Association Between Obesity-Mediated Atrial Fibrillation and Therapy With Sodium Channel Blocker Antiarrhythmic Drugs | Atrial Fibrillation | JAMA Cardiology | JAMA Network
[Skip to Navigation]
Access to paid content on this site is currently suspended due to excessive activity being detected from your IP address 34.204.186.91. Please contact the publisher to request reinstatement.
1.
Benjamin  EJ, Wolf  PA, D’Agostino  RB, Silbershatz  H, Kannel  WB, Levy  D.  Impact of atrial fibrillation on the risk of death: the Framingham Heart Study.  Circulation. 1998;98(10):946-952. doi:10.1161/01.CIR.98.10.946PubMedGoogle ScholarCrossref
2.
Flegal  KM, Carroll  MD, Kit  BK, Ogden  CL.  Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010.  JAMA. 2012;307(5):491-497. doi:10.1001/jama.2012.39PubMedGoogle ScholarCrossref
3.
Chatterjee  NA, Giulianini  F, Geelhoed  B,  et al.  Genetic obesity and the risk of atrial fibrillation: causal estimates from mendelian randomization.  Circulation. 2017;135(8):741-754. doi:10.1161/CIRCULATIONAHA.116.024921PubMedGoogle ScholarCrossref
4.
January  CT, Wann  LS, Alpert  JS,  et al; American College of Cardiology/American Heart Association Task Force on Practice Guidelines.  2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society.  J Am Coll Cardiol. 2014;64(21):e1-e76. doi:10.1016/j.jacc.2014.03.022PubMedGoogle ScholarCrossref
5.
Duytschaever  M, Demolder  A, Phlips  T,  et al.  Pulmonary vein isolation with vs without continued antiarrhythmic drug treatment in subjects with recurrent atrial fibrillation (POWDER AF): results from a multicentre randomized trial.  Eur Heart J. 2018;39(16):1429-1437. doi:10.1093/eurheartj/ehx666PubMedGoogle ScholarCrossref
6.
Darghosian  L, Free  M, Li  J,  et al.  Effect of omega-three polyunsaturated fatty acids on inflammation, oxidative stress, and recurrence of atrial fibrillation.  Am J Cardiol. 2015;115(2):196-201. doi:10.1016/j.amjcard.2014.10.022PubMedGoogle ScholarCrossref
7.
Lieve  KV, Verkerk  AO, Podliesna  S,  et al.  Gain-of-function mutation in SCN5A causes ventricular arrhythmias and early onset atrial fibrillation.  Int J Cardiol. 2017;236:187-193. doi:10.1016/j.ijcard.2017.01.113PubMedGoogle ScholarCrossref
8.
Musa  H, Kline  CF, Sturm  AC,  et al.  SCN5A variant that blocks fibroblast growth factor homologous factor regulation causes human arrhythmia.  Proc Natl Acad Sci U S A. 2015;112(40):12528-12533. doi:10.1073/pnas.1516430112PubMedGoogle ScholarCrossref
9.
Darbar  D, Kannankeril  PJ, Donahue  BS,  et al.  Cardiac sodium channel (SCN5A) variants associated with atrial fibrillation.  Circulation. 2008;117(15):1927-1935. doi:10.1161/CIRCULATIONAHA.107.757955PubMedGoogle ScholarCrossref
10.
Ellinor  PT, Nam  EG, Shea  MA, Milan  DJ, Ruskin  JN, MacRae  CA.  Cardiac sodium channel mutation in atrial fibrillation.  Heart Rhythm. 2008;5(1):99-105. doi:10.1016/j.hrthm.2007.09.015PubMedGoogle ScholarCrossref
11.
Makiyama  T, Akao  M, Shizuta  S,  et al.  A novel SCN5A gain-of-function mutation M1875T associated with familial atrial fibrillation.  J Am Coll Cardiol. 2008;52(16):1326-1334. doi:10.1016/j.jacc.2008.07.013PubMedGoogle ScholarCrossref
12.
Yoshida  K, Solomon  DH, Kim  SC.  Active-comparator design and new-user design in observational studies.  Nat Rev Rheumatol. 2015;11(7):437-441. doi:10.1038/nrrheum.2015.30PubMedGoogle ScholarCrossref
13.
Chalazan  B, Mol  D, Sridhar  A,  et al.  Genetic modulation of atrial fibrillation risk in a Hispanic/Latino cohort.  PLoS One. 2018;13(4):e0194480. doi:10.1371/journal.pone.0194480PubMedGoogle Scholar
14.
Alzahrani  Z, Ornelas-Loredo  A, Darbar  SD,  et al.  Association between family history and early-onset atrial fibrillation across racial and ethnic groups.  JAMA Netw Open. 2018;1(5):e182497. doi:10.1001/jamanetworkopen.2018.2497PubMedGoogle Scholar
15.
Nagueh  SF, Smiseth  OA, Appleton  CP,  et al.  Recommendations for the evaluation of left ventricular diastolic function by echocardiography: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging.  J Am Soc Echocardiogr. 2016;29(4):277-314. doi:10.1016/j.echo.2016.01.011PubMedGoogle ScholarCrossref
16.
do Carmo  JM, da Silva  AA, Rushing  JS, Pace  B, Hall  JE.  Differential control of metabolic and cardiovascular functions by melanocortin-4 receptors in proopiomelanocortin neurons.  Am J Physiol Regul Integr Comp Physiol. 2013;305(4):R359-R368. doi:10.1152/ajpregu.00518.2012PubMedGoogle ScholarCrossref
17.
Li  XF, Lytton  J.  An essential role for the K+-dependent Na+/Ca2+-exchanger, NCKX4, in melanocortin-4-receptor–dependent satiety.  J Biol Chem. 2014;289(37):25445-25459. doi:10.1074/jbc.M114.564450PubMedGoogle ScholarCrossref
18.
Banach  M, Popławska  M, Borowicz-Reutt  KK.  Sotalol enhances the anticonvulsant action of valproate and diphenylhydantoin in the mouse maximal electroshock model.  Pharmacol Rep. 2017;69(6):1173-1177. doi:10.1016/j.pharep.2017.05.005PubMedGoogle ScholarCrossref
19.
Martin  CA, Zhang  Y, Grace  AA, Huang  CL.  In vivo studies of Scn5a+/− mice modeling Brugada syndrome demonstrate both conduction and repolarization abnormalities.  J Electrocardiol. 2010;43(5):433-439. doi:10.1016/j.jelectrocard.2010.05.015PubMedGoogle ScholarCrossref
20.
Kim  S, Popkin  BM.  Commentary: understanding the epidemiology of overweight and obesity—a real global public health concern.  Int J Epidemiol. 2006;35(1):60-67. doi:10.1093/ije/dyi255PubMedGoogle ScholarCrossref
21.
Hayashi  K, Konno  T, Tada  H,  et al.  Functional characterization of rare variants implicated in susceptibility to lone atrial fibrillation.  Circ Arrhythm Electrophysiol. 2015;8(5):1095-1104. doi:10.1161/CIRCEP.114.002519PubMedGoogle ScholarCrossref
22.
Kim  GE, Ross  JL, Xie  C,  et al.  LKB1 deletion causes early changes in atrial channel expression and electrophysiology prior to atrial fibrillation.  Cardiovasc Res. 2015;108(1):197-208. doi:10.1093/cvr/cvv212PubMedGoogle ScholarCrossref
23.
Lin  CS, Pan  CH.  Regulatory mechanisms of atrial fibrotic remodeling in atrial fibrillation.  Cell Mol Life Sci. 2008;65(10):1489-1508. doi:10.1007/s00018-008-7408-8PubMedGoogle ScholarCrossref
24.
Van Wagoner  DR.  Oxidative stress and inflammation in atrial fibrillation: role in pathogenesis and potential as a therapeutic target.  J Cardiovasc Pharmacol. 2008;52(4):306-313. doi:10.1097/FJC.0b013e31817f9398PubMedGoogle ScholarCrossref
25.
Darbar  D, Roden  DM.  Symptomatic burden as an endpoint to evaluate interventions in patients with atrial fibrillation.  Heart Rhythm. 2005;2(5):544-549. doi:10.1016/j.hrthm.2005.01.028PubMedGoogle ScholarCrossref
26.
Martin  DT, Bersohn  MM, Waldo  AL,  et al; IMPACT Investigators.  Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices.  Eur Heart J. 2015;36(26):1660-1668. doi:10.1093/eurheartj/ehv115PubMedGoogle ScholarCrossref
27.
Israel  CW, Grönefeld  G, Ehrlich  JR, Li  YG, Hohnloser  SH.  Long-term risk of recurrent atrial fibrillation as documented by an implantable monitoring device: implications for optimal patient care.  J Am Coll Cardiol. 2004;43(1):47-52. doi:10.1016/j.jacc.2003.08.027PubMedGoogle ScholarCrossref
28.
Euler  DE, Friedman  PA.  Atrial arrhythmia burden as an endpoint in clinical trials: is it the best surrogate? lessons from a multicenter defibrillator trial.  Card Electrophysiol Rev. 2003;7(4):355-358. doi:10.1023/B:CEPR.0000023138.85821.63PubMedGoogle ScholarCrossref
29.
Calkins  H, Kuck  KH, Cappato  R,  et al; Heart Rhythm Society Task Force on Catheter and Surgical Ablation of Atrial Fibrillation.  2012 HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design.  Heart Rhythm. 2012;9(4):632-696.e21, e621. doi:10.1016/j.hrthm.2011.12.016PubMedGoogle ScholarCrossref
30.
Inohara  T, Shrader  P, Pieper  K,  et al.  Association of atrial fibrillation clinical phenotypes with treatment patterns and outcomes: a multicenter registry study.  JAMA Cardiol. 2018;3(1):54-63. doi:10.1001/jamacardio.2017.4665PubMedGoogle ScholarCrossref
31.
Darbar  D, Motsinger  AA, Ritchie  MD, Gainer  JV, Roden  DM.  Polymorphism modulates symptomatic response to antiarrhythmic drug therapy in patients with lone atrial fibrillation.  Heart Rhythm. 2007;4(6):743-749. doi:10.1016/j.hrthm.2007.02.006PubMedGoogle ScholarCrossref
32.
Parvez  B, Chopra  N, Rowan  S,  et al.  A common β1-adrenergic receptor polymorphism predicts favorable response to rate-control therapy in atrial fibrillation.  J Am Coll Cardiol. 2012;59(1):49-56. doi:10.1016/j.jacc.2011.08.061PubMedGoogle ScholarCrossref
33.
Echouffo-Tcheugui  JB, Shrader  P, Thomas  L,  et al.  Care patterns and outcomes in atrial fibrillation patients with and without diabetes: ORBIT-AF Registry.  J Am Coll Cardiol. 2017;70(11):1325-1335. doi:10.1016/j.jacc.2017.07.755PubMedGoogle ScholarCrossref
34.
Chalazan  B, Dickerman  D, Sridhar  A,  et al.  Relation of body mass index to symptom burden in patients with atrial fibrillation.  Am J Cardiol. 2018;122(2):235-241. doi:10.1016/j.amjcard.2018.04.011PubMedGoogle ScholarCrossref
35.
McCauley  MD, Darbar  D.  Race and socioeconomic status regulate lifetime risk of atrial fibrillation.  Circ Arrhythm Electrophysiol. 2018;11(7):e006584. doi:10.1161/CIRCEP.118.006584PubMedGoogle Scholar
Original Investigation
November 27, 2019

Association Between Obesity-Mediated Atrial Fibrillation and Therapy With Sodium Channel Blocker Antiarrhythmic Drugs

Author Affiliations
  • 1Division of Cardiology, Department of Medicine, University of Illinois at Chicago
  • 2Division of Epidemiology and Biostatistics, University of Illinois at Chicago
  • 3Department of Medicine, Jesse Brown Veterans Affairs Medical Center, Chicago, Illinois
JAMA Cardiol. 2020;5(1):57-64. doi:10.1001/jamacardio.2019.4513
Key Points

Question  Does obesity mediate response to sodium channel vs potassium channel blocker antiarrhythmic drugs in patients with atrial fibrillation and in mice with diet-induced obesity?

Findings  In this cohort study of 311 patients, those with obesity had greater recurrence (30%) of atrial fibrillation compared with those who were not obese who received sodium channel blocker antiarrhythmic drugs (6%); significant factors associated with failure to respond to antiarrhythmic drugs were use of sodium channel blockers, obesity, female sex, and hyperthyroidism. Mice with obesity showed reduced association of flecainide acetate in suppressing pacing-induced atrial fibrillation vs sotalol hydrochloride.

Meaning  Possible reduced response to sodium channel blocker antiarrhythmic drugs in suppressing atrial fibrillation in patients with obesity has important clinical implications.

Abstract

Importance  The association between obesity, an established risk factor for atrial fibrillation (AF), and response to antiarrhythmic drugs (AADs) remains unclear.

Objective  To test the hypothesis that obesity differentially mediates response to AADs in patients with symptomatic AF and in mice with diet-induced obesity (DIO) and pacing induced AF.

Design, Setting, and Participants  An observational cohort study was conducted including 311 patients enrolled in a clinical-genetic registry. Mice fed a high-fat diet for 10 weeks were also evaluated. The study was conducted from January 1, 2018, to June 2, 2019.

Main Outcomes and Measures  Symptomatic response was defined as continuation of the same AAD for at least 3 months. Nonresponse was defined as discontinuation of the AAD within 3 months of initiation because of poor symptomatic control of AF necessitating alternative rhythm control therapy. Outcome measures in DIO mice were pacing-induced AF and suppression of AF after 2 weeks of treatment with flecainide acetate or sotalol hydrochloride.

Results  A total of 311 patients (mean [SD] age, 65 [12] years; 120 women [38.6%]) met the entry criteria and were treated with a class I or III AAD for symptomatic AF. Nonresponse to class I AADs in patients with obesity was less than in those without obesity (30% [obese] vs 6% [nonobese]; difference, 0.24; 95% CI, 0.11-0.37; P = .001). Both groups had similar symptomatic response to a potassium channel blocker AAD. On multivariate analysis, obesity, AAD class (class I vs III AAD [obese] odds ratio [OR], 4.54; 95% Wald CI, 1.84-11.20; P = .001), female vs male sex (OR, 2.31; 95% Wald CI, 1.07-4.99; P = .03), and hyperthyroidism (OR, 4.95; 95% Wald CI, 1.23-20.00; P = .02) were significant indicators of the probability of failure to respond to AADs. Pacing induced AF in 100% of DIO mice vs 30% (P < .001) in controls. Furthermore, DIO mice showed a greater reduction in AF burden when treated with sotalol compared with flecainide (85% vs 25%; P < .01).

Conclusions and Relevance  Results suggest that obesity differentially mediates response to AADs in patients and in mice with AF, possibly reducing the therapeutic effectiveness of sodium channel blockers. These findings may have implications for the management of AF in patients with obesity.

×