Proteomics-based computational modelling of myocardial metabolism for personalized phenotyping and outcome prediction in advanced heart failure

Abstract

Abstract Background Perturbations of myocardial metabolism and energy depletion are well-established hallmarks of heart failure (HF), yet methods for their systematic assessment remain limited in humans [1]. Computational modelling of the heart of individual patients represents an emerging precision medicine approach for personalized diagnosis of specific disease mechanisms and tailored clinical decision-making [2,3]. Purpose To determine the ability of computational modelling of patient-specific myocardial metabolism to assess individual bioenergetic phenotypes and their prognostic implications in HF. Methods Based on proteomics-derived enzyme intensity profiles in cardiac biopsies, we recently developed a computational model of myocardial metabolism capturing all pathways along energy-delivering substrates are processed to generate ATP [4]. Using this framework, we studied the individual energetic state in 17 sex-matched nonfailing controls and 48 patients with advanced HF (LVEF 20±10%, 72.9% non-ischemic aetiology, 52.1% females, 29.8% diabetics) undergoing heart transplantation (n=25) or ventricular assist device (VAD) implantation (n=23). The bioenergetic impact of alterations in substrate availability and myocardial workload were simulated, and the model’s ability to predict myocardial reverse remodelling after VAD implantation was assessed. Results Compared to controls, HF patients had a reduced ATP production capacity (p<0.01), although there was considerable interindividual variance, ranging from severely decreased to normal values. While overall oxygen consumption was lower in failing hearts, the amount of ATP generated per mole of oxygen did not differ (p=0.16). Maximum uptake capacity of fatty acids, glucose, and amino acids was comparable. By contrast, maximum uptake capacity of ketone bodies and lactate was reduced in the failing heart (p for both <0.05). There was a shift from fatty acid to glucose utilization in most HF patients, depending on substrate availability and myocardial workload. The ratio of fatty acid to glucose oxidation distinguished patients with LVEF recovery >10% after VAD implantation (C-index 0.93, p<0.01; Picture 1B) and correlated significantly with improvement in post-operative LVEF (r=0.67, p<0.01). ATP production capacity was not associated with reverse remodelling after VAD implantation. Conclusions Computational modelling identified a subset of advanced HF patients with preserved myocardial metabolism despite a similar degree of systolic dysfunction. Myocardial substrate preference but not ATP production capacity was associated with myocardial recovery after VAD implantation, which suggests a potential for substrate manipulation as a therapeutic approach in those with deranged fuel use. Computational assessment of myocardial metabolism in HF may improve understanding of disease heterogeneity, individual risk stratification, and guidance of personalized clinical decision-making in the future.