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. This study examined whether computational modelling of patient-specific myocardial metabolism is suitable for assessing individual bioenergetic phenotypes and their clinical implications. Personalised computational models were created based on proteomics-derived enzyme quantities in 136 cardiac biopsies (advanced HF patients and controls). The research team simulated different substrate availability and myocardial workload and tested the models' ability to predict the myocardial response following left ventricular assist device (LVAD) implantation. They identified a subgroup with advanced HF whose metabolism was largely preserved despite severe dysfunction. Substrate preference was associated with the myocardial response after LVAD implantation and closely related to recovery. Computational assessment of myocardial metabolism in HF may improve understanding of HF disease heterogeneity. Risks can be assessed on an individual basis and treatments can be planned in a more targeted manner.
Link to the publication: Computational modelling of myocardial metabolism in patients with advanced heart failure (Beyhoff N. et al., Eur J Heart Fail, 2025)
