Study Population

The study included 470,478 participants with individual-level data, including 367,641 participants who were enrolled in the U.K. Biobank study and 102,837 participants who were enrolled in one of the 14 prospective cohort or case–control studies as part of the Database of Genotypes and Phenotypes program of the National Center for Biotechnology Information. The study also included 184,305 participants enrolled in one of the 48 prospective cohort, case–control, or cross-sectional studies included the Coronary Artery Disease Genomewide Replication and Meta-Analysis plus the Coronary Artery Disease (CARDIoGRAMplusC4D) consortium and 159,208 participants enrolled in one of the 18 studies included in the Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) consortium for whom summary-level data were available.10-13 Written informed consent was obtained from all the participants in each of the contributing studies. A description of the included studies and the genotyping platforms that were used in each study is provided in Table S1 in the Supplementary Appendix, available with the full text of this article at NEJM.org.

Genetic Instruments

We constructed the genetic score for ACLY by combining all variants within ACLY (and also within 500 kb on either side of it) that were associated with plasma LDL cholesterol levels at a level of significance of less than 0.05, conditional on all other variants included in the score, and that were in low linkage disequilibrium (r2<0.3).14,15 Of the two alternative ACLY variants (or alleles) at each locus, the exposure allele was defined as the allele associated with a lower plasma LDL cholesterol level.15,16 For each study participant, we calculated an ACLY genetic score by adding the number of LDL cholesterol–lowering alleles that he or she had inherited at each variant that was included in the ACLY score, weighted by the conditional effect of each variant on plasma LDL cholesterol levels measured in milligrams per deciliter. For comparison, we also constructed genetic scores for HMGCR and NPC1L1, which encode proteins targeted by statins and ezetimibe, respectively, as previously described.6-8

Study Outcomes

The primary efficacy outcome for the study was major cardiovascular events (defined as a composite of the first occurrence of myocardial infarction, coronary revascularization, ischemic stroke, or coronary death). The key secondary outcome was myocardial infarction. The primary safety outcome was any type of cancer, and the key secondary safety outcome was diabetes. Changes in plasma lipid levels, lipoprotein levels, and the lipoprotein particle concentration were used to compare the lipidomic signatures of the ACLY and HMGCR scores. A description of the data used in each analysis is provided in Table S2 in the Supplementary Appendix.

Study Design and Oversight

The primary analysis measured the association between the ACLY score and changes in plasma lipid levels, lipoprotein levels, and the risk of major cardiovascular events. To assess for an effect modification between ATP citrate lyase inhibition and either HMGCR or NPC1L1 inhibition, we measured the association between the ACLY genetic score and plasma lipoprotein levels and the risk of major cardiovascular events stratified according to the HMGCR and NPC1L1 genetic scores, respectively. To evaluate the combined effect of ATP citrate lyase inhibition with either HMGCR or NPC1L1 inhibition, we evaluated the combined association of the ACLY and HMGCR or NPC1L1 scores with changes in plasma lipid levels, lipoprotein levels, and the risk of major cardiovascular events using a two-by-two factorial mendelian randomization analysis.6-9 Although this study was funded by the manufacturer of bempedoic acid, the sponsor had no access to the data; had no role in the design, conduct, or analysis of the study; had no role in the drafting of the manuscript or its content; and did not participate in the decision to submit the manuscript for publication.

Statistical Analysis

We estimated the association of each genetic score with continuous outcomes, including plasma lipid and lipoprotein levels, using linear regression, and we estimated the association of each genetic score with the risk of major cardiovascular events and other dichotomous outcomes using logistic regression. All regression analyses were performed separately in each of the included studies with adjustment for age, sex, and the first five principal components of ancestry for participants with individual-level data, or with the ratio of effect estimates method for studies that provided summary-level data. These effect estimates were then combined across studies in a fixed-effects inverse-variance–weighted meta-analysis to produce summary estimates of effect with the use of a previously reported method that accounts for correlation among variants included in a genetic score.17 To compare the association of the genetic scores with changes in plasma lipoprotein levels and the risk of cardiovascular events, we adjusted each effect-size estimate to scale the results for a standard decrement of 10 mg per deciliter (0.26 mmol per liter) in the LDL cholesterol level.

All analyses were performed with the use of Stata software, version 14 (StataCorp), R software, version 3.2.2 (R Foundation for Statistical Computing), and SNP & Variation Suite software, version 8.1.4 (Golden Helix). A detailed description of the methods, including a description of both mendelian randomization and factorial mendelian randomization, is provided in the Methods section in the Supplementary Appendix.