A Genetic Risk Score for Atrial Fibrillation Predicts the Response to Catheter Ablation
- Author:
Won Seok CHOE
1
;
Jun Hyuk KANG
;
Eue Keun CHOI
;
Seung Yong SHIN
;
Steven A LUBITZ
;
Patrick T ELLINOR
;
Seil OH
;
Hong Euy LIM
Author Information
- Publication Type:Original Article
- From:Korean Circulation Journal 2019;49(4):338-349
- CountryRepublic of Korea
- Language:English
-
Abstract:
BACKGROUND AND OBJECTIVES:The association of susceptibility loci for atrial fibrillation (AF) with AF recurrence after ablation has been reported, although with controversial results. In this prospective cohort analysis, we aimed to investigate whether a genetic risk score (GRS) can predict the rhythm outcomes after catheter ablation of AF.
METHODS:We determined the association between 20 AF-susceptible single nucleotide polymorphisms (SNPs) and AF recurrence after catheter ablation in 746 patients (74% males; age, 59±11 years; 56% paroxysmal AF). A GRS was calculated by summing the unweighted numbers of risk alleles of selected SNPs. A Cox proportional hazard model was used to identify the association between the GRS and risk of AF recurrence after catheter ablation.
RESULTS:AF recurrences after catheter ablation occurred in 168 (22.5%) subjects with a median follow-up of 23 months. The GRS was calculated using 5 SNPs (rs1448818, rs2200733, rs6843082, rs6838973 at chromosome 4q25 [PITX2] and rs2106261 at chromosome 16q22 [ZFHX3]), which showed modest associations with AF recurrence. The GRS was significantly associated with AF recurrence (hazard ratio [HR] per each score, 1.13; 95% confidence interval [CI], 1.03–1.24). Patients with intermediate (GRS 4–6) and high risks (GRS 7–10) showed HRs of 2.00 (95% CI, 0.99–4.04) and 2.66 (95% CI, 1.32–5.37), respectively, compared to patients with low risk (GRS 0–3).
CONCLUSIONS:Our novel GRS using 5 AF-susceptible SNPs was strongly associated with AF recurrence after catheter ablation in Korean population, beyond clinical risk factors. Further efforts are warranted to construct a generalizable, robust genetic prediction model which can guide the optimal treatment strategies.
