Network Analysis and Structural Equation Modelling of Genetic and Environmental Factors in Hypertrophic Cardiomyopathy Progression

Abstract Background: Hypertrophic cardiomyopathy (HCM) is when the heart muscle becomes thickened, which affects the function of the heart. It affects approximately 1 in 500 individuals in the general population. It is a multifactorial nature but is mainly caused by abnormal genes in the heart muscles. Many patients with HCM are non-symptomatic, and it is diagnosed typically with echocardiogram. HCM is marked by left ventricular hypertrophy, myocardial fibrosis and elevated risk of sudden cardiac death. The goal is to create a comprehensive analytical framework that clarifies the intricate relationships that exist between molecular pathways, environmental influences, genetic variations and clinical outcomes in HCM. Methods: We analysed 50 HCM patients using network analysis and structural equation modelling. We modelled HCM as a complex biological system using multilayered network analysis. Clinical symptoms, environmental triggers, molecular and cellular processes and genetic predisposition are interconnected in this framework. These layers were linked to uncover disease-supporting hubs, bottlenecks and subnetworks. Integrative approaches provide systems-level knowledge of HCM pathophysiology beyond reductionist, single-factor models. Results: The SEM revealed significant gene-environment interactions, with sarcomere mutations driving disease through sarcomere dysfunction and energy depletion, while environmental factors (exercise, diet) modulated progression through hypertrophic signalling. The model improved risk stratification accuracy (82% vs. 65% in traditional models) and identified three phenotypic subtypes. Conclusion: The multifaceted character of HCM, where environmental factors and genetic abnormalities interact to define disease progression, is highlighted by this study. By including these elements, risk assessment is improved and tailored care is informed.