{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Beyhoff N"],"funding":["Deutsche Gesellschaft für Kardiologie-Herz und Kreislaufforschung.","Deutsches Zentrum für Herz-Kreislaufforschung","Deutsche Gesellschaft für Kardiologie-Herz und Kreislaufforschung"],"pagination":["3411-3422"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12803576"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(12)"],"pubmed_abstract":["Aims
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 aimed to determine the ability of computational modelling of patient-specific myocardial metabolism to assess individual bioenergetic phenotypes and their clinical implications in HF.Methods and results
Based on proteomics-derived enzyme quantities in 136 cardiac biopsies, personalised computational models of myocardial metabolism were generated in two independent cohorts of advanced HF patients together with sex- and body mass index-matched non-failing controls. The bioenergetic impact of dynamic changes in substrate availability and myocardial workload were simulated, and the"],"journal":["European journal of heart failure"],"pubmed_title":["Computational modelling of myocardial metabolism in patients with advanced heart failure."],"pmcid":["PMC12803576"],"funding_grant_id":["DGK07/2021","81Z0100212"],"pubmed_authors":["Baczko I","Kintscher U","Neubauer S","Holzhutter HG","Potapov E","Knosalla C","Kuehne T","Braun VM","Tyler DJ","Grune T","Kirchner M","Finnigan LEM","Berndt N","Rider OJ","Milting H","Beyhoff N","Mertins P","Raman B"],"additional_accession":[]},"is_claimable":false,"name":"Computational modelling of myocardial metabolism in patients with advanced heart failure.","description":"Aims
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 aimed to determine the ability of computational modelling of patient-specific myocardial metabolism to assess individual bioenergetic phenotypes and their clinical implications in HF.Methods and results
Based on proteomics-derived enzyme quantities in 136 cardiac biopsies, personalised computational models of myocardial metabolism were generated in two independent cohorts of advanced HF patients together with sex- and body mass index-matched non-failing controls. The bioenergetic impact of dynamic changes in substrate availability and myocardial workload were simulated, and the","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-07-15T13:19:47.376Z","creation":"2026-07-05T03:08:37.787Z"},"accession":"S-EPMC12803576","cross_references":{"pubmed":["40662214"],"doi":["10.1002/ejhf.3746"]}}