Multi-scale computational models integrating biophysical data have the potential to predict heart phenotypes from genotype and improve clinical management of familial hypertrophic cardiomyopathy.
Multi-scale computational models linking genotype to phenotype in familial hypertrophic cardiomyopathy hold promise for personalized risk stratification and therapy.
Familial hypertrophic cardiomyopathy (FHC) is an inherited disorder affecting roughly one in 500 people. Its hallmark is abnormal thickening of the ventricular wall, leading to serious complications that include heart failure and sudden cardiac death. Treatment is complicated by variation in the severity, symptoms and risks for sudden death within the patient population. Nearly all of the genetic lesions associated with FHC occur in genes encoding sarcomeric proteins, indicating that defects in cardiac muscle contraction underlie the condition. Detailed biophysical data are increasingly available for computational analyses that could be used to predict heart phenotypes based on genotype. These models must integrate the dynamic processes occurring in cardiac cells with properties of myocardial tissue, heart geometry and haemodynamic load in order to predict strain and stress in the ventricular walls and overall pump function. Recent advances have increased the biophysical detail in these models at the myofilament level, which will allow properties of FHC-linked mutant proteins to be accurately represented in simulations of whole heart function. The short-term impact of these models will be detailed descriptions of contractile dysfunction and altered myocardial strain patterns at the earliest stages of the disease-predictions that could be validated in genetically modified animals. Long term, these multi-scale models have the potential to improve clinical management of FHC through genotype-based risk stratification and personalized therapy.
Campbell et al. (Wed,) conducted a review in Familial hypertrophic cardiomyopathy (FHC). Multi-scale computational models was evaluated. Multi-scale computational models integrating biophysical data have the potential to predict heart phenotypes from genotype and improve clinical management of familial hypertrophic cardiomyopathy.