Identification of genetic markers for treatment success in heart failure patients: Insight from cardiac resynchronization therapy

Background —Cardiac resynchronization therapy (CRT) can improve ventricular size, shape and mass and reduce mitral regurgitation by reverse remodelling of the failing ventricle. About 30% of patients do not respond to this therapy for unknown reasons. In this study, we aimed at the identification and classification of CRT responder by the use of genetic variants and clinical parameters. Methods and Results —Out of 1,421 CRT patients, 207 subjects were consecutively selected and CRT responder and non-responder were matched for their baseline parameters before CRT. Treatment success of CRT was defined as a decrease in left ventricular end systolic volume (LVESV) >15% at follow-up echocardiography compared to LVESV at baseline. All other changes classified the patient as CRT non-responder. A genetic association study was performed, which identified 4 genetic variants to be associated with the CRT responder phenotype at the allelic (p ATPIB1 ), rs5443 ( GNB3 ), rs5522 ( NR3C2 ) and rs7325635 ( TNFSF11 ). Machine learning algorithms were used for the classification of CRT patients into responder and non-responder status, including combinations of the identified genetic variants and clinical parameters. Conclusions —We demonstrated that rule induction algorithms can successfully be applied for the classification of heart failure patients in CRT responder and non-responder status using clinical and genetic parameters. Our analysis included information on alleles and genotypes of 4 genetic loci, rs3766031 ( ATPIB1 ), rs5443 ( GNB3 ), rs5522 ( NR3C2 ) and rs7325635 ( TNFSF11 ), pathophysiologically associated with remodelling of the failing ventricle.