Introduction: Type 2 diabetes affects (T2D) over half a billion people worldwide and is a major risk factor for cardiac arrhythmias and heart failure. Early detection of cardiac abnormalities and understanding the underlying mechanisms are essential to prevent further cardiac deterioration in T2D.
Aim: To quantify subclinical cardiac remodelling in patients with T2D and develop an in-silico framework to investigate the mechanisms driving these changes at multiple scales.
Methods: Using UK Biobank data, we conducted a matched cross-sectional study comparing ECG and cardiac magnetic resonance imaging biomarkers between 1781 patients with T2D and healthy controls. We integrated experimental and clinical data into adapted state-of-the-art human-based modelling and simulation tools. These included the ToR-ORd ventricular cardiomyocyte model, a deep learning-based 3D anatomical reconstruction pipeline, and inference-based personalisation of cardiac activation times, to replicate the diabetic cardiac phenotype.
Results: Despite no cardiovascular disease, patients with T2D showed significant cardiac differences, notably a higher resting heart rate (66 vs. 61 bpm, p < 0.001), longer QTc interval (424 vs. 420ms, p < 0.001), lower stroke volume (72 vs. 78ml, p < 0.001) and thicker left ventricular wall (6.1 vs. 5.9mm, p < 0.001). Diabetes-specific ionic changes implemented in the ToR-ORd model resulted in prolonged action potentials, aligning with QTc prolongation, and reduced active tension, consistent with lower stroke volume. The reconstructed patient-specific cardiac anatomy and torso captured key features such as chest size and ventricular wall thickness. Personalised cardiac activation times were successfully computed, enabling whole-heart electrophysiology simulations and ECG reconstruction.
Conclusion: By integrating population-level Big Data analysis and patient-specific modelling and simulation, we identified early cardiac abnormalities in T2D and developed a multi-scale framework for in-silico studies of the diabetic heart. This approach may help improve our understanding and management of cardiac health in this high-risk population.