Analyzing the causal relationship between lipid-lowering drug target genes and epilepsy: a Mendelian randomization study

Background

Previous research has yielded conflicting results on the link between epilepsy risk and lipid-lowering medications. The aim of this study is to determine whether the risk of epilepsy outcomes is causally related to lipid-lowering medications predicted by genetics.

Methods

We used genetic instruments as proxies to the exposure of lipid-lowering drugs, employing variants within or near genes targeted by these drugs and associated with low-density lipoprotein cholesterol (LDL cholesterol) from a genome-wide association study. These variants served as controlling factors. Through drug target Mendelian randomization, we systematically assessed the impact of lipid-lowering medications, including HMG-CoA reductase (HMGCR) inhibitors, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, and Niemann-Pick C1-like 1 (NPC1L1) inhibitors, on epilepsy.

Results

The analysis demonstrated that a higher expression of HMGCR was associated with an elevated risk of various types of epilepsy, including all types (OR = 1.17, 95% CI:1.03 to 1.32, p = 0.01), focal epilepsy (OR = 1.24, 95% CI:1.08 to 1.43, p = 0.003), and focal epilepsy documented with lesions other than hippocampal sclerosis (OR = 1.05, 95% CI: 1.01 to 1.10, p = 0.02). The risk of juvenile absence epilepsy (JAE) was also associated with higher expression of PCSK9 (OR = 1.06, 95% CI: 1.02 to 1.09, p = 0.002). For other relationships, there was no reliable supporting data available.

Conclusion

The drug target MR investigation suggests a possible link between reduced epilepsy vulnerability and HMGCR and PCSK9 inhibition.

Robust use of phenotypic heterogeneity at drug target genes for mechanistic insights: Application of cis-multivariable Mendelian randomization to GLP1R gene region

Phenotypic heterogeneity at genomic loci encoding drug targets can be exploited by multivariable Mendelian randomization to provide insight into the pathways by which pharmacological interventions may affect disease risk. However, statistical inference in such investigations may be poor if overdispersion heterogeneity in measured genetic associations is unaccounted for. In this work, we first develop conditional F statistics for dimension-reduced genetic associations that enable more accurate measurement of phenotypic heterogeneity. We then develop a novel extension for two-sample multivariable Mendelian randomization that accounts for overdispersion heterogeneity in dimension-reduced genetic associations. Our empirical focus is to use genetic variants in the GLP1R gene region to understand the mechanism by which GLP1R agonism affects coronary artery disease (CAD) risk. Colocalization analyses indicate that distinct variants in the GLP1R gene region are associated with body mass index and type 2 diabetes (T2D). Multivariable Mendelian randomization analyses that were corrected for overdispersion heterogeneity suggest that bodyweight lowering rather than T2D liability lowering effects of GLP1R agonism are more likely contributing to reduced CAD risk. Tissue-specific analyses prioritized brain tissue as the most likely to be relevant for CAD risk, of the tissues considered. We hope the multivariable Mendelian randomization approach illustrated here is widely applicable to better understand mechanisms linking drug targets to diseases outcomes, and hence to guide drug development efforts.