Improving heart failure with preserved ejection fraction (HFpEF) treatment using a mathematical model of physiology

Lead Investigator: John Clemmer, University of Mississippi Medical Center
Title of Proposal Research: Improving heart failure with preserved ejection fraction (HFpEF) treatment using a mathematical model of physiology
Vivli Data Request: 9909
Funding Source: (NIH) National Institute on Minority Health and Health Disparities R00 MD014738
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Heart failure (HF) is a complicated disease, and HF patients can have largely different ejection fractions. Ejection fraction refers to how well your heart pumps blood. It is the amount of blood pumped out of your heart’s lower chambers (ventricles) to the body’s organs each time it contracts. Some people with a normal ejection fraction (50-70% of blood pumped out) also have HF. This is known as HF with preserved ejection fraction (HFpEF).

Black Americans are consistently underrepresented in clinical trials including HF trials. Black Americans have higher rates of kidney disease and HFpEF at younger ages. Although kidney disease is a known cause of HF, the relative importance of renal function during HFpEF treatment in blacks is unclear. Our overarching hypothesis is that kidney disease plays a major role in causing the disproportionate HFpEF burden, hospitalization, and death seen in blacks as compared to whites.

To address this hypothesis, we will use a large mathematical model to investigate the role of the cardiovascular and kidney systems during HFpEF treatment. The current model, HumMod (free download at, is comprised of 14 organ systems, and includes the nervous system, endocrine system (hormones and glands that secrete them), circulatory physiology (the heart, blood volume, and blood vessels), and the kidneys. I have created tools that generate and analyze large sets of computer-generated (virtual) patients. With these techniques HumMod has been used for hypothesis generation and for understanding underlying physiological mechanisms that are not able to be determined in either whole animal or human experiments. This proposed work will use these tools and this mathematical model of human physiology to develop a realistic virtual population for studying heart failure and its treatment. Published data from my laboratory show that the model is robust and can realistically simulate HFpEF, kidney disease, changes in salt intake, and multiple types of treatments including the drug that was investigated in the EMPEROR-Preserved Trial: the sodium-glucose cotransporter 2 (SGLT2) inhibitor.
SGLT2i blocks kidney reabsorption of sugar and was initially an antidiabetic drug but went on to show superior kidney and heart protection even in non-diabetic patients. With the EMPEROR-Preserved Trial individual patient data, I will be able to train a race-specific virtual population model (namely white and black populations) and virtually investigate heart and kidney mechanisms for the benefits seen with SGLT2 inhibitor. The aim of the study is to artificially increase the Black population sample in the EMPEROR-Preserved Trial to determine the efficacy of SGLT2 inhibitor on this underrepresented subgroup in the trial (only 4% of the population), who are subject to a higher than average disease burden.

Requested Studies:

A Phase III Randomised, Double-blind Trial to Evaluate Efficacy and Safety of Once Daily Empagliflozin 10 mg Compared to Placebo, in Patients With Chronic Heart Failure With Preserved Ejection Fraction (HFpEF)
Data Contributor: Boehringer Ingelheim
Study ID: NCT03057951
Sponsor ID: 1245.110