Causal relationship between inflammatory factors and cerebral small vessel disease: Univariate, multivariate, and summary-data-based mendelian randomization analysis.

OBJECTIVE: To explore the impact of inflammatory factors on the incidence of cerebral small vessel disease (CSVD), we performed a mendelian randomization (MR) study to analyze the causal relationship between multiple inflammatory factors and CSVD imaging markers and utilized summary-data-based mendelian randomization (SMR) analysis to infer whether the impact of instrumental variables (IVs) on disease is mediated by gene expression or DNA methylation. METHODS: Using public databases such as UKB and IEU, and original genome-wide association studies, we obtained IVs related to exposure (inflammatory factors) and outcome (CSVD imaging markers). We performed the inverse variance weighted, weighted median, and MR-Egger methods to assess causal effects between exposure and outcome in univariate MR analysis. To evaluate their heterogeneity, a series of sensitivity analyses were conducted, including the Cochrane Q test, MR-Egger intercept test, MR-Presso, and leave-one-out analysis. We also applied mediation and multivariate MR analysis to explore the interactions between positive exposures on the same outcome. Additionally, we conducted the SMR, which utilizes instruments within or near relevant genes in blood or brain tissues, to elucidate the causal associations with CSVD markers. RESULTS: ABO Univariate MR of multiple cohorts revealed that the risk of small vessel stroke (SVS) increases with elevated levels of TNF-related apoptosis-inducing ligand (TRAIL, OR, 1.23, 95% CI, 1.08-1.39) and interleukin-1 receptor-like 2, (IL-1RL2, OR, 1.29, 95% CI, 1.04-1.61). IL-18 was a potential risk factor for extensive basal ganglia perivascular space burden (BGPVS, OR, 1.02, 95% CI, 1.00-1.05). Moreover, the risk of extensive white matter perivascular space burden (WMPVS) decreased with rising levels of E-selectin (OR, .98, 95% CI, .97-1.00), IL-1RL2 (OR, .97, 95% CI, .95-1.00), IL-3 receptor subunit alpha (IL-3Ra, OR, .98, 95% CI, .97-1.00), and IL-5 receptor subunit alpha (IL-5Ra, OR, .98, 95% CI, .97-1.00). Mediation and multivariate MR analysis indicated that E-selectin and IL-3Ra might interact during the pathogenesis of WMPVS. SMR estimates showed that TRAIL-related IVs rs5030044 and rs2304456 increased the risk of SVS by increasing the expression of gene Kininogen-1 (KNG1) in the cerebral cortex, particularly in the frontal cortex (ßsmr = .10, Psmr = .003, FDR = .04). Instruments (rs507666 and rs2519093) related to E-selectin and IL-3Ra could increase the risk of WMPVS by enhancing DNA methylation of the gene ABO in blood tissue (ßsmr = .01-.02, Psmr = .001, FDR = .01-.03). CONCLUSION: According to MR and SMR analysis, higher levels of TRAIL increased the risk of SVS by upregulating gene expression of KNG1 in brain cortex tissues. In addition, protective effects of E-selectin and IL-3a levels on WMPVS were regulated by increased DNA methylation of gene ABO in blood tissue.

Twenty-three medication-taking traits and stroke: A comprehensive Mendelian randomization study.

Background: Certain medication categories may increase the risk of stroke. Nonetheless, the evidence regarding the causal relationship of medication-taking in promoting stroke and subtypes is deficient. Methods: We evaluated the causal effect of a genetic predisposition for certain medication categories on stroke and subtypes (ischemic and hemorrhagic categories) by a two-sample Mendelian randomization (MR) analysis. Data for 23 medication categories were gathered from a genome-wide association study (GWAS) involving 318,177 patients. The Medical Research Council Integrative Epidemiology Unit Open GWAS database and the FinnGen consortium were used to gather GWAS data for stroke and subtypes. Inverse variance weighted, MR-Egger, and weighted median were used for the estimation of causal effects. Cochran's Q test, MR-Egger intercept test, and leave-one-out analysis were used for sensitivity analyses. Results: Ten medication categories were linked to a high stroke risk. Nine categories were linked to a high-risk ischemic stroke. Five categories were associated with small vessel ischemic stroke. Nine categories were positively associated with large artery atherosclerotic ischemic stroke. Three categories causally increased the possibility of cardioembolic ischemic stroke. Four categories were associated with intracerebral hemorrhage. Four categories were associated with nontraumatic intracranial hemorrhage. Three categories were causally associated with subarachnoid hemorrhage (SAH). Four categories were associated with the combination of SAH, unruptured cerebral aneurysm, and aneurysm operations SAH. Conclusions: This study confirms that some medication categories lead to a greater risk of strokes. Meanwhile, it has an implication for stroke screening as well as direct clinical significance in the design of conduction of future randomized controlled trials.