Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites

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Our core methodology centered on inverse-variance weighted MR, supplemented by other methods. This approach was crucial in clarifying the potential intermediary functions of metabolites in the genetic links between traits of immune cells and epilepsy. We found a causal relationship between immune cells and epilepsy. Specifically, the genetically predicted levels of CD64 on CD14-CD16 are positively correlated with the risk of epilepsy (p < 0.001, OR = 1.0826, 95% CI 1.0361–1.1312). Similarly, metabolites also exhibit a causal relationship with both immune cells (OR = 1.0438, 95% CI:1.0087–1.0801, p = 0.0140) and epilepsy (p = 0.0334, OR = 1.0897, 95% CI: 1.0068–1.1795), and sensitivity analysis was conducted to further validate these relationships. Importantly, our intermediate MR results suggest that the metabolite Paraxanthine to linoleate (18:2n6) ratio may mediate the causal relationship between immune cell CD64 on CD14-CD16 and epilepsy, with a mediation effect of 5.05%. The results suggest the importance of specific immune cell levels and metabolites in understanding epilepsy's pathogenesis. This is significant for understanding the pathogenesis of epilepsy and its prevention and treatment. Biological sciences/Genetics Biological sciences/Immunology Biological sciences/Neuroscience Epilepsy Mendelian Randomization Immune cells Metabolites Figures Figure 1 Figure 2 Introduction Epilepsy, a chronic neurologic condition, is marked by atypical electrical brain activity that results in recurrent seizures, known as spontaneous recurrent seizures (SRS) 1 . Clinical manifestations of epilepsy include convulsions, loss of consciousness, myoclonus (sudden muscle jerks), aura, automatisms, hypotonia (decreased muscle tone), altered sensations, and prolonged muscle contractions 2–4 . Per the 2017 definition from the International League Against Epilepsy (ILAE), epilepsy is categorized as generalized epilepsy, focal epilepsy, combined generalized and focal epilepsy, and unknown epilepsy 5 . Epilepsy affects people of all ages, regions, and races, although it is more commonly diagnosed in children and adolescents. Its prevalence worldwide ranges from 0.5–1.0% 5 . Various factors can contribute to the development of epilepsy, including genetic predisposition, acquired brain injuries, infections, and metabolic or immune disorders 2 . Despite these known factors, the precise cause of epilepsy remains unclear. In the pathogenesis of epilepsy, inflammation, and immune responses are vital factors 6–8 . During epileptic seizures, the secretion of inflammatory mediators and the activation of immune cells can lead to excessive neuronal excitation and abnormal synaptic connectivity 9–11 . A complex interaction exists between immune cells and neurons, known as neuro-immune modulation 12 . Immune cells can release various cytokines and chemokines, which may affect neuronal excitability and stability, thereby influencing epileptic seizures 8,13 . However, the existence of a direct causal relationship between epilepsy and immune cells remains uncertain. Thus, a study design that minimizes biases is necessary to elucidate this potential causality. Moreover, given the intricate relationship between immune cells and metabolites 14–16 , and findings that certain metabolites, such as circulating metabolites 17 , can serve as biomarkers for epilepsy, it is conceivable that metabolites may act as potential mediators between immune cells and epilepsy. Mendelian Randomization (MR) is a research method grounded in Mendel's Second Law 18–20 . It utilizes the random allocation of alleles from parents to offspring during the creation of gametes, facilitating the establishment of links between traits of exposure, such as intermediate phenotypes, and outcomes of diseases 18–20 . This method employs genetic variation as a key variable, serving as an instrument to deduce the impact of exposure factors on outcomes based on observational data 18 . The inherent randomness of allele distribution in Mendelian Randomization naturally minimizes non-random errors or misleading factors, and prevents causal inversion through the principles of Mendelian inheritance 18 . Therefore, MR provides a reliable mechanism for drawing causal inferences from observational data. Thus, our objectives were to (i) ascertain if there is a causal link between immune cells and epilepsy, and (ii) evaluate the degree to which metabolites mediate the effects of immune cells on epilepsy. Methods Study design Our analysis was founded on publicly accessible data that have been previously approved by the institutional review boards of the corresponding studies. This meant that no further approvals were necessary for our work. Detailed descriptions of the results from our analysis can be found in both the main article and the supplementary materials accompanying it. To ensure dependable outcomes, a two-sample MR study must satisfy three fundamental assumptions: ( 1 ) The instrumental variables (IVs) ought to exhibit a notable correlation with immune cells; ( 2 ) IVs should remain uncorrelated with any confounding variables capable of impacting the connection between exposure and outcome factors; ( 3 ) IVs should solely influence outcomes via the pathway of immune cells. Our study initially utilized a two-sample MR to ascertain the causal association between immune cells, metabolites, and epilepsy. Subsequently, to further investigate the potential impact of immune cells on epilepsy mediated through metabolites, we employed a mediating MR approach, also known as a two-step MR. Data sources The immune cell exposure data is sourced from OpenGWAS, which includes 126 billion genetic associations from 14,582 complete GWAS datasets 21 . The intermediary (metabolite) data comes from GWAS Catalog 22 . The outcome (epilepsy) data is derived from FinnGen GWAS 23 R10, including 12,891 cases and 312,803 controls, with all individuals being of European descent. Selection for genetic variation For immune cells to outcome, immune cells to metabolites, and metabolites to outcome, we selected instrumental variables (IVs) with a p-value threshold of < 1.0×10^-5. To ensure independent loci for the IVs, we used the "TwoSampleMR" package, setting the linkage disequilibrium (LD) threshold for the 1000 Genomes EUR data to R^2 < 0.001, with a clumping distance of 10,000 kb. For the reverse direction (epilepsy to immune cells), IVs were chosen with a p-value threshold of < 5.0×10^-6. Similarly, to obtain independent loci for the IVs, we applied the "TwoSampleMR" package with the LD threshold for the 1000 Genomes EUR data set to R^2 < 0.001, and a clumping distance of 10,000 kb. The results revealed no evidence of reverse causation. For MR analysis, palindromic and ambiguous SNPs were omitted from IVs 24 . Furthermore, we computed the F statistic (i.e., the proportion of variability attributed to Single Nucleotide Polymorphisms in each exposure), represented as \(F=\left(\frac{N-K-1}{K}\right)\left(\frac{{R}^{2}}{1-{R}^{2}}\right)\) , where K stands for the count of genetic variants and N denotes the sample size 25 . We excluded instrumental variables with low strength (F-statistics < 10) 25 . Statistical analysis Although the primary emphasis of our research was on outcomes using the inverse variance weighting (IVW) method 26 , it was crucial to confirm that the directions of the results were consistent across all methods used, including IVW. For a thorough secondary analysis, we applied various methods like MR-Egger regression, weighted median, simple mode, weighted mode, and MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO) 20,27–29 , to conduct a sensitivity check of our IVW results. Pleiotropy and heterogeneity analysis Our study began with employing the MR-PRESSO 29 method to pinpoint and remove outliers. We then reanalyzed the data without these outliers. Next, to investigate the influence of each Single Nucleotide Polymorphism (SNP) on the link between exposure and outcome, we executed a leave-one-out sensitivity analysis, systematically omitting one SNP at a time. Additionally, the MR-Egger regression test was applied to detect horizontal pleiotropy in the Mendelian randomization analysis, particularly focusing on the importance of the intercept term's statistical significance 30 . To explore heterogeneity within our findings, we calculated the Cochran Q statistic, considering P = 0.05 as the significance threshold 31 . All these statistical procedures were performed using R (version 4.2.1), utilizing the MR and MR-PRESSO packages. Results 1.Association of immune cells with epilepsy We first screened for exposure tools related to the outcome (epilepsy) from 731 immune cells. After excluding palindromic SNPs and those identified by MR Steiger filtering as having incorrect causal directions, there are a total of 34 qualified immune cells (Table 1). We then conducted analyses using MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, and illustrated these in forest plots(Supplementary Fig. 1). Here, we selected the immune cell subgroup CD64 on CD14-CD16 (id ebi-a-GCST90002001) as our exposure tool variable. In analyses such as MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, we looked for results with p-values below the commonly used threshold for statistical significance (e.g., 0.05). In this case, the IVW method had the lowest p-value at 0.0004, indicating statistical significance in this analysis. We examined the odds ratio (OR) and the 95% confidence interval (CI). In the IVW method, the OR was 1.0826, with a 95% CI of 1.0361 to 1.1312, indicating not only a positive direction of effect but also that the confidence interval does not include 1, suggesting an association between CD64 on CD14-CD16 and the outcome variable. Additionally, the OR remained above 1 across different estimation methods, indicating consistent result directions across methods, i.e., the exposure increases the risk of epilepsy. To validate this, we also conducted sensitivity analysis (Fig. 1a). Moreover, our MR analysis results showed no evidence of reverse causation between CD64 on CD14-CD16 and the outcome (epilepsy). 2.Association of metabolites with epilepsy From 1,400 metabolites, we identified exposure tools related to the outcome (epilepsy). After excluding palindromic SNPs and those identified by MR Steiger filtering as having incorrect causal directions, there are a total of 75 qualified metabolites(Table 2). We then performed analyses using MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, and presented these in forest plots༈Supplementary Fig. 2༉. We chose the ratio of Paraxanthine to linoleate (18:2n6) (id GCST90200982) as the exposure tool for the outcome of epilepsy. In all methods, the IVW's p-value was the lowest (0.0334), indicating statistical significance. Across different estimation methods, the OR was consistently above 1, indicating a consistent association between this ratio and an increased risk of epilepsy. Although some methods had confidence intervals close to 1, the IVW method provided a sufficiently wide confidence interval (1.0068–1.1795), along with a narrower interval, suggesting higher precision and adding credibility to the result. We further performed a sensitivity analysis to verify this result (Fig. 1b). 3.Selected relationship between immune cells and positive metabolites . The results show that the selected immune cell subgroup CD64 on CD14-CD16 (OR = 1.0438, 95% CI:1.0087–1.0801, p = 0.0140) influences the aforementioned positive metabolite Paraxanthine to linoleate (18:2n6) ratio, as demonstrated by the IVW method (Fig. 2a). Since we have verified the connections between "immune cells → epilepsy" and "immune cells → metabolites," we hypothesize that certain metabolites, such as the Paraxanthine to linoleate (18:2n6) ratio, may play a modulating role in the relationship between immune cells and epilepsy. We additionally performed a sensitivity analysis to reinforce this finding (Fig. 1c). 4. The intermediary role of metabolites in the causal relationship between immune cells and epilepsy. A two-step method was used to analyze the mediating role of the metabolite Paraxanthine to linoleate (18:2n6) ratio in the causal relationship between the immune cell subgroup CD64 on CD14-CD16 and epilepsy. As shown in Fig. 2b, the metabolite accounts for 5.05% of the effect. This indicates that the metabolite may play a certain mediating role in the connection between immune cells and epilepsy, but this role is limited. Discussion In this research, we employed MR analysis to investigate the causal link between immune cells and epilepsy, and to investigate whether this relationship is mediated through metabolites. Our findings suggest a causative link between genetically forecasted levels of immune cells (specifically CD64 on CD14-CD16) and increased risk of epilepsy, indicating that an increase of one standard deviation in CD64 on CD14-CD16 levels is associated with an approximate 8.26% increase in epilepsy risk under constant conditions. Notably, about 5.05% of this effect appears to be mediated through the metabolite Paraxanthine to linoleate (18:2n6) ratio. Studies show that inflammation is a major factor in epilepsy 32,33 . For instance, a study discussed the role of P2X7 receptors in epilepsy and neuroinflammation, particularly their expression on various cells in the brain and their involvement in the inflammatory process during epileptic seizures 13 . Other studies 34 have demonstrated that tumor necrosis factor-alpha inhibits epileptic seizures through specific receptors, while interleukin-1 receptor antagonists have been found to exhibit antiepileptic effects in mice. Overexpression of cytokines in the brain, such as interleukin-6, is associated with the induction of neurological disorders in animal models. This suggests a close link between immune responses and neural health. The migration of immune cells to the central nervous system and their role in neurological disorders is also a significant area of research. Additionally, studies 35 have explored the role of the blood-brain barrier in immune functions and dysfunctions, which is crucial for understanding how peripheral immune responses affect the central nervous system. These studies collectively emphasize the intricate interplay between the immune system and neurological disorders, especially epilepsy. When immune cell activity changes, different metabolites may be released, which can influence the risk of epilepsy by affecting the function of neural cells. For example, certain metabolites might alter neuronal excitability or affect neural conduction pathways 36 . A study utilized 1 H-NMR and DI/LC-MS/MS technologies to identify and quantify 212 metabolites 37 . The results showed that among these 212 detected metabolites, 14 exhibited significant concentration changes between epilepsy patients and the control group (p < 0.05, q < 0.05) 37 .Therefore, by studying these changes in metabolites, scientists can better understand the connection between the immune system and the onset of epilepsy, and how modulating these metabolic pathways might reduce the risk of epilepsy. This study found that the Paraxanthine to linoleate (18:2n6) ratio may play a certain mediating role in the relationship between immune cells and epilepsy. This suggests that this metabolite might be a key factor linking changes in immune activity and epilepsy risk. The variation in this ratio may reflect the activation state of immune cells, impacting the function of the nervous system. For instance, if changes in this ratio lead to increased excitability of neural cells or altered neural communication pathways, this might increase the risk of epilepsy. Therefore, changes in the Paraxanthine to linoleate (18:2n6) ratio not only reflect the state of the immune system but may also be an important biomarker for predicting or intervening in epilepsy risk. This discovery underscores the potential importance of metabolites in neuroimmunology and disease prevention. In our research, we were the first to employ mediation MR technology to explore the causal association among metabolites, immune cells, and epilepsy. We employed multiple typical sensitivity analyses and excluded the likelihood of reverse causation. Our initial results indicate a causal link between immune cells and epilepsy and its intermediaries, further supporting the theoretical basis for treating and preventing epilepsy and proposing new methods for its management. For example, the risk of epilepsy onset can be managed by initially regulating specific metabolites through diet, exercise, or other methods. While our study includes several advantages, such as a substantial sample size and the utilization of various sensitivity analysis techniques to ensure the credibility of our research and mitigate confounding variables, it is essential to acknowledge the presence of unavoidable limitations. Firstly, although the sample size is substantial, participants are entirely of European descent, potentially limiting considerations of genetic diversity and environmental influences. Secondly, despite our efforts to identify and remove outlier variables, we cannot completely exclude the possibility of horizontal pleiotropy influencing our findings. Thirdly, while our results suggest a potential mediation effect, the fact that the p-value is greater than 0.05 and the confidence interval includes negative numbers may be due to the presence of unknown moderating variables. Perhaps future research could focus on including participants from a more diverse range of ethnicities and regions, expanding the sample size, and conducting experiments at the cellular level, with animal models, as well as clinical trials. Advanced analytical methods or stricter statistical controls could be used to reduce the impact of potential biases. Additionally, the robustness of research results could be further validated by using different statistical models or tools. Conclusion Our study has established a causal link between immune cells and epilepsy, with specific levels of immune cells (such as CD64 on CD14-CD16) being associated with an increased risk of epilepsy. Additionally, the ratio of Paraxanthine to linoleate (18:2n6) appears to exert a moderating influence on this relationship, although its impact is limited. Subsequent investigations should prioritize larger sample sizes, deeper exploration of mechanisms, controlling potential confounding factors, and conducting comparative studies across different populations to validate and extend the current findings. This is significant for understanding the pathogenesis of epilepsy and its prevention and treatment. Abbreviations Abbreviations Full form MR Mendelian Randomization SRS Spontaneous Recurrent Seizures ILAE International League Against Epilepsy IVs Instrumental Variables LD Linkage Disequilibrium IVW Inverse Variance Weighting SNP Single Nucleotide Polymorphism OR Odds Ratio CI Confidence Interval Declarations Acknowledgments The authors thank all the researchers for sharing their data. Authors' contributions L.D. conceived, initiated, and supervised the project. J.C. wrote a draft of the manuscript. H.Y., H.L.,H.Y.,S.L., Q.W., X.Z.and R.Z.collected and analyzed the data. The authors read and approved the final manuscript. Competing interests The authors declare that they have no competing interests Availability of data and materials The data supporting the findings of this study are openly available on the following platforms: the IEU OpenGWAS Project (https://gwas.mrcieu.ac.uk), the GWAS Catalog (https://www.ebi.ac.uk/gwas/), and the FinnGen GWAS R10 version (https://www.finngen.fi/en/access_results). Additional data can be found in the article and its supplementary materials. For further inquiries, please contact the corresponding authors. Funding This work was supported by the China National Natural Sciences Foundation (Grant No. 82360875), China National Natural Sciences Foundation (Grant No. 81960858) and Guangxi Science and Technology Base and talent Project (Grant No. 2021AC18028). References Fisher R S, Boas W van E, Blume W, Elger C, Genton P, Lee P, et al. 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Tables Table 1 Genetic Association Analysis of Epilepsy with Immune Cell Types and Markers. id pvalue Types or Markers of Immune Cells ebi-a-GCST90001396 0.008878046 IgD+ CD38- AC ebi-a-GCST90001531 0.00517134 CD33dim HLA DR- AC ebi-a-GCST90001532 0.006606124 Basophil AC ebi-a-GCST90001541 0.044654038 Naive CD4+ %CD4+ ebi-a-GCST90001551 0.004667347 Naive CD8br AC ebi-a-GCST90001563 0.00216603 CM DN (CD4-CD8-) AC ebi-a-GCST90001570 0.031477456 EM DN (CD4-CD8-) %DN ebi-a-GCST90001580 0.027067671 CD14+ CD16+ monocyte AC ebi-a-GCST90001581 0.038268065 CD14- CD16- AC ebi-a-GCST90001587 0.031598029 CD16+ monocyte %monocyte ebi-a-GCST90001626 0.008344311 HLA DR+ CD4+ %lymphocyte ebi-a-GCST90001644 0.021711251 B cell %lymphocyte ebi-a-GCST90001645 0.020119915 NK AC ebi-a-GCST90001651 0.043506543 Granulocyte AC ebi-a-GCST90001658 0.011535717 CD39+ CD4+ %T cell ebi-a-GCST90001669 0.005021942 CD28+ CD45RA- CD8dim AC ebi-a-GCST90001678 0.020511711 CD28- CD25++ CD8br AC ebi-a-GCST90001726 0.040757543 CD19 on IgD+ CD38- ebi-a-GCST90001730 0.030657637 CD19 on IgD+ CD38dim ebi-a-GCST90001741 0.042287047 CD19 on IgD+ ebi-a-GCST90001783 0.017714312 CD25 on IgD+ CD38br ebi-a-GCST90001787 0.026205839 CD25 on IgD- CD38- ebi-a-GCST90001788 0.002511127 CD25 on IgD- CD38br ebi-a-GCST90001899 0.043891698 CD28 on CD4 Treg ebi-a-GCST90001926 0.03128246 CD127 on granulocyte ebi-a-GCST90001934 0.005542075 CD25 on CD45RA+ CD4 not Treg ebi-a-GCST90001976 0.014318799 FSC-A on NKT ebi-a-GCST90002001 0.00039017 CD64 on CD14- CD16- ebi-a-GCST90002064 0.020624656 CD4 on resting Treg ebi-a-GCST90002069 0.049771099 CD4 on CD39+ secreting Treg ebi-a-GCST90002105 0.025971632 HLA DR on plasmacytoid DC ebi-a-GCST90002106 0.004962448 HLA DR on DC ebi-a-GCST90002108 0.003396284 HLA DR on CD33br HLA DR+ CD14- ebi-a-GCST90002109 0.017140626 HLA DR on CD33br HLA DR+ CD14dim Table 2 Genetic Association Analysis of Epilepsy and Metabolic Levels. id pvalue Reported Trait GCST90199637 0.035004621 N-acetylglutamate levels GCST90199644 0.000457545 Theobromine levels GCST90199653 0.04408322 1-myristoyl-2-palmitoyl-gpc (14:0/16:0) levels GCST90199681 0.021563403 2-hydroxyoctanoate levels GCST90199683 0.009643237 Indoleacetate levels GCST90199700 0.006991842 3-hydroxylaurate levels GCST90199711 0.03945197 Docosadienoate (22:2n6) levels GCST90199723 0.011948843 5-dodecenoate (12:1n7) levels GCST90199744 0.010790532 Eicosenoate (20:1) levels GCST90199747 0.023626463 Isovalerylcarnitine (C5) levels GCST90199753 0.029679753 Gamma-glutamyltryptophan levels GCST90199763 0.004365508 1-methylxanthine levels GCST90199823 0.004535746 O-cresol sulfate levels GCST90199832 0.019507094 Dimethylarginine (sdma + adma) levels GCST90199843 0.045989215 Chiro-inositol levels GCST90199847 0.01794526 4-allylphenol sulfate levels GCST90199848 0.021558422 Succinylcarnitine levels GCST90199859 0.003985094 "5alpha-androstan-3alpha,17beta-diol disulfate levels" GCST90199960 0.006826377 N-formylanthranilic acid levels GCST90199972 0.043217479 "Sphingomyelin (d18:1/24:1, d18:2/24:0) levels" GCST90200014 0.048344808 3beta-hydroxy-5-cholestenoate levels GCST90200032 0.011255736 2-hydroxybutyrate/2-hydroxyisobutyrate levels GCST90200053 0.024210085 "Sphingomyelin (d18:1/21:0, d17:1/22:0, d16:1/23:0) levels" GCST90200072 0.049970989 Thioproline levels GCST90200092 0.045776627 Glycocholate glucuronide (1) levels GCST90200094 0.036420663 Ascorbic acid 2-sulfate levels GCST90200099 0.034174041 N-oleoylserine levels GCST90200107 0.028807959 Gamma-glutamyl-alpha-lysine levels GCST90200115 0.000513124 N-palmitoyl-heptadecasphingosine (d17:1/16:0) levels GCST90200131 0.048118224 "Sphingomyelin (d18:1/19:0, d19:1/18:0) levels" GCST90200175 0.020203189 Glycine conjugate of C10H14O2 (1) levels GCST90200203 0.031194186 Indoleacetoylcarnitine levels GCST90200212 0.048093597 Glucuronide of piperine metabolite C17H21NO3 (5) levels GCST90200244 0.012916584 5-hydroxymethyl-2-furoylcarnitine levels GCST90200311 0.036126849 4-acetamidobutanoate levels GCST90200319 0.032188976 "Cys-gly, oxidized levels" GCST90200337 0.048487501 Taurochenodeoxycholate levels GCST90200338 0.044597138 4-hydroxyphenylacetate levels GCST90200367 0.006025932 3-Hydroxybutyrate levels GCST90200369 0.026651347 2-hydroxyphenylacetate levels GCST90200377 0.00511502 Histidine levels GCST90200402 GCST90200420 0.043643867 0.010843509 Lysine levels Taurine levels GCST90200431 0.00895426 Alanine levels GCST90200433 0.019690958 Cytosine levels GCST90200473 0.015151642 X-11795 levels GCST90200506 0.002750803 X-12812 levels GCST90200539 0.040536944 X-14939 levels GCST90200558 0.02635407 X-18888 levels GCST90200648 0.037850225 X-24585 levels GCST90200655 0.025381338 X-25828 levels GCST90200663 0.038075027 X-25420 levels GCST90200669 0.049614027 X-25519 levels GCST90200673 0.014707482 Carnitine C4 levels GCST90200676 0.039475199 N-acetyl-L-glutamine levels GCST90200680 0.002303833 5-acetylamino-6-formylamino-3-methyluracil levels GCST90200717 0.036735217 3-phosphoglycerate to adenosine 5'-diphosphate (ADP) ratio GCST90200724 0.021637868 Cholate to taurocholate ratio GCST90200737 0.014854875 Adenosine 5'-diphosphate (ADP) to 2'-deoxyuridine ratio GCST90200746 0.025084551 Adenosine 5'-diphosphate (ADP) to gluconate ratio GCST90200760 0.046036017 Methionine to methionine sulfoxide ratio GCST90200763 0.042195841 Phosphate to N-acetylneuraminate ratio GCST90200765 0.044466635 Phosphate to alanine ratio GCST90200780 0.02403292 Alanine to pyruvate ratio GCST90200782 0.02219789 Mannose to trans-4-hydroxyproline ratio GCST90200807 0.011330952 Carnitine to ergothioneine ratio GCST90200903 0.049106155 Phosphate to glycerol ratio GCST90200914 0.031096816 Tyrosine to pyruvate ratio GCST90200919 0.012454706 Caffeine to theophylline ratio GCST90200929 0.039771623 Glycerol to carnitine ratio GCST90200982 0.033377915 Paraxanthine to linoleate (18:2n6) ratio GCST90200984 0.016856574 Cholesterol to benzoate ratio GCST90200987 0.039659483 Benzoate to oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] ratio GCST90200997 0.039762413 Histidine to glutamine ratio GCST90201002 0.012976684 Maltose to sucrose ratio Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Aug, 2024 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2024 Reviews received at journal 12 Jun, 2024 Reviews received at journal 10 Jun, 2024 Reviewers agreed at journal 10 Jun, 2024 Reviews received at journal 10 Jun, 2024 Reviewers agreed at journal 29 May, 2024 Reviewers agreed at journal 27 May, 2024 Reviewers invited by journal 23 May, 2024 Editor assigned by journal 23 May, 2024 Editor invited by journal 02 May, 2024 Submission checks completed at journal 02 May, 2024 First submitted to journal 28 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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(b) MR leave-one-out sensitivity analysis for metabolites (GCST90200982) on epilepsy. (c) MR leave-one-out sensitivity analysis for ebi-a-GCST90002001 on GCST90200982.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4336289/v1/12e2681701d67710bad5db2f.jpeg"},{"id":56069154,"identity":"154859c2-5a87-4c67-8a5c-f49751605bf1","added_by":"auto","created_at":"2024-05-08 06:53:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135753,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Forest plot of different MR methods. (b) Depiction of the Mediating Role of the Metabolite (Paraxanthine to Linoleate (18:2n6) Ratio) in the Causal Relationship between Immune Cells (CD64 on CD14- CD16-) and Epilepsy, with MR Estimates of the Associations among Metabolites, Immune Cells, and Epilepsy provided by the Inverse-Variance Weighted Method.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4336289/v1/e9d0250c5cf201386e455df6.jpeg"},{"id":63300273,"identity":"2575ccbf-b876-4dd6-8315-7eb89430de52","added_by":"auto","created_at":"2024-08-26 16:13:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4336289/v1/4d16fcf9-9026-42a9-9d33-23c521b7d07d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEpilepsy, a chronic neurologic condition, is marked by atypical electrical brain activity that results in recurrent seizures, known as spontaneous recurrent seizures (SRS)\u003csup\u003e1\u003c/sup\u003e. Clinical manifestations of epilepsy include convulsions, loss of consciousness, myoclonus (sudden muscle jerks), aura, automatisms, hypotonia (decreased muscle tone), altered sensations, and prolonged muscle contractions\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e. Per the 2017 definition from the International League Against Epilepsy (ILAE), epilepsy is categorized as generalized epilepsy, focal epilepsy, combined generalized and focal epilepsy, and unknown epilepsy\u003csup\u003e5\u003c/sup\u003e. Epilepsy affects people of all ages, regions, and races, although it is more commonly diagnosed in children and adolescents. Its prevalence worldwide ranges from 0.5\u0026ndash;1.0%\u003csup\u003e5\u003c/sup\u003e. Various factors can contribute to the development of epilepsy, including genetic predisposition, acquired brain injuries, infections, and metabolic or immune disorders\u003csup\u003e2\u003c/sup\u003e. Despite these known factors, the precise cause of epilepsy remains unclear.\u003c/p\u003e \u003cp\u003eIn the pathogenesis of epilepsy, inflammation, and immune responses are vital factors\u003csup\u003e6\u0026ndash;8\u003c/sup\u003e. During epileptic seizures, the secretion of inflammatory mediators and the activation of immune cells can lead to excessive neuronal excitation and abnormal synaptic connectivity\u003csup\u003e9\u0026ndash;11\u003c/sup\u003e. A complex interaction exists between immune cells and neurons, known as neuro-immune modulation\u003csup\u003e12\u003c/sup\u003e. Immune cells can release various cytokines and chemokines, which may affect neuronal excitability and stability, thereby influencing epileptic seizures\u003csup\u003e8,13\u003c/sup\u003e. However, the existence of a direct causal relationship between epilepsy and immune cells remains uncertain. Thus, a study design that minimizes biases is necessary to elucidate this potential causality. Moreover, given the intricate relationship between immune cells and metabolites\u003csup\u003e14\u0026ndash;16\u003c/sup\u003e, and findings that certain metabolites, such as circulating metabolites\u003csup\u003e17\u003c/sup\u003e, can serve as biomarkers for epilepsy, it is conceivable that metabolites may act as potential mediators between immune cells and epilepsy.\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR) is a research method grounded in Mendel's Second Law\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. It utilizes the random allocation of alleles from parents to offspring during the creation of gametes, facilitating the establishment of links between traits of exposure, such as intermediate phenotypes, and outcomes of diseases\u003csup\u003e18\u0026ndash;20\u003c/sup\u003e. This method employs genetic variation as a key variable, serving as an instrument to deduce the impact of exposure factors on outcomes based on observational data\u003csup\u003e18\u003c/sup\u003e. The inherent randomness of allele distribution in Mendelian Randomization naturally minimizes non-random errors or misleading factors, and prevents causal inversion through the principles of Mendelian inheritance\u003csup\u003e18\u003c/sup\u003e. Therefore, MR provides a reliable mechanism for drawing causal inferences from observational data. Thus, our objectives were to (i) ascertain if there is a causal link between immune cells and epilepsy, and (ii) evaluate the degree to which metabolites mediate the effects of immune cells on epilepsy.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eOur analysis was founded on publicly accessible data that have been previously approved by the institutional review boards of the corresponding studies. This meant that no further approvals were necessary for our work. Detailed descriptions of the results from our analysis can be found in both the main article and the supplementary materials accompanying it.\u003c/p\u003e \u003cp\u003eTo ensure dependable outcomes, a two-sample MR study must satisfy three fundamental assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) The instrumental variables (IVs) ought to exhibit a notable correlation with immune cells; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) IVs should remain uncorrelated with any confounding variables capable of impacting the connection between exposure and outcome factors; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) IVs should solely influence outcomes via the pathway of immune cells.\u003c/p\u003e \u003cp\u003eOur study initially utilized a two-sample MR to ascertain the causal association between immune cells, metabolites, and epilepsy. Subsequently, to further investigate the potential impact of immune cells on epilepsy mediated through metabolites, we employed a mediating MR approach, also known as a two-step MR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eThe immune cell exposure data is sourced from OpenGWAS, which includes 126\u0026nbsp;billion genetic associations from 14,582 complete GWAS datasets\u003csup\u003e21\u003c/sup\u003e. The intermediary (metabolite) data comes from GWAS Catalog\u003csup\u003e22\u003c/sup\u003e. The outcome (epilepsy) data is derived from FinnGen GWAS\u003csup\u003e23\u003c/sup\u003e R10, including 12,891 cases and 312,803 controls, with all individuals being of European descent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSelection for genetic variation\u003c/h2\u003e \u003cp\u003eFor immune cells to outcome, immune cells to metabolites, and metabolites to outcome, we selected instrumental variables (IVs) with a p-value threshold of \u0026lt;\u0026thinsp;1.0\u0026times;10^-5. To ensure independent loci for the IVs, we used the \"TwoSampleMR\" package, setting the linkage disequilibrium (LD) threshold for the 1000 Genomes EUR data to R^2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with a clumping distance of 10,000 kb.\u003c/p\u003e \u003cp\u003eFor the reverse direction (epilepsy to immune cells), IVs were chosen with a p-value threshold of \u0026lt;\u0026thinsp;5.0\u0026times;10^-6. Similarly, to obtain independent loci for the IVs, we applied the \"TwoSampleMR\" package with the LD threshold for the 1000 Genomes EUR data set to R^2\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and a clumping distance of 10,000 kb. The results revealed no evidence of reverse causation. For MR analysis, palindromic and ambiguous SNPs were omitted from IVs \u003csup\u003e24\u003c/sup\u003e. Furthermore, we computed the F statistic (i.e., the proportion of variability attributed to Single Nucleotide Polymorphisms in each exposure), represented as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(F=\\left(\\frac{N-K-1}{K}\\right)\\left(\\frac{{R}^{2}}{1-{R}^{2}}\\right)\\)\u003c/span\u003e\u003c/span\u003e, where K stands for the count of genetic variants and N denotes the sample size\u003csup\u003e25\u003c/sup\u003e. We excluded instrumental variables with low strength (F-statistics\u0026thinsp;\u0026lt;\u0026thinsp;10) \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAlthough the primary emphasis of our research was on outcomes using the inverse variance weighting (IVW) method\u003csup\u003e26\u003c/sup\u003e, it was crucial to confirm that the directions of the results were consistent across all methods used, including IVW. For a thorough secondary analysis, we applied various methods like MR-Egger regression, weighted median, simple mode, weighted mode, and MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO)\u003csup\u003e20,27\u0026ndash;29\u003c/sup\u003e, to conduct a sensitivity check of our IVW results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePleiotropy and heterogeneity analysis\u003c/h2\u003e \u003cp\u003eOur study began with employing the MR-PRESSO\u003csup\u003e29\u003c/sup\u003e method to pinpoint and remove outliers. We then reanalyzed the data without these outliers. Next, to investigate the influence of each Single Nucleotide Polymorphism (SNP) on the link between exposure and outcome, we executed a leave-one-out sensitivity analysis, systematically omitting one SNP at a time. Additionally, the MR-Egger regression test was applied to detect horizontal pleiotropy in the Mendelian randomization analysis, particularly focusing on the importance of the intercept term's statistical significance \u003csup\u003e30\u003c/sup\u003e. To explore heterogeneity within our findings, we calculated the Cochran Q statistic, considering P\u0026thinsp;=\u0026thinsp;0.05 as the significance threshold\u003csup\u003e31\u003c/sup\u003e. All these statistical procedures were performed using R (version 4.2.1), utilizing the MR and MR-PRESSO packages.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1.Association of immune cells with epilepsy\u003c/h2\u003e \u003cp\u003eWe first screened for exposure tools related to the outcome (epilepsy) from 731 immune cells. After excluding palindromic SNPs and those identified by MR Steiger filtering as having incorrect causal directions, there are a total of 34 qualified immune cells (Table\u0026nbsp;1). We then conducted analyses using MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, and illustrated these in forest plots(Supplementary Fig.\u0026nbsp;1). Here, we selected the immune cell subgroup CD64 on CD14-CD16 (id ebi-a-GCST90002001) as our exposure tool variable. In analyses such as MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, we looked for results with p-values below the commonly used threshold for statistical significance (e.g., 0.05). In this case, the IVW method had the lowest p-value at 0.0004, indicating statistical significance in this analysis.\u003c/p\u003e \u003cp\u003eWe examined the odds ratio (OR) and the 95% confidence interval (CI). In the IVW method, the OR was 1.0826, with a 95% CI of 1.0361 to 1.1312, indicating not only a positive direction of effect but also that the confidence interval does not include 1, suggesting an association between CD64 on CD14-CD16 and the outcome variable. Additionally, the OR remained above 1 across different estimation methods, indicating consistent result directions across methods, i.e., the exposure increases the risk of epilepsy. To validate this, we also conducted sensitivity analysis (Fig.\u0026nbsp;1a). Moreover, our MR analysis results showed no evidence of reverse causation between CD64 on CD14-CD16 and the outcome (epilepsy).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.Association of metabolites with epilepsy\u003c/h2\u003e \u003cp\u003eFrom 1,400 metabolites, we identified exposure tools related to the outcome (epilepsy). After excluding palindromic SNPs and those identified by MR Steiger filtering as having incorrect causal directions, there are a total of 75 qualified metabolites(Table\u0026nbsp;2). We then performed analyses using MR Egger, Weighted median, IVW, Simple mode, and Weighted mode, and presented these in forest plots༈Supplementary Fig.\u0026nbsp;2༉. We chose the ratio of Paraxanthine to linoleate (18:2n6) (id GCST90200982) as the exposure tool for the outcome of epilepsy. In all methods, the IVW's p-value was the lowest (0.0334), indicating statistical significance. Across different estimation methods, the OR was consistently above 1, indicating a consistent association between this ratio and an increased risk of epilepsy. Although some methods had confidence intervals close to 1, the IVW method provided a sufficiently wide confidence interval (1.0068\u0026ndash;1.1795), along with a narrower interval, suggesting higher precision and adding credibility to the result. We further performed a sensitivity analysis to verify this result (Fig.\u0026nbsp;1b).\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.Selected relationship between immune cells and positive metabolites\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe results show that the selected immune cell subgroup CD64 on CD14-CD16 (OR\u0026thinsp;=\u0026thinsp;1.0438, 95% CI:1.0087\u0026ndash;1.0801, p\u0026thinsp;=\u0026thinsp;0.0140) influences the aforementioned positive metabolite Paraxanthine to linoleate (18:2n6) ratio, as demonstrated by the IVW method (Fig.\u0026nbsp;2a). Since we have verified the connections between \"immune cells \u0026rarr; epilepsy\" and \"immune cells \u0026rarr; metabolites,\" we hypothesize that certain metabolites, such as the Paraxanthine to linoleate (18:2n6) ratio, may play a modulating role in the relationship between immune cells and epilepsy. We additionally performed a sensitivity analysis to reinforce this finding (Fig.\u0026nbsp;1c).\u003c/p\u003e \u003cp\u003e \u003cb\u003e4. The intermediary role of metabolites in the causal relationship between immune cells and epilepsy.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA two-step method was used to analyze the mediating role of the metabolite Paraxanthine to linoleate (18:2n6) ratio in the causal relationship between the immune cell subgroup CD64 on CD14-CD16 and epilepsy. As shown in Fig.\u0026nbsp;2b, the metabolite accounts for 5.05% of the effect. This indicates that the metabolite may play a certain mediating role in the connection between immune cells and epilepsy, but this role is limited.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this research, we employed MR analysis to investigate the causal link between immune cells and epilepsy, and to investigate whether this relationship is mediated through metabolites. Our findings suggest a causative link between genetically forecasted levels of immune cells (specifically CD64 on CD14-CD16) and increased risk of epilepsy, indicating that an increase of one standard deviation in CD64 on CD14-CD16 levels is associated with an approximate 8.26% increase in epilepsy risk under constant conditions. Notably, about 5.05% of this effect appears to be mediated through the metabolite Paraxanthine to linoleate (18:2n6) ratio.\u003c/p\u003e \u003cp\u003eStudies show that inflammation is a major factor in epilepsy\u003csup\u003e32,33\u003c/sup\u003e. For instance, a study discussed the role of P2X7 receptors in epilepsy and neuroinflammation, particularly their expression on various cells in the brain and their involvement in the inflammatory process during epileptic seizures\u003csup\u003e13\u003c/sup\u003e. Other studies\u003csup\u003e34\u003c/sup\u003e have demonstrated that tumor necrosis factor-alpha inhibits epileptic seizures through specific receptors, while interleukin-1 receptor antagonists have been found to exhibit antiepileptic effects in mice. Overexpression of cytokines in the brain, such as interleukin-6, is associated with the induction of neurological disorders in animal models. This suggests a close link between immune responses and neural health. The migration of immune cells to the central nervous system and their role in neurological disorders is also a significant area of research. Additionally, studies\u003csup\u003e35\u003c/sup\u003e have explored the role of the blood-brain barrier in immune functions and dysfunctions, which is crucial for understanding how peripheral immune responses affect the central nervous system. These studies collectively emphasize the intricate interplay between the immune system and neurological disorders, especially epilepsy.\u003c/p\u003e \u003cp\u003eWhen immune cell activity changes, different metabolites may be released, which can influence the risk of epilepsy by affecting the function of neural cells. For example, certain metabolites might alter neuronal excitability or affect neural conduction pathways\u003csup\u003e36\u003c/sup\u003e. A study utilized \u003csup\u003e1\u003c/sup\u003eH-NMR and DI/LC-MS/MS technologies to identify and quantify 212 metabolites\u003csup\u003e37\u003c/sup\u003e. The results showed that among these 212 detected metabolites, 14 exhibited significant concentration changes between epilepsy patients and the control group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, q\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e37\u003c/sup\u003e.Therefore, by studying these changes in metabolites, scientists can better understand the connection between the immune system and the onset of epilepsy, and how modulating these metabolic pathways might reduce the risk of epilepsy. This study found that the Paraxanthine to linoleate (18:2n6) ratio may play a certain mediating role in the relationship between immune cells and epilepsy. This suggests that this metabolite might be a key factor linking changes in immune activity and epilepsy risk. The variation in this ratio may reflect the activation state of immune cells, impacting the function of the nervous system. For instance, if changes in this ratio lead to increased excitability of neural cells or altered neural communication pathways, this might increase the risk of epilepsy. Therefore, changes in the Paraxanthine to linoleate (18:2n6) ratio not only reflect the state of the immune system but may also be an important biomarker for predicting or intervening in epilepsy risk. This discovery underscores the potential importance of metabolites in neuroimmunology and disease prevention.\u003c/p\u003e \u003cp\u003eIn our research, we were the first to employ mediation MR technology to explore the causal association among metabolites, immune cells, and epilepsy. We employed multiple typical sensitivity analyses and excluded the likelihood of reverse causation. Our initial results indicate a causal link between immune cells and epilepsy and its intermediaries, further supporting the theoretical basis for treating and preventing epilepsy and proposing new methods for its management. For example, the risk of epilepsy onset can be managed by initially regulating specific metabolites through diet, exercise, or other methods.\u003c/p\u003e \u003cp\u003eWhile our study includes several advantages, such as a substantial sample size and the utilization of various sensitivity analysis techniques to ensure the credibility of our research and mitigate confounding variables, it is essential to acknowledge the presence of unavoidable limitations. Firstly, although the sample size is substantial, participants are entirely of European descent, potentially limiting considerations of genetic diversity and environmental influences. Secondly, despite our efforts to identify and remove outlier variables, we cannot completely exclude the possibility of horizontal pleiotropy influencing our findings. Thirdly, while our results suggest a potential mediation effect, the fact that the p-value is greater than 0.05 and the confidence interval includes negative numbers may be due to the presence of unknown moderating variables. Perhaps future research could focus on including participants from a more diverse range of ethnicities and regions, expanding the sample size, and conducting experiments at the cellular level, with animal models, as well as clinical trials. Advanced analytical methods or stricter statistical controls could be used to reduce the impact of potential biases. Additionally, the robustness of research results could be further validated by using different statistical models or tools.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study has established a causal link between immune cells and epilepsy, with specific levels of immune cells (such as CD64 on CD14-CD16) being associated with an increased risk of epilepsy. Additionally, the ratio of Paraxanthine to linoleate (18:2n6) appears to exert a moderating influence on this relationship, although its impact is limited. Subsequent investigations should prioritize larger sample sizes, deeper exploration of mechanisms, controlling potential confounding factors, and conducting comparative studies across different populations to validate and extend the current findings. This is significant for understanding the pathogenesis of epilepsy and its prevention and treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAbbreviations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eFull form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMendelian Randomization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSpontaneous Recurrent Seizures\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eILAE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInternational League Against Epilepsy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInstrumental Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLinkage Disequilibrium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInverse Variance Weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eOdds Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eConfidence Interval\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the researchers for sharing their data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eL.D. conceived, initiated, and supervised the project. J.C. wrote a draft of the manuscript. H.Y., H.L.,H.Y.,S.L., Q.W., X.Z.and R.Z.collected and analyzed the data. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the findings of this study are openly available on the following platforms: the IEU OpenGWAS Project (https://gwas.mrcieu.ac.uk), the GWAS Catalog (https://www.ebi.ac.uk/gwas/), and the FinnGen GWAS R10 version (https://www.finngen.fi/en/access_results). Additional data can be found in the article and its supplementary materials. For further inquiries, please contact the corresponding authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China National Natural Sciences Foundation (Grant No. 82360875), China National Natural Sciences Foundation (Grant No. 81960858) and Guangxi Science and Technology Base and talent Project (Grant No. 2021AC18028).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFisher R S, Boas W van E, Blume W, Elger C, Genton P, Lee P, et al. Epileptic Seizures and Epilepsy: Definitions Proposed by the International League Against Epilepsy (ILAE) and the International Bureau for Epilepsy (IBE)[J]. Epilepsia, 2005, 46(4): 470\u0026ndash;472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScheffer I E, Berkovic S, Capovilla G, Connolly M B, French J, Guilhoto L, et al. 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Metabolites, 2020, 10(6): 261.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Genetic Association Analysis of Epilepsy with Immune Cell Types and Markers.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"519\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eTypes or Markers of Immune Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.008878046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eIgD+ CD38- AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.00517134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD33dim HLA DR- AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.006606124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eBasophil AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.044654038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNaive CD4+ %CD4+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.004667347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNaive CD8br AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.00216603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCM DN (CD4-CD8-) AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.031477456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eEM DN (CD4-CD8-) %DN\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.027067671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD14+ CD16+ monocyte AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.038268065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD14- CD16- AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.031598029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD16+ monocyte %monocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.008344311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHLA DR+ CD4+ %lymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.021711251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eB cell %lymphocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.020119915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eNK AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.043506543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eGranulocyte AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.011535717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD39+ CD4+ %T cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.005021942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD28+ CD45RA- CD8dim AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.020511711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD28- CD25++ CD8br AC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.040757543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD19 on IgD+ CD38-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.030657637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD19 on IgD+ CD38dim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.042287047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD19 on IgD+\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.017714312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD25 on IgD+ CD38br\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.026205839\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD25 on IgD- CD38-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.002511127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD25 on IgD- CD38br\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.043891698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD28 on CD4 Treg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.03128246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD127 on granulocyte\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.005542075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD25 on CD45RA+ CD4 not Treg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90001976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.014318799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eFSC-A on NKT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.00039017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD64 on CD14- CD16-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002064\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.020624656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD4 on resting Treg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.049771099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eCD4 on CD39+ secreting Treg\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.025971632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHLA DR on plasmacytoid DC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.004962448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHLA DR on DC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.003396284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHLA DR on CD33br HLA DR+ CD14-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"31.346153846153847%\" valign=\"top\"\u003e\n \u003cp\u003eebi-a-GCST90002109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003e0.017140626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.73076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHLA DR on CD33br HLA DR+ CD14dim\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 Genetic Association Analysis of Epilepsy and Metabolic Levels.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"565\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003epvalue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eReported Trait\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.035004621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eN-acetylglutamate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.000457545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eTheobromine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.04408322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e1-myristoyl-2-palmitoyl-gpc (14:0/16:0) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.021563403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e2-hydroxyoctanoate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.009643237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eIndoleacetate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.006991842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e3-hydroxylaurate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.03945197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eDocosadienoate (22:2n6) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.011948843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e5-dodecenoate (12:1n7) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.010790532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eEicosenoate (20:1) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.023626463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eIsovalerylcarnitine (C5) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.029679753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGamma-glutamyltryptophan levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.004365508\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e1-methylxanthine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.004535746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eO-cresol sulfate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.019507094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eDimethylarginine (sdma + adma) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.045989215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eChiro-inositol levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.01794526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e4-allylphenol sulfate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.021558422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eSuccinylcarnitine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.003985094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026quot;5alpha-androstan-3alpha,17beta-diol disulfate levels\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.006826377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eN-formylanthranilic acid levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90199972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.043217479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026quot;Sphingomyelin (d18:1/24:1, d18:2/24:0) levels\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.048344808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e3beta-hydroxy-5-cholestenoate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.011255736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e2-hydroxybutyrate/2-hydroxyisobutyrate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.024210085\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026quot;Sphingomyelin (d18:1/21:0, d17:1/22:0, d16:1/23:0) levels\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.049970989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eThioproline levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.045776627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGlycocholate glucuronide (1) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.036420663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eAscorbic acid 2-sulfate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.034174041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eN-oleoylserine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.028807959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGamma-glutamyl-alpha-lysine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.000513124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eN-palmitoyl-heptadecasphingosine (d17:1/16:0) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.048118224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026quot;Sphingomyelin (d18:1/19:0, d19:1/18:0) levels\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.020203189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGlycine conjugate of C10H14O2 (1) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.031194186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eIndoleacetoylcarnitine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.048093597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGlucuronide of piperine metabolite C17H21NO3 (5) levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.012916584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e5-hydroxymethyl-2-furoylcarnitine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.036126849\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e4-acetamidobutanoate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.032188976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026quot;Cys-gly, oxidized levels\u0026quot;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.048487501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eTaurochenodeoxycholate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.044597138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e4-hydroxyphenylacetate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.006025932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e3-Hydroxybutyrate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.026651347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e2-hydroxyphenylacetate levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.00511502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eHistidine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200402\u003c/p\u003e\n \u003cp\u003eGCST90200420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.043643867\u003c/p\u003e\n \u003cp\u003e0.010843509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eLysine levels\u003c/p\u003e\n \u003cp\u003eTaurine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.00895426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eAlanine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.019690958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCytosine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.015151642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-11795 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.002750803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-12812 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.040536944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-14939 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.02635407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-18888 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.037850225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-24585 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.025381338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-25828 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.038075027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-25420 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.049614027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eX-25519 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.014707482\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCarnitine C4 levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.039475199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eN-acetyl-L-glutamine levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.002303833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e5-acetylamino-6-formylamino-3-methyluracil levels\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200717\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.036735217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003e3-phosphoglycerate to adenosine 5\u0026apos;-diphosphate (ADP) ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.021637868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCholate to taurocholate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.014854875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eAdenosine 5\u0026apos;-diphosphate (ADP) to 2\u0026apos;-deoxyuridine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.025084551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eAdenosine 5\u0026apos;-diphosphate (ADP) to gluconate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.046036017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eMethionine to methionine sulfoxide ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.042195841\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate to N-acetylneuraminate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.044466635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate to alanine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.02403292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eAlanine to pyruvate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.02219789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eMannose to trans-4-hydroxyproline ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.011330952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCarnitine to ergothioneine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.049106155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003ePhosphate to glycerol ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.031096816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eTyrosine to pyruvate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.012454706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCaffeine to theophylline ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.039771623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eGlycerol to carnitine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.033377915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eParaxanthine to linoleate (18:2n6) ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.016856574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eCholesterol to benzoate ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.039659483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eBenzoate to oleoyl-linoleoyl-glycerol (18:1 to 18:2) [2] ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90200997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.039762413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eHistidine to glutamine ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.45390070921986%\" valign=\"top\"\u003e\n \u003cp\u003eGCST90201002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.390070921985817%\" valign=\"top\"\u003e\n \u003cp\u003e0.012976684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.156028368794324%\" valign=\"top\"\u003e\n \u003cp\u003eMaltose to sucrose ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Epilepsy, Mendelian Randomization, Immune cells, Metabolites","lastPublishedDoi":"10.21203/rs.3.rs-4336289/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4336289/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur study investigated the causal relationship between immune cells, metabolites, and epilepsy using two-sample Mendelian Randomization (MR) and mediation MR analysis of 731 immune cell traits and 1,400 metabolites. Our core methodology centered on inverse-variance weighted MR, supplemented by other methods. This approach was crucial in clarifying the potential intermediary functions of metabolites in the genetic links between traits of immune cells and epilepsy. We found a causal relationship between immune cells and epilepsy. Specifically, the genetically predicted levels of CD64 on CD14-CD16 are positively correlated with the risk of epilepsy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, OR\u0026thinsp;=\u0026thinsp;1.0826, 95% CI 1.0361\u0026ndash;1.1312). Similarly, metabolites also exhibit a causal relationship with both immune cells (OR\u0026thinsp;=\u0026thinsp;1.0438, 95% CI:1.0087\u0026ndash;1.0801, p\u0026thinsp;=\u0026thinsp;0.0140) and epilepsy (p\u0026thinsp;=\u0026thinsp;0.0334, OR\u0026thinsp;=\u0026thinsp;1.0897, 95% CI: 1.0068\u0026ndash;1.1795), and sensitivity analysis was conducted to further validate these relationships. Importantly, our intermediate MR results suggest that the metabolite Paraxanthine to linoleate (18:2n6) ratio may mediate the causal relationship between immune cell CD64 on CD14-CD16 and epilepsy, with a mediation effect of 5.05%. The results suggest the importance of specific immune cell levels and metabolites in understanding epilepsy's pathogenesis. This is significant for understanding the pathogenesis of epilepsy and its prevention and treatment.\u003c/p\u003e","manuscriptTitle":"Mendelian Randomization Analysis of the Causal Relationship Between Immune Cells and Epilepsy: The Mediating Role of Metabolites","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-08 06:53:24","doi":"10.21203/rs.3.rs-4336289/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-19T03:36:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-12T13:51:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-10T11:00:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246688418638585142025345615745896794797","date":"2024-06-10T06:41:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-10T06:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188961528212506395306249130583925438795","date":"2024-05-30T00:10:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"142202478528017503150353169629604493525","date":"2024-05-27T16:04:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-05-23T05:00:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T04:43:40+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-05-02T04:45:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-02T04:41:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-04-28T05:10:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"88e804fd-9865-40d0-9907-5cd6f3ca6f96","owner":[],"postedDate":"May 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":31616969,"name":"Biological sciences/Genetics"},{"id":31616971,"name":"Biological sciences/Immunology"},{"id":31616974,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2024-08-26T16:04:14+00:00","versionOfRecord":{"articleIdentity":"rs-4336289","link":"https://doi.org/10.1038/s41598-024-70370-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2024-08-23 15:57:42","publishedOnDateReadable":"August 23rd, 2024"},"versionCreatedAt":"2024-05-08 06:53:24","video":"","vorDoi":"10.1038/s41598-024-70370-1","vorDoiUrl":"https://doi.org/10.1038/s41598-024-70370-1","workflowStages":[]},"version":"v1","identity":"rs-4336289","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4336289","identity":"rs-4336289","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}