Previous studies have identified metabolites as biomarkers or potential therapeutic targets for traumatic brain injury (TBI). However, the causal association between them remains unknown. Therefore, we investigated the causal effect of serum metabolites and cerebrospinal fluid (CSF) metabolites on TBI susceptibility through Mendelian randomization (MR). Genetic variants related to metabolites and TBI were extracted from a corresponding genome-wide association study (GWAS). Causal effects were estimated through the inverse variance weighted approach, supplemented by a weighted median, weight mode, and the MR–Egger test. In addition, sensitivity analyses were further performed to evaluate the stability of the MR results, including the MR–Egger intercept, leave-one-out analysis, Cochrane’s Q-test, and the MR-PRESSO global test. Metabolic pathway analysis was applied to uncover the underlying pathways of the significant metabolites in TBI. In blood metabolites, substances such as 4-acetaminophen sulfate and kynurenine showed positive links, whereas beta-hydroxyisovalerate and creatinine exhibited negative correlations. CSF metabolites such as N-formylanthranilic acid were positively related, while kynurenate showed negative associations. The metabolic pathway analysis highlighted the potential biological pathways involved in TBI. Of these 16 serum metabolites, 11 CSF metabolites and metabolic pathways may serve as useful circulating biomarkers in clinical screening and prevention, and may be candidate molecules for the exploration of mechanisms and drug targets.

Background: The gut microbiome (GM) has been reported to play a role in traumatic brain injury (TBI). To investigate the causal relationship between GMs, inflammatory mediators, and TBI, a comprehensive Mendelian randomization (MR) analysis was conducted. Methods: We utilized Genome-Wide Association Study (GWAS) summary statistics to examine the causal relationships between GM and TBI. To assess the potential causal associations between GM and TBI, we employed the inverse-variance-weighted, MR-Egger, and weighted median methods. Mediation analysis was used to assess the possible mediating factors. Several sensitivity analyses methods were implemented to verify the stability of the results. Additionally, we utilized FUMA GWAS to map single-nucleotide polymorphisms to genes and conduct transcriptomic MR analysis. Results: We identified potential causal relationships between nine bacterial taxa and TBI. Notably, class Methanobacteria, family Methanobacteriaceae, and order Methanobacteriales (p = 0.0003) maintained a robust positive correlation with TBI. This causal association passed false discovery rate (FDR) correction (FDR < 0.05). Genetically determined 1 inflammatory protein, 30 immune cells and 3 inflammatory factors were significantly causally related to TBI. None of them mediated the relationship between GMs and TBI. The outcome of the sensitivity analysis corroborated the findings. Regarding the mapped genes of significant GMs, genes such as CLK4, MTRF1, NAA16, SH3BP5, and ZNF354A in class Methanobacteria showed a significant causal correlation with TBI. Conclusions: Our study reveals the potential causal effects of nine GMs, especially Methanogens on TBI, and there was no link between TBI and GM through inflammatory protein, immune cells, and inflammatory factors, which may offer fresh insights into TBI biomarkers and therapeutic targets through specific GMs.

Background: Epilepsy is a chronic neurological disorder that can significantly impair quality of life. Previous studies have demonstrated the positive effects of sodium–glucose co-transporter 2 (SGLT2) inhibitors on various systems, including the nervous system. SGLT2 inhibitors have been shown to reduce seizure activity in pentylenetetrazol-induced epilepsy models in rats. Recent study results showed that glucose-lowering drugs including SGLT2 inhibitors reduced risk of seizures and epilepsy. Methods: Genetic variants of SGLT2 inhibition were selected as exposure instrumental variables (IVs) and used type 2 diabetes (T2DM) as validation. Two-sample Mendelian Randomization (MR) analysis was conducted to explore the causal relationship between SGLT2 inhibition and epilepsy. In addition, we performed a two-step MR to evaluate whether possible circulating metabolites mediate the effects of SGLT2 inhibition on epilepsy. Results: The two-sample MR analysis showed that genetically proxied SGLT2 inhibition was associated with lower odds of epilepsy under MR assumptions [IVW: OR = 0.30 (95% CI: 0.13, 0.71), p = 0.0064]. Exploratory two-step analyses identified 3-methylglutaconate and N-acetylleucine as candidate metabolic signals; however, neither signal remained statistically significant after Benjamini–Hochberg correction. Conclusion: Our study suggests that SGLT2 is associated with a reduced risk of epilepsy, and the underlying mechanism may be the potential antioxidant and anti-inflammatory effects of SGLT2. In addition, circulating 3-methylglutaconate and N-acetylleucine should therefore be regarded as candidate metabolic signals rather than established mediators.

Background: Epidemiological studies have reported the association between ischemic stroke (IS) and intracerebral hemorrhage (ICH). However, their shared genetic basis remains incompletely characterized. This study quantifies the genetic overlap between them. Methods: We analyzed European-ancestry GWAS summary statistics for IS and ICH. Genetic correlation was estimated using linkage disequilibrium score regression (LDSC), with local correlation assessed by SUPER GeNetic covariance Analyzer (SUPERGNOVA). Partitioned heritability was evaluated across 96 functional categories. Shared loci were identified through cross-trait meta-analysis and evaluated by Bayesian colocalization. Transcriptome-wide association analysis (TWAS) was performed to identify shared gene-expression associations. Bidirectional MR with multiple pleiotropy-robust methods was used to explore causality. Tissue type-specific analysis was performed to investigate shared tissue enrichment. Results: LDSC showed a significant positive genetic correlation between IS and ICH (r_g = 0.57, p = 2.13 × 10⁻⁸). SUPERGNOVA identified significant local correlations at chromosomes 2q33 and 4q25. Partitioned heritability suggested shared genetic enrichment across five functional domains. Cross-trait analysis identified 25 candidate novel pleiotropic SNPs and strong colocalization at 1q22 (rs2736613 near PMF1). TWAS identified four overlapping genes between stroke subtypes, including FAM117B, ICA1L, NBEAL1, and WDR12, mainly located at 2q33. MR results suggested shared liability rather than direct causality. Tissue-specific analyses identified nominal enrichment signals, although none survived multiple-testing correction. Conclusions: Our findings support substantial shared genetic susceptibility between IS and ICH, likely reflecting pleiotropic mechanisms and shared vascular liability rather than a direct directional causal relationship. Further functional studies are needed to clarify the underlying biological mechanisms.