Electromechanical Drivers of Diastolic Dysfunction During Hypertrophic Cardiomyopathy Progression

Aims: Diastolic dysfunction in hypertrophic cardiomyopathy reflects changes in sarcomeric function, electrophysiology, ventricular structure, and myocardial mechanics. Their effects on filling remain difficult to determine clinically. We sought to dissect these effects in MYH7-related non-obstructive hypertrophic cardiomyopathy progression. Methods and results: We developed a human biventricular electromechanical model linking electrophysiology and calcium handling to contraction, ventricular mechanics, haemodynamics, electrocardiography, and myocardial strain. Stage I represented MYH7 sarcomeric dysfunction before hypertrophy; Stage II added septal hypertrophy; Stage III added increased myocardial stiffness. Secondary ionic remodelling was examined at each stage and severity. Sarcomeric dysfunction alone generated a low-volume hypercontractile phenotype before overt hypertrophy. Septal hypertrophy further constrained chamber filling, whereas myocardial stiffening exacerbated diastolic dysfunction and regional mechanical heterogeneity. Secondary ionic remodelling produced marked repolarisation abnormalities but, importantly, also prolonged calcium-tension dynamics and impaired relaxation, reducing ventricular filling to an extent comparable with structural remodelling in some disease states. Increasing ionic-remodelling severity progressively amplified these effects. Despite preserved ejection fraction throughout progression, ventricular volumes decreased while global and regional myocardial deformation deteriorated. Conclusion: Diastolic dysfunction was evident before overt hypertrophy and worsened as structural, mechanical, and ionic abnormalities were added. Preserved ejection fraction did not reflect reduced ventricular filling or deterioration in myocardial strain. Regional mechanical measures may therefore reveal disease burden not captured by ejection fraction alone. By separating individual disease mechanisms, in silico modelling may help link measurable biomarkers to their underlying causes and support mechanism-based patient stratification.