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.

Grant Number: 5U01HL160226-05
NIH Institute/Center: NIH
Principal Investigator: Barry Borlaug