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. 2023 Nov 14;31(2):e16123. doi: 10.1111/ene.16123

Exploring causal association between circulating inflammatory cytokines and functional outcomes following ischemic stroke: A bidirectional Mendelian randomization study

Huacong Liu 1, Zhaoxing Liu 1,2, Yumeng Huang 1,2, Qian Ding 2, Zhenyi Lai 1, Xiaowen Cai 1, Shengtao Huang 1, Lianjun Yin 3, Xiaoyan Zheng 4,✉, Yong Huang 1,✉, Junqi Chen 3,4,✉
PMCID: PMC11235604  PMID: 37961927

Abstract

Objectives

Previous observational studies have indicated correlations between various inflammatory cytokines and functional outcomes following ischemic stroke (IS); however, the causality remains unclear. We aimed to further evaluate the causal association between 41 circulating inflammatory cytokines and functional outcomes following IS.

Methods

Two‐sample bidirectional Mendelian randomization (MR) analysis was used in this study. The genetic variation of 41 circulating inflammatory cytokines were derived from genome‐wide association study (GWAS) data of European ancestry (n = 8293). The corresponding genetic association of functional outcomes following IS were derived from European ancestry GWAS data (n = 6021).

Results

Inverse variance weighted (IVW) analysis showed that genetically predicted increased levels of regulation and activation in normal T‐cell expression and secretion factor (RANTES/CCL5) and eosinophilic chemotactic factor (EOTAXIN/CCL11) were positively correlated with the increased adverse functional outcomes (modified Rankin Scale [mRS≥3] following IS (OR: 1.40, 95% CI: 1.002–1.96, p = 0.049; OR: 1.33, 95% CI: 1.15–1.54, p = 0.0001). Interleukin 18 (IL‐18) level might be the downstream consequence of adverse functional outcomes following IS (β: −0.09, p = 0.039). Other inflammatory cytokines and functional outcomes following IS did not appear to be causally related.

Conclusions

This study suggests a causality between inflammation and adverse functional outcomes following IS. RANTES (CCL5) and EOTAXIN (CCL11) may be the upstream factors of adverse functional outcomes following IS, while IL‐18 may be the downstream effect of adverse functional outcomes following IS. Whether these cytokines can be used to predict or improve adverse functional outcomes after IS requires further researches.

Keywords: functional outcomes, GWAS, inflammatory cytokines, ischemic stroke, Mendelian randomization

INTRODUCTION

Ischemic stroke (IS) is a common clinical cerebrovascular disease. IS accounts for approximately 80% of all strokes, with remarkably high morbidity, mortality, and disability rate, causing substantial medical and economic burdens to society [1]. The mortality and disability rates after IS have decreased, with the improvement and popularization of secondary stroke prevention guidelines, but a certain residual risk persists [2]. It is necessary to continuously explore new risk factors related to adverse outcomes following IS, to further improve the prognosis for IS, and optimize diagnosis and treatment strategies.

Aside from traditional risk factors such as blood pressure, smoking, and sleep [3, 4, 5], as an indispensable part of the pathophysiology of stroke, inflammation assumes a pivotal role in the pathogenesis and prognosis of the disease [6]. Several cytokines and growth factors are involved in the regulation of inflammatory responses [7]. Circulating inflammatory cytokines may provide valuable predictive information for improving post‐stroke outcomes [8], which is also one of the tremendous potential stroke prevention and treatment strategies [9]. The majority of relevant research has predominantly focused on investigating the impact of inflammatory factors on the risk of IS [10, 11, 12]. Prior studies have shown associations between the risk of IS and inflammatory cytokines, such as MCP‐1 and IL‐4 [11, 12]. Limited studies have explored the association between levels of circulating inflammatory cytokines and functional outcomes after IS. Circulating inflammatory cytokines could potentially be one of the mechanisms underlying a poor functional prognosis after IS: elevated concentrations of inflammatory cytokines in the circulation have the potential to traverse the blood–brain barrier compromised by ischemic events and engage with central immune cells, specifically microglia, leading to the initiation of an inflammatory cascade. This cascade can induce or intensify chronic inflammation on either a localized or whole‐brain level, consequently impacting the process of functional recuperation following stroke [9]. Observational studies have suggested that individuals experiencing acute IS, who exhibit higher systemic immune inflammatory indices, may encounter unfavorable short‐term and long‐term prognostic outcomes [13]. Circulating inflammatory factors such as IL‐6 and IL‐37 may be associated with adverse functional outcomes following IS [14, 15]. However, the results of observational studies may be confounded by confounding factors and reverse causality. There is still controversy as to whether inflammatory cytokines are the initial factor or downstream effects of adverse functional outcomes following IS, which still needs further studies for resolution.

Mendelian randomization (MR) analysis utilizes single nucleotide polymorphisms (SNPs) as genetic instrumental variables to represent exposure factors [16]. By employing MR analysis, the impact of reverse causality and confounding factors on the findings is mitigated in comparison to observational studies, thereby offering more robust evidence for causal inference [16, 17]. To evaluate the potential association between circulating inflammatory cytokines and functional outcomes following IS, as well as the direction of this association, we employed a two‐sample bidirectional MR analysis. We first extracted validated genetic instrumental variables of 41 inflammatory cytokines from the genome‐wide association study (GWAS) data and analyzed their associations with functional outcomes in 3 months after IS, and further explored the direction of causality by reversing exposures and outcomes. The study evidence was provided for targeting specific inflammatory cytokines to prevent adverse functional outcomes following IS.

METHODS

Ethics and informed consent

The data used in this study were obtained from two published GWAS studies. These GWAS studies were approved by ethics committees and all subjects involved in the studies were informed and provided written consent.

MR hypothesis

Figure 1 concisely describes the two‐sample bidirectional MR study design of this study. The MR study design consisted of three core assumptions: (1) relevance: the variables selected as genetic instruments are closely associated with exposures; (2) independence: genetic variation is not associated with confounding factors; and (3) exclusion restrictions: genetic variation affects the outcomes only through exposures, instead of other pathways [18]. This study used the published GWAS data of 41 circulating inflammatory cytokines and functional outcomes following IS. First, genetic instrumental variables for each circulating inflammatory cytokine were selected to infer their causal association with functional outcome following IS. Second, genetic instrumental variables associated with functional outcomes following IS were used to infer the causality between functional outcome and each of the circulating inflammatory cytokines.

FIGURE 1.

FIGURE 1

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Mendelian randomization study design. SNP, single nucleotide polymorphism.

Screening of genetic instrumental variables

This study first used a threshold of p < 5 × 10−8 to screen SNPs strongly related to each of the circulating inflammatory cytokines and functional outcomes following IS. However, only a few SNPs were identified, when certain circulating inflammatory cytokines were considered as exposures. Thus, a threshold of p < 5 × 10−6 was selected to screen for exposed genetic instrumental variables. Furthermore, the linkage disequilibrium of instrumental variables was removed to ensure mutual independence of these instrumental variables (r 2 < 0.001, kb = 10,000). The F‐values of the SNPs were calculated to identify the presence of weak instrumental variable bias. There was no weak instrumental bias with F > 10 [19].

GWAS data

GWAS data of functional outcomes following IS

This study used publicly available GWAS data on functional outcomes following IS from the Genetics of Ischemic Stroke Functional Outcomes (GISCOME) network, which included 6021 IS patients of European ancestry [20]. The modified Rankin Scale (mRS) was used to evaluate the functional outcomes in the 3 months after IS. A lower mRS score (0–2) indicates a better functional outcome (n = 3741), and a higher score (3–6) indicates an adverse functional outcome (n = 2280). This study used the results adjusted for age, gender, ancestry, and baseline stroke severity for the primary MR analysis. As a comparison, supplementary MR analyses were performed on GWAS data that were not adjusted for stroke severity.

GWAS data of 41 circulating inflammatory cytokines

The data regarding the 41 circulating inflammatory cytokines were obtained from the reanalyzed outcomes of a serum cytokine‐associated GWAS [21, 22], which can be accessed at https://doi.org/10.5523/bris.3g3i5smgghp0s2uvm1doflkx9x. This GWAS study consists of findings from the Finnish Cardiovascular Risk in Young Adults Study (n = 1980; mean age: male 37.4, female 37.5 years) and the FINRISK survey (FINRISK1997: n = 4608; mean age: male 48.3, female 47.3 years; FINRISK2002: n = 1705; mean age: male 60.4, female 60.1 years), which were collected from 1980 to 2011 and included a total of 8293 individuals of European ancestry. The GWAS data underwent adjustments for age, sex, and the top 10 genetic principal components in order to capture the relationships between genetic variants and inflammatory cytokines. Notably, there were no shared samples between the two GWAS datasets employed in this study.

Statistical analysis

This study employed inverse variance weighting (IVW) as the chief methodology for MR analysis. To assess the presence of heterogeneity among SNPs, Cocrane Q‐tests were employed. Supplementary analyses were conducted using the weighted median and MR‐Egger methods. The MR‐Egger intercept test was utilized to ascertain horizontal pleiotropy [23], while MR‐PRESSO was employed to identify outlier SNPs [24]. The PhenoScanner database was employed for the purpose of searching and screening SNPs associated with confounding factors (blood pressure, diabetes, triglycerides, etc.) or outcome at the level of whole‐genome significance (p < 5 × 10−8) [25]. These identified SNPs were subsequently excluded from MR analysis to ensure the reliability and consistency of the results. To assess the robustness of the findings, a leave‐one‐out analysis was conducted. Additionally, supplementary sensitivity analyses were conducted using weighted mode and simple mode analyses. In cases where the IVW analysis yielded significant results (p < 0.05) and no evidence of horizontal pleiotropy or heterogeneity was observed, the findings could be considered positive, even if the other methods did not yield significant results, as long as the direction of the β‐values remained consistent [26]. In situations where horizontal pleiotropy was present but no heterogeneity was detected, the MR‐Egger method was employed. In instances where heterogeneity was observed without the presence of polytropy, the analysis employed either the weighted median method or the random effects IVW method. The study utilized R 4.2.2 software and the R packages “TwosampleMR” and “MR‐PRESSO” for analysis.

RESULTS

Genetic predictors of 41 circulating inflammatory cytokines and functional outcomes following IS

In order to ensure an adequate number of SNPs for subsequent MR analysis, a significance threshold of p < 5 × 10 −6 was selected when screening SNPs related to each circulating inflammatory cytokine and functional outcome following IS. The F‐statistics for SNPs of the 41 inflammatory cytokines were 20.78–345.09; the F‐statistics for SNPs of functional outcomes following IS were 20.99–22.00. The F‐statistic for all used instrumental variables was greater than 10, indicating no weak instrumental variable bias (Tables S1–S3).

Effects of 41 circulating inflammatory cytokines on the functional outcomes following IS

When 41 circulating inflammatory cytokines were used as exposures, and functional outcomes following IS were used as outcomes, the main results of the MR analysis were presented in Figure 2 and Table S4. IVW results showed that genetically predicted increases in RANTES (CCL5) levels were positively associated with increased adverse functional outcomes following IS (OR: 1.40, 95% CI: 1.002–1.96, p = 0.049). Genetically predicted increases in EOTAXIN (CCL11) levels were positively associated with increased adverse functional outcomes following IS (OR: 1.33, 95% CI: 1.15–1.54, p = 0.0001). The MR‐Egger analysis did not yield any evidence of potential horizontal pleiotropy (RANTES: p = 0.67; EOTAXIN: p = 0.62) or heterogeneity (RANTES: p = 0.26; EOTAXIN: p = 1.00) for RANTES and EOTAXIN (Table S5). There were no outliers in SNPs of RANTES and EOTAXIN by MR‐PRESSO analysis. The leave‐one‐out analysis demonstrated the stability of the results (Figure S1). The direction of the β‐values of MR‐Egger, simple model, weighted median, and weighted model analyses results were consistent. A scatterplot of the causality of RANTES and EOTAXIN on functional outcomes following IS was shown in Figure 3.

FIGURE 2.

FIGURE 2

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Causality of 41 circulating inflammatory cytokines on functional outcomes following ischemic stroke. *p < 0.05 in inverse variance weighted (IVW) results. CI, confidence interval; NA, insufficient SNPs for MR analysis; OR, odds ratio; SNP, single nucleotide polymorphism.

FIGURE 3.

FIGURE 3

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The scatterplot of the causality of RANTES and EOTAXIN on functional outcomes following ischemic stroke (IS). (a) The exposure is RANTES, the outcome is the functional outcomes following IS. (b) The exposure is EOTAXIN, the outcome is the functional outcomes following IS. MR, Mendelian randomization; SNP, single nucleotide polymorphism.

There was no evidence that other circulating inflammatory cytokines are associated with functional outcomes following IS. In addition, this study used the outcome data that were not adjusted for stroke severity for supplementary analysis. IVW results showed that RANTES and EOTAXIN retained a positive association with adverse functional outcomes following IS (OR: 1.39, 95% CI: 1.03–1.88, p = 0.031; OR: 1.20, 95% CI: 1.03–1.40, p = 0.018), suggesting that the findings possess robustness and reliability.

Effects of the functional outcome following IS on 41 circulating inflammatory cytokines

When functional outcomes after IS were used as exposures, and the 41 circulating inflammatory cytokines were used as outcomes, the main results of the MR analysis are shown in Figure 4 and Table S6. The IVW results showed that genetically predicted increases in adverse functional outcomes following IS were negatively associated with IL‐18 levels (β: −0.09, p = 0.039). MR‐Egger analysis did not reveal potential horizontal pleiotropy (p = 0.59) and heterogeneity (p = 0.87) of SNPs for exposure factors. MR‐PRESSO analyses did not reveal outliers in SNPs for exposure factors (Table S7). The leave‐one‐out analysis demonstrated the stability of the results (Figure S2). The direction of the β‐values of MR‐Egger, simple model, weighted median, and weighted model analyses results were consistent. A scatterplot of the causality of functional outcomes following IS and IL‐18 is shown in Figure 5.

FIGURE 4.

FIGURE 4

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Causality of the functional outcome following ischemic stroke and 41 circulating inflammatory cytokines. *p < 0.05 in inverse variance weighted (IVW) results. CI, confidence interval; SNP, single nucleotide polymorphism.

FIGURE 5.

FIGURE 5

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The scatterplot of the causality of functional outcomes following ischemic stroke (IS) and interleukin‐18 (IL‐18). The exposure is the functional outcome following IS, and the outcome is IL‐18 levels. MR, Mendelian randomization; SNP, single nucleotide polymorphism.

There is no evidence that adverse functional outcomes following IS were associated with other circulating inflammatory cytokines. In addition, this study used exposure data that were not adjusted for stroke severity for supplementary analysis. IVW results showed no association between adverse functional outcomes following IS and IL‐18 levels. This difference is possibly due to certain biases in the exposure data that were not adjusted for stroke severity. Considering that the baseline of stroke severity may affect the functional outcomes following IS, the results of MR analysis of exposure data adjusted for stroke severity were used as the main results of this study.

DISCUSSION

This study employed the two‐sample bidirectional MR analysis method to investigate the causal effect between circulating inflammatory cytokines and functional outcomes subsequent to IS. The study first evaluated the causal association between 41 circulating inflammatory cytokines (including chemokines, interleukins, and growth factors) and functional outcomes following IS. The results revealed that genetically predicted increased RANTES (CCL5) and EOTAXIN (CCL11) levels were positively associated with adverse functional outcomes following IS. When adverse functional outcomes following IS are considered as the exposure variable, they might suggestively cause a decrease in IL‐18 levels through pathogenic pathways.

Effects of circulating inflammatory cytokines on functional outcomes following IS

The results of the causality of the 41 circulating inflammatory cytokines on the adverse functional outcomes following IS showed that RANTES (CCL5) and EOTAXIN (CCL11) may be the upstream factors of adverse functional outcomes following IS.

RANTES, also identified as CCL5, is one of the chemokines that promote the directional migration of white blood cells, T cells, eosinophils, and basophils to sites of injury or inflammation, acting as a pro‐inflammatory molecule in disease [27, 28]. The inflammatory response is one of the significant pathological processes in IS, and post‐stroke inflammation affects the prognosis of IS. RANTES serves the function of the inflammatory, microvascular, and tissue injury responses to cerebral ischemia–reperfusion [27]. The RANTES‐mediated systemic inflammatory response after IS exacerbates ischemic cerebral injury, resulting in delayed inflammation resolution and increasing cerebral microvascular injury [29]. Experimental stroke models have shown that the cerebral infarct volume of RANTES‐deficient mice was significantly decreased [27]. These findings support the study results that increased RANTES levels are positively associated with increased adverse functional outcomes following IS. Furthermore, aside from the mechanistic research basis, several observational studies have provided evidence of the association between RANTES and the risk of IS as well as functional prognosis [29, 30, 31]. A prospective study has revealed that higher circulating RANTES levels can predict the risk of IS [30]. RANTES is an influencing factor in the risk of IS, and it may also come into play in post‐stroke inflammatory pathological changes, thereby affecting post‐stroke functional outcomes. Previous investigations have indicated that heightened levels of CCL5 in the bloodstream of individuals with IS may serve as a predictive factor for both 3‐month mortality and unfavorable outcomes [31, 32]. This study indicated that higher RANTES levels play an adverse role in the functional prognosis of IS, providing a targeted basis for clinical intervention to improve the functional prognosis after IS. Further studies are necessary to analyze whether targeting RANTES can improve poor functional prognosis following IS.

EOTAXIN (CCL11), a pro‐inflammatory chemokine, can be involved in the pathophysiological process of IS through the inflammatory response and neurogenesis pathway [33, 34]. Cytokines such as CCL11 in ischemic tissues play a pro‐inflammatory role in acute brain injury by recruiting and activating inflammatory cells [33]. Circulating CCL11 has the ability to traverse the intact blood–brain barrier and reach the neurogenic niche, where it exerts a detrimental effect on neurogenesis [35]. The higher genetically predicted levels of circulating CCL11 may play a pro‐inflammatory role through the peripheral immune system, and may also cross the blood–brain barrier into the central nervous system to affect post‐stroke neurogenesis repair. Studies have shown that CCL11 significantly increased infarct volume in adult MCAO mice [34]. Machine learning analysis has found that CCL11 is one of the predictive factors for edema volume after acute IS [36]. This evidence, to some extent, explains that increased EOTAXIN levels is positively associated with adverse functional outcomes subsequent to IS. Two observational study conducted in Chinese and Indian populations demonstrated an association between CCL11 genetic polymorphisms and an augmented risk of IS [37, 38]. The study further indicated the predictive role of CCL11 in adverse functional outcomes subsequent to IS. Nevertheless, research has indicated that decreased levels of CCL11 following stroke can independently predict heightened stroke severity and unfavorable functional prognosis in IS individuals after 12 months [39]. This finding suggests that there exist disparities in the inflammatory pathological mechanisms following IS in mice and humans, and the utility of CCL11 levels as a prognostic indicator for functional outcomes after IS remains a subject of ongoing debate. This difference could be associated with different times for observing functional prognosis, and it is plausible that CCL11 levels undergo temporal fluctuations subsequent to IS. Additionally, it is important to consider the influence of confounding factors and the possibility of reverse causal relationships. Further investigation is required to elucidate the predictive significance of CCL11 in functional outcomes following IS.

Previous studies have investigated the causal relationship between inflammatory cytokines and the risk of IS [11, 12]. The findings indicate that elevated levels of MCP‐1 predicted by genetics are linked to a higher risk of IS, while genetically predicted elevated levels of IL‐4 have a protective effect against this risk [11, 12]. Additionally, a previous study suggests a positive correlation between RANTES and EOTAXIN levels and the risk of IS, although this trend does not reach statistical significance [12]. This study found a causal relationship between RANTES, EOTAXIN, and adverse functional outcomes following IS. The main reason for the difference between the results of previous studies is that this study used functional prognosis after IS as the outcome, rather than the risk of IS. This to some extent suggests that RANTES and EOTAXIN play a role in the pathological process after IS, thereby affecting the functional outcomes after stroke.

Effects of functional outcomes following IS on circulating inflammatory cytokines

This study revealed a negative causality between adverse functional outcomes following IS and IL‐18 in the analysis of reverse causal effects. IL‐18 is a pro‐inflammatory cytokine, which plays a key role in the inflammatory pathological process of IS through inflammatory signal transduction [40, 41]. Prior studies have suggested that elevated IL‐18 levels promote the progression of IS [42]. The results of this study showed that IL‐18 is not an initiating factor for adverse functional outcomes following IS, but a downstream factor. The negative association in the MR results contradicts the pro‐inflammatory effects of IL‐18 and has not been reported in prior observational studies. IL‐18 knockout did not result in a decrease in infarct size or enhancement in functional recovery in mice experiencing ischemic cerebral injury, suggesting that IL‐18 may not have a direct role in the development of acute ischemic cerebral injury [43]. Genetically predicted patients with adverse functional outcomes after IS may have low immune capacities, making it difficult to activate cytokines levels such as IL‐18 after stroke or increase susceptibility to complications such as pulmonary infections, resulting in poor post‐stroke prognosis [44]. In addition, this negative association may also be associated with study sample size and population ethnicity. More studies are required to further explore the potential mechanisms of the negative association between adverse functional outcomes following IS and IL‐18.

Study limitations

There are multiple limitations inherent in this study. First, the impact of inflammatory cytokines on functional prognosis following IS in various subtypes remains uncertain and necessitates further investigation due to the absence of GWAS data related to functional prognosis after IS in different subtypes. Second, differences in the timing of evaluation for National Institutes of Health Stroke Scale (NIHSS) baseline (0–10 days after stroke onset) may lead to measurement errors when obtaining genetic data on functional outcomes in IS, which may also affect risk estimates. Third, a significance threshold with a p‐value of 5 × 10−6 was used in the exposed GWAS data, as the number of genome‐wide significant SNPs was few under the threshold of p < 5 × 10−8 such that conducting further MR analysis becomes challenging. Fourth, the results of MR‐Egger, weighted median, simple model, and weighted model estimation were not significant in this study. Given the substantially greater statistical power of the IVW method compared to other MR analysis methods, and the adherence of this study to the criterion of consistent β direction in MR findings, the results can be deemed significant also. Fifth, it is important to note that the scope of this study was limited to investigating the correlation between circulating inflammatory cytokines and adverse functional outcomes following IS. However, it is crucial to acknowledge that the effects of neuroinflammation on such outcomes may vary. The absence of available GWAS data hinders the exploration of central neuroinflammation. Consequently, further GWAS studies focusing on neuroinflammation are imperative for conducting robust MR analyses. Sixth, the GWAS data used in the study were all from European populations, indicating that the results of this study may not be applicable to individuals of other ancestries. To confirm the results of this study, more studies are necessary, and we should attempt to use these results in clinical practice for diagnosis and treatment strategies.

CONCLUSIONS

This study suggests a causality between inflammation and adverse functional outcomes following IS. RANTES (CCL5) and EOTAXIN (CCL1) may be the upstream factors of adverse functional outcomes following IS, while IL‐18 may be the downstream effect of adverse functional outcomes following IS. Further studies are needed to determine whether these cytokines can be used to predict or prevent functional prognosis after IS.

AUTHOR CONTRIBUTIONS

Junqi Chen, Yong Huang, and Xiaoyan Zheng designed the study and reviewed the manuscript. Huacong Liu, Zhaoxing Liu, and Yumeng Huang performed the statistical analysis and drafted the manuscript. Qian Ding and Lianjun Yin edited the figures and tables in the manuscript. Xiaowen Cai, Shengtao Huang, and Zhenyi Lai verified the data and statistical analysis results. All authors contributed to the article and approved the submitted version.

FUNDING INFORMATION

This research was financially supported by the National Natural Science Foundation of China (Grant No. k120290048), the scientific research project of Traditional Chinese Medicine Bureau of Guangdong Province (Grant No. 20221262), and the 2023 Annual Guangdong Provincial Medical Research Fund Project (Grant No. C2023102).

CONFLICT OF INTEREST STATEMENT

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.

Supporting information

Figure S1.

Figure S2.

Table S1.

Table S2.

Table S3.

Table S4.

Table S5.

Table S6.

Table S7.

ENE-31-e16123-s001.zip (191.3KB, zip)

ACKNOWLEDGMENTS

The authors sincerely thank related investigators for sharing the GWAS summary statistics included in this study.

Liu H, Liu Z, Huang Y, et al. Exploring causal association between circulating inflammatory cytokines and functional outcomes following ischemic stroke: A bidirectional Mendelian randomization study. Eur J Neurol. 2024;31:e16123. doi: 10.1111/ene.16123

Huacong Liu, Zhaoxing Liu, and Yumeng Huang contributed equally to this work and share first authorship.

Xiaoyan Zheng, Yong Huang, and Junqi Chen contributed equally to this work and share last authorship.

Contributor Information

Xiaoyan Zheng, Email: zxy18825147762@smu.edu.cn.

Yong Huang, Email: nanfanglihuang@163.com.

Junqi Chen, Email: meixibao@126.com.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article and Supplementary Material; further inquiries can be directed to the corresponding author.

REFERENCES

  • 1. Phipps MS, Cronin CA. Management of acute ischemic stroke. BMJ. 2020;368:l6983. [DOI] [PubMed] [Google Scholar]
  • 2. Pan Y, Li Z, Li J, et al. Residual risk and its risk factors for ischemic stroke with adherence to guideline‐based secondary stroke prevention. J Stroke. 2021;23(1):51‐60. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3. Zhang Z, Wang M, Gill D, Zhu W, Liu X. Genetically predicted sleep traits and functional outcome after ischemic stroke: a Mendelian randomization study. Neurology. 2023;100(11):e1159‐e1165. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4. Zhang Z, Wang M, Gill D, Liu X. Genetically predicted smoking and alcohol consumption and functional outcome after ischemic stroke. Neurology. 2022;99:e2693‐e2698. [DOI] [PubMed] [Google Scholar]
  • 5. Liu H, Zhang X, Zhou Y, et al. Association between blood pressure and different antihypertensive drugs with outcome after ischemic stroke: a Mendelian randomization study. Int J Stroke. 2023:17474930231185695. doi: 10.1177/17474930231185695 [DOI] [PubMed] [Google Scholar]
  • 6. Gu HQ, Yang KX, Li JJ, et al. Mediation effect of stroke recurrence in the association between post‐stroke interleukin‐6 and functional disability. CNS Neurosci Ther. 2023;29:3579‐3587. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7. Esenwa CC, Elkind MS. Inflammatory risk factors, biomarkers and associated therapy in ischaemic stroke. Nat Rev Neurol. 2016;12(10):594‐604. [DOI] [PubMed] [Google Scholar]
  • 8. Ramiro L, Simats A, García‐Berrocoso T, Montaner J. Inflammatory molecules might become both biomarkers and therapeutic targets for stroke management. Ther Adv Neurol Disord. 2018;11:1756286418789340. doi: 10.1177/1756286418789340 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9. Alsbrook DL, Di Napoli M, Bhatia K, et al. Neuroinflammation in acute ischemic and hemorrhagic stroke. Curr Neurol Neurosci Rep. 2023;23(8):407‐431. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10. Lin J, Wang Y, Wang Y, Pan Y. Inflammatory biomarkers and risk of ischemic stroke and subtypes: a 2‐sample Mendelian randomization study. Neurol Res. 2020;42(2):118‐125. [DOI] [PubMed] [Google Scholar]
  • 11. Georgakis MK, Gill D, Rannikmäe K, et al. Genetically determined levels of circulating cytokines and risk of stroke. Circulation. 2019;139(2):256‐268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12. Li Y, Lu J, Wang J, Deng P, Meng C, Tang H. Inflammatory cytokines and risk of ischemic stroke: a Mendelian randomization study. Front Pharmacol. 2021;12:779899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13. Wang N, Yang Y, Qiu B, et al. Correlation of the systemic immune‐inflammation index with short‐ and long‐term prognosis after acute ischemic stroke. Aging (Albany NY). 2022;14(16):6567‐6578. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14. Li J, Lin J, Pan Y, et al. Interleukin‐6 and YKL‐40 predicted recurrent stroke after ischemic stroke or TIA: analysis of 6 inflammation biomarkers in a prospective cohort study. J Neuroinflammation. 2022;19(1):131. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15. Coveney S, Murphy S, Belton O, et al. Inflammatory cytokines, high‐sensitivity C‐reactive protein, and risk of one‐year vascular events, death, and poor functional outcome after stroke and transient ischemic attack. Int J Stroke. 2022;17(2):163‐171. [DOI] [PubMed] [Google Scholar]
  • 16. Holmes MV, Ala‐Korpela M, Smith GD. Mendelian randomization in cardiometabolic disease: challenges in evaluating causality. Nat Rev Cardiol. 2017;14(10):577‐590. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17. Davey SG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89‐R98. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18. Emdin CA, Khera AV, Kathiresan S. Mendelian randomization. JAMA. 2017;318(19):1925‐1926. [DOI] [PubMed] [Google Scholar]
  • 19. Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol. 2011;40(3):755‐764. [DOI] [PubMed] [Google Scholar]
  • 20. Söderholm M, Pedersen A, Lorentzen E, et al. Genome‐wide association meta‐analysis of functional outcome after ischemic stroke. Neurology. 2019;92(12):e1271‐e1283. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21. Ahola‐Olli AV, Würtz P, Havulinna AS, et al. Genome‐wide association study identifies 27 loci influencing concentrations of circulating cytokines and growth factors. Am J Hum Genet. 2017;100(1):40‐50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22. Corbin L, Timpson N. Cytokines GWAS results. University of Bristol; 2020. doi: 10.5523/bris.3g3i5smgghp0s2uvm1doflkx9x [DOI] [Google Scholar]
  • 23. Bowden J, Davey SG, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512‐525. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693‐698. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25. Kamat MA, Blackshaw JA, Young R, et al. PhenoScanner V2: an expanded tool for searching human genotype‐phenotype associations. Bioinformatics. 2019;35(22):4851‐4853. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26. Wang S, Zhu H, Pan L, et al. Systemic inflammatory regulators and risk of acute‐on‐chronic liver failure: a bidirectional mendelian‐randomization study. Front Cell Dev Biol. 2023;11:1125233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27. Terao S, Yilmaz G, Stokes KY, et al. Blood cell‐derived RANTES mediates cerebral microvascular dysfunction, inflammation, and tissue injury after focal ischemia‐reperfusion. Stroke. 2008;39(9):2560‐2570. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28. Bacon KB, Premack BA, Gardner P, Schall TJ. Activation of dual T cell signaling pathways by the chemokine RANTES. Science. 1995;269(5231):1727‐1730. [DOI] [PubMed] [Google Scholar]
  • 29. Dénes A, Humphreys N, Lane TE, Grencis R, Rothwell N. Chronic systemic infection exacerbates ischemic brain damage via a CCL5 (regulated on activation, normal T‐cell expressed and secreted)‐mediated proinflammatory response in mice. J Neurosci. 2010;30(30):10086‐10095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30. Canouï‐Poitrine F, Luc G, Mallat Z, et al. Systemic chemokine levels, coronary heart disease, and ischemic stroke events: the PRIME study. Neurology. 2011;77(12):1165‐1173. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31. Kong Q, Ma X, Lyu JX, Wang C, du XY, Guan YQ. Plasma RANTES level is correlated with cardio‐cerebral atherosclerosis burden in patients with ischemic cerebrovascular disease. Chronic Dis Transl Med. 2020;6(1):46‐54. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32. Montecucco F, Lenglet S, Gayet‐Ageron A, et al. Systemic and intraplaque mediators of inflammation are increased in patients symptomatic for ischemic stroke. Stroke. 2010;41(7):1394‐1404. [DOI] [PubMed] [Google Scholar]
  • 33. Chen C, Chu SF, Liu DD, et al. Chemokines play complex roles in cerebral ischemia. Neurochem Int. 2018;112:146‐158. [DOI] [PubMed] [Google Scholar]
  • 34. Lieschke S, Zechmeister B, Haupt M, et al. CCL11 differentially affects post‐stroke brain injury and neuroregeneration in mice depending on age. Cell. 2019;9(1):66. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35. Erickson MA, Morofuji Y, Owen JB, Banks WA. Rapid transport of CCL11 across the blood‐brain barrier: regional variation and importance of blood cells. J Pharmacol Exp Ther. 2014;349(3):497‐507. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36. Martha SR, Cheng Q, Fraser JF, et al. Expression of cytokines and chemokines as predictors of stroke outcomes in acute ischemic stroke. Front Neurol. 2019;10:1391. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37. Zhao N, Liu X, Wang Y, et al. Association of inflammatory gene polymorphisms with ischemic stroke in a Chinese Han population. J Neuroinflammation. 2012;9:162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38. Roy S, Das S, Munshi A, Kaul S, Jyothy A. Association of‐1382A>G CCL11 gene variant with ischemic stroke, its subtypes and hemorrhagic stroke in a south Indian population. Neurol India. 2014;62(4):387‐392. [DOI] [PubMed] [Google Scholar]
  • 39. Roy‐O'Reilly M, Ritzel RM, Conway SE, et al. CCL11 (Eotaxin‐1) levels predict long‐term functional outcomes in patients following ischemic stroke. Transl Stroke Res. 2017;8(6):578‐584. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40. Felderhoff‐Mueser U, Schmidt OI, Oberholzer A, Bührer C, Stahel PF. IL‐18: a key player in neuroinflammation and neurodegeneration? Trends Neurosci. 2005;28(9):487‐493. [DOI] [PubMed] [Google Scholar]
  • 41. Zhu H, Hu S, Li Y, et al. Interleukins and ischemic stroke. Front Immunol. 2022;13:828447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42. Hao Y, Ding J, Hong R, et al. Increased interleukin‐18 level contributes to the development and severity of ischemic stroke. Aging (Albany NY). 2019;11(18):7457‐7472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43. Wheeler RD, Boutin H, Touzani O, Luheshi GN, Takeda K, Rothwell NJ. No role for interleukin‐18 in acute murine stroke‐induced brain injury. J Cereb Blood Flow Metab. 2003;23(5):531‐535. [DOI] [PubMed] [Google Scholar]
  • 44. Simats A, Liesz A. Systemic inflammation after stroke: implications for post‐stroke comorbidities. EMBO Mol Med. 2022;14(9):e16269. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Figure S1.

Figure S2.

Table S1.

Table S2.

Table S3.

Table S4.

Table S5.

Table S6.

Table S7.

ENE-31-e16123-s001.zip (191.3KB, zip)

Data Availability Statement

The original contributions presented in the study are included in the article and Supplementary Material; further inquiries can be directed to the corresponding author.

Articles from European Journal of Neurology are provided here courtesy of John Wiley & Sons Ltd on behalf of European Academy of Neurology (EAN)

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