Why the study?
Heart failure remains a major clinical challenge, and mathematical modeling can help describe pathophysiological processes, quantify treatment outcomes, and capture cardiorenal fluid and solute homeostasis including sex-specific differences.
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Design
Review
Mathematical modeling of the cardiorenal system offers a predictive framework to understand heart failure pathophysiology, SGLT2 inhibitor effects, and sex-specific differences, potentially improving therapeutic strategies.
Heart failure (HF), which is a major clinical and public health challenge, commonly develops when the myocardial muscle is unable to pump an adequate amount of blood at typical cardiac pressures to fulfill the body’s metabolic needs, and compensatory mechanisms are compromised or fail to adjust. Treatments consist of targeting the maladaptive response of the neurohormonal system, thereby decreasing symptoms by relieving congestion. Sodium–glucose co-transporter 2 (SGLT2) inhibitors, which are a recent antihyperglycemic drug, have been found to significantly improve HF complications and mortality. They act through many pleiotropic effects, and show better improvements compared to others existing pharmacological therapies. Mathematical modeling is a tool used to describe the pathophysiological processes of the disease, quantify clinically relevant outcomes in response to therapies, and provide a predictive framework to improve therapeutic scheduling and strategies. In this review, we describe the pathophysiology of HF, its treatment, and how an integrated mathematical model of the cardiorenal system was built to capture body fluid and solute homeostasis. We also provide insights into sex-specific differences between males and females, thereby encouraging the development of more effective sex-based therapies in the case of heart failure.
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Ndiaye et al. (2023) studied this question.
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