Metabolic Pathways in Heart Failure
Principal Investigator: Dr Svati Shah
Approved Research ID: 48785
Approval date: May 29th 2019
Aim/s: We aim to use the data available from the UK Biobank to determine the role of mitochondrial function in heart failure (HF) and diabetes (DM) and create a score, using the genetics, to predict who would be at risk for heart failure in the presence of diabetes. Also, using data we've previously generated, we would look into the metabolic differences between patients with heart failure caused by diabetes and other causes of heart failure. Background: The common role of metabolic dysregulation in DM and in HF has been studied and is well established. Previous studies have shown that patients with DM have a higher risk of HF than patients without DM and vice versa. However, despite what is published on the metabolic similarities of the diseases, data is lacking on the use of genetic data to explain this overlap. With the use of metabolomics, a scientific study of a large number of metabolites in the blood, we, and others, have identified a branched-chain amino acid (BCAA-a family of amino acids, which are the building blocks of proteins) and other pathways that is involved in the development of cardiometabolic diseases like DM and HF. Current evidence points to significant alterations of the BCAA metabolic pathways in the stressed or failing heart. However, the importance of these pathways during the progression of HF caused by diabetes is unknown. A number of studies have found an association between HF and the development of diabetes, however the studies available serves as a start point to further explore if heart failure increases the risk for diabetes. Duration: The anticipated project should take approximately 4 years after obtaining access to the data Public health impact: Despite the medications currently available, a large number of people are still affected by both heart failure and diabetes. This project may potentially open doors to novel therapeutic modalities or approaches to managing them to further improve outcomes. Additionally, it will help clarify the overlap between the two conditions and help identify those individuals with DM who are at greatest risk of developing HF, which could lead to personalized approaches for HF prevention.