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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Neurosci.</journal-id>
<journal-title>Frontiers in Neuroscience</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Neurosci.</abbrev-journal-title>
<issn pub-type="epub">1662-453X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fnins.2024.1397430</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Neuroscience</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Causal links between gut microbiomes, cytokines and risk of different subtypes of epilepsy: a Mendelian randomization study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Qiu</surname> <given-names>Youjia</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref rid="fn0004" ref-type="author-notes"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author" equal-contrib="yes">
<name><surname>Song</surname> <given-names>Bingyi</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref rid="fn0004" ref-type="author-notes"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author">
<name><surname>Xie</surname> <given-names>Minjia</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Tao</surname> <given-names>Yuchen</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
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<contrib contrib-type="author">
<name><surname>Yin</surname> <given-names>Ziqian</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Menghan</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Ma</surname> <given-names>Chao</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Chen</surname> <given-names>Zhouqing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Wang</surname> <given-names>Zhong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<aff id="aff1"><sup>1</sup><institution>Department of Neurosurgery, The First Affiliated Hospital of Soochow University</institution>, <addr-line>Suzhou</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Suzhou Medical College of Soochow University</institution>, <addr-line>Suzhou</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0001">
<p>Edited by: Avinash Veerappa, University of Nebraska Medical Center, United States</p>
</fn>
<fn fn-type="edited-by" id="fn0002">
<p>Reviewed by: Jai Chand Patel, University of Nebraska Medical Center, United States</p>
<p>Di Xie, Shanghai Jiao Tong University, China</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Zhong Wang, <email>wangzhong761@163.com</email></corresp>
<corresp id="c002">Zhouqing Chen, <email>zqchen6@163.com</email></corresp>
<fn fn-type="equal" id="fn0004"><p><sup>&#x2020;</sup>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>24</day>
<month>05</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>18</volume>
<elocation-id>1397430</elocation-id>
<history>
<date date-type="received">
<day>07</day>
<month>03</month>
<year>2024</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>05</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Qiu, Song, Xie, Tao, Yin, Wang, Ma, Chen and Wang.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Qiu, Song, Xie, Tao, Yin, Wang, Ma, Chen and Wang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Objective</title>
<p>Recent research suggests a potential link between the gut microbiome (GM) and epilepsy. We undertook a Mendelian randomization (MR) study to determine the possible causal influence of GM on epilepsy and its various subtypes, and explore whether cytokines act as mediators.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>We utilized Genome-Wide Association Study (GWAS) summary statistics to examine the causal relationships between GM, cytokines, and four epilepsy subtypes. Furthermore, we assessed whether cytokines mediate the relationship between GM and epilepsy. Significant GMs were further investigated using transcriptomic MR analysis with genes mapped from the FUMA GWAS. Sensitivity analyses and reverse MR were conducted for validation, and false discovery rate (FDR) correction was applied for multiple comparisons.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>We pinpointed causal relationships between 30 GMs and various epilepsy subtypes. Notably, the Family Veillonellaceae (OR:1.03, 95%CI:1.02&#x2013;1.05, <italic>p</italic>&#x2009;=&#x2009;0.0003) consistently showed a strong positive association with child absence epilepsy, and this causal association endured even after FDR correction (<italic>p</italic>-FDR&#x2009;&#x003C;&#x2009;0.05). Seven cytokines were significantly associated with epilepsy and its subtypes. A mediating role for cytokines has not been demonstrated. Sensitivity tests validated the primary MR analysis outcomes. Additionally, no reverse causality was detected between significant GMs and epilepsy. Of the mapped genes of notable GMs, genes like BLK, FDFT1, DOK2, FAM167A, ZSCAN9, RNGTT, RBM47, DNAJC21, SUMF1, TCF20, GLO1, TMTC1, VAV2, and RNF14 exhibited a profound correlation with the risk factors of epilepsy subtypes.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>Our research validates the causal role of GMs and cytokines in various epilepsy subtypes, and there has been no evidence that cytokines play a mediating role between GM and epilepsy. This could provide fresh perspectives for the prevention and treatment of epilepsy.</p>
</sec>
</abstract>
<kwd-group>
<kwd>gut microbiome</kwd>
<kwd>subtype of epilepsy</kwd>
<kwd>cytokines</kwd>
<kwd>Mendelian randomization</kwd>
<kwd>mapped genes</kwd>
<kwd>FUMA</kwd>
</kwd-group>
<counts>
<fig-count count="4"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="67"/>
<page-count count="11"/>
<word-count count="7868"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Gut-Brain Axis</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<label>1</label>
<title>Introduction</title>
<p>Epilepsy is a widespread and severe neurological disorder marked by sudden, simultaneous abnormal neuron activity in the brain. The rate of active epilepsy typically ranges between 4 and 12 per 1,000 (<xref ref-type="bibr" rid="ref52">Thijs et al., 2019</xref>). Epilepsy&#x2019;s incidence shows a bimodal distribution, with the highest risk seen in infants and the elderly (<xref ref-type="bibr" rid="ref52">Thijs et al., 2019</xref>). Epilepsy remains a significant cause of disability and death, posing a considerable global societal burden. It can be categorized into focal epilepsy (FE) and generalized epilepsy (GE). The latter is subdivided into motor and non-motor (absence) epilepsy. In GE, the epileptogenic networks are widespread, covering thalamocortical structures bilaterally (<xref ref-type="bibr" rid="ref4">Asadi-Pooya, 2019</xref>). By contrast, FE networks are limited to a single hemisphere, often involving limbic or neocortical regions (<xref ref-type="bibr" rid="ref2">Allers et al., 2015</xref>). Both childhood absence epilepsy (CAE) and juvenile absence epilepsy (JAE) are part of an epileptic syndrome, commonly presenting with brief staring, rhythmic blinking, or motor automatisms and changes in the electroencephalogram (<xref ref-type="bibr" rid="ref21">Kessler and McGinnis, 2019</xref>). While 65% of epilepsy patients find relief with antiepileptic medication (<xref ref-type="bibr" rid="ref24">Kwan et al., 2011</xref>), about a third face drug resistance (<xref ref-type="bibr" rid="ref37">Perucca et al., 2023</xref>). The frequent seizures, cognitive challenges, psychological disorders, and drug side effects profoundly impact these patients&#x2019; quality of life (<xref ref-type="bibr" rid="ref59">Wang et al., 2023</xref>). Therefore, further exploring the etiology of epilepsy and new therapeutic options for patients has become necessary.</p>
<p>Recently, mounting evidence indicates a strong link between gut microbiomes (GMs) and human health (<xref ref-type="bibr" rid="ref20">J&#x00E4;rbrink-Sehgal and Andreasson, 2020</xref>). Research reveals that GM affects the central nervous system&#x2019;s physiology, neurochemistry, behavior, and cognitive growth. The theory suggests that communication between the GM and the brain is facilitated through diverse channels, including the central and enteric nervous systems and endocrine and immune pathways (<xref ref-type="bibr" rid="ref17">Grenham et al., 2011</xref>). This intricate relationship, the gut-brain axis, is maintained through multiple signaling pathways and connectivity networks. Studies have highlighted the role of GM and the gut-brain axis in neurological disorders such as depression, Alzheimer&#x2019;s disease, and Parkinson&#x2019;s disease. The hypothesis has been advised that GM dysbiosis is linked to development of epilepsy (<xref ref-type="bibr" rid="ref45">Sorboni et al., 2022</xref>). The composition of GM in individuals with epilepsy is different from that of healthy control subjects, both before and after treatment (<xref ref-type="bibr" rid="ref16">Gong et al., 2022</xref>). Additionally, disturbances at the level of specific GM taxa can influence the activity of neurons associated with epilepsy (<xref ref-type="bibr" rid="ref10">Darch and McCafferty, 2022</xref>). The ketogenic diet (KD), a non-pharmacological option for those with treatment-resistant epilepsy (<xref ref-type="bibr" rid="ref43">Soca&#x0142;a et al., 2021</xref>), can reduce seizure attacks by 50%, as observed in 22&#x2013;55% of adults and 60&#x2013;75% of children (<xref ref-type="bibr" rid="ref33">&#x00D6;zcan et al., 2022</xref>). Yet, whether alteration in GMs induced by KD is protective or detrimental remains debatable.</p>
<p>In general, neuroinflammation is a normal response that helps maintain homeostasis. However, excessive or persistent neuroinflammation can lead to cellular dysfunction (<xref ref-type="bibr" rid="ref62">Xanthos and Sandk&#x00FC;hler, 2014</xref>). Related studies have shown that neuroinflammatory responses are not only the result of epileptic seizures or brain neuropathology but are involved in their production (<xref ref-type="bibr" rid="ref39">Prabowo et al., 2013</xref>; <xref ref-type="bibr" rid="ref34">Pauletti et al., 2019</xref>). Activated microglia and astrocytes are the major cell types contributing to neuroinflammation (<xref ref-type="bibr" rid="ref53">Vezzani et al., 2011</xref>, <xref ref-type="bibr" rid="ref56">2015</xref>). Several bioactive products from the GM can trigger the release of inflammatory factors by activating microglia (<xref ref-type="bibr" rid="ref22">Kim et al., 2012</xref>). Therefore, we hypothesized that cytokines may be mediators in the pathway from GM to epilepsy. Cytokines are inflammatory regulators and, therefore, important intermediate phenotypes in inflammatory diseases. The pro-inflammatory cytokines IL-1&#x03B2; and IL-6 are important contributors to the inflammatory response in the brain. There is evidence that epilepsy is associated with elevated levels of pro-inflammatory cytokines (<xref ref-type="bibr" rid="ref54">Vezzani et al., 2013</xref>). Moreover, microglia may, with the help of the above cytokines, play a role in the pathology of epilepsy through the IL-1 receptor/Toll-like receptor signaling, COX-2, and the TGF-&#x03B2; signaling process (<xref ref-type="bibr" rid="ref55">Vezzani et al., 2016</xref>; <xref ref-type="bibr" rid="ref3">Alvim et al., 2021</xref>; <xref ref-type="bibr" rid="ref44">Soltani Khaboushan et al., 2022</xref>).</p>
<p>Robust evidence from randomized controlled trials (RCTs) verifies the connection between GM dysfunction and epilepsy onset. However, the nature of this relationship, whether causal or not, requires further study. Mendelian randomization (MR) is an advanced statistical method designed to explore causative associations between variables. It uses single nucleotide polymorphisms (SNPs) as instrumental variables. This method can help negate reverse causality and confounding factor effects. Still, no MR studies have focused on children with juvenile absence epilepsy, who often exhibit higher drug resistance (<xref ref-type="bibr" rid="ref50">Symonds et al., 2021</xref>). Thus, our research employed a MR analysis using data from a published Genome-Wide Association Study (GWAS) to explore the potential causal link between GM and various epilepsy subtypes.</p>
</sec>
<sec sec-type="materials|methods" id="sec6">
<label>2</label>
<title>Materials and methods</title>
<sec id="sec7">
<label>2.1</label>
<title>Ethical approval</title>
<p>The GWAS data utilized in this study were publicly available and lacked identifiable details. The data had already been approved by an ethics committee, eliminating the need for additional ethical clearance for our research.</p>
</sec>
<sec id="sec8">
<label>2.2</label>
<title>Study design</title>
<p>We employed two-sample MR to explore the relationship between GM and various epilepsy subtypes such as FE, GE, JAE, and CAE. Our research follows STROBE-MR guidance (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S1</xref>). The study&#x2019;s flowchart is depicted in <xref ref-type="fig" rid="fig1">Figure 1</xref>. For reliable MR study outcomes, three criteria must be met: (1) There must be a significant association between genetic variants and the specific exposure. (2) Genetic variants must not be linked to confounding factors associated with the outcomes. (3) Genetic variants should only affect the outcomes through exposure factors and no alternative pathways. IVs satisfying these criteria are deemed fit for MR analysis, ensuring accurate causal inference.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>The overview of the MR analysis design (Created with <ext-link xlink:href="http://BioRender.com" ext-link-type="uri">BioRender.com</ext-link>). IV, instrumental variable; MR, Mendelian randomization; SNPs, single-nucleotide polymorphisms; IVW, inverse-variance weighted; MR-PRESSO, MR pleiotropy residual sum and outlier.</p>
</caption>
<graphic xlink:href="fnins-18-1397430-g001.tif"/>
</fig>
</sec>
<sec id="sec9">
<label>2.3</label>
<title>Data source</title>
<p>Summary statistics for GMs were sourced from an extensive GWAS by the MiBioGen consortium, which included 18,340 individuals of European ethnic backgrounds across 11 countries and analyzed 122,110 genetic variation loci (<xref ref-type="bibr" rid="ref23">Kurilshikov et al., 2021</xref>). We accessed data on 211 gut microbial taxa, covering five taxonomic levels from genus to phylum. The adjustment was made for sex, age, study-specific covariates, and the primary genetic principal components reflecting population stratification (<xref ref-type="bibr" rid="ref23">Kurilshikov et al., 2021</xref>). GWAS data for FE and GE were procured from FinnGen Research (<ext-link xlink:href="https://r9.finngen.fi/" ext-link-type="uri">https://r9.finngen.fi/</ext-link>) and released in September 2022. Epilepsy diagnoses in the FinnGen dataset used the G40 code from the 10th edition of the International Classification of Diseases (ICD). The summary data for CAE and JAE came from the IEU open GAWS project (<ext-link xlink:href="https://gwas.mrcieu.ac.uk/" ext-link-type="uri">https://gwas.mrcieu.ac.uk/</ext-link>). As data on GMs and different subtypes of epilepsy were obtained from different databases, the populations with GMs and epilepsy did not overlap. Genetic data for cytokines were from a previous GWAS (8,337 individuals) and included 41 inflammatory cytokines (<xref ref-type="bibr" rid="ref1">Ahola-Olli et al., 2017</xref>). This study amalgamated findings from both The Cardiovascular Risk in Young Finns Study and FINRISK surveys, wherein participants averaged 37&#x2009;years and 60&#x2009;years, respectively. Under the framework of our MR design, a comprehensive summary of the exposures and outcomes subjected to analysis is provided in <xref ref-type="table" rid="tab1">Table 1</xref>.</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Details of the exposure and outcome.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top"><bold>Trait</bold></th>
<th align="left" valign="top"><bold>Consortium</bold></th>
<th align="center" valign="top"><bold>Samples</bold></th>
<th align="center" valign="top"><bold>Case</bold></th>
<th align="center" valign="top"><bold>Control</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="bottom" colspan="5"><bold>Exposure</bold>
</td>
</tr>
<tr>
<td align="left" valign="bottom">211 GM taxa</td>
<td align="left" valign="bottom">MiBioGen</td>
<td align="center" valign="bottom">18,340</td>
<td align="center" valign="bottom">/</td>
<td align="center" valign="bottom">/</td>
</tr>
<tr>
<td align="left" valign="bottom">41 cytokines</td>
<td align="left" valign="bottom">Ahola-Olli et al.</td>
<td align="center" valign="bottom">8,337</td>
<td align="center" valign="bottom">/</td>
<td align="center" valign="bottom">/</td>
</tr>
<tr>
<td align="left" valign="bottom" colspan="5"><bold>Outcome</bold>
</td>
</tr>
<tr>
<td align="left" valign="bottom">Generalized epilepsy</td>
<td align="left" valign="bottom">FinnGen (R9)</td>
<td/>
<td align="center" valign="bottom">2,632</td>
<td align="center" valign="bottom">300,631</td>
</tr>
<tr>
<td align="left" valign="bottom">Focal epilepsy</td>
<td align="left" valign="bottom">FinnGen (R9)</td>
<td/>
<td align="center" valign="bottom">1,297</td>
<td align="center" valign="bottom">300,631</td>
</tr>
<tr>
<td align="left" valign="bottom">Child absence epilepsy</td>
<td align="left" valign="bottom">IEU OpenGWAS project</td>
<td align="center" valign="bottom">30,470</td>
<td align="center" valign="bottom">793</td>
<td align="center" valign="bottom">29,677</td>
</tr>
<tr>
<td align="left" valign="bottom">Juvenile absence epilepsy</td>
<td align="left" valign="bottom">IEU OpenGWAS project</td>
<td align="center" valign="bottom">30,092</td>
<td align="center" valign="bottom">415</td>
<td align="center" valign="bottom">29,677</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>GM, gut microbiome; GWAS, genome-wide association study.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec10">
<label>2.4</label>
<title>Identification of IVs</title>
<p>To enhance the precision of our results, we conducted a thorough data screening process for the information extracted from MiBioGen. Owing to the limited number of IVs meeting a stringent threshold of <italic>p</italic>&#x2009;&#x003C;&#x2009;5&#x2009;&#x00D7;&#x2009;10<sup>&#x2212;8</sup>, we adjusted the threshold to <italic>p</italic>&#x2009;&#x003C;&#x2009;1&#x2009;&#x00D7;&#x2009;10<sup>&#x2212;5</sup> to obtain a more substantial number of IVs derived from GMs for our analysis. By increasing the number of IVs included, the efficacy of the statistical analyses can be improved. Similarly, we set the threshold to <italic>p</italic>&#x2009;&#x003C;&#x2009;5&#x2009;&#x00D7;&#x2009;10<sup>&#x2212;6</sup> to expand the number of available IVs for each cytokine. Additionally, we set <italic>r</italic><sup>2</sup> to 0.01 and the clumping window width kb&#x2009;=&#x2009;10,000 to ensure no linkage disequilibrium among IVs. Further refinements to our IVs included removing palindromic SNPs and any not present in the results, allowing us to assess whether the selected IVs had any associations with potential confounding risk factors. Lastly, the F-statistics were calculated using the subsequent formula to evaluate the potential bias arising from weak instruments: (<italic>F</italic>&#x2009;=&#x2009;beta<sup>2</sup>/se<sup>2</sup>).</p>
</sec>
<sec id="sec11">
<label>2.5</label>
<title>Primary analyses</title>
<p>We used the inverse variance weighted (IVW) method as our primary means to evaluate the causal relationship. Other methods, including MR-Egger, weighted mode, weighted median, and simple mode, were also employed. MR-Egger detects and adjusts for possible horizontal pleiotropy, but its results can be skewed by the presence of outlying genetic variables (<xref ref-type="bibr" rid="ref48">Sun et al., 2023</xref>). The weighted median method ensures causality estimate stability by correcting potential errors when up to 50% of the IVs might be invalid and may provide better causality detection than the MR-Egger under certain conditions (<xref ref-type="bibr" rid="ref29">Morris et al., 2022</xref>). In addition, reverse MR was conducted to rule out reverse causality. The odds ratio (OR) with its 95% confidence interval (95% CI) indicates GM&#x2019;s causal effect on epilepsy. We adjusted the <italic>p</italic>-value using the false discovery rate (FDR) procedure for multiple comparisons (<xref ref-type="bibr" rid="ref63">Xiang et al., 2021</xref>).</p>
</sec>
<sec id="sec12">
<label>2.6</label>
<title>Mediation analysis</title>
<p>Mediation analysis aims to evaluate the pathway from exposure to outcome through a mediator, which helps explore the potential mechanisms by which exposure affects outcome (<xref ref-type="bibr" rid="ref7">Carter et al., 2021</xref>). After assessing causal effects using two-sample analysis, the selected GM and cytokines with significant causal impact on epilepsy will be included in the mediation analysis. If GM has a causal effect on cytokines, we will explore whether cytokines are mediators in the pathway from GM to epilepsy. Specifically, a two-step MR analysis was conducted to examine the mediating pathways from GM to epilepsy. In the first step, the IV of GM was used to estimate the causal effect of exposure on potential mediating variables. In the second step, the causal effect of mediating variables on epilepsy risk was estimated. Finally, the indirect effect of GM on epilepsy was investigated by cytokines. The two-step MR is similar to the product of coefficient methods. Two MR estimates were calculated: &#x03B2;1: causal effect of exposure on the mediator; &#x03B2;2: causal effect of the mediator on the outcome; &#x03B2;3: causal effect of exposure on the outcome (<xref ref-type="bibr" rid="ref41">Relton and Davey Smith, 2012</xref>; <xref ref-type="bibr" rid="ref6">Burgess et al., 2015</xref>). These two estimates can be multiplied to estimate indirect effects, and the percentage of mediation was calculated by applying the following formula: (&#x03B2;1&#x2009;&#x00D7;&#x2009;&#x03B2;2)/(&#x03B2;3).</p>
</sec>
<sec id="sec13">
<label>2.7</label>
<title>Sensitivity analyses</title>
<p>Sensitivity analyses were performed to evaluate the robustness of MR estimates. We assessed the heterogeneity of the IVs through the calculation of I<sup>2</sup> statistics and the application of Cochran&#x2019;s Q test. The MR-Egger intercept and MR pleiotropy residual sum and outlier (MR-PRESSO) tests were employed to pinpoint pleiotropy and to identify potential outliers with heterogeneity among the IVs (<xref ref-type="bibr" rid="ref26">Li et al., 2023</xref>). Furthermore, a leave-one-out analysis determined if a single SNP influenced the MR findings. This approach was carried out to assess whether the estimates were affected by biases or driven by outliers (<xref ref-type="bibr" rid="ref26">Li et al., 2023</xref>). All MR calculations were conducted using R software version 4.3.0 and the R package MendelR (7.8.0).</p>
</sec>
<sec id="sec14">
<label>2.8</label>
<title>Mapping SNPs to genes</title>
<p>To better understand GMs&#x2019; effect on epilepsy, we integrated significant SNPs from each GM as primary SNPs in the FUMA GWAS (<xref ref-type="bibr" rid="ref60">Watanabe et al., 2017</xref>). These SNPs were then related to genes using FUMA integrated SNPGENE tool. A protein&#x2013;protein interaction (PPI) network was created using the STRING database, with a recommended minimum interaction score of 0.4 and default settings for other parameters (<xref ref-type="bibr" rid="ref51">Szklarczyk et al., 2021</xref>). The PPI network was visualized in Cytoscape (V3.9.1).</p>
</sec>
<sec id="sec15">
<label>2.9</label>
<title>Transcriptomic MR analysis</title>
<p>To delve deeper into the role of genes derived from the positive GMs and CAE GWAS, we executed a transcriptomic MR analysis. Cis-expression quantitative trait loci (cis-eQTL) data related to these genes were sourced from the eQTLGen Consortium (<ext-link xlink:href="https://eqtlgen.org/" ext-link-type="uri">https://eqtlgen.org/</ext-link>), comprising 16,987 genes from an extensive pool of 31,684 blood samples, mainly from a healthy European population (<xref ref-type="bibr" rid="ref57">V&#x00F5;sa et al., 2021</xref>). For cis-eQTL MR, using a very stringent correlation criterion could exclude causal variants. Hence, these expression quantitative trait loci (eQTLs) underwent clumping based on pairwise linkage disequilibrium (LD) threshold, with an r-squared (<italic>r</italic><sup>2</sup>) value threshold of less than 0.1. Subsequently, the SNPs linked to these genes were used as IVs for MR between these IVs and epilepsy. We used FDR correction for multiple comparisons, and a result of <italic>p</italic>-FDR&#x2009;&#x003C;&#x2009;0.05 indicated statistical significance.</p>
</sec>
</sec>
<sec sec-type="results" id="sec16">
<label>3</label>
<title>Results</title>
<sec id="sec17">
<label>3.1</label>
<title>Details of GMs</title>
<p>A total of 211 GMs spanning five biological levels were included in this study. From these, 15 bacterial traits with unspecified characteristics were excluded. Consequently, 196 bacterial traits from the IEU and FinnGen datasets were incorporated into the MR analysis. The <italic>F</italic> value for all IVs exceeded 10, signifying no bias from weak IVs. Details about IVs can be found in <xref ref-type="supplementary-material" rid="SM1">Supplementary Tables S2</xref>, S3, and the MR results for the 196 GMs are presented in <xref ref-type="fig" rid="fig2">Figure 2</xref> and <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S4</xref>. <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S5</xref> shows the whole results of MR analysis between cytokines and epilepsy.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>Heat map of GM taxa causally associated with subtypes of epilepsy. Red represents risk factors, while blue represents protective factors. <bold>(A)</bold> Heat map of GM taxa causally associated with GE; <bold>(B)</bold> Heat map of GM taxa causally associated with FE; <bold>(C)</bold> Heat map of GM taxa causally associated with CAE; <bold>(D)</bold> Heat map of GM taxa causally associated with JAE. GM, gut microbiome; IVW, inverse-variance weighted method; WM, weighted median; OR, odds ratios; SM, simple mode; WMODE, weighted mode.</p>
</caption>
<graphic xlink:href="fnins-18-1397430-g002.tif"/>
</fig>
</sec>
<sec id="sec18">
<label>3.2</label>
<title>MR estimates</title>
<p>In FE, the genera <italic>Bilophila</italic> (OR: 1.24, 95% CI: 1.04&#x2013;1.48, <italic>p</italic>&#x2009;=&#x2009;0.02), <italic>Bifidobacterium</italic> (OR: 1.26, 95% CI: 1.04&#x2013;1.51, <italic>p</italic>&#x2009;=&#x2009;0.02), <italic>Eubacterium nodatum group</italic> (OR: 1.10, 95% CI: 1.01&#x2013;1.21, <italic>p</italic>&#x2009;=&#x2009;0.04), and <italic>Erysipelatoclostridium</italic> (OR:1.14, 95% CI:1.002&#x2013;1.30, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.05) were positively associated with FE, while <italic>Flavonifractor</italic> (OR:0.78, 95% CI:0.62&#x2013;0.99 <italic>p</italic>&#x2009;=&#x2009;0.04) was negatively correlated with FE. In GE, six GMs were identified. Of these, class <italic>Gammaproteobacteria</italic> (OR: 2.05, 95% CI: 1.02&#x2013;4.14, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.05), genus <italic>Marvinbryantia</italic> (OR: 2.04, 95% CI: 1.35&#x2013;3.10, <italic>p</italic>&#x2009;=&#x2009;0.001), genus <italic>Oxalobacter</italic> (OR: 1.30, 95% CI: 1.04&#x2013;1.64, <italic>p</italic>&#x2009;=&#x2009;0.02), genus <italic>Ruminococcaceae Ucg013</italic> (OR: 1.59, 95% CI: 1.02&#x2013;2.45, <italic>p</italic>&#x2009;=&#x2009;0.04), and order <italic>Mollicutes RF9</italic> (OR: 1.48, 95% CI:1.03&#x2013;2.13, <italic>p</italic>&#x2009;=&#x2009;0.03) were associated with an increased risk of GE; however, the genus <italic>Phascolarctobacterium</italic> (OR: 0.58, 95% CI: 0.37&#x2013;0.91, <italic>p</italic>&#x2009;=&#x2009;0.02) was associated with a risk reduction of GE.</p>
<p>Nine GMs were identified in CAE. The family <italic>Veillonellaceae</italic> (OR: 1.03, 95% CI: 1.02&#x2013;1.05, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001), genus <italic>Desulfovibrio</italic> (OR: 1.03, 95% CI: 1.00&#x2013;1.06, <italic>p</italic>&#x2009;=&#x2009;0.03), genus <italic>Oscillibacter</italic> (OR: 1.02, 95% CI: 1.001&#x2013;1.04, <italic>p</italic>&#x2009;=&#x2009;0.04), genus <italic>Anaerostipes</italic> (OR: 1.03, 95% CI:1.0004&#x2013;1.06, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.05), and phylum <italic>Verrucomicrobia</italic> (OR:1.02, 95% CI: 1.003&#x2013;1.039, <italic>p</italic>&#x2009;=&#x2009;0.03) were associated with an increased risk of CAE, while the class <italic>Bacteroidia</italic> (OR:0.97, 95% CI:0.95&#x2013;0.995, <italic>p</italic>&#x2009;=&#x2009;0.02), genus <italic>Ruminococcaceae NK4A214 group</italic> (OR: 0.97, 95% CI:0.94&#x2013;0.996, <italic>p</italic>&#x2009;=&#x2009;0.03), phylum <italic>Bacteroidetes</italic> (OR: 0.97, 95% CI: 0.95&#x2013;0.99, <italic>p&#x2009;=</italic> 0.01), and order <italic>Bacteroidales</italic> (OR: 0.97, 95% CI: 0.95&#x2013;0.995, <italic>p</italic>&#x2009;=&#x2009;0.02) were associated with a reduced risk of CAE. For JAE, 10 GMs were identified to have a causal association. Of these, the genus <italic>Ruminococcaceae UCG004</italic> (OR: 1.02, 95% CI: 1.003&#x2013;1.03, <italic>p</italic>&#x2009;=&#x2009;0.02), genus <italic>Candidatus soleaferrea</italic> (OR: 1.01, 95% CI: 1.001&#x2013;1.02, <italic>p</italic>&#x2009;=&#x2009;0.03), and genus <italic>Lachnospiraceae UCG010</italic> (OR: 1.02, 95% CI: 1.001&#x2013;1.03, <italic>p&#x2009;=</italic> 0.04) were positively correlated with JAE; while family <italic>Rhodospirillaceae</italic> (OR: 0.99, 95% CI: 0.98&#x2013;0.996, <italic>p</italic>&#x2009;=&#x2009;0.007), family <italic>Prevotellaceae</italic> (OR: 0.99, 95% CI: 0.97&#x2013;0.999, <italic>p&#x2009;=</italic> 0.03), genus <italic>Parabacteroides</italic> (OR: 0.97, 95% CI: 0.95&#x2013;0.99, <italic>p</italic>&#x2009;=&#x2009;0.01), genus <italic>Ruminococcaceae UCG010</italic> (OR: 0.98, 95% CI: 0.95&#x2013;0.998, <italic>p</italic>&#x2009;=&#x2009;0.03), genus <italic>Eggerthella</italic> (OR: 0.99, 95% CI: 0.97&#x2013;0.999, <italic>p</italic>&#x2009;=&#x2009;0.03), genus <italic>Eubacterium nodatum group</italic> (OR:0.99, 95% CI: 0.99&#x2013;0.9998, <italic>p&#x2009;=</italic> 0.04), and order <italic>Rhodospirillales</italic> (OR: 0.99, 95% CI: 0.98&#x2013;0.998, <italic>p</italic>&#x2009;=&#x2009;0.02) were negatively associated with JAE. Of 30 GMs, only family <italic>Veillonellaceae</italic> in CAE passed FDR correction (<italic>p</italic>-FDR&#x2009;&#x003C;&#x2009;0.05). The MR results of positive GMs in different epilepsy subtypes are shown in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S6</xref>.</p>
<p>In exploring the causal relationship between cytokines and epilepsy, we found that Interleukin-1-receptor antagonist (OR: 1.03, 95% CI: 1.01&#x2013;1.04, <italic>p</italic>&#x2009;&#x003C;&#x2009;0.001) and Vascular endothelial growth factor (OR: 1.18, 95% CI: 1.03&#x2013;1.35, <italic>p</italic>&#x2009;=&#x2009;0.02) have a protective effect on epilepsy. Besides, Fibroblast growth factor basic (OR: 0.80, 95% CI:0.67&#x2013;0.97, <italic>p</italic>&#x2009;=&#x2009;0.02) can increase the risk of FE (<xref ref-type="fig" rid="fig3">Figure 3</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>The positive results of MR analysis between cytokines and different subtypes of epilepsy. IL-1Ra, Interleukin-1-receptor antagonist levels; FGF-b, Fibroblast growth factor basic levels; VEGF, Vascular endothelial growth factor levels; SNP, single-nucleotide polymorphisms; OR, odds ratios; CI, confidence interval.</p>
</caption>
<graphic xlink:href="fnins-18-1397430-g003.tif"/>
</fig>
</sec>
<sec id="sec19">
<label>3.3</label>
<title>Mediation analysis</title>
<p>In this study, both GM and cytokines were causally associated with epilepsy. This seems to indicate that cytokines play a mediating role in the pathway between GM and dementia. This mediation analysis was based on the significant correlation between GM and cytokines. However, we did not observe a causal relationship between GM and cytokines significantly associated with epilepsy (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S7</xref>). Therefore, there is insufficient evidence to prove that GM has an indirect effect on epilepsy through cytokines.</p>
</sec>
<sec id="sec20">
<label>3.4</label>
<title>Sensitivity analyses</title>
<p>The MR-PRESSO and MR-Egger intercept analysis confirmed the absence of potential horizontal pleiotropy, as detailed in <xref ref-type="table" rid="tab2">Table 2</xref> and <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S8</xref>. Based on the Cochrane Q test and <italic>I<sup>2</sup></italic> statistics, no evidence of heterogeneity was found among the selected IVs and their relationship with epilepsy. Results from the scatter plot are provided in the <xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S1</xref>. The leave-one-out analysis did not identify any outlier SNPs (<xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S2</xref>). In conclusion, these findings underline a consistent and robust causal link between GM and epilepsy.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Horizontal pleiotropy analysis for IVs of 30 GM taxa associated with epilepsy.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th align="left" valign="top" rowspan="2"><bold>Exposure</bold></th>
<th align="left" valign="top" rowspan="2"><bold>Outcome</bold></th>
<th align="center" valign="top" colspan="2"><bold>MR-Egger intercept test</bold></th>
<th align="center" valign="top" colspan="2"><bold>MR-PRESSO global test</bold></th>
</tr>
<tr>
<th align="center" valign="top"><bold>Egger_ intercept</bold></th>
<th align="center" valign="top"><bold><italic>p</italic>-value</bold></th>
<th align="center" valign="top"><bold>RSS obs</bold></th>
<th align="center" valign="top"><bold><italic>p</italic>-value</bold></th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="middle">Genus Phascolarctobacterium id.2168</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">5.68</td>
<td align="center" valign="middle">0.68</td>
<td align="center" valign="middle">7.76</td>
<td align="center" valign="middle">0.50</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Oxalobacter id.2978</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">8.16</td>
<td align="center" valign="middle">0.52</td>
<td align="center" valign="middle">10.06</td>
<td align="center" valign="middle">0.57</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Ruminococcaceae UCG013 id.11370</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">10.55</td>
<td align="center" valign="middle">0.31</td>
<td align="center" valign="middle">11.04</td>
<td align="center" valign="middle">0.54</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Marvinbryantia id.2005</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">5.76</td>
<td align="center" valign="middle">0.33</td>
<td align="center" valign="middle">12.50</td>
<td align="center" valign="middle">0.43</td>
</tr>
<tr>
<td align="left" valign="middle">Order Mollicutes RF9 id.11579</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">12.72</td>
<td align="center" valign="middle">0.24</td>
<td align="center" valign="middle">15.28</td>
<td align="center" valign="middle">0.34</td>
</tr>
<tr>
<td align="left" valign="middle">Class Gammaproteobacteria id.3303</td>
<td align="left" valign="middle">GE</td>
<td align="center" valign="middle">5.61</td>
<td align="center" valign="middle">0.23</td>
<td align="center" valign="middle">10.28</td>
<td align="center" valign="middle">0.26</td>
</tr>
<tr>
<td align="left" valign="middle">Genus <italic>Eubacterium nodatum</italic> group id.11297</td>
<td align="left" valign="middle">FE</td>
<td align="center" valign="middle">0.03</td>
<td align="center" valign="middle">0.41</td>
<td align="center" valign="middle">6.44</td>
<td align="center" valign="middle">0.88</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Bifidobacterium id.436</td>
<td align="left" valign="middle">FE</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.47</td>
<td align="center" valign="middle">19.22</td>
<td align="center" valign="middle">0.17</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Bilophila id.3170</td>
<td align="left" valign="middle">FE</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.60</td>
<td align="center" valign="middle">15.34</td>
<td align="center" valign="middle">0.40</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Erysipelatoclostridium id.11381</td>
<td align="left" valign="middle">FE</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.77</td>
<td align="center" valign="middle">10.49</td>
<td align="center" valign="middle">0.83</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Flavonifractor id.2059</td>
<td align="left" valign="middle">FE</td>
<td align="center" valign="middle">0.02</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">3.71</td>
<td align="center" valign="middle">0.69</td>
</tr>
<tr>
<td align="left" valign="middle">Family Veillonellaceae id.2172</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.69</td>
<td align="center" valign="middle">5.29</td>
<td align="center" valign="middle">0.94</td>
</tr>
<tr>
<td align="left" valign="middle">Phylum Bacteroidetes id.905</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.67</td>
<td align="center" valign="middle">6.41</td>
<td align="center" valign="middle">0.70</td>
</tr>
<tr>
<td align="left" valign="middle">Phylum Verrucomicrobia id.3982</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.48</td>
<td align="center" valign="middle">4.09</td>
<td align="center" valign="middle">0.83</td>
</tr>
<tr>
<td align="left" valign="middle">Class Bacteroidia id.912</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.30</td>
<td align="center" valign="middle">5.84</td>
<td align="center" valign="middle">0.75</td>
</tr>
<tr>
<td align="left" valign="middle">Order Bacteroidales id.913</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.30</td>
<td align="center" valign="middle">5.84</td>
<td align="center" valign="middle">0.76</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Anaerostipes id.1991</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.37</td>
<td align="center" valign="middle">4.94</td>
<td align="center" valign="middle">0.48</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Desulfovibrio id.3173</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.04</td>
<td align="center" valign="middle">0.40</td>
<td align="center" valign="middle">NA</td>
<td align="center" valign="middle">NA</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Ruminococcaceae NK4A214 group id.11358</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.96</td>
<td align="center" valign="middle">1.11</td>
<td align="center" valign="middle">0.90</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Oscillibacter id.2063</td>
<td align="left" valign="middle">CAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.93</td>
<td align="center" valign="middle">12.74</td>
<td align="center" valign="middle">0.19</td>
</tr>
<tr>
<td align="left" valign="middle">Family Prevotellaceae id.960</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.09</td>
<td align="center" valign="middle">7.96</td>
<td align="center" valign="middle">0.71</td>
</tr>
<tr>
<td align="left" valign="middle">Family Rhodospirillaceae id.2717</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.55</td>
<td align="center" valign="middle">12.10</td>
<td align="center" valign="middle">0.55</td>
</tr>
<tr>
<td align="left" valign="middle">Order Rhodospirillales id.2667</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.72</td>
<td align="center" valign="middle">12.79</td>
<td align="center" valign="middle">0.48</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Eggerthella id.819</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.44</td>
<td align="center" valign="middle">1.81</td>
<td align="center" valign="middle">0.83</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Parabacteroides id.954</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.79</td>
<td align="center" valign="middle">1.92</td>
<td align="center" valign="middle">0.81</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Ruminococcaceae UCG004 id.11362</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.67</td>
<td align="center" valign="middle">5.43</td>
<td align="center" valign="middle">0.63</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Ruminococcaceae UCG010 id.11367</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">&#x2212;0.01</td>
<td align="center" valign="middle">0.42</td>
<td align="center" valign="middle">NA</td>
<td align="center" valign="middle">NA</td>
</tr>
<tr>
<td align="left" valign="middle">Genus <italic>Eubacterium nodatum</italic> group id.11297</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.29</td>
<td align="center" valign="middle">5.86</td>
<td align="center" valign="middle">0.74</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Candidatus Soleaferrea id.11350</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.01</td>
<td align="center" valign="middle">0.47</td>
<td align="center" valign="middle">5.89</td>
<td align="center" valign="middle">0.57</td>
</tr>
<tr>
<td align="left" valign="middle">Genus Lachnospiraceae UCG010 id.11330</td>
<td align="left" valign="middle">JAE</td>
<td align="center" valign="middle">0.00</td>
<td align="center" valign="middle">0.62</td>
<td align="center" valign="middle">3.51</td>
<td align="center" valign="middle">0.73</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>GE, generalized epilepsy; FE, focal epilepsy; CAE, child absence epilepsy; JAE, juvenile absence epilepsy; MR-PRESSO, MR pleiotropy residual sum and outlier.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec21">
<label>3.5</label>
<title>Reverse Mendelian randomization</title>
<p>1To examine reverse causality, we considered epilepsy subtypes as exposure and significant GMs as outcomes. After adjusting for linkage disequilibrium, we sourced 17, 11, 14, and 8 SNPs associated with CAE, JAE, FE, and GE from the GWAS database. As demonstrated in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S9</xref>, no reverse causality between epilepsy subtypes and GMs was evident (<italic>p</italic>&#x2009;&#x003E;&#x2009;0.05).</p>
</sec>
<sec id="sec22">
<label>3.6</label>
<title>Mapping SNPs to genes</title>
<p>To delve deeper into biologically significant findings, the IVs, used as genetic variants, were functionally annotated using the FUMA GWAS tool. The identified genes are listed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S10</xref>. Using STRING, we constructed a PPI network for these mapped genes across different epilepsy subtypes, with outcomes displayed in <xref ref-type="supplementary-material" rid="SM1">Supplementary Figure S3</xref>.</p>
</sec>
<sec id="sec23">
<label>3.7</label>
<title>Transcriptomic MR analysis</title>
<p>We sourced SNPs linked to gene expression (eQTLs) from the eQTLGen consortium. Transcriptomic MR analysis of the family <italic>Veillonellaceae</italic> and CAE remained significant post-FDR correction. Of the 631 mapped genes, 501 were derived from cis-eQTL summary statistics. Post FDR correction, genes like FDFT1, DOK2, FAM167A, and ZSCAN9 in the genus <italic>Oscillibacter</italic> showed a positive correlation with CAE risk, while BLK indicated a negative correlation. For JAE, RNGTT from the family <italic>Rhodospirillaceae</italic> and RBM47 from the genus of <italic>Ruminococcaceae UCG010</italic> showed positive associations. DNAJC in genus <italic>Erysipelatoclostridium</italic> was associated with FE risk. In the case of GE, genes such as SUMF1 and TCF20 from order <italic>Mollicutes RF9</italic> and VAV2 from the genus <italic>Ruminococcaceae UCG13</italic> were positively associated; conversely, GLO1 from the genus <italic>Oxalobacter</italic> and TMTC from the genus <italic>Ruminococcaceae UCG13</italic> indicated a reduced risk for GE. Comprehensive results from the five methods are consolidated in <xref ref-type="supplementary-material" rid="SM1">Supplementary Table S11</xref>; the directionality across these methods was consistent (<xref ref-type="fig" rid="fig4">Figure 4</xref>). Heterogeneity testing showed nonsignificant differences, suggesting no notable heterogeneity in the MR estimates (<xref ref-type="supplementary-material" rid="SM1">Supplementary Table S12</xref>).</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>Forest plots for transcriptomic MR analysis of significant mapped genes. CAE, child absence epilepsy; JAE, juvenile absence epilepsy; FE, focal epilepsy; GE, generalized epilepsy; IVW, inverse-variance weighted method; OR, odds ratios; CI, confidence interval; FDR: false discovery rate correction.</p>
</caption>
<graphic xlink:href="fnins-18-1397430-g004.tif"/>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="sec24">
<label>4</label>
<title>Discussion</title>
<p>In this study, we conducted MR analyses to investigate the potential causal relationship between GMs and different epilepsy subtypes. We found that 30 GMs were associated with four epilepsy subtypes using extensive GWAS statistics, and no reverse causality was identified. Notably, the family <italic>Veillonellaceae</italic> in CAE displayed a significant causal association after FDR correction. Three types of cytokines have significant effects on epilepsy. These results could provide insights into the therapeutic role of epilepsy.</p>
<p>The role of GM in maintaining the state of epilepsy has gained increasing recognition. Yet, due to limited clinical trials examining the differences in the metagenome/metabolome between epilepsy patients and healthy individuals, the influence of GM on epilepsy remains elusive. In our conclusions, no reverse causality was found for epilepsy on GM. Considering modifications related to several external factors (e.g., diet and age), it is not easy to conduct relevant animal or clinical studies. Although some studies have found increased alpha diversity and abundance of thick-walled phyla in patients with drug-resistant epilepsy and altered abundance of <italic>Prevotella</italic>, <italic>Ruminococcus</italic>, and other flora in patients with cerebral palsy with epilepsy (<xref ref-type="bibr" rid="ref36">Peng et al., 2018</xref>; <xref ref-type="bibr" rid="ref19">Huang et al., 2019</xref>). The results of the available studies are not uniform, and it is therefore difficult to define relevant criteria (<xref ref-type="bibr" rid="ref42">Russo, 2022</xref>). Previous research has highlighted the importance of <italic>Veillonellaceae</italic> in developing the intestinal microecosystem during early life (<xref ref-type="bibr" rid="ref27">Lou et al., 2022</xref>). While direct evidence connecting <italic>Veillonellaceae</italic> and epilepsy is scarce, its influence on normal brain functions is documented. Furthermore, <italic>Veillonellaceae</italic>, part of the phylum <italic>Firmicutes</italic>, shows increased abundance in patients with drug-resistant epilepsy, which also decreases after antiepileptic treatment (<xref ref-type="bibr" rid="ref8">Cheraghmakani et al., 2021</xref>). Research has identified a prevalent genus, <italic>Marvinbryantia</italic>, in epileptic animals, which correlates positively with excitatory neurotransmitters (<xref ref-type="bibr" rid="ref8">Cheraghmakani et al., 2021</xref>). Our findings show that a positive causal relationship between the genus <italic>Marvinbryantia</italic> and GE aligns with these experimental results. Oliveira et al. observed a negative correlation between <italic>Marvinbryantia</italic> and d-glucose and lactate. This negative association may disrupt carbohydrate metabolism, possibly contributing to the mechanisms underlying epilepsy (<xref ref-type="bibr" rid="ref14">Fei et al., 2020</xref>). Additionally, <italic>Marvinbryantia</italic> is a pro-inflammatory bacterium, possibly exacerbating inflammation and precipitating epileptic conditions (<xref ref-type="bibr" rid="ref31">Oliveira et al., 2022</xref>). Our MR analysis highlighted <italic>Parabacteroides</italic>, belonging to the family <italic>Tannerellaceae</italic>, as a potential protective agent for JAE. Previously linked to increased ketosis and metabolic enhancement in humans (<xref ref-type="bibr" rid="ref11">David et al., 2014</xref>), this bacterium might explain the seizure control seen with the KD (<xref ref-type="bibr" rid="ref5">Bough and Rho, 2007</xref>). Olson et al. posited that the KD&#x2019;s anti-seizure effects arise from increasing specific bacterial species, particularly <italic>Parabacteroides merdae</italic>. Together, these bacteria reduce the &#x03B3;-glutamylation of amino acids and boost the hippocampus&#x2019;s gamma-aminobutyric acid/glutamate ratio, preventing epilepsy (<xref ref-type="bibr" rid="ref32">Olson et al., 2018</xref>).</p>
<p>Interestingly, 28 other GMs with nominal causal associations, such as Ruminococcus, echoed past findings. Similar to <italic>Veillonellaceae</italic>, <italic>Ruminococcus</italic>, another microbiome from phylum <italic>Firmicutes</italic>, increases in individuals with autism spectrum disorders (<xref ref-type="bibr" rid="ref58">Wang et al., 2013</xref>). <italic>Ruminococcus</italic> causes reduced levels of N-acetyl aspartate, a marker of neuronal health, and is typically diminished in individuals diagnosed with epilepsy (<xref ref-type="bibr" rid="ref67">Zhang et al., 2016</xref>; <xref ref-type="bibr" rid="ref30">Mudd et al., 2017</xref>). The presence of this microorganism also correlates positively with glutamate and glutamine, both closely associated with the pathogenesis of epilepsy (<xref ref-type="bibr" rid="ref49">Sun et al., 2016</xref>). Furthermore, <italic>Ruminococcus</italic> is associated with reduced levels of 5-hydroxytryptophan, which inhibits T-type calcium channels and subsequently reduces epileptiform discharges (<xref ref-type="bibr" rid="ref38">Petersen et al., 2017</xref>). Additionally, the abundance of the phylum <italic>Verrucomicrobia</italic>, <italic>Bacteroides</italic>, and its related genus (including <italic>Bacteroides</italic> and <italic>Barnesiella</italic>) was observed to be higher in the drug-resistant epilepsy group than in the control group, aligning with our findings (<xref ref-type="bibr" rid="ref36">Peng et al., 2018</xref>). Notably, subjects with cerebral palsy and epilepsy, potential risk factors for FE and GE, exhibited an elevated presence of <italic>Eubacterium</italic> (<xref ref-type="bibr" rid="ref35">Peng et al., 2023</xref>). Moreover, <italic>Anaerostipes</italic> was found to be more prevalent in epilepsy patients (<xref ref-type="bibr" rid="ref15">Gong et al., 2021</xref>). However, it is noteworthy that while <italic>Lachnospiraceae</italic> is more abundant in healthy individuals (<xref ref-type="bibr" rid="ref13">Ding et al., 2021</xref>), a reduced presence was noted in temporal lobe epilepsy patients (<xref ref-type="bibr" rid="ref61">Wei et al., 2023</xref>). This observation contrasts with our MR findings and necessitates further exploration.</p>
<p>Currently, the exact mechanism by which the GM contributes to epilepsy has not been determined. Although the available data do not confirm that cytokines are mediators between GM and epilepsy, we observed many results that are consistent with previous studies. As a protective factor in epilepsy, the IL-1 receptor antagonist anakinra can be used as a treatment for refractory epilepsy (<xref ref-type="bibr" rid="ref66">Yamanaka et al., 2021</xref>), and vascular endothelial growth factor is considered an attractive target for epilepsy treatment (<xref ref-type="bibr" rid="ref25">Lange et al., 2016</xref>). On the other hand, we have found that IL-2 increases the risk of epilepsy. As a factor that plays an important immune role, elevated levels of IL-2 over-activate signaling pathways, leading to a worse prognosis in temporal lobe epilepsy (<xref ref-type="bibr" rid="ref28">Mazumder et al., 2019</xref>).</p>
<p>The precise mechanisms driving the relationship between GMs and epilepsy are not yet fully understood, but emerging evidence provides some initial insights into possible mechanisms. Various bioactive products from GMs have the potential to influence the brain either directly or indirectly. As mentioned above, GM can mediate neuroinflammation, and pro-inflammatory cytokines may contribute to epilepsy by triggering oxidative stress, increased PIC secretion and BBB disruption (<xref ref-type="bibr" rid="ref54">Vezzani et al., 2013</xref>). For instance, lipopolysaccharides can directly affect the central nervous system by activating Toll-like receptors on microglia and triggering the release of inflammatory factors (<xref ref-type="bibr" rid="ref22">Kim et al., 2012</xref>). In addition, enteroendocrine signaling and microbial metabolism are among the mechanisms that may contribute to epilepsy. By increasing the release of excitatory neurotransmitters such as glutamate and decreasing inhibitory neurotransmitters such as GABA, neurotransmission can be disrupted (<xref ref-type="bibr" rid="ref46">Su et al., 2015</xref>; <xref ref-type="bibr" rid="ref40">Rana and Musto, 2018</xref>). SCFAs are also thought to be protective against epilepsy (<xref ref-type="bibr" rid="ref13">Ding et al., 2021</xref>). From an autoimmune perspective, GM can regulate microglia maturation as well as astrocyte activation (<xref ref-type="bibr" rid="ref12">de Theije et al., 2011</xref>). This implies that GM can regulate epilepsy development by modulating innate immunity, adaptive immunity and inflammatory mechanisms (<xref ref-type="bibr" rid="ref13">Ding et al., 2021</xref>). Notably, <italic>Bacteroidetes</italic> have been linked to conditions such as encephalitis and autoimmunity (<xref ref-type="bibr" rid="ref65">Xu et al., 2020</xref>), which could play a role in epilepsy development. A theory also suggests that GMs might affect the hypothalamic&#x2013;pituitary&#x2013;adrenal axis, thereby increasing the likelihood of epilepsy (<xref ref-type="bibr" rid="ref47">Sudo et al., 2004</xref>).</p>
<p>Combined with existing theories, our study has profound clinical implications and provides new ideas for the clinical management of epilepsy. The risk of epilepsy can be reduced by the use of probiotics. In the field of pharmacological microbiology, GMs and their secreted cytokines can be used as drug targets. He et al. used FMT to improve epilepsy in a 22-year-old girl (<xref ref-type="bibr" rid="ref18">He et al., 2017</xref>). Additionally, GMs may serve as promising biomarkers for identifying the prognosis of epilepsy patients. For instance, a smaller Bacteroidetes/Firmicutes ratio may increase the risk of seizures and lead to poor prognosis (<xref ref-type="bibr" rid="ref9">Citraro et al., 2021</xref>). The different abundance composition of GMs in the intestine can also be an indicator for evaluating the efficacy of KD (<xref ref-type="bibr" rid="ref64">Xie et al., 2017</xref>). Furthermore, this study also has several strengths. First, it comprehensively examines the causal relationship between GM and various epilepsy subtypes. Unlike observational studies, this analysis is less vulnerable to confounding factors and reverse causality. Second, the paper&#x2019;s statistical robustness is evident with a substantial GWAS data sample size for exposure and outcome and significant estimated effects for each genetic variable (<italic>F</italic>-value &#x003E;10). Besides, a rigorous FDR correction was applied in the MR analysis to minimize type I errors. Third, we included cytokines to analyze possible mediating factors from GM to epilepsy and annotated genetic variants through FUMA. However, certain limitations warrant consideration. The GWAS data on GMs encompass various diseases and age groups without specific gender and age stratification. We included only individuals of European descent due to the absence of large-scale GWAS data for other ancestries, which might make these findings less generalizable. Lastly, while the MR analysis parallels the insights from RCT studies, further validation through animal experiments remains essential.</p>
</sec>
<sec sec-type="conclusions" id="sec25">
<label>5</label>
<title>Conclusion</title>
<p>In conclusion, our MR study underscores potential causal connections between GMs and various epilepsy subtypes (GE, FE, JAE, and CAE), suggesting that the dysbiosis of the family <italic>Veillonellaceae</italic> might play a role in CAE onset. A mediating role of the cytokines has not been shown to exist between GM and epilepsy. Further studies are essential to understand the potential mechanisms by which GM might influence epilepsy treatment.</p>
</sec>
<sec sec-type="data-availability" id="sec26">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="author-contributions" id="sec27">
<title>Author contributions</title>
<p>YQ: Conceptualization, Formal analysis, Writing &#x2013; original draft. BS: Conceptualization, Formal analysis, Writing &#x2013; original draft. MX: Data curation, Writing &#x2013; original draft. YT: Data curation, Writing &#x2013; original draft. ZY: Methodology, Software, Writing &#x2013; original draft. MW: Methodology, Software, Writing &#x2013; original draft. CM: Supervision, Validation, Writing &#x2013; review &#x0026; editing. ZC: Supervision, Validation, Writing &#x2013; review &#x0026; editing. ZW: Funding acquisition, Resources, Supervision, Writing &#x2013; review &#x0026; editing.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec28">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the National Natural Science Foundation of China (Grant No. 82171309).</p>
</sec>
<ack>
<p>We want to acknowledge the MiBioGen consortium for providing GM-related GWAS summary data. We wish to acknowledge the participants and investigators of the FinnGen study. We want to acknowledge Ahola-Olli et al. for providing cytokine-related GWAS summary data. We want to acknowledge the eQTLGen Consortium for the cis-expression quantitative trait loci data.</p>
</ack>
<sec sec-type="COI-statement" id="sec29">
<title>Conflict of interest</title>
<p>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.</p>
</sec>
<sec sec-type="disclaimer" id="sec30">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec31">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnins.2024.1397430/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnins.2024.1397430/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Data_Sheet_1.ZIP" id="SM1" mimetype="application/zip" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
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