Unraveling the Genetic Basis and Cardiovascular Impact of Lipoprotein(a) in Diverse Populations

Philadelphia Va Medical Center, Philadelphia PA

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Abstract

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of morbidity and mortality in the United States, disproportionately affecting Veteran and minority populations. Lipoprotein(a) [Lp(a)], a complex lipoprotein particle found in the circulation, has emerged as a novel causal risk factor for ASCVD. The Black population has >3-4-fold increased levels of Lp(a) compared to other populations, and continues to be disproportionately affected by ASCVD, with earlier age-of-onset and higher mortality. While genetic variation is thought to strongly influence Lp(a) levels, a key knowledge gap remains: the genetic causes and consequences of elevated Lp(a) have not been systematically characterized across diverse populations. The overarching objective of this proposal is to characterize the population-specific genetic architecture of Lp(a), leveraging this understanding to quantify the downstream consequences on ASCVD outcomes across vascular beds and identify individuals who will benefit most from emerging Lp(a)-lowering therapies. In Aim 1 we will perform genetic association studies of Lp(a) across genetic biobanks (including the VA Million Veteran Program) and cohort studies to characterize the impact of DNA sequence variation on Lp(a) levels among >350,000 individuals of diverse ancestry. We will leverage these genetic associations within a Mendelian randomization framework to quantify the impact of Lp(a) on ASCVD across vascular beds and compare these effects across populations. In Aim 2, we will apply newly developed statistical genetics techniques to identify genomic loci where genetic and environmental modifiers act to influence Lp(a) levels. We will compare these loci across populations to pinpoint novel susceptibility patterns that increase or mitigate risk of Lp(a)-related ASCVD. In Aim 3, we will assess whether clinical and genomic models can accurately identify individuals with undiagnosed elevations in Lp(a). We will develop polygenic risk scores for Lp(a), quantify their performance across diverse populations, and determine whether these scores improve the performance of models designed to predict Lp(a) levels based on clinical variables alone. We will then apply these clinical and genomic prediction models to the VA Corporate Data Warehouse and MVP datasets to identify Veterans with undiagnosed elevations in Lp(a). Successful completion of this project will clarify the role of Lp(a) on ASCVD across vascular beds among diverse populations, identifying opportunities to reduce the burden of ASCVD. These outcomes will lay the groundwork for future precision-medicine approaches to specifically prevent and treat Lp(a)-associated ASCVD among the Veteran population.