While there have been notable advancements in the management of atherosclerotic cardiovascular disease (ASCVD), a crucial gap remains in identifying and managing apparently healthy individuals at elevated risk of developing atherosclerosis. More importantly, there is a need to address also individuals who already exhibit early-stage atherosclerotic lesions, detectable by imaging modalities, but have not yet experienced a clinical event (i.e., subclinical atherosclerosis)1. The notion that atherosclerosis often remains asymptomatic until advanced stages poses a major challenge for early detection and intervention2. While traditional risk factors such as hypertension, dyslipidaemia, diabetes and smoking are well-established, a substantial proportion of individuals with subclinical atherosclerosis may go undetected using conventional risk assessment tools3. Current clinical risk algorithms, including the Framingham risk score (FRS), the Systematic COronary Risk Evaluation 2 (SCORE2) system, and the Second Manifestations of ARTerial disease 2 (SMART2), rely purely on traditional risk factors for cardiovascular disease[4], [5], [6], [7], [8]. However, these algorithms have limitations in accurately predicting future events, as the included traditional risk factors fail to encompass the complexity of pathophysiological processes underlying atherosclerosis and do not consider plaque vulnerability9. This highlights the urgent need for innovative approaches to identify and manage subclinical atherosclerosis in apparently healthy individuals.

Here, we review novel tools enhancing risk stratification including biomarkers, genetics, imaging techniques, and artificial intelligence (AI) for personalised intervention strategies. By examining these innovative approaches, this review seeks to provide insights into current strategies and future directions in primary cardiovascular risk assessment and management of apparently healthy individuals with or without subclinical atherosclerosis.