Association of lipoprotein profile with cardiovascular disease and its risk factors
Approved Research ID: 100430
Approval date: April 18th 2023
Cardiovascular disease (CVD) is the leading cause of death globally. The previous two decades of cardiovascular research has largely focused on reducing 'bad cholesterol', also known as low-density lipoprotein cholesterol. Several therapeutic interventions have been developed to effectively reduce this bad cholesterol, however, even in those patients with normal or low levels of bad cholesterol the incidence of cardiovascular disease remains unacceptably high. This phenomenon is termed residual risk and to further understand this residual risk we aim to use more detailed lipoprotein measures. Nuclear magnetic resonance (NMR) spectroscopy is able to provide a comprehensive analysis of not only total plasma cholesterol or triglyceride concentration but also precise details on lipoprotein particle size, number of particles and their composition. In smaller cohorts, NMR-derived lipoprotein parameters have been shown to have superior predictive power compared to standard lipid measurements for future CVD events. However, as of yet no one has confirmed these findings in a large, diverse cohort such as that of the UK Biobank.
Therefore, the aim of this study is to comprehensively assess the relationship of lipoprotein distribution with CVD and its risk factors. Risk factors for CVD that will be investigated in this study included diabetes, inflammation and subclinical atherosclerosis. This project should take approximately 24 months to complete.
By providing robust evidence on the association of lipoprotein distribution with CVD and its risk factors, results of this study will help better understand the mechanisms of disease at play in CVD. Better understanding of disease mechanisms will aid drug development and hopefully enhance therapeutic options available for the treatment/prevention of CVD. Moreover, by testing the predictive power of various lipoprotein variables we aim to improve risk prediction for CVD in both the primary and secondary prevention settings. This will facilitate early intervention of disease and ultimately improve long-term health outcomes for many.