Glycometabolic cardiac dysfunction in HFpEF: Lessons from multi-omics studies

Heart Failure with preserved Ejection Fraction (HFpEF) is a complex condition that stems from intricate biochemical changes in the heart tissue. The loss of metabolic flexibility, a hallmark of unhealthy myocardium, may play a role in its progression. However, the impact of myocardial metabolic changes on the development of HFpEF and the relationship with its diverse clinical presentations remains unclear. The heterogeneous nature of HFpEF poses a challenge to research and management, highlighting the pressing need for a deeper understanding of its pathophysiology and more accurate differentiation of its phenotypes. Multi-omics, driven by artificial intelligence and machine learning, is a source of inspiration in the field of HFpEF research. This method has the potential to reveal insights into the metabolic changes and phenotypes of HFpEF that were previously inaccessible. By revealing non-traditional biomarkers that go beyond basic clinical and demographic criteria, it also inspires the development of targeted therapies for specific patient groups. This review aims to explore the current understanding of how myocardial metabolic changes and metabolic inflexibility contribute to the pathogenesis of HFpEF. By drawing on the latest multi-omics studies, it also aims to identify an omics signature for HFpEF that could be instrumental in unearthing new biomarkers for diagnosis, phenotyping, risk stratification, and the development of tailored therapies, thereby advancing personalized medicine in the field of HFpEF.