Mayo Clinic HeartShare Clinical Center

PROJECT SUMMARY/ABSTRACT This is Mayo Clinic’s application to participate in the NIH HeartShare Research Consortium as a HeartShare Clinical Center (CC). Our goal is to collaborate with the other HeartShare Investigators to elucidate the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF) and discover novel diagnostic and therapeutic approaches. Multiple pathophysiologic processes may ultimately lead to different HFpEF phenotypes, though the specific mechanisms remain largely undefined. It is also not known whether standard clinical information can identify patients with different mechanistic etiologies, which is necessary to provide targeted therapies in clinical trials and eventually in clinical practice. Our proposal outlines four specific aims. In Specific Aim 1: We document that Mayo Clinic has the resources and the Mayo HeartShare Team has the expertise and track record of productivity in HFpEF and relevant related diseases, clinical research, patient recruitment and retention, data science, and collaborative team science to help drive the success of HeartShare Network. In Specific Aim 2: We propose a broad mechanistic phenotyping protocol providing quantitative variables reflective of senescence, systemic disease processes, and multi-organ integrity (L2 data), which are used as input variables in unsupervised machine learning (ML) models. We hypothesize that this approach will allow identification of unique HFpEF pathophysiologic phenogroups (clusters). We also propose invasive hemodynamic signatures, trans-cardiac gradients of circulating biomarkers and myocardial, adipose and skeletal muscle tissue characterization (L3 data) be obtained in a subset within each HFpEF pathophysiologic phenogroup. We hypothesize these L3 data will enhance identification of targeted therapeutic strategies. Lastly, we outline supervised ML using EHR data to develop automatable algorithms to accurately identify the HeartShare HFpEF pathophysiological phenogroups derived using L2 data. We hypothesize that if successful, this approach will enhance translation of HeartShare findings by allowing automated identification of patients in the different HFpEF phenogroups for enrollment in clinical trials of agents targeting their specific pathophysiology. In Specific Aim 3: We propose that use of circulating proteins alone (n=5000; defined by the SOMAScanTM Aptamer based platform) as input variables for unsupervised ML models will identify unique HFpEF pathophysiologic phenotypes (clusters). In Specific Aim 4: We outline the Mayo HeartShare Research Skills Development Program. Providing HFpEF clinical investigators a short-term intensive immersion experience by collaboration with a data scientist intern in the Mayo Cardiovascular Disease AI Internship or a long term dedicated program in data science as a Mayo Kern Center Scholar in Data Science will equip a new generation of HFpEF investigators with a robust data science toolbox to drive future discovery.