The association between immune cells and epilepsy：Evidence from Mendelian randomization study and meta-analysis

doi:10.21203/rs.3.rs-4697272/v1

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The association between immune cells and epilepsy：Evidence from Mendelian randomization study and meta-analysis

https://doi.org/10.21203/rs.3.rs-4697272/v1

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Epilepsy is one of the most common serious brain diseases, affecting more than 70 million people. Complex immune-brain interactions can affect the development and function of epilepsy.Two-sample Mendelian randomized (MR) analysis was used to determine the causal relationship between 731 immune cells as exposure data and epilepsy, focal epilepsy (FE), and generalized epilepsy(GE) as outcome data based on the Genome-Wide Association Studies (GWAS) and Finngen R10 dataset. The main methods to evaluate causality were inverse variance weighted (IVW) and P < 0.05 was included in the primary results. Comprehensive sensitivity analyses were used to verify the robustness of results. Finngen R10 dataset and GWAS dataset was respectively used as discovery dataset and validation dataset to select common positive immune cell phenotypes. Finally, reverse MR analysis and meta-analysis were conducted to obtain more accurate results. The final results showed that CD19 on IgD + CD38- is the risk factor for epilepsy (P = 0.031 in the discovery dataset; P = 0.032 in the validation dataset) and CD38 on CD20- (P = 0.007 in the discovery dataset; P = 0.035 in the validation dataset) and CD64 on CD14 + CD16- monocyte (P = 0.019 in the discovery dataset; P = 0.020 in the validation dataset) respectively served as protective factor and risk element for GE based on two different datasets. The meta-analysis of the IVW results from both datasets further support the causal effects of CD19 on IgD + CD38- on epilepsy (odds ratio (OR) = 1.037,95% confidence interval (CI) = 1.012–1.063, P = 0.003), CD38 on CD20- on GE (OR = 0.877,95%CI = 0.777–0.989, P = 0.032) and CD64 on CD14 + CD16- monocyte on GE (OR = 1.044,95%CI = 1.017–1.071, P = 0.001).

Epilepsy

MR analysis

meta-analysis

immune-brain interactions

Epilepsy is a chronic central nervous system disease that affects people of all ages and is distributed globally [1]. Epilepsy is one of the most common brain diseases, affecting more than 70 million people worldwide [2]. It is characterized by abnormal discharges of neurons in the brain, persistent susceptibility to spontaneous seizures and many neurobiological, cognitive and psychosocial consequences. The WHO's 2010 Global Burden of Disease study ranks epilepsy as the second most burdensome neurologic disorder worldwide in terms of disability-adjusted life years[3]. The diagnosis of epilepsy is very complex, there is no unified gold standard, detailed medical history and EEG examination is the key to the diagnosis. For most patients with epilepsy, antiepileptic drugs are the main treatment[2]. Although studies have shown that more than 2/3 patients may not have seizures when taking antiepileptic drugs, about 1/3 of patients develop drug-resistant epilepsy[4]. Therefore, it is necessary to further explore the pathogenesis of epilepsy and explore new treatment targets, which can be diagnosed and treated early, which may help to control symptoms and improve prognosis before serious complications occur.

The 2017 International League Against Epilepsy (ILAE) classified the types of epilepsy into the following generalized epilepsy (GE), focal epilepsy (FE) and unknown[5], which clarified the etiology of epilepsy: structural, genetic, infectious, metabolic, and immune, as well as an unknown group. Infectious and immune play an important role in epilepsy. More and more studies have shown that specific inflammatory mediators and immune cells play an important role in the occurrence and recurrence of seizures as well as related neuropathology and complications[6–9]. Therefore, complex immune-brain interactions that affect neurodevelopment, and function may have causal and therapeutic effects on nervous system diseases including epilepsy. In recent years, studies have shown an increased risk of epilepsy in systemic autoimmune diseases, which may affect the occurrence and development of epilepsy through abnormal activation of the immune system[10]. Cytokines play an important role in infection and inflammation, are important media for communication between the brain and the immune system, and play a key role in epilepsy[11]. Inflammation is the physiological response of the immune system to harmful stimuli to protect tissue from damage, maintain homeostasis, and promote neurogenesis and repair in brain tissue. Adaptive immune cells, such as regulatory T cells (Tregs), helper T cells (Th) and B cells, contribute to the process of neuroinflammation[12]. Neuroinflammation is a double-edged sword that protects the central nervous system (CNS) as an immune response and is harmful when overactivated. It is well known that both innate immunity and adaptive immunity can be activated during CNS injury, which in turn may lead to epilepsy. [6, 7]。Inflammatory cytokines, including IL-1β, IL-6 and TNF-α, play an important role in the pathogenesis of epilepsy, but their relationship with epilepsy is still inconsistent. In some animal experiments, most studies have shown that IL-1β has a pro-convulsant effect[13], and a few studies have shown that IL-1β has an anti-convulsant effect[14]. In human studies, IL-1β plasma levels are low in patients with epilepsy, and it remains controversial that plasma and cerebrospinal fluid concentrations of IL-1β are unchanged or elevated from baseline after a seizure[15–17]. In most animal studies, a consensus has been reached on the increase of IL-6 in serum and cerebrospinal fluid during seizures. However, some studies have shown that the hippocampal concentration of IL-6 is similar to that of the control group[18, 19]. In human studies, most studies support elevated levels of IL-6 in serum and cerebrospinal fluid in patients after a seizure [20, 21]. Animal studies have shown that TNF-α has both anticonvulsant and pro-convulsant effects, which may be related to its concentration[16]. Low concentrations of TNF-α may activate the pro-convulsant effect mainly through p55, while high concentrations of TNF-α may exert the anticonvulsant effect through the p75 pathway[22, 23].In a systematic review, most animal and human studies support the increased expression of TNF- α in patients with epilepsy, which is related to a higher susceptibility to seizures, but there is a lack of consistency[8]. However, so far, the results of the study on the relationship between immune inflammation and epilepsy are not consistent, and further research is needed.

Nevertheless, the specific contribution of immune cells to epilepsy warrants further exploration. Similar to the randomized controlled trial (RCT), the Mendelian randomization (MR) study is an analytical method of epidemiological etiological inference based on Mendelian independent distribution to explore the causal relationship between exposure and outcome[24]. In the MR study, single nucleotide polymorphisms (SNPs) are regarded as an instrumental variables (IVs) to estimate the causal relationship between exposure and outcome[25]. Previous studies have found the causal relationship between human blood metabolites, gut microbiota, circulating lipids, psychiatric disorders and epilepsy[26–30]. In recent years, more and more studies have focused on the relationship between immune cells and epilepsy, but no research has been done to explore the relationship between immune cells and epilepsy at the genetic level. Here, we conducted a comprehensive two-sample MR analysis and meta-analysis, using large-scale Genome-wide association study (GWAS) dataset and Finngen R10 dataset to summarize statistical dataset of immune cells and epilepsy to determine the causal relationship between immune cell phenotypes and epilepsy, which can provide new insights for the prevention and treatment of epilepsy.

Study design

The overall flow chart of this study is shown in Fig. 1. Based on the two-sample MR analysis and meta-analysis, we evaluated the causal relationship between 731 immune cell characteristic immune cell phenotypes and epilepsy. MR uses genetic variation to represent risk factors, and the study needs to meet the following three hypotheses: (1) instrumental variables (IVs) is closely related to exposure factors, (2) IVs is not related to confounding factors, and (3) IVs does not affect the results through ways other than exposure. Specifically, we identified the immune cells that have causal effects on epilepsy, generalized epilepsy and focal epilepsy through two-sample MR analysis. Our results are reported according to the STROBE-MR guidelines[31]. The study included in our analysis was approved by the relevant institutional review committee and the participants provided informed consent.

GWAS and Finngen R10 data sources for Epilepsy

On the one hand, we obtain the GWAS aggregate statistics of epilepsy, GE and FE from the International League Against Epilepsy Consortium on Complex Epilepsies[32]. The study analyzed 44,889 participants from Caucasian and Asian and African, including 15,212 epilepsy cases, stratified into generalized epilepsy (3,769 cases), focal epilepsy (9,671 cases) and unclassified (1,772 cases) and 29,677 controls. The proportion of females in the case group and control group was 53.4% and 51.6% respectively. For detailed information about the cohort, please refer to its official website (https://gwas.mrcieu.ac.uk/).On the other hand, we also can obtain the Finngen R10 aggregate statistics of epilepsy, GE and FE from the MR-Base website (http://www.mrbase.org/).The study included 12,891 cases and 312,803 controls for epilepsy, 7,526 cases and 399,290 controls for generalized epilepsy and 1,413 cases and 399,287 controls for focal epilepsy. More detailed information can be accessed on the official website (https://www.finngen.fi/fi).Table 1 shows the details of the exposure and outcome analyzed in this MR study.

Table 1

Details of the exposure and outcome.
Trait	Consortium	Samples	Case	Control
Exposure
731 immune cells	GWAS	3,757	/	/
Outcome
Epilepsy	GWAS	44,889	15,212	29,677
Generalized epilepsy	GWAS	33,446	3,769	29,677
Focal epilepsy	GWAS	39,348	9,671	29,677
Epilepsy	Finngen_R10	325,694	12,891	312,803
Generalized epilepsy	Finngen_R10	406,816	7,526	399,290
Focal epilepsy	Finngen_R10	400,700	1,413	399,287

Immunity-wide GWAS data sources

GWAS summary statistics for each immune trait are publicly available from the GWAS Catalog (accession numbers from GCST90001391 to GCST9000212)[33]. A total of 731 immunophenotypes including absolute cell (AC) counts (n = 118), median fluorescence intensities (MFI) reflecting surface antigen levels (n = 389), morphological parameters (MP) (n = 32) and relative cell (RC)counts (n = 192) were included. Specifically, the MFI, AC and RC features include B cells, CDCs, mature stages of T cells, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells) and Treg panels, while the MP features include CDC and TBNK panels. The report studied the effects of about 22 million variants on 731 immune cell traits in 3,757 Sardinian participants, and detected 122 significant independent association signals for 459 cell traits. For detailed information can browse its official website (https://gwas.mrcieu.ac.uk/).

Selection of instrumental variables

According to recent research[34], Selection of immune cells-related IVs for MR analyses followed specific criteria: (a)Since the number of IVs obtained under the strict threshold (p < 5× 10^− 8) was extremely minimal, we adopted a more comprehensive threshold (p < 1×10^− 5) to obtain relatively more IVs to achieve relatively robust results; (b) To ensure each IV’s independence, SNPs within a window size of 10000 kb at a threshold of r² < 0.001 were pruned to mitigate linkage disequilibrium (LD);(c) We calculated the F-statistic of IVs to assess the degree of weak instrumental bias. If the F-statistic > 10, it was considered that no bias was caused by weak IVs. In addition, selection of epilepsy-related IVs for reverse MR analyses followed specific criteria[35]: (a) IVs obtained under the threshold (p < 1 × 10^− 5); (b) SNPs within a window size of 10000 kb at a threshold of r² < 0.001 were pruned to mitigate LD.

Statistical analysis

All analyses were performed in R 4.3.3 software. The Finngen R10 dataset and GWAS dataset were used for statistical analysis as discovery dataset and validation dataset, respectively. To evaluate the causal association between 731 immune cell phenotypes and epilepsy, inverse variance weighting (IVW), were mainly performed by using the ‘TwoSampleMR’ package (version 0.5.8)[26, 28, 34]. Another four additional MR methods, MR-Egger, weighted median, simple mode and weighted mode, would be performed to supplement the final results. Finally, the results of causal associations were presented as odds ratios (OR) and 95% confidence intervals (95% CI). The significance threshold was set at p < 0.05. In order to test the stability of causality and evaluate the validity of the hypothesis, we further carried out several sensitivity analyses and statistical tests.MR-PRESSO global test and MR-Egger intercept were employed to identify outliers and horizontal pleiotropic effects[36, 37]. In addition, the leave-one-out analysis was performed to identify SNPs with potential extreme influence on estimates and assess the robustness of the results. Finally, scatter plots were used to show that the results were not affected by outliers and funnel plots were used to demonstrated the robustness of the correlation and no heterogeneity[28].

Bonferroni correction was the most common method to adjust for multiple testing[38]. We initially adopted Bonferroni correction to solve the problem of multiple tests and established a significance threshold of p ≤ 6.84× 10^− 5(0.05/731) as statistically significant, but no positive results were found. In our MR study, to validate the robustness of the MR results, Finngen R10 dataset was used as discovery dataset, and GWAS dataset was used as validation dataset to select common positive immune cell phenotypes for inclusion in the final results. According to the final results, reverse MR analysis and meta-analysis were conducted to consolidate the findings.

When we conducted a follow-up analysis of the final results of MR using meta-analysis, it was mainly performed by using the 'meta' package (version 4.3.3). If P ≥ 0.05 and I² ≤ 50%, the heterogeneity between studies is low and a fixed-effect model is used, otherwise a random-effect model is used. P < 0.05 was considered statistically significant.

Exploration of the causal effect of immune cell phenotypes on epilepsy

To assess the influence of different immune cell phenotypes on the risk of epilepsy, Two-Sample MR and meta-analysis were conducted to study the relationship between 731 immune cells and epilepsy, FE, and GE.

First, we performed MR analysis to assess the causal association of 731 immune cells with epilepsy based on the GWAS and Finngen R10 datasets. Regarding the discovery dataset, the primary results showed that 34 immune cell phenotypes were associated with epilepsy of Finngen R10 dataset. Supplementary Table 1 can provide detailed information. Figure 2a is the circle map of five different MR method. Figure 3a is the forest map the most dominant method of IVW. For the validation dataset, the primary results showed that 35 immune cell phenotypes were associated with epilepsy of GWAS dataset. Supplementary Table 2 can provide detailed information. Figure 2b is the circle map of five different MR method. Figure 3b is the forest map to show the most dominant method of IVW. Combining the MR Results of the discovery dataset and the validation dataset, we found that CD19 on IgD + CD38- (OR = 1.032,95%CI = 1.003–1.062, P = 0.031 in the Finngen R10 dataset; OR = 1.052,95%CI = 1.004–1.102, P = 0.032 in the GWAS dataset) was associated with an increased risk for epilepsy. Next, we used IVW method to evaluate the heterogeneity results showed that the heterogeneity of all the results was small. (p > 0.05). Further, the MR-Egger intercept test and MR-PRESSO global test suggested that our MR analysis was unaffected by horizontal pleiotropy (p > 0.05). The results were shown in the Table 2. The Scatter plots, forest plot, leave-one-out analysis and funnel plots suggested the robustness of the final results (Supplementary Fig. 1 and Supplementary Fig. 2).

Subsequently, we further evaluated the causal association of 731 immune cells with generalized epilepsy based on the GWAS and Finngen R10 datasets. Regarding the discovery dataset, the primary results showed that 31 immune cell phenotypes were associated with generalized epilepsy of Finngen R10 dataset. Supplementary Table 3 can provide detailed information. Figure 2c is the circle map of five different MR method. Figure 3c is the forest map the most dominant method of IVW. For the validation dataset, the primary results showed that 45 immune cell phenotypes were associated with generalized epilepsy of GWAS dataset. Supplementary Table 4 can provide detailed information. Figure 2d is the circle map of five different MR method. Figure 3d is the forest map to show the most dominant method of IVW. Combining the MR Results of the discovery dataset and the validation dataset, we found that CD38 on CD20- (OR = 0.807,95%CI = 0.690–0.944, P = 0.007 in the Finngen R10 dataset; OR = 0.918,95%CI = 0.847–0.994, P = 0.035 in the GWAS dataset) and CD64 on CD14 + CD16- monocyte (OR = 1.038,95%CI = 1.006–1.071, P = 0.019 in the Finngen R10 dataset; OR = 1.057,95%CI = 1.009–1.108, P = 0.020 in the GWAS dataset) respectively served as protective factor and risk element for generalized epilepsy. Next, we used IVW method to evaluate the heterogeneity results showed that the heterogeneity of all the results was small. (p > 0.05). Further, the MR-Egger intercept test and MR-PRESSO global test suggested that our MR analysis was unaffected by horizontal pleiotropy (p > 0.05). The results were shown in the Table 2. The Scatter plots, forest plot, leave-one-out analysis and funnel plots suggested the robustness of the final results (Supplementary Fig. 3, Supplementary Fig. 4, Supplementary Fig. 5 and Supplementary Fig. 6).

Finally, we assessed the causal association of 731 immune cells with focal epilepsy based on the GWAS and Finngen R10 datasets. Regarding the discovery dataset, the primary results showed that 32 immune cell phenotypes were associated with focal epilepsy of Finngen R10 dataset. Supplementary Table 5 can provide detailed information. Figure 2e is the circle map of five different MR method. Figure 3e is the forest map the most dominant method of IVW. For the validation dataset, the primary results showed that 33 immune cell phenotypes were associated with focal epilepsy of GWAS dataset. Supplementary Table 6can provide detailed information. Figure 2f is the circle map of five different MR method. Figure 3f is the forest map to show the most dominant method of IVW. Unfortunately, combining the MR Results of the discovery dataset and the validation dataset, we did not find any immune cells associated with focal epilepsy.

Table 2

The results of heterogeneity and horizontal pleiotropy tests between immune cells on epilepsy.
Exposure		Heterogeneity test		horizontal pleiotropy test
		MR Egger regression	IVW model	MR Egger intercept	MR PRESSO test
CD19 on IgD + CD38-	Epilepsy (GWAS)	0.923	0.981	0.375	0.883
CD19 on IgD + CD38-	Epilepsy (Finngen)	0.703	0.741	0.706	0.726
CD38 on CD20-	Generalized epilepsy (GWAS)	0.403	0.293	0.856	0.460
CD38 on CD20-	Generalized epilepsy (Finngen)	0.870	0.837	0.688	0.870
CD64 on CD14 + CD16- monocyte	Generalized epilepsy (GWAS)	0.681	0.584	0.671	0.811
CD64 on CD14 + CD16- monocyte	Generalized epilepsy (Finngen)	0.389	0.434	0.174	0.423

Exploration of the causal effect of epilepsy onset on immune cells

To explore the causal effects of epilepsy, GE and FE on immune cell phenotypes, reverse two-sample MR analysis was performed, and the IVW method was used as the main analysis (Fig. 4). The results showed that there was no any causal relationship between epilepsy and immune cells in either the GWAS and Finngen R10 datasets.

Meta-analysis to validate the robustness of immune cell phenotypes on epilepsy

Figure 5a, Fig. 5b and Fig. 5c respectively show the meta-analysis of the IVW results from both datasets further support the causal effects of CD19 on IgD + CD38- on epilepsy (OR = 1.037,95%CI = 1.012–1.063, P = 0.003), CD38 on CD20- on generalized epilepsy (OR = 0.877,95%CI = 0.777–0.989, P = 0.032) and CD64 on CD14 + CD16- monocyte on generalized epilepsy (OR = 1.044,95%CI = 1.017–1.071, P = 0.001). From the meta-analysis results, the results were accurate and the heterogeneity was low (I² = 0.0%, τ2 = 0.0%, P = 0.49 of CD19 on IgD + CD38- on epilepsy, I² = 51%, τ2 = 0.0%, P = 0.15 of CD38 on CD20- on generalized epilepsy and I² = 0.0%, τ2 = 0.0%, P = 0.52 of CD64 on CD14 + CD16- monocyte on generalized epilepsy).

The development and progression of epilepsy involve a highly complex process in which immune cells play crucial roles[6, 7]. Based on large publicly available genetic data, our study comprehensively assessed the causal associations between 731 immune cell traits and epilepsy. According to the 2017 ILAE classification criteria, epilepsy can be divided into GE, FE and unclassified epilepsy[5]. In a genome-wide mega-analysis study, FE and GE were divided into several different subtypes for separate studies[32]. The GE included in this study from the GWAS database included the following types: juvenile myoclonic epilepsy, childhood absence epilepsy, juvenile absence epilepsy and generalized epilepsy with tonic-clonic seizures. FE includes the following types: focal epilepsy documented lesion other than hippocampal sclerosis, focal epilepsy documented lesion negative and focal epilepsy documented hippocampal sclerosis[32]. At the broadest level, FE attacks occur in restricted parts of the brain, a form that has not traditionally been seen as a genetic form, while GE occurs in bilateral networks where evidence of a genetic component is very strong, but genes are difficult to identify. To our knowledge, this is the first large-scale genetic consortia-based MR analysis to establish causality between multiple immunophenotypes and epilepsy. In our study, we identified different immune cells associated with epilepsy (34 immune cells of Finngen R10 dataset and 45 immune cells of GWAS dataset), generalized epilepsy (31 immune cells of Finngen R10 dataset and 35 immune cells of GWAS dataset) and focal epilepsy (32 immune cells of Finngen R10 dataset and 33 immune cells of GWAS dataset) among the four types of immune traits (MFI, RC, AC, and MP). Finally, by combining Finngen and GWAS datasets with meta-analysis, we focus on one identical immunophenotypes (CD19 on IgD + CD38-) on epilepsy and two identical immunophenotypes (CD38 on CD20- and CD64 on CD14 + CD16- monocyte) on generalized epilepsy.

CD19 on IgD + CD38- in the B cell panel has been found to be associated with increased the risk of epilepsy in this study. CD19 is a marker expressed on almost all B cell lineages, while IgD is part of the B cell receptor (BCR) and CD38 is a surface molecule associated with proliferation and apoptosis of B cells[39, 40]. As part of the immune system, B cells may participate in the regulation of immune response through the production of antibodies or cytokines, and may also participate in the neuroinflammatory process, affecting the excitability of neurons through the release of pro-inflammatory or anti-inflammatory cytokines, thus affecting the onset of epilepsy[41]. CD19B cells are a key factor of central nervous system autoimmunity and participate in antibody production of autoimmune diseases, which has been confirmed in mice autoimmune encephalitis experiments[42, 43]. A study examining the immune mechanism of temporal lobe epilepsy due to limbic encephalitis (LE) showed an increase in CD19 + B cells in plasma and cerebrospinal fluid of antibody-negative LE patients[44]. In a previously published MR Study that explored the causal relationship between immune cells and breast cancer, CD19 on IgD-CD38br was found to be associated with an increased risk of breast cancer, similar to our findings in this study[45]. Although the results of this study provide certain clues to understanding the role of CD19 on IgD + CD38- in epilepsy, further research is needed to elucidate the mechanism.

CD38 on CD20- in the B cell panel and CD64 on CD14 + CD16- monocyte in the monocyte panel has been found to be associated with a decreased and increased risk of generalized epilepsy, respectively. CD38 is a pluripotent cell surface receptor and enzyme on the surface of B cells, which affects immune regulation by regulating signaling pathways related to the activation, proliferation and differentiation of B cells, and participates in the occurrence and development of diseases[46]. CD38 plays an important role in immune regulation, intracellular signal transduction, cellular metabolism, and inflammatory responses[47]. Previous studies have shown that CD38 can be involved in the occurrence and development of a variety of diseases, such as neuroinflammation and neurodegeneration, multiple myeloma, leukemia, cancer, cardiovascular diseases and allergic airway diseases[47–50]. CD38 is expressed in brain cells, including neurons, astrocytes, and microglia, and is associated with intracellular NAD + homeostasis[51]. At present, although no direct evidence has been found linking CD38 on CD20- to epilepsy, given the role of CD38 in immune regulation, as well as the interaction between the immune system and the nervous system, it can be speculated that CD38 may indirectly affect the development of epilepsy by affecting the immune response.

CD64 is an FC-γ receptor, also known as IgG Fc fragment Receptor 1 (FC-γ RI), which mediates antibody-dependent phagocytosis of immune cells, release of inflammatory mediators, and antibody-dependent cytotoxicity, and plays an important role in inflammation and immune responses[52].Human circulating monocytes are an important part of the immune system, and the most common subtypes include the classical (CD14 + + CD16−), intermediate (CD14 + + CD16+), and nonclassical (CD14 + CD16++) monocytes[53]. CD14 + CD16- monocyte belongs to the classical subset, constituting 10% of peripheral monocytes, which can participate in various immune responses of inflammation and tissue repair[54, 55]. Previously published MR Studies exploring immune cells and osteoarthritis and myasthenia gravis have shown that CD64 on CD14 + CD16- monocyte is a risk factor, consistent with the results of this study[56, 57]. Inflammation and immunity play an important role in the development and maintenance of epilepsy, and CD64 on CD14 + CD16-monocyte may be involved in the development of epilepsy by influencing immune cells and inflammatory responses[58].

In recent years, more and more studies have begun to pay attention to the relationship between immune cells and epilepsy, but the role of which immune cells in which types of epilepsy is still unclear. Our study utilized MR analysis based on the published results of large GWAS cohorts and Finngen cohorts, relying on genetic variants, so it has high statistical efficiency to explore the causal effect of different immune cells on different subtypes of epilepsy. The integration of reverse MR analysis minimized confounding and reverse causation bias, which yielded robust results and strengthened the final causal inference. This MR Study can provide a valuable reference for further research on the relationship between immune cells and epilepsy. The limitations of the present study should be noted: (1) Since the number of IVs fulfilling the strict threshold (p < 5 × 10^− 8) was extremely small, a relatively lenient threshold (p < 1 × 10^− 5) was adopted for screening IVs. (2) This study only included individuals of Caucasian, Asian and African ancestry, so extrapolating the findings to other racial groups is limiting. (3) The number of cases of the two subtypes of epilepsy under strict definition (generalized epilepsy and focal epilepsy) is relatively small, so future analysis based on a larger sample size of GWAS summary data is necessary to increase the confidence of the results. (4) In this study, significance is declared at p < 0.05 unadjusted. We hope to make up for this deficiency through meta-analysis. This study was an exploratory study and did not adjust for P-values to find as many potential positive results as possible. We will further verify the results in future studies.

In conclusion, the primary genetic evidence from this study have demonstrated the causal associations between several immunophenotypes and epilepsy through a comprehensive bidirectional MR analysis and meta-analysis, highlighting the complex pattern of interactions between the immune cells and epilepsy. Furthermore, our research may aid in identifying biochemical markers for the prediction, screening, early diagnosis of epilepsy, which may provide a new avenue for researchers to explore the biological mechanisms of epilepsy. Nevertheless, in the future, more research in vivo vitro experiments should be carried out to validate the functional roles of these immune cells in epilepsy.

Author contributions

Conceptualization: Xiangyi Wang, Shenggen Chen

Data curation: Xiangyi Wang, Man Li

Formal analysis: Xiangyi Wang, Luyan Wu

Investigation: Xiangyi Wang, Yuying Zhang

Methodology: Xiangyi Wang, Shenggen Chen

Supervision: Xiangyi Wang, Chaofeng Zhu

Visualization: Xiangyi Wang, Wanhui Lin

Writing – original draft: Xiangyi Wang

Writing – review & editing: Chaofeng Zhu, Huapin Huang

All the authors have read and agreed to the published version of the manuscript.

Conflict of interest statement

The authors have no actual or potential conflicts of interest to declare.

Funding

This study was financially supported by a research fund from the Excellent Young Scholars Cultivation Project of Fujian Medical University Union Hospital (2022XH037), National key clinical specialty (Grant No. 21281003), Clinical Research Center for Precision Diagnosis and Treatment of Neurological Diseases of Fujian Province(2022Y2005), Provincial Clinical Key Specialty Construction Project (2128200737).

Data Availability

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Further inquiries can be directed to the corresponding author.

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The association between immune cells and epilepsy：Evidence from Mendelian randomization study and meta-analysis

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