Cardiac digital twins: a tool to investigate the function and treatment of the diabetic heart.

Abstract

Diabetes increases the risk of cardiovascular disease (CVD) due to its multi-scale and diverse effects on cardiomyocyte metabolism and function, the circulation, and the kidneys. The complex relationship between organ systems affected by diabetes and associated comorbidities leads to challenges in estimating cardiovascular risk and stratifying optimal treatment strategies at the individual patient level. Most recently, sodium-glucose transport protein 2 (SGLT2) inhibitors and glucagon-like peptide-1 (GLP1) receptor agonists have been shown to offer substantial cardiac benefits. However, the direct or indirect mechanisms through which these agents protect the heart remain unclear, posing a challenge to patient selection. Amidst a growing burden of diabetes and increased therapeutic armamentarium, there is an important unmet need to develop more precise methods and technologies to understand the effects of diabetes and anti-diabetic treatment on the heart with faster timelines than conventional randomised controlled trials. Cardiac computational models could be used to improve our understanding of the cardiac changes in diabetes and to predict how a patient's heart will respond to anti-diabetic treatment. In this review, we provide an overview of current cardiac computational models to investigate the diabetic heart and the cardiac effects of anti-diabetic treatment. We discuss how multi-scale and multi-physics models could be applied in future to support the development of novel therapeutic approaches and further improve the treatment of diabetic patients with different CVD risk.