Jp, Henriques

Abstract The persistent global rise in atherosclerotic cardiovascular disease (ASCVD) challenges the effectiveness of current risk factor-based algorithms in primary prevention. Most myocardial infarctions occur in individuals without prior symptoms or recognized standard modifiable risk factors, underscoring the need to improve risk stratification. Coronary CT imaging, including coronary artery calcium scoring (CACS) and coronary computed tomography angiography (CCTA), enables direct non-invasive visualization of coronary atherosclerosis, supporting a shift from population-based prevention towards personalized, disease-based risk assessment and targeted preventive treatment. CCTA provides comprehensive evaluation of plaque burden, composition, stenosis severity, and high-risk plaque (HRP) features, thereby refining risk stratification and guiding personalized treatment decisions. The presence and total burden of coronary atherosclerotic plaque are considered the primary CCTA-derived markers for guiding preventive therapy, while noncalcified plaque burden and HRP features may support treatment intensification. Advances in CT technology, including photon-counting CT, and the application of artificial intelligence-based tools may further refine risk assessment by enabling accurate and reproducible plaque quantification and characterization. Although no randomized trials have yet demonstrated that CCTA-based screening improves clinical outcomes in asymptomatic individuals compared with standard care, the same is true of traditional cardiovascular risk scores. Three large ongoing randomized trials are currently evaluating whether CCTA-guided, personalized prevention strategies can reduce ASCVD events. The results are eagerly awaited and may redefine the role of CCTA in asymptomatic individuals.

Abstract Aims Lipoprotein(a) [Lp(a)] is a causal risk factor for cardiovascular events. However, the effect of Lp(a) on coronary plaque composition and high-risk plaque features has not been fully characterized. This study aimed to investigate the association between Lp(a) and coronary atherosclerotic plaque phenotype at the plaque level. Methods and results This study included 710 patients who underwent coronary computed tomography angiography (CCTA) and had Lp(a) measured between 2008 and 2024. CCTA scans were analysed with a previously validated artificial intelligence-based algorithm (AI-QCT, Cleerly Inc.). The association of Lp(a) with non-calcified and calcified plaque volumes and HRP features was evaluated at the plaque level using generalized estimating equation models adjusted for traditional cardiovascular risk factors. Among the 710 patients, the mean age was 56 ± 10 years and 379 (53%) were male. In total, 3642 plaques were identified. In the adjusted plaque-level analysis, elevated Lp(a) (≥150 nmol/L) was associated with an increase in non-calcified plaque volume per plaque (P = 0.009), but not with an increase in calcified plaque volume (P = 0.81). Furthermore, elevated Lp(a) (≥150 nmol/L) was strongly associated with the presence of high-risk plaque at the plaque level (adjusted odds ratio: 1.78, 95% CI: 1.25–2.54, P = 0.001), whereas low-density lipoprotein-cholesterol and high-sensitivity C-reactive protein were not (P > 0.05). Conclusion In this plaque-level CCTA study of 3642 individual plaques, elevated Lp(a) levels were independently associated with increased non-calcified plaque volume and with the presence of high-risk plaque, but not with calcified plaque volume. These findings elucidate the impact of Lp(a) on unstable plaque phenotypes, even in patients without prior cardiovascular events.