AB1088 ASSOCIATION OF GENETICALLY PROXIED LIPID-LOWERING DRUG TARGETS WITH RISK OF AUTOIMMUNE DISEASES: A MENDELIAN RANDOMIZATION STUDY
Authors: L. Chen, J. Zhang
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Lipid metabolism has a unique role in autoimmune diseases(ADs)<sup>1</sup>. Previous observational studies have shown that lipid-lowering drugs had protective effects against ADs<sup>2</sup>. While blood lipids had a significant impact on ADs, the causal association between lipid-lowering drugs and the risk of ADs was still elusive. Mendelian randomization (MR) studies were an alternative method that estimated the causal effects of exposure on the risk of diseases with genetic variants, including single nucleotide polymorphisms (SNPs), by using genetic variants as an instrument to perform causal inference between an exposure and an outcome<sup>3</sup>. <h3>Objectives:</h3> To investigate the causal association between genetically proxied lipid-lowering drugs(HMGCR inhibitiors, PCSK9 inhibitors and NPC1L1 inhibitors) and ADs. <h3>Methods:</h3> A two-sample MR analysis was undertaken using summary data derived from genome-wide association studies (GWAS) pertaining to systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), systemic sclerosis (SSc) and coronary artery disease(CAD). GWAS summary-level data for ADs were obtained from the UK Biobank, accessed through the IEU Open GWAS project. The use of SNPs situated in the vicinity of drug target genes such as HMGCR, PCSK9, and NPC1L1 served as surrogates for statins, PCSK9 inhibitors, and ezetimibe treatment, respectively. The main outcome was determined using the inverse-variance weighted (IVW) approach. In addition to IVW, the additional MR analysis methods (simple mode, weighted mode, weighted median, and MR Egger) were used. Furthermore, sensitivity analysis was conducted to assess the stability and reliability of the MR analysis findings. For the instrument from LDL cholesterol GWAS, we performed a positive control study by examining the association of exposures of interest with CAD. Figure 1 depicts an overview of the study. <h3>Results:</h3> A total of 7, 12, and 3 SNPs within or nearby genes HMGCR, PCSK9, and NPC1L1 were selected from a GWAS summary of LDL cholesterol levels in the Global Lipids Genetics Consortium, respectively. In Figure 2, IVW-MR analysis found suggestive evidence for the association of the increased expression of the PCSK9 gene in blood with the higher risk of SLE (OR = 1.739, 95% CI = 1.226−2.468, P = 0.002), indicating that PCSK9 inhibitors might lower the risk of SLE and further supporting a possible protective effect of PCSK9 inhibitors against SLE. In contrast, genetically determined HMGCR gene was associated with low odds of SSc(OR = 0.152, 95% CI = 0.042–0.561, P = 0.004),indicating that HMGCR inhibitors might higher the risk of SSc.The horizontal pleiotropy test (HMGCR, intercept = -0.040, P = 0.685; PCSK9, intercept = -0.001, P = 0.976; NPC1L1, intercept = 0.042, P = 0.874) revealed no directional pleiotropy. According to the Cochran's Q test (HMGCR, Q = 2.424, P = 0.787; PCSK9, Q = 13.682, P = 0.187; NPC1L1, Q = 1.989, P = 0.368), there was no heterogeneity. The leave-one-out study demonstrated the reliability and stability of the obtained result. No consistent evidence from MR analyses was found for other association with RA. The three medications substantially decreased the risk of CAD, as anticipated. <h3>Conclusion:</h3> This two-sample MR study suggested a potential causal relationship between PCSK9 inhibition and the reduced risk of SLE, while genetically proxied inhibition of HMGCR was significantly associated with higher odds of SSc. <h3>REFERENCES:</h3> [1] Anaya, J.M., et al., Common mechanisms of autoimmune diseases (the autoimmune tautology). Autoimmun Rev, 2012. 11(11): 781-784. [2] Kwon, M.J., et al., Incident Rheumatoid Arthritis Following Statin Use: From the View of a National Cohort Study in Korea. J Pers Med, 2022. 12(4): 559. [3] Lawlor, D.A., et al., Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med, 2008. 27(8): 1133-1163. <h3>Acknowledgements:</h3> We would like to thank the original study participants and investigators who supplied the GWAS data. <h3>Disclosure of Interests:</h3> <b>None declared.</b>