Gene Expression Profiles for the Identification of Prevalent Atrial Fibrillation Conference Paper uri icon

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abstract

  • Background Diagnosis of atrial fibrillation ( AF ) can be difficult, requiring cumbersome investigations. We aimed to determine the association of established whole‐blood gene expression scores with prevalent AF and to evaluate their performance for the identification of AF in a SIRS (Steroids in Cardiac Surgery) trial cohort. Methods and Results Whole‐blood, transcriptome‐wide gene expression profiling was performed using the Illumina Human HT ‐12 Expression BeadChip in 416 participants (65% men) before surgery, including 91 with a diagnosis of AF . An AF gene score ( GS ) calculated from 7 genes reported to be upregulated in AF and a validated GS for biological age based on 1254 genes related to aging were both independently associated with AF diagnosis before surgery in multivariate logistic regression analyses adjusting for known risk factors ( P =0.0006 and P =0.003). Addition of AF and biological age GSs to clinical risk factors led to significant improvement in area under the receiver operating characteristic curve (from 0.77 to 0.80; P =0.03), continuous net reclassification improvement index ( P <0.0001), and integrated discrimination improvement index ( P =0.0002). When stratifying AF by subtype, AF GS was mainly associated with paroxysmal AF ( P =0.003), whereas the biological age GS was mainly associated with permanent AF ( P =0.017). Conclusions We validated the existence of a blood gene expression signature for prevalent AF and showed that biological age derived from gene expression is significantly associated with prevalent AF . These findings suggest a potential utility of blood gene expression for the identification of patients with AF , particularly paroxysmal AF . This result could have implications for the prevention and management of cryptogenic stroke. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 00427388.

publication date

  • July 2017