A large reduction in Lp(a) is necessary to achieve a meaningful reduction of CHD risk
Association of LPA Variants With Risk of Coronary Disease and the Implications for Lipoprotein(a)-Lowering Therapies: A Mendelian Randomization Analysis
Introduction and Methods
Higher plasma lipoprotein(a) [Lp(a)] concentrations have been associated with a higher risk of coronary heart disease (CHD) in a dose-dependent manner, and data suggest that this association is causal [1,2]. On the other hand, lowering Lp(a) concentrations by 20% to 35% with niacin, cholesterol ester transfer protein inhibitors, and PCSK9 inhibitors, did not reduce the risk of CV events as would be expected [3,4]. It is not clear which degree of Lp(a) concentration lowering might lead to a clinically meaningful reduction of CV events.
In this Mendelian randomization analysis, it was evaluated how much Lp(a) must be reduced pharmacologically to produce a clinically meaningful reduction in CHD risk, in order to determine who is most likely to benefit from treatment with Lp(a)-lowering therapy. For this purpose, 48,333 participants of European descent from 5 studies were genotyped, out of which 20,793 had CHD, and the findings were validated using data from the Coronary Artery Disease Genome Wide Replication and Meta-analysis and the Coronary Artery Disease (C4D) Genetics (CARDIOGRAMplusC4D) consortium, including up to 62,240 patients and 127,299 controls.
A weighted LPA genetic score was calculated, based on which participants were divided into deciles, and the association between each decile of genetically predicted Lp(a) concentration and the risk of CHD was assessed. The absolute reduction in Lp(a) concentration necessary to alter the CHD risk similarly to a 38.67 mg/dL decrease in LDL-c level was estimated.
- Fixed changes in absolute Lp(a) concentrations led to equal odds ratios (ORs) for CHD regardless of the starting Lp(a) concentration, supporting a log-linear association between the risk of CHD and absolute changes in Lp(a) concentration.
- Each 10 mg/dL lower genetically predicted Lp(a) level was associated with a 5.8% lower risk of CHD (OR: 0.942; 95%CI: 0.933-0.951; P=3 × 10−37).
- Using the LDL-c genetic score, a 10-mg/dL genetically predicted lower LDL-c level was associated with a 14.5% lower risk of CHD (OR: 0.855; 95%CI: 0.818-0.893; P=2 × 10−12).
- Thus, a 38.67 mg/dL decrease in LDL-c levels has an equivalent CHD risk reduction effect with a 101.5 mg/dL (95%CI: 71.0-137.0) decrease in Lp(a) concentration.
- The relative risk reduction of lowering Lp(a) concentration is likely to be independent of lowering LDL-c levels with statins, PCSK9 inhibitors and ezetimibe.
In this Mendelian randomization analysis, the association of genetically predicted Lp(a) with CHD risk was linearly proportional to the absolute change in Lp(a) concentration. To achieve clinically meaningful reductions in the risk of CHD similar to those accomplished by lowering LDL-c levels by 38.67 mg/dL (1 mmol/L), Lp(a) concentrations have to be decreased by approximately 100 mg/dL.
In his editorial article, O’Donnel  starts by reminding us that ‘the aim of precision medicine is to direct the right treatment to the right person at the right dose at the right time.’ He summarizes the findings of Burgess et al, emphasizes the need for further studies in diverse patient populations and examining the associations between Lp(a) lowering and other atherosclerotic outcomes beyond CHD, and he concludes: ‘Where should MR data be placed in the spectrum of evidence ranging from observational studies to large-scale randomized trials for clinical guidelines? At present, these data may be viewed as a stronger level of observational data. While MR studies may lend support or cast doubt on whether an observational association may infer causality, data from human randomized clinical trials remain the strongest level of evidence for clinical practice. Nevertheless, the opportunity to harness MR data for all types of precision medicine questions will certainly increase as immense samples of populations with deep genomic data become available from biobanks, such as the UK Biobank and the VA Million Veteran Program.’