Background The failing human heart is characterized by metabolic abnormalities, but these defects remains incompletely understood. In animal models of HF there is a switch from a predominance of fatty acid utilization to the more oxygen-sparing carbohydrate metabolism. Recent studies have reported decreases in myocardial lipid content, but inclusion of diabetics and nondiabetics obscures the distinction of adapations to metabolic derangements from adaptations to heart failure per se. Methods and Results We performed both unbiased and targeted myocardial lipid surveys using liquid chromatography-mass spectroscopy in non-diabetic, lean, predominantly non-ischemic advanced HF patients at the time of heart transplantation or LVAD implantation. We identified significantly decreased concentrations of the majority of myocardial lipid intermediates, including long-chain acylcarnitines, the primary subset of energetic lipid substrate for mitochondrial fatty acid oxidation. We report for the first time significantly reduced levels of intermediate and anaplerotic acyl-CoA species incorporated into Krebs cycle, while the myocardial concentration of acetyl-CoA was significantly increased in end-stage heart failure. In contrast, we observed an increased abundance of ketogenic β-hydroxybutyryl CoA, in association with increased myocardial utilization of β-hydroxybutyrate. We observed a significant increase in the expression of the gene encoding succinyl-CoA: 3oxoacid-CoA transferase (SCOT), the rate limiting enzyme for myocardial oxidation of βOHB and acetoacetate. Conclusions These findings indicate increased ketone utilization in the severely failing human heart independent of diabetes, support the role of ketone bodies as an alternative fuel and myocardial ketone oxidation as a key metabolic adaptation in the failing human heart.

Heart failure (HF) is a leading cause of morbidity and mortality worldwide. A small proportion of HF cases are attributable to monogenic cardiomyopathies and existing genome-wide association studies (GWAS) have yielded only limited insights, leaving the observed heritability of HF largely unexplained. We report results from a GWAS meta-analysis of HF comprising 47,309 cases and 930,014 controls. Twelve independent variants at 11 genomic loci are associated with HF, all of which demonstrate one or more associations with coronary artery disease (CAD), atrial fibrillation, or reduced left ventricular function, suggesting shared genetic aetiology. Functional analysis of non-CAD-associated loci implicate genes involved in cardiac development (MYOZ1, SYNPO2L), protein homoeostasis (BAG3), and cellular senescence (CDKN1A). Mendelian randomisation analysis supports causal roles for several HF risk factors, and demonstrates CAD-independent effects for atrial fibrillation, body mass index, and hypertension. These findings extend our knowledge of the pathways underlying HF and may inform new therapeutic strategies.

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal Mendelian Long QT Syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals we identified 35 common variant QT interval loci, that collectively explain ∼8-10% of QT variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 novel QT loci in 298 unrelated LQTS probands identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode for proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies novel candidate genes for ventricular arrhythmias, LQTS,and SCD.

Heart failure is a complex clinical syndrome and has become the most common reason for adult hospitalization in developed countries. Two subtypes of heart failure, ischemic heart disease (ISCH) and dilated cardiomyopathy (DCM), have been studied using microarray platforms. However, microarray has limited resolution. Here we applied RNA sequencing (RNA-Seq) to identify gene signatures for heart failure from six individuals, including three controls, one ISCH and two DCM patients. Using genes identified from this small RNA-Seq dataset, we were able to accurately classify heart failure status in a much larger set of 313 individuals. The identified genes significantly overlapped with genes identified via genome-wide association studies for cardiometabolic traits and the promoters of those genes were enriched for binding sites for transcriptions factors. Our results indicate that it is possible to use RNA-Seq to classify disease status for complex diseases such as heart failure using an extremely small training dataset.