Repurposing lipid-lowering drugs as potential treatment for acne vulgaris: a Mendelian randomization study.

Background: Acne vulgaris, a chronic inflammatory skin condition predominantly seen in teenagers, impacts more than 640 million people worldwide. The potential use of lipid-lowering medications as a treatment for acne vulgaris remains underexplored. This study seeks to investigate the impact of lipid-lowering therapies on the risk of developing acne vulgaris using two-sample Mendelian randomization (MR) analysis.
Method: The two-sample MR method was employed for analysis, and information on lipid-lowering drugs was obtained from the DrugBank and ChEMBL databases. The summary data for blood low-density lipoprotein (LDL) and triglycerides were sourced from the Global Lipids Genetics Consortium, while genome-wide association studies (GWAS) summary data for acne vulgaris were obtained from the FinnGen database. Heterogeneity was examined using the Q-test, horizontal pleiotropy was assessed using MR-Presso, and the robustness of analysis results was evaluated using leave-one-out analysis.
Results: The MR analysis provided robust evidence for an association between lowering LDL cholesterol through two drug targets and acne vulgaris, with PCSK9 showing an odds ratio (OR) of 1.782 (95%CI: 1.129-2.812, p  = 0.013) and LDL receptor (LDLR) with an OR of 1.581 (95%CI: 1.071-2.334, p  = 0.021). Similarly, targeting the lowering of triglycerides through lipoprotein lipase (LPL) was significantly associated with an increased risk of acne vulgaris, indicated by an OR of 1.607 (95%CI: 1.124-2.299, p  = 0.009).
Conclusion: The current MR study presented suggestive evidence of a positive association between drugs targeting three genes (PCSK9, LDLR, and LPL) to lower lipids and a reduced risk of acne vulgaris.
Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
(Copyright © 2024 Fang, Lei, Zhang and Zhang.)