Cardiovascular disease (CVD) remains a leading cause of mortality worldwide. Digital twins, which combine mechanistic modeling and machine learning, offer a promising approach for personalized CVD medicine. This paper presents a personalized approach to CVD management through a multi-scale digital twin framework. The framework integrates physiological models with patient-specific information, enabling preliminary investigations into individual risk prediction and virtual treatment response. Further progress in this area could lead to a paradigm shift in CVD prevention and treatment.