Genetic Insights into Sepsis: Mendelian Randomization Analysis of Cerebrospinal Fluid Metabolites.

Abstract

Background: Sepsis, a life-threatening response to infection leading to systemic inflammation and organ dysfunction, has been hypothesized to be influenced by metabolic alterations in cerebrospinal fluid (CSF). Despite extensive research, the specific metabolic pathways contributing to sepsis remain unclear. This study aims to elucidate the causal relationships between CSF metabolites and sepsis risk using Mendelian Randomization (MR), offering insights that could lead to novel therapeutic strategies.

Methods: We conducted a two-sample MR analysis using genetic variants as instrumental variables (IVs) to investigate 338 CSF metabolites identified through a genome-wide association study. Data on sepsis-related outcomes were extracted from the genome-wide association study (GWAS) catalog encompassing 486,484 individuals of European descent. IVs were rigorously selected based on stringent genetic association and linkage disequilibrium criteria. Statistical analyses, including inverse variance weighting (IVW) and weighted median methods, were performed using the 'TwoSampleMR' package in R software, supplemented by comprehensive sensitivity analyses to ensure the robustness of our findings.

Results: Our analysis identified 19 CSF metabolites causally associated with sepsis risk. Notably, metabolites such as 1-palmitoyl-2-stearoyl-gpc (16:0/18:0) and 2-hydroxyglutarate showed significant negative correlations with sepsis risk. The reverse MR analysis further revealed that sepsis could negatively impact certain CSF metabolite levels, particularly Ribonate, suggesting a bidirectional relationship. These relationships were substantiated by rigorous statistical testing and sensitivity analyses confirming the absence of horizontal pleiotropy and the stability of our results across various MR methods.

Conclusions: This study demonstrates significant causal associations between specific CSF metabolites and the risk of developing sepsis, highlighting the potential for these metabolites to serve as biomarkers or therapeutic targets. The bidirectional nature of these findings also suggests that sepsis itself may alter metabolic profiles, offering further avenues for intervention.