Causal impact of statins on susceptibility to osteoarthritis: insights from a two-sample Mendelian randomization
Kefu Yu1, Ziming Li2, Weizhong Shi1
1Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, No.119 South Fourth Ring West Road, Fengtai District, Beijing, 100070, People's Republic of China.
Background:
Osteoarthritis is a widely prevalent cause of pain and disability among older adults. It is an incurable condition, and most treatments are aimed at alleviating symptoms.
Aim:
This study aimed to investigate the impact of statins on osteoarthritis by using a two-sample Mendelian randomization approach, using genetic variants associated with statin use as instrumental variables.
Method:
Information on single nucleotide polymorphisms associated with statin medication was obtained from the FinnGen study, and data on osteoarthritis were sourced from the UK Biobank. The inverse variance weighted method was used as the primary analytical approach for the Mendelian randomization analysis. Sensitivity analyses were conducted to evaluate horizontal pleiotropy and heterogeneity. To examine the genetic relationship between statins and osteoarthritis, linkage disequilibrium score regression-based estimates were used.
Results:
Mendelian randomization analysis indicated a positive effect of statin use on the treatment of osteoarthritis (odds ratio 0.951, 95% confidence interval 0.914-0.99, p < 0.05). This conclusion was supported by various Mendelian randomization methods. Sensitivity analyses revealed no significant directional pleiotropy or influential single nucleotide polymorphisms that could compromise the overall causal inference. Linkage disequilibrium score regression-based estimates suggested a modest genetic correlation between statin use and osteoarthritis (Rg = 0.098, Se = 0.034, p < 0.05), thus reinforcing the robustness of the Mendelian randomization analysis.
Conclusion:
Statins reduce the risk of osteoarthritis, aligning with the results of observational studies. Further research is essential to validate these results and explore the underlying mechanisms in detail.
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