: Ovid Technologies (Wolters Kluwer Health)
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  Data: BACKGROUND: Atrial fibrillation (AF) is a common and clinically heterogeneous arrhythmia. Machine learning algorithms can define data-driven disease subtypes in an unbiased fashion, but whether these AF subgroups align with underlying mechanisms, such as polygenic liability to AF or inflammation, and associate with clinical outcomes is unclear. METHODS: We identified individuals with AF in a large biobank linked to electronic health records and genome-wide genotyping. We applied an unsupervised coclustering machine learning algorithm to 35 curated and uncorrelated clinical features to identify distinct phenotypic AF clusters. The clinical inflammatory status of the clusters was defined using measured biomarkers (CRP, ESR, WBC, Neutrophil %, Platelet count, RDW) within 6 months of first AF mention. Polygenic risk scores for AF and for cytokine levels were used to assess the genetic liability of clusters to AF and inflammation, respectively. Clinical outcomes were collected from electronic health records up to the last medical contact. RESULTS: The analysis included 23 271 subjects with AF, of which 6023 had available genome-wide genotyping. The machine learning algorithm identified 3 phenotypic clusters that were distinguished by increasing prevalence of comorbidities, particularly renal disease and coronary artery disease. Polygenic liability to AF across clusters was highest in the low comorbidity cluster. Clinically measured inflammatory biomarkers were highest in the high comorbidity cluster. There was no difference between groups in genetically predicted levels of inflammatory biomarkers. Cluster assignment was associated with mortality, stroke, bleeding, and use of cardiac implantable electronic devices after AF diagnosis. CONCLUSIONS: Patients within AF subgroups identified by clustering were distinguished by comorbidity burden and associated with risk of clinically important outcomes, polygenic liability to AF, and clinical inflammation, but not with genetically predicted inflammatory cytokine levels. Our ...
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