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

ABSTRACT:

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

SUBMITTER: Davogustto G 

PROVIDER: S-EPMC12880694 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature