Cardiovascular disease (CVD) remains the leading cause of morbidity and mortality worldwide, despite widespread implementation of structured primary prevention programs targeting cardiometabolic risk factors. While such interventions improve intermediate risk factors, their effects on major adverse cardiovascular events (MACE) have been modest and heterogeneous. One plausible explanation is biological heterogeneity, whereby individuals with different levels of inherited susceptibility derive different benefit from preventive interventions. This project aims to investigate the role of genetic susceptibility in modifying the effectiveness of structured cardiovascular prevention. Using genome-wide genotype data from approximately 7,000 participants in the population-based Healthy Aging Initiative (HAI), we will perform genotype imputation using the Haplotype Reference Consortium (HRC) reference panel to achieve dense genomic coverage. Standard quality control procedures will be applied prior to imputation to ensure high-quality genotype data. Following imputation, polygenic risk scores (PRS) for myocardial infarction (coronary artery disease) and stroke will be constructed using large-scale, externally validated genome-wide association study (GWAS) summary statistics from international consortia. Linkage disequilibrium–aware methods will be used to derive robust PRS, which will be standardized and analysed both as continuous measures and categorical risk strata. These genetic data will be integrated with rich phenotypic information and nationwide register-based follow-up for adjudicated cardiovascular outcomes. The primary aim is to evaluate whether participation in a structured cardiovascular prevention program is associated with reduced risk of MACE compared with matched population controls, and whether this effect is modified by inherited genetic susceptibility. Secondary analyses will quantify differences in absolute risk reduction across genetic risk groups to assess the potential for genetically informed targeting of preventive interventions. The computational requirements of this project include large-scale genotype imputation and PRS computation, necessitating access to secure high-performance computing infrastructure. The NAISS Bianca system provides an appropriate environment for handling sensitive genetic data in compliance with GDPR and institutional regulations. By integrating genome-wide genetic data with real-world prevention data and nationwide outcomes, this project addresses a fundamental question in precision medicine: whether genetic risk information can improve targeting and effectiveness of preventive strategies. The results have the potential to inform more efficient and personalized approaches to cardiovascular disease prevention in clinical practice.