Abstract 19162: Metabolites Reporting on Mitochondrial Function Predict Response to Biventricular Pacing

Background. Biventricular pacing has been shown to improve morbidity and mortality in patients with impaired systolic left ventricular function and congestive heart failure who have evidence of dyssynchronous ventricular contraction. However, a significant percentage of patients implanted with a biventricular pacer will not have a beneficial response. Novel molecular technologies may help identify biomarkers to better predict who will benefit most from this therapy. Methods. Individuals undergoing biventricular pacemaker implantation were selected from the ElectroPhysiology GENetics (EPGEN) biorepository (N=120). Targeted, quantitative mass-spectrometry based metabolomic profiling of 63 metabolites was performed on baseline, frozen, fasting plasma samples collected at the time of the index procedure. Positive response to pacing was defined as a 5% improvement in left ventricular ejection fraction or a ≥1 class improvement in New York Heart Association functional class at six months after device implantation. Principal components analysis (PCA) was used to reduce the large number of individual metabolites into a smaller set of uncorrelated factors. Logistic regression was used to test the association between PCA-derived factors and response. Results. A positive response to biventricular pacing was observed in 55 individuals at 6 months (N=65 showing no positive response). PCA identified 13 metabolite factors clustering in biologically plausible pathways. Of these, factor 1 (composed of medium and long chain acylcarnitines, medium and long chain dicarboxylacylcarnitines, ketones and β-hydroxybutyrate) was associated with positive response to pacing (p=0.007). Analyses of the individual metabolites composing this factor showed similar results (p=0.03-0.003). Conclusions. We have identified a cluster of metabolites, many of which are derived from mitochondrial metabolism, that predict a positive response to biventricular pacing. These novel biomarkers may assist in better risk stratification and patient selection for this therapy.