Impact of addressing modifiable risk factors on the 10-year risk of cardiovascular disease in individuals with familial hypercholesterolemia: a causal analysis utilising UK biobank

Abstract Background Familial hypercholesterolemia (FH) is a prevalent autosomal dominant disorder characterised by elevated low-density lipoprotein cholesterol (LDL-C) levels from birth. These elevated LDL-C levels significantly amplify the lifetime risk of cardiovascular disease (CVD). While the impact of early and effective initiation of lipid-lowering medication (LLM) on CVD risk is well documented in genetically confirmed FH, the additional impact of addressing modifiable lifestyle risk factors (MLRF) in that patient population remains largely unquantified. Methods This study quantified the potential effects of successfully managing 4 MLRF (smoking, obesity, alcohol consumption, and low physical activity) on the 10-year incidence of a first CVD diagnosis in adults with genetically confirmed FH treated with LLM within the UK Biobank. The synthesised causal estimates of these 4 MLF were obtained using two recent causal analysis frameworks, namely the doWhy causal calculus framework and the Causal Forest Double Machine Learning Method. Results In 660 FH individuals (mean age 56±8 years; 60.15% women), smoking cessation and reducing body mass index below 30 kg/m2 delivered the most substantial risk reductions (pooled estimates, adjusted for the 3 other MLRF: -6.5% and -3.2%, respectively; both p<0.001), followed by initiating moderate or high physical activity (-1.7%; p<0.001; Figure). Notably, cessation of alcohol consumption significantly increased CVD risk (+2.5%; p<0.001), aligning with existing literature. Conclusion In over 10 years, reductions in CVD risk ranging from 1.7% to 6.5% were achievable through smoking cessation, BMI reduction, and enhanced physical activity in FH individuals. Leveraging causal inference methodologies facilitates the discernment of which risk factors offer the most pronounced additional benefits beyond lipid-lowering interventions in this high-risk population.Forest Plot of Causal Effect Estimates