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The causal association between COVID-19 and ischemic stroke: a mendelian randomization study

Virology Journal volume 21, Article number: 280 (2024) Cite this article

Abstract

Background

Current observational data indicates that ischemic stroke (IS) affects a significant proportion of people with COVID-19. The current study sought to evaluate the causal relationship between COVID-19 and IS.

Methods

A two-sample Mendelian randomization (2 S-MR) approach was used to probe the relationship between genetic determinants of three COVID-19 parameters (SARS-CoV‐2 infection, COVID-19 hospitalization, and severe COVID-19) and the incidence of IS based on genome-wide association studies (GWAS) data. Using this 2 S-MR technique, expression quantitative trait loci (eQTL) and GWAS studies were further assessed for overlap to identify common causative genes associated with severe COVID-19 and IS.

Results

IVW approaches indicated the genetic variants linked to COVID-19 hospitalization (OR 1.04, 95% CI 1.01–1.08, p = 0.023) and severe COVID-19 (OR 1.03, 95% CI 1.01–1.05, p = 0.007) were both significantly linked to greater odds of IS. In contrast, there was no causal association between genetic SARS-CoV-2 infection susceptibility and the occurrence of IS (OR 0.99, 95% CI 0.92–1.06, p = 0.694). Ten shared causal genes (TNFSF8, CFL2, TPM1, C15orf39, LHFPL6, FAM20C, SPAG9, KCNJ2, PELI1, and HLA-L) were established as possible mediators of the interplay between severe COVID-19 and the development of IS, with these genes primarily being enriched in immune-related and renin-angiotensin-aldosterone system pathways.

Conclusion

These findings indicate a possible causative relationship between IS risk and COVID-19 severity, offering crucial new information for managing COVID-19 patients. Promising options for therapeutic therapies for severe COVID-19 complicated by IS include the common genes found in the present study.

Background

The COVID-19 pandemic, caused by SARS-CoV-2, has precipitated a global crisis of public health [1], with the World Health Organization estimating 774,469,939 confirmed COVID-19 diagnoses and 7,026,465 deaths worldwide [2]. The symptoms of COVID-19 infections can differ significantly, as can their severity and prognosis, ranging from predominantly asymptomatic to lethal episodes of illness. Most patients experience mild to severe symptoms, including cough, fever, and dyspnea. However, severe disease affects approximately 10–20% of patients and may advance to acute respiratory distress syndrome, acute lung damage, significant hypoxemia, and potentially fatal outcomes [1]. Mortality rates for patients with severe COVID-19 experiencing respiratory failure that necessitates ventilation (i.e., those with a PaO2/FIO2 ≦ 200 mmHg) can be as high as 40–60% [1, 3]. As with other forms of infectious illness, host genetic factors can have a marked impact on COVID-19 infection risk, disease severity, and prognostic outcomes.

Patients with COVID-19 often present with a range of neurological symptoms that can include insomnia, dizziness, headaches, altered consciousness, and cerebrovascular event incidence [4]. Lodigian et al. explored venous and arterial thromboembolic complication rates among 388 patients with COVID-19, observing ischemic stroke (IS) incidence in 2.5% of cases [5]. Katz et al. determined that COVID-19 was independently associated with an increased risk of stroke and mortality in hospitalized patients in their retrospective analysis, with the primary manifestations being ischemic stroke (83.7%) and nonfocal neurological symptoms (67.4%) [6]. IS even reportedly impacts some younger COVID-19 patients without any relevant history of susceptibility [7]. In one systemic review incorporating data from 26 studies, IS was found to affect an average of 1.5% of COVID-19 patients, with rates anywhere from 0.1 to 6.9% among hospitalized individuals [8]. The wide variation in incidence rates could be explained by the specific location of the studies in the hospital (typical COVID-19 wards, stroke warfare, critical care units, etc.). As a result, it might represent the seriousness of COVID-19 infections. However, a comparison of IS rates in COVID-19 and influenza patients by Merkler et al. [9] found that a significantly higher proportion of adult COVID-19 patients who had undergone hospitalization or ER visits had IS than did influenza patients (1.6% vs. 0.2%), suggesting that the relationship between COVID-19 and IS is not just coincidental. However, most of these earlier investigations were observational, leaving them open to reverse causality and confounding variables. The precise nature of the interplay between COVID-19 and IS thus remains to be effectively clarified.

Leveraging the fact that genetic variants are randomly assigned at the time of conception, Mendelian randomization (MR) studies exploit these variants as instrumental variables (IVs) to find possible causal links between a given exposure factor (EF) and a disease or related outcome variable. Randomly administering these IVs allows researchers to neutralize the effects of confounding factors and screen for causal correlations [10]. As a result, three COVID-19-related traits were selected as EFs, while single nucleotide polymorphisms (SNPs) served as the IVs, and IS was the outcome of interest such that a two-sample MR (2 S-MR) approach could be used to determine whether these EFs were causally related to IS incidence (Step-1 in Fig. 1). Following the integration of expression quantitative trait loci (eQTLs) as EFs, further 2 S-MR analyses were conducted with IS and severe COVID-19 (sCOVID-19) acting as outcomes of interest to further investigate for causative genes linking these diseases (Step-2 in Fig. 1). Determining a causal relationship between the severity of COVID-19 and the incidence of IS may inform the clinical management of patients with COVID-19 infection. Furthermore, the overlapped genes found through this approach may serve as crucial mediators of sCOVID-19-related pathways that influence IS susceptibility.

Fig. 1
figure 1

Flowchart of the study’s design

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Materials and methods

Data sources

This study was conducted using GWAS summary data from populations of European ancestry. The collaborative COVID-19 Host Genetic Initiative (HGI), a resource created to help relay data related to the genetic factors that influence disease outcomes in COVID-19 patients, was one of the publicly accessible datasets related to COVID-19. This allowed for the prior identification of several COVID-19 risk loci [11]. The most extensive GWAS datasets for the three COVID-19 traits of interest for this study were accessed through the GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads/summary-statistics), with these traits including SARS-CoV-2 infection (ID: GCST011074, ncases = 32,494 and ncontrols = 1,316,207), COVID-19 hospitalization (ID: GCST011081, ncases = 9986 and ncontrols = 1,877,672), and sCOVID-19 (ID: GCST011075, ncases = 5,101 and ncontrols = 1,383,241). There is variation in the definitions of sCOVID-19 between research papers and healthcare systems. Patients were classified as having sCOVID-19 for this study if they needed invasive or noninvasive ventilation. There were 11,929 cases and 472,192 controls in the used GWAS-related IS dataset (ID: GCST90018864), with no overlapping cohorts.

The eQTL study summary statistics were used to gain insight into the relationship between particular genetic variants and the expression levels of genes of interest. Given tissue type-specific variations in gene expression patterns, these eQTL studies tend to be tissue-specific, utilizing appropriate probes to measure gene expression. The eQTLs for a given gene are defined as any SNPs within 1 Mb of the corresponding probe found to be significantly related to gene expression (P < 5 × 10− 8). EQTL data were accessed manually through the GTEx project-based IEU OpenGWAS database (https://gwas.mrcieu.ac.uk/) [12]. The GTEx project, which supports this collection, sought to characterize human transcriptome data from several participants across diverse cell types and primary tissues. This is particularly pertinent to the current investigation, as gene-specific tissue expression may have systemic implications, and COVID-19 has been documented to affect nearly all organs. The expression data from all accessible tissues was utilized for this analysis to ensure the most comprehensive coverage.

Instrumental variable selection

IVs were selected for inclusion in this study if: (1) they were SNPs that were significantly associated with the corresponding EFs at a genome-wide significance level (P < 5 × 10− 6 for the step-1 MR analysis; P < 5 × 10− 8 for the step-2 MR); (2) they did not exhibit significant linkage disequilibrium (LD; r2 > 0.001), thus ensuring independence; and (3) they were adequately powered as determined by an F-statistic (beta2/se2) > 10.

MR and sensitivity analyses

After the appropriate IVs were filtered out, the input data were utilized for 2 S-MR univariate analyses. The Harmonise_data function (TwoSampleMR package) was employed to harmonize effect equivalents and sizes. Subsequently, the primary studies were conducted using an inverse variance-weighted (IVW) model. IVW approaches estimate causal effects for a given IV based on the Wald ratio, employing a fixed-effect model for the meta-aggregation of multiple causal forecasts for a single IV. The often employed IVW estimates correlate to the aggregated causal effect estimate, enabling this method to provide causal estimates devoid of directed pleiotropy. This work employed complementary Mendelian randomization techniques to elucidate the sensitivity of these causal connections, including MR-Egger, simple mode, weighted mode, and weighted median methods.

MR-Egger regression analyses were used to evaluate any potential horizontal pleiotropy, with such pleiotropy being evident when the Egger intercept was a significantly non-zero value. The MR-PRESSO Global test assessed potential pleiotropy, utilizing 1000 simulations to calculate the associated P-values. When global pleiotropy was substantial, any anomalous SNPs were identified by a local outlier test. Heterogeneity was determined using Cochran’s Q test. Leave-one-out analysis assessed the dependency of MR results on a specific variation. Visual examinations of forest and funnel plots were performed to detect potential heterogeneity or horizontal pleiotropy.

Shared causal gene selection and functional enrichment analysis

After conducting distinct analyses of genes causally associated with sCOVID-19 and IS, the intersection of these gene lists was examined to uncover common genes that may connect sCOVID-19 to the onset of IS. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the common genes were conducted using the R ‘clusterProfiler’ package, applying a false discovery rate (FDR) of less than 0.05 and an adjusted P-value of less than 0.05 to establish significance. GO terms included cellular component (CC), molecular function (MF), and biological process (BP) terms, displaying the top 10 most enriched terms in each category. GeneMANIA (http://www.genemania.org/) was also used to establish a co-expression network for these shared genes.

Statistical analysis

The TwoSampleMR package (v 0.5.6) in R (v 4.2.1) was used to perform these 2 S-MR analyses, with an adjusted P < 0.05 defining statistical significance.

Results

MR analyses of the relationship between COVID-19 and IS

After a rigorous screening process, a total of 19, 19, and 36 SNPs were established as IVs for the respective SARS-CoV-2 infection, COVID-19 hospitalization, and sCOVID-19 EFs (Supplementary file 1). In an IVW analysis, there was a significant correlation found between genetic vulnerability to COVID-19 hospitalization (OR 1.04, 95% CI 1.01–1.08, = 0.023) and sCOVID-19 (OR 1.03, 95% CI 1.01–1.05, p = 0.0037) with increased risk of IS. On the other hand, there was no correlation between the risk of IS and genetic vulnerability to SARS-CoV-2 infection (OR 0.99, 95% CI 0.92–1.06, p = 0.694) (Fig. 2 and Supplementary file 2).

Fig. 2
figure 2

Scatter plots of MR analyses of causal effects for SARS-CoV-2 infection (a), COVID-19 hospitalization (b), and sCOVID-19 (c) on the risk of IS

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The directionality of causal effect estimates was consistent across approaches in sensitivity analysis. The MR-Egger regression results indicated no evidence of directional pleiotropy (MR-Egger intercept < 0.01, p > 0.05). Simultaneously, the MR-PRESSO and leave-one-out methodologies did not identify any outlier instrumental variables utilized in these MR analyses (Supplementary file 2).

MR analyses of genes causally linked to sCOVID-19 and IS incidence

Causal eQTL-associated genes related to sCOVID-19 and IS were next identified, and 26,152 SNPs were selected as IVs to conduct these MR analyses (Supplementary file 3). Using the IVW method, 263 genes that were significantly causally linked with IS risk were identified, including 140 related to a reduction in IS risk (including CD248, SLC25A44, and CYB561) as well as 123 associated with greater IS risk (including CDC16, SPART, and C9orf72) (Fig. 3 and Supplementary file 4).

Fig. 3
figure 3

MR analyses of genes causally linked to sCOVID-19 and IS incidence

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IVW MR analyses similarly revealed 221 genes that were significantly linked to the risk of sCOVID-19, including 129 negatively related to such risk (including PPCDC, LIMK2, and CXCR6) as well as 92 positively associated with such risk (including NLRP3, TMED6, and C1orf198) (Fig. 3 and Supplementary file 5).

Numerous sensitivity studies were performed to identify and mitigate pleiotropy in these causal effect estimations. Cochran’s Q test indicated no significant heterogeneity among the SNPs utilized for these causal impact analyses, and pleiotropy assessment found no indication of horizontal pleiotropy for these SNPs (Supplementary File 5). Leave-one-out analyses were conducted to ascertain if any specific SNP influenced these causal estimations. The results remained constant when MR analyses were performed without these particular SNPs, affirming that these SNPs were pertinent to the predicted significant causal linkages (Supplementary file 5).

Shared causal gene selection and functional enrichment analysis

Based on the causal genes associated with sCOVID-19 and IS that overlapped with one another, 10 total genes potentially involved in the incidence of these two diseases were identified (KCNJ2, CFL2, TPM1, LHFPL6, PELI1, FAM20C, SPAG9, C15orf39, TNFSF8, and HLA-L). The ORs for linking CFL2 to genetically predicted IS and sCOVID-19 were 1.04 (95% CI 1.00-1.07) and 1.15 (95% CI 1.01–1.32), respectively, while the corresponding ORs for FAM20C were 1.03 (95% CI 1.00-1.06) and 1.18 (95% CI 1.01–1.37), respectively, suggesting that these variants likely increase the risk of both IS and sCOVID-19. In contrast, the remaining causally liked genes were associated with reductions in the risk of both IS and sCOVID-19, including SPAG9 (OR 0.89 [95% CI 0.80–0.99] and OR 0.67 [95% CI 0.54–0.86], respectively), TNFSF8 (OR 0.96 [95% CI 0.92-1.00] and OR 0.78 [95% CI 0.63–0.96] for IS and sCOVID-19, respectively), KCNJ2 (OR 0.91 [95% CI 0.87–0.96] and OR 0.75 [95% CI 0.63–0.90], respectively), TPM1 (OR 0.95 [95% CI 0.90–0.99] and OR 0.85 [95% CI 0.74–0.99], respectively), C15orf39 (OR 0.87 [95% CI 0.80–0.96] and OR 0.65 [95% CI 0.44–0.96], respectively), LHFPL6 (OR 0.91 [95% CI 0.83–0.99] and OR 0.76 [95% CI 0.59–0.98], respectively), PELI1 (OR 0.94 [95% CI 0.90–0.99] and OR 0.79 [95% CI 0.66–0.95], respectively) and HLA-L (OR 0.88 [95% CI 0.83–0.92] and OR 0.72 [95% CI 0.57–0.91], respectively) (Fig. 4).

Fig. 4
figure 4

Forest plots of a shared causal gene associated with sCOVID-19 (a) and IS (b)

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These ten overlapping causative genes, including actin filament control, striated muscle cell formation, and differentiation, were enriched in BP in GO analysis. Furthermore, they exhibited enrichment in the KEGG pathways related to renin secretion, cardiomyopathy, pertussis, and FcγR-mediated phagocytosis (Fig. 5 and Supplementary file 6). In summary, the renin-angiotensin-aldosterone system (RAAS) and immune response activity were primarily regulated by shared causal genes associated with sCOVID-19 and complicated with IS. GeneMANIA was also employed to evaluate these overlapping genes’ co-expression networks and associated functions. The resulting network consisted of a center node representing these shared genes, surrounded by 20 nodes corresponding to the genes significantly correlated. The C15orf39 interacted strongly with HSP90B1, whereas TPM1 interacted strongly with HNMT and TPM4 (Fig. 6).

Fig. 5
figure 5

GO (a) and KEGG (b) enrichment analyses of the shared caucal genes

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Fig. 6
figure 6

Co-expression network for the shared causal genes

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Discussion

Observational studies have demonstrated that patients who experience sCOVID-19 are also more susceptible to IS. A recent meta-analysis indicated that the risk of stroke increased by 500% for those who were confined to the ICU due to sCOVID-19 (OR 5.1, 95% CI 2.72–9.54) [13]. Observational research, however, is not well-suited to distinguishing between causal associations and those resulting from confounding factors or reverse causation. Consistent with past studies [5, 8–9, 13], the present MR approach revealed that COVID-19 hospitalization and sCOVID-19 were causally associated with an elevated IS risk level. No comparable causal relationship was observed regarding genetic susceptibility to SARS-CoV-2 infection. This report presents genetic evidence linking COVID-19 severity to the risk of ischemic stroke through a comprehensive MR approach. Furthermore, ten genes (KCNJ2, CFL2, TPM1, LHFPL6, PELI1, FAM20C, SPAG9, C15orf39, TNFSF8, and HLA-L) have been identified as potentially causal in the incidence of IS among patients with severe COVID-19. This finding offers significant insight into the underlying mechanisms of this association, suggesting these genes may serve as promising targets for therapeutic interventions in cases of severe COVID-19 complicated by IS.

Factors suggested to play a role in IS incidence among COVID-19 patients include alternative RAAS pathway activity and immune-mediated thrombosis or hypercoagulopathy [14–15]. This report identifies shared causal genes primarily associated with the immune response and the activation of the RAAS pathway. Hemostasis is intricately linked to the activity of the immune response and the process of inflammation. In instances of COVID-19, both the innate and adaptive immune systems are activated, leading to significant activation of inflammatory cells, including macrophages and neutrophils, alongside complement activation and the release of various pro-inflammatory cytokines, such as Interleukin (IL)-1, IL-2, IL-6, IL-8, IL-10, and IL-17, resulting in a phenomenon known as a cytokine storm [15–16]. Activated immune cells are capable of causing damage to endothelial cells through the exposure of tissue factor (TF) and the induction of microvascular thrombosis. In a hyperinflammatory condition, locally activated platelets can induce the release of tissue factor-coated neutrophil extracellular traps (NETs), initiating the extrinsic coagulation cascade and resulting in thrombin production [17]. Inappropriate host immune responses in this context lead to interactions among platelets, endothelial cells, and various immune system components, resulting in hypercoagulability and excessive microvascular immune-mediated thrombosis. Angiotensin-converting enzyme 2 (ACE2) facilitates the entry of SARS-CoV-2 into cells, which is essential for the viral replication process [18]. It also serves a necessary role as a suppressor of angiotensin II (Ang II) in the RAAS pathway [19]. In healthy individuals, angiotensin II is converted into Ang (1–7) via ACE2 [15, 19]. ACE2 dysfunction may result from SARS-CoV-2 binding in COVID-19 patients, potentially impacting Ang II conversion into Ang (1–7) [20], with Ang II accumulating in affected individuals. When Ang II binds to its receptor (AT1R), it functions as a potent vasoconstrictor that can adversely affect various tissues by promoting oxidative stress, inflammation, fibrosis, and vascular remodeling [20]. Further research on IS in animals has shown that Ang (1–7) can activate the Mas receptor and AT2R to locally produce anti-inflammatory, antioxidant, and vasodilatory effects within the grain [21]. The ability of SARS-CoV-2 to disrupt these neuroprotective functions of ACE2 may thus culminate in the occurrence of strokes.

The CFL2 gene encodes cofilin-2, an actin-binding protein expressed in a range of eukaryotic cells. Cofilins are attributed with numerous functions, playing a critical role in cell stress responses, locomotion, and cytokinesis, and are pertinent in various pathological contexts [22]. In mouse model systems, myocardial infarction results in cofilin-2 upregulation and cardiac NLRP3 inflammasome upregulation [23]. Inflammatory cytokines can promote the upregulation of CFL2 in cells treated with lipopolysaccharide (LPS). In the context of infection, cofilin-2 can exert damaging pro-apoptotic effects through its ability to promote mitochondrial cytochrome c release [25–26]. L-cofilin-2 also reportedly plays a role in LPS-induced immunosuppressive responses [23]. The Golgi casein kinase Fam20C phosphorylates the SxE/pS motifs of proteins that are secreted [24]. Multiple Fam20C substrates associated with coagulation were found using the phosphoproteomic analysis of serum and plasma samples. Upon vascular and tissue injury, thrombin cleaves fibrinogen to generate fibrin peptides, resulting in blood clot formation and cessation of bleeding [24]. The ability of phosphorylated fibrinogen to bind to thrombin has been reported to be enhanced, allowing for the release of a more significant number of fibrin peptides, resulting in more rapid coagulation [25]. Fam20C is reportedly capable of the direct phosphorylation of the gamma and alpha chains of fibrinogen in vitro, in addition to phosphorylating two SxE sites within the von Willebrand factor (vWF) A2 domain (pSer1517 and pSer1613). The modifications facilitate platelet adhesion at the site of vascular injury, thereby contributing to coagulation. Fam20C can phosphorylate the C3 and C4 complement proteins. Liu et al. identified a positive correlation between the infiltration of macrophages, neutrophils, and dendritic cells and the expression of Fam20C. They also proposed that Fam20C may be involved in Treg activation and the induction of T cell exhaustion [26]. Inflammatory and coagulation crosstalk may thus explain the roles that CFL2 and Fam20C play in sCOVID-19 and IS development.

The E3 ubiquitin ligase pellino1 (Peli1) exhibits a high degree of conservation and functions through its ability to mediate the ubiquitin modification of target proteins [27]. Peli1 has been demonstrated to play a crucial role in regulating various inflammatory signaling pathways involving Toll-like receptors (TLRs), IL-1 receptors, MAPKs, PI3K/AKT, and NF-kB [27]. Peli1 is proposed to contribute to infections, coagulation, and immunological responses by regulating glycolysis, DNA damage, autophagy, necrosis, pyroptosis, and apoptosis [27]. Peli1 can promote inflammatory activity by affecting the IL-1R and TLR pathways, activating NF-κB. In contrast, it can also interact with the TGF-β-induced Smad6 protein to exert anti-inflammatory effects [28]. Yang et al. explored transcriptomic datasets from patients with COVID-19. They determined that PELI1 upregulation was only evident in cases of moderate COVID-19 but not sCOVID-19, suggesting a link between this gene and a reduction in disease severity [29]. PELI1 also serves as a vital regulator of stroke pathogenesis. In large-artery atherothrombotic stroke patients, PELI1 expression was downregulated, consistent with the inhibition of IS [30].

KCNJ2, located on chromosome 17 in humans, encodes the Kir2.1 K + channel, expressed by mononuclear cells in peripheral blood. Its expression levels are positively connected with ventricular expression and inversely correlated with IL-1 and CRP levels in patients with acute infections [31]. Kir2.1 modulates the plasma membrane potential (Vm) of macrophages, and this regulatory function is crucial for food intake and subsequent pro-inflammatory metabolic reprogramming. In the absence of Kir2.1 activity, the depolarization of macrophage Vm induces a condition of caloric restriction, leading to the depletion of epigenetic substrates and altering the histone methylation status of metabolism-responsive inflammatory gene clusters, thereby inhibiting their transcriptional activation [32]. Pharmacological efforts to target Kir2.1 can protect against LPS- or bacteria-induced inflammation in sepsis model systems while protecting against sterile inflammation in human samples [32]. As Kir2.1 plays a selective anti-inflammatory role, it can reportedly decrease the size of infarcts in MCAO animal models [33]. TNFRSF8, a type I transmembrane glycoprotein belonging to the TNRSF superfamily, possesses a cytosolic domain that contains a TNFR-associated factor (TRAF) binding domain. This domain can interact with TRAF1 and TRAF2, increasing the activation of NF-κB and thereby positively influencing T cell activity [34]. TNFRSF8 is an essential regulator of T cell-mediated immune responses directed against intracellular bacteria. TNFRSF8-deficient mice with M.avium infections present with a reduction in IFN-γ-producing cell numbers, a more significant bacterial burden, and abnormal inflammation [35]. There is a potential relationship between TPM1 and long COVID-19. According to Gui et al., when the influenza A virus is present, SPAG9 can stimulate necroptotic, pyroptotic, and apoptotic cell death by interacting with DAI via the c-Jun N-terminal kinase pathway [36]. The expression of LHFPL6 was positively correlated with M2 macrophage abundance, with these cells serving as robust immunosuppressive agents [37]. C15orf39 has been established as a novel substrate of MAPKs, which can regulate hemorrhagic and ischemic vascular diseases [38]. However, relatively little is known about the link between these variables and the pathophysiology of sCOVID-19 or IS.

This study has some limitations. Firstly, the generalizability of these findings is unknown, as they were conducted using data from a European population. Furthermore, the sample size of the GWAS data was somewhat constrained, limiting the number of SNPs available as instrumental variables. These GWAS studies also neglected to account for all risk variables associated with sCOVID-19, such as obesity and diabetes, potentially influencing the study outcomes. Furthermore, the control groups were not screened, raising the possibility that some healthy controls had moderate or asymptomatic infections. The outcomes of eQTL-based analyses are less likely to have been impacted by confounding variables. Thirdly, while efforts to reduce bias, including additional sensitivity analyses, were implemented, it is impossible to exclude the possibility that these results stem from horizontal pleiotropy. Therefore, these findings and candidate genes should only be considered “potentially causal,” highlighting the necessity of future validation research in larger patient cohorts.

Conclusion

In conclusion, the results of this study support a model wherein the impact that COVID-19 has on the risk of IS is closely associated with the severity of infection. Patients with a genetic susceptibility to COVID-19 hospitalization and sCOVID-19 may be at a higher risk of IS, but there is no comparable link for sensitivity to SARS-CoV-2 infection. Furthermore, ten causative genes were identified, which may mediate the relationship between sCOVID-19 and IS via modulating immune-related and RAAS pathways. These results serve as a significant resource for clinical strategies in managing COVID-19 patients while also elucidating the probable mechanisms behind COVID-19 complicated by IS, hence guiding medicinal development initiatives.

Data availability

Publicly available datasets were analyzed in this study. The summary statistics were retrieved from the OpenGWAS databases.

Abbreviations

MR:

Mendelian randomization

sCOVID-19:

Severe COVID-19

IS:

Ischemic stroke

eQTLs:

Expression quantitative trait loci

IVW:

Inverse variance weighted

SNPs:

Single nucleotide polymorphisms

GWAS:

Genome-wide association study

ICI:

Immune cells infiltration

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Acknowledgements

The authors would like to express their gratitude to BMCSCI (http://www.bmcscience.com/) for the expert linguistic services provided.

Funding

This study is supported by the Nanjing Medical Science and Technology Development Fund (NO. YKK22239) and Talent Introduction Special Funds (NO.08, received by Liang Chen).

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Authors and Affiliations

  1. Department of Critical Care Medicine, Zhongda Hospital Lishui Branch, Nanjing Lishui People’s Hospital, Southeast University, Nanjing, 211200, China

    Zhaojie Zhang

  2. Department of Gastroenterology, Jiangsu Province People’ s Hospital, Nanjing, 211029, China

    Jie Hua

  3. Department of Infectious Diseases, Taikang Xianlin Drum Tower Hospital, Affiliated Hospital of Medical College of Nanjing University, NO 188 Lingshan North Road, Qixia District, Nanjing, 210046, China

    Liang Chen

Authors
  1. Zhaojie Zhang
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  2. Jie Hua
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Contributions

SJ Z: conception, design, data collection, analysis, manuscript writing, and revision. JH: design, data collection, manuscript writing and revision. LC: conception, design, data collection, analysis, manuscript writing, manuscript revision, fund acquisition, and supervision. All authors read and approved the final version.

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Correspondence to Liang Chen.

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Zhang, Z., Hua, J. & Chen, L. The causal association between COVID-19 and ischemic stroke: a mendelian randomization study. Virol J 21, 280 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12985-024-02548-y

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12985-024-02548-y

Keywords

Virology Journal

ISSN: 1743-422X