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.

Introduction: Bell’s palsy is a common acute peripheral neurological disorder causing unilateral facial paralysis. Its exact etiology remains unknown, but it is linked to inflammation, immune responses, infections, and ischemia. This study explores the potential causal relationship between Bell’s palsy and peripheral blood inflammatory proteins, metabolites, and immune cell characteristics. Methods: Genetic data for Bell’s palsy were obtained from the Finnish database (version R10) and IEU OpenGWAS. A two-sample Mendelian randomization (MR) approach was applied, analyzing 4907 plasma proteins, 731 immune cell traits, 91 inflammatory proteins, and 1400 metabolites. The Finnish dataset served as the discovery cohort, while the IEU OpenGWAS dataset acted as the validation cohort. Bioinformatics analyses included protein–protein interaction (PPI) networks, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, colocalization, and Linkage Disequilibrium Score Regression (LDSC) to identify candidate proteins and explore potential therapeutic targets. Results: MR analysis identified 70 inflammatory proteins, 77 metabolites, and 26 immune cell traits as potentially causally associated with Bell’s palsy. After external validation, BLVRB, HMOX2, TNFRSF12A, DEFB128, ITM2A, VEGF-A, and DDX58 remained significantly associated (p < 0.05). PPI network analysis led to 31 candidate proteins, and six core proteins (JAK2, IL27RA, OSM, CCL19, SELL, VCAM-1) were identified. Conclusions: Our study identifies causal relationships between inflammatory proteins, metabolites, immune cells, and Bell’s palsy, highlighting that the JAK/STAT signaling pathway may be a potentially critical target for intervention in Bell’s palsy, and that its modulation may provide new directions and opportunities for therapeutic strategies and drug discovery for the disease.

Observational studies highlighted a strong association between gut microbiota (GM) and the onset of neuromyelitis optica spectrum disorder (NMOSD), however, the causality remains unclear. This study investigated the causal relationship between them through two-sample Mendelian randomization (MR) based on genome-wide association studies (GWAS). Two-step MR was used to investigate the potential role of inflammatory cytokines between the causal relationship. Several sensitive analysis were performed to validate the robustness of MR. The MR analysis revealed phylum Tenericutes ( P = .0357); class Mollicutes ( P = .0357); genus Eubacterium rectale group ( P = .0487); genus Barnesiella ( P = .03); genus Eubacterium xylanophilum group ( P = .037); and genus Ruminococcus torques group ( P = .0179) were positively associated with the risk of NMOSD, family Clostridiales vadin BB60 group ( P = .0244); genus Eggerthella ( P = .0214); and genus Intestinibacter ( P = .0308) were negatively correlated with NMOSD. Among 41 inflammatory cytokines, MR showed significant causal effects of Macrophage colony-stimulating factor 1 levels ( P = .00256), C-X-C motif chemokine 11 levels ( P = .0478), SIR2-like protein 2 levels ( P = .00338) and Tumor necrosis factor receptor superfamily member 9 levels ( P = .0234) on NMOSD. Sensitivity analyses confirmed the robustness of these findings. However, mediated MR analysis did not find evidence supporting inflammatory proteins as mediators in the GM-NMOSD pathway. The study presents evidence for a causal relationship between GM, inflammatory proteins, and NMOSD. Notably, inflammatory proteins do not mediate the pathway from GM to NMOSD, contributing to a deeper understanding of the interplay between GM and NMOSD.

Postoperative intracranial infection (ICI) is a serious complication that occurs after craniotomy, typically caused by the invasion of microorganisms such as bacteria into the sterile cranial cavity. Aneurysm clipping is one of the primary treatment methods for intracranial aneurysms, and ICI can significantly impact patient prognosis. Our investigation aims to systematically identify the determinants of postoperative ICI after aneurysm clipping and develop a robust predictive model for clinical risk assessment. To eliminate potential confounding factors introduced by aneurysm rupture and subarachnoid hemorrhage, our study focuses exclusively on patients with unruptured intracranial anterior circulation aneurysms. We conducted a retrospective analysis of clinical data from 428 patients with anterior circulation aneurysms. Based on the occurrence of postoperative ICI, patients were stratified into non-infected group and infected group. Univariate and multivariate statistical analyses were performed to evaluate the following variables: gender, age, body mass index (BMI), hypertension, diabetes mellitus, aneurysm location, number of aneurysm clips applied, operative duration, intraoperative blood loss, cerebrospinal fluid (CSF) leakage, and postoperative intracranial hemorrhage or cerebral infarction in the surgical region. Subsequently, a predictive nomogram was constructed based on the multivariate regression results to generate a robust predictive model. Among 428 patients with anterior circulation aneurysms, 38 developed postoperative ICI. Univariate analysis revealed that BMI, diabetes mellitus, operative duration, intraoperative blood loss, CSF leakage, and postoperative cerebral hemorrhage or infarction were significant factors influencing ICI. In contrast, variables such as gender, age, hypertension, and the number of aneurysm clips applied demonstrated no statistically significant association. Subsequent logistic regression analysis identified elevated BMI, diabetes mellitus, prolonged operative duration, substantial intraoperative blood loss, and postoperative CSF leakage as independent risk factors for ICI in UIA patients. A receiver operating characteristic (ROC) curve was constructed based on the predicted probabilities of ICI, yielding an area under the curve (AUC) of 0.8756, indicating strong predictive accuracy. Postoperative ICI in patients with anterior circulation aneurysms is influenced by multiple factors, including BMI, diabetes mellitus, operative duration, intraoperative blood loss, and CSF leakage. A predictive model constructed based on the relative impact of these factors may assist clinicians in anticipating potential infection events during the perioperative period.

1. Post-traumatic stress disorder (PTSD) is a chronic psychiatric disorder that develops following exposure to traumatic events and represents a substantial global public health burden. According t...