Partitioning the Genetic Architecture of Plasma Lipoprotein(a) and Kringle IV Type 2 Repeats: Implications for Therapeutic Lowering

Circulating low-density lipoprotein cholesterol (LDL-c)3 remains the most widely accepted clinical risk factor and target of pharmacotherapy used in the management of lipid and lipoprotein-related risk of atherosclerotic cardiovascular disease (ASCVD). However, in the past decade, it has become increasingly clear that other lipid and lipoprotein risk pathways exist outside of the scope of what is captured by LDL-c (1). Like LDL-c, many of these lipids and lipoproteins can be up-taken into atherosclerotic plaque, where they incite an inflammatory cascade leading to the genesis and propagation of atherosclerosis. As LDL-c continues to decline in the population with the broadening use of pharmacotherapy and lifestyle interventions, it is anticipated that the relative importance of many of these non-LDL-c lipid and lipoprotein risk factors will become increasingly important. Therefore, understanding and characterizing such pathways has taken on greater relevance in the primary and secondary prevention of ASCVD. Lipoprotein(a) [Lp(a)] is likely one such risk factor (2), although the cholesterol content of Lp(a) is included in Friedewald-calculated LDL-c and in most direct measurements of LDL-c. Lp(a) is a complex LDL-like lipoprotein consisting of an apo(a) molecule covalently bound to another apolipoprotein, apoB. Plasma concentrations of Lp(a) are predominantly under the influence of genetic variations in the LPA gene coding for apo(a). Apo(a) is a surface protein that varies in size between individuals due to polymorphisms causing a variable number of kringle IV type 2 repeats (KIVRs), which are translated into proteins of varying size [apo(a) isoforms]. An inverse correlation exists between the size of the apo(a) isoform and the Lp(a) plasma concentration, such that large apo(a) isoforms are associated with low Lp(a) plasma concentrations and vice versa, with up to a 1000-fold difference in Lp(a) concentration noted between individuals. Approximately half of the genetically based variations in Lp(a) concentration are related to variations in the LPA gene, and the other half are related to the variable number of KIVRs (2). Epidemiologic studies have linked increased Lp(a) with risk for atherothrombosis (3). Several studies have also observed that genetic variants that lead to naturally increased Lp(a) were also associated with risk of ASCVD, consistent with a potentially causal association (4, 5). Mechanisms of risk could relate to proinflammatory and prothrombotic effects, as well as to phospholipid oxidation (6). The enthusiasm for Lp(a) as a potential therapeutic target was further extended with observations from genome-wide association studies (GWAS) that Lp(a) variants were associated with the development of calcific aortic stenosis (7). Several available pharmacotherapies, including proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, cholesterol ester transfer protein (CETP) inhibitors, mipomersen, and niacin reduce Lp(a) by approximately 20%–40%. Indevelopment antisense therapeutics can also reduce Lp(a) by 80%–90% (6, 8). Targeting these alternate lipid and lipoprotein-related risk pathways may be of incremental value beyond LDL-c reduction, even in select individuals without increased LDL-c. This was highlighted by observations from the JUPITER trial, where even among individuals with very effective LDL-c lowering on high-intensity statins (mean on-statin LDL-c 54 mg/dL; 1.4 mmol/L), high Lp(a) concentrations were associated with a 20%–30% increased relative risk of incident ASCVD events (9). Intriguingly, statin therapy shifted the Lp(a) distribution positively, suggesting that statins may increase Lp(a) concentrations in some individuals (9). In parallel with these observations, however, low Lp(a) concentrations have been associated with increased risk of incident diabetes mellitus. Among initially healthy women participating in the Women's Health Study, baseline Lp(a) concentration was inversely associated with incident diabetes, with an adjusted increased relative risk of 28% among those in the lowest vs the highest quintiles, which was replicated in the Copenhagen City Heart Study of men and women (10). The association was subsequently also validated in another Danish cohort, the Copenhagen General Population Study (11). These observations have raised the potential concern that therapies that lower Lp(a) concentration may have the adverse effect of increasing diabetes risk. The mechanisms underlying the increased risk of diabetes with low Lp(a) are unknown, although they are consistent across studies and do not appear to be confounded by standard factors. Furthermore, it is uncertain whether this potential risk of diabetes is related to Lp(a) itself or to other factors, such as the isoform size of Lp(a) or KIVR number. Hence, Tolbus et al. undertook a Mendelian randomization study aimed to partition the genetic architecture of Lp(a) concentration and composition by identifying genetic variants selectively associated with Lp(a) concentration or KIVR number (12). They performed genotyping in 8411 individuals from the Copenhagen City Heart Study for 778 single-nucleotide polymorphisms (SNPs) in the LPA gene region and examined the association of these SNPs with plasma Lp(a) concentrations and KIVR number. They identified 3 candidate SNPs that were selectively associated with low Lp(a) concentration alone (without an effect on KIVR number; rs12209517, rs12194138, rs641990), and 3 that were selectively associated with KIVR number (rs1084651, rs9458009, and rs9365166). By separating these genetic effects, they performed an elegant Mendelian randomization analysis separating the effects of natural variation in the components of the Lp(a) molecule in relation to subsequent diabetes risk. Tolbus et al. observed that individuals with SNPs selectively associated with Lp(a) concentration and not KIVR number (i.e., those with an allele score of 4–6 vs 0–2, in whom Lp(a) concentrations were naturally low) had an odds ratio (95% CI) for diabetes of 1.03 (0.86–1.23). Conversely, individuals with SNPs selectively associated with KIVR number and not Lp(a) concentration (in whom KIVR number was naturally high) had an odds ratio (95% CI) of 1.42 (1.17–1.69). Hence, it appeared that KIVR number and associated Lp(a) isoform size, and not the concentration of Lp(a) per se, that may be the more important determinant of diabetes risk. This contrasts with recent findings from another Mendelian randomization study that found that both smaller isoform size and greater Lp(a) concentration were independent risk factors for coronary heart disease risk (13). Numerous questions arise from these novel findings. First, the potential pathophysiologic mechanisms underlying these observations remain very much open to investigation. Second, while seemingly reassuring that pharmacotherapies that lower Lp(a) may not confer excess diabetes risk, it is uncertain what other structural changes in Lp(a) may occur in response to Lp(a)-modulating therapies and what effects these may have on diabetes risk. Furthermore, there are potential inherent limitations of Mendelian randomization studies. Pleiotropic effects of the instrumental SNPs—that is, additional effects of the SNP on other diabetes risk pathways that may go unmeasured—can influence the observed associations. Whether this could be the case here will require further investigation. Also, linkage disequilibrium can imply that variants in genes in close proximity to each other may segregate (travel together) during recombination or random assortment. Hence, it may be a geographically related genetic variant in another gene that underlies the observed association with the SNP under study. Reassuringly, the investigators point out that the LPA KIV-2 SNPs that were associated with diabetes were not in linkage disequilibrium with other genes implicated in risk of diabetes. How should these findings be accelerated into clinical practice and research? The study findings highlight the important complex roles that lipid and lipoprotein species (beyond LDL-c) play in modulating cardiometabolic risk. When there is discordance between LDL-c and LDL particle number (or apoB), ASCVD risk tracks with the particle number (14), and genetics studies have recently supported that finding (15). In the current study, Tolbus et al. provided a sophisticated genetic approach to suggest that Lp(a)–diabetes risk tracks with discordance in KIVR number and not with Lp(a) concentration, providing an important step in better understanding Lp(a)-related risk. Finally, in evaluating emerging pharmacotherapies for their potential efficacy and adverse effects, investigators should consider incorporating assays of Lp(a) isoform size in addition to Lp(a) concentration as intermediate biomarkers along the pathway of cardiometabolic risk. Approaching these common pathways of disease with an inclusive lens will be important to identify and optimally treat ASCVD and diabetes. low-density lipoprotein cholesterol atherosclerotic cardiovascular disease kringle IV type 2 repeats genome-wide association studies proprotein convertase subtilisin/kexin type 9 cholesterol ester transfer protein single-nucleotide polymorphism.

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