The interplay between cytokines and stroke: a bi-directional Mendelian randomization study

Abstract

Stroke, the second leading cause of death and disability, causes massive cell death in the brain followed by secondary inflammatory injury initiated by disease associated molecular patterns released from dead cells. Nonetheless, the evidence regarding the causal relationship between inflammatory cytokines and stroke subtypes is obscure. To leverage large scale genetic association data to investigate the interplay between circulating cytokines and stroke, we adopted a two-sample bi-directional Mendelian randomization (MR) analysis. Firstly, we performed a forward MR analysis to examine the associations of genetically determined 31 cytokines with 6 stroke subtypes. Secondly, we conducted a reverse MR analysis to check the associations of 6 stroke subtypes with 31 cytokines. In the forward MR analysis, genetic evidence suggests that 21 cytokines were significantly associated with certain stroke subtype risk with |β| ranging from 1.90 × 10^-4 to 0.74. In the reverse MR analysis, our results found that five stroke subtypes (intracerebral hemorrhage (ICH), large artery atherosclerosis ischemic stroke (LAAS), lacunar stroke (LS), cardioembolic ischemic stroke (CEI), small-vessel ischemic stroke (SV)) caused significantly changes in 16 cytokines with |β| ranging from 1.08 × 10^-4 to 0.69. In particular, those five stroke subtypes were statistically significantly associated with C-reactive protein (CRP). In addition, ICH, LAAS, LS and SV were significantly correlated with vascular endothelial growth factor (VEGF), while LAAS, LS, CEI and SV were significantly related to fibroblast growth factor (FGF). Moreover, integrated bi-directional MR analysis, these factors (IL-3Rα, IL-6R, IL-6Rα, IL-1Ra, insulin-like growth factor-1(IGF-1), IL-12Rβ2) can be used as predictors of some specific stroke subtypes. As well as, IL-16 and C-C motif chemokine receptor 7 (CCR7) can be used as prognostic factors of stroke. Our findings prognostic identify potential pharmacological opportunities, including perturbation of circulating cytokines for both predicting stroke risk and post stroke treatment effects. As we conducted a comprehensive search and analysis of stroke subtype and cytokines in the existing publicly available GWAS database, the results have good population-generalizability.

Keywords: Causality; Cytokine; Inflammation; Mendelian randomization; Stroke.

Figure 1

Study design of the two-sample bi-directional MR for the effect of genetically predicted the relationship between cytokines and stroke subtypes. Hypothesis 1: the genetic variant (SNP) is robustly and strongly associated with the exposure of interest; Hypothesis 2: the genetic variant must be independent of confounders; Hypothesis 3: the genetic variant influences the outcome only through the exposure of interest, not through other pathways. SNPs, single-nucleotide polymorphisms. ICH intracerebral hemorrhage, SAH subarachnoid hemorrhage, LAAS large artery atherosclerosis ischemic stroke, LS lacunar stroke, CEI cardioembolic ischemic stroke, SV small-vessel ischemic stroke.

Figure 2

Effects of potential causal cytokines on six stroke subtypes outcomes. MR analyses of the effect of cytokines on stroke outcomes. The squares are the causal estimates on the OR scale, and the whiskers represent the 95% confidence intervals for these ORs. (A) OR < 1 indicates a negative correlation between cytokines and stroke risk. (B) OR > 1 indicates a positive correlation between cytokines and stroke risk. (C) Cytokines with OR value greater than 1 or less than 1 with different stroke subtypes. OR odds ratio. Other abbreviations can be found in the Abbreviation.

Figure 3

Mendelian randomization effect size estimates (Z-scores) of genetically predicted five categories of cytokines traits on six stroke subtypes outcome and causal risk factors for stroke. Colors in each lattice of the heatmap represent the effect size (Z-score), with genetically predicted increased cytokines level associated with a higher risk of stroke outcomes colored in red and lower risk of outcomes colored in blue. The darker the color, the larger the effect size. *Indicates that the causal association is significant. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 4

Effects of five potential causal stroke subtypes on cytokines outcomes. MR analyses of the effect of stroke on cytokines outcomes. (A–E) represents the OR values of statistically significant results in ICH (A), LAAS (B), LS (C), CEI (D), SV (E) and cytokines, respectively. The squares are the causal estimates on the OR scale, and the whiskers represent the 95% confidence intervals for these ORs. The abbreviations can be found in the Abbreviation.

Figure 5

Mendelian randomization effect size estimates (Z-scores) of genetically predicted six stroke subtype traits on 31 cytokines outcome. Colors in each lattice of the heatmap represent the effect size (Z-score), with genetically predicted stroke associated with a higher level of cytokines colored in red and lower level of cytokines colored in blue. The darker the color the larger the effect size. *Indicates that the causal association is significant. *P < 0.05, **P < 0.01, ***P < 0.001. The abbreviations can be found in the Abbreviation.