Causal Effects of 1400 Blood Metabolites And Sleep Disorders: A Mendelian Randomization Study

Abstract

Background: There is growing evidence that sleep disorders (SD) affect changes in the body's metabolic microbiota, such as serum levels of glucose, lipid metabolites, insulin, adrenaline, and cortisol, which are all modified by sleep. However, the causal relationship between these serum metabolites and SD remains undetermined. Methods: A comprehensive two-sample Mendelian randomization (MR) study was performed to investigate the causal relationship between blood microbiota characteristics and SD. Additionally, reverse MR analyses were performed to mitigate the effects of reverse causality, genetic correlation, and linkage disequilibrium (LD). Moreover, the screened significant metabolites were further examined in an additional dataset. Finally, potential metabolic pathways associated with SD were exposed by enrichment analysis. Results: In a survey of 1400 metabolites, 201 exhibited statistical significance (P<0.05) in an inverse variance weighted (IVW) model. Among these, 18 known metabolites and four unknown metabolites were found to be significantly associated with SD (PFDR<0.05), with six of them being validated in the validation set. Enrichment analysis revealed the involvement of five typical metabolites in 23 central metabolic pathways (P<0.05). Notably, glycine, D-lysine, cyclic adenosine monophosphate, guanosine, and arachidonic acid could serve as key markers based on their roles in the pathways, disease characterization prediction, and localization. Furthermore, inverse Mendelian randomization analysis indicated that six known metabolites and three unknown metabolites were significantly associated with SD (PFDR<0.2), and these metabolites were implicated in 15 core metabolic pathways (P<0.05). Among them, cortisone, cortisol, N-acetyl-L-aspartic acid, and arachidonic acid played crucial roles. These findings provide novel insights for early screening, prevention, and treatment of SD. Conclusions:This study reveals the potential relationship between blood microbiota and SD using a two-sample Mendelian randomization approach, and the findings provide new insights into the role of genetic exposure interactions in the development of SD.