Risk factor clusters associated with atherosclerotic cardiovascular events: a latent class analysis in the EPIC-Norfolk cohort

Abstract Introduction Risk factors for atherosclerosis often cluster within individuals. While traditional single-risk-factor and clinically evident risk-factor combinations have been extensively investigated, such approaches can overlook latent risk factor clusters that may reflect different underlying biological pathways. The relationship between data-driven risk factor clusters and incident cardiovascular disease (CVD) events in apparently healthy individuals remains largely unknown. We therefore identified latent risk factor clusters and investigated their association with first atherosclerotic CVD event incidence. Methods We included participants without CVD at baseline from the European Prospective Investigation into Cancer and Nutrition (EPIC-Norfolk) population cohort. Latent class analysis was used to identify clusters based on 9 co-occurring risk factors. CVD events were defined as first occurrence of hospitalisation or death due to ischemic heart disease (IHD), ischemic stroke, hemorrhagic stroke, peripheral arterial disease (PAD), or aortic aneurysm (AA). Associations between clusters and first CVD incidence were estimated using multivariable Fine–Gray competing risk regression. Participants were followed until the first event, non-CVD death (as a competing risk), or end of follow-up (March 15, 2018). Results Six risk factor clusters were identified: cluster 1 (15%, n=3601) of young women with low risk factor burden, cluster 2 (21%, n=4792) of young women with mildly elevated blood pressure, cluster 3 (13%, n=2828) of men with impaired kidney function, cluster 4 (15%, n=3504) of individuals with overweight and elevated triglycerides, cluster 5 (19%, n=4683) of older individuals with elevated blood pressure, and cluster 6 (18%, n=4173) of older individuals with high risk factor burden (Figure 1). Relative to cluster 1, the highest risk of a first CVD event was observed in cluster 6 (aHR 1.79, 95%CI 1.58-2.03), followed by cluster 5 (aHR 1.62, 95%CI 1.43-1.82), cluster 4 (aHR 1.49, 95%CI 1.32-1.69), cluster 3 (aHR 1.28 95%CI 1.12-1.46) and cluster 2 (aHR 1.15 95%CI 1.02-1.30) (Figure 2). Conclusion Six risk factor clusters were identified in individuals without prior CVD, each showing different associations with cardiovascular events. This latent class approach offers a more nuanced understanding of how co-occurring risk factors for atherosclerosis contribute to CVD development and may support cluster-targeted prevention strategies.For image description, please refer to the figure legend and surrounding text. For image description, please refer to the figure legend and surrounding text.