Investigation of causal effects of blood metabolites on insomnia and circadian rhythm sleep wake disorders

Abstract

Insomnia (IS) and circadian rhythm sleep-wake disorders (CRSWD) are complex disorders with limited and unsatisfactory treatment options that can even cause some side effects. By analyzing blood metabolites to reveal underlying biological processes, studies of sleep and the complex interactions between its influencing factors can be elucidated. Therefore, we hope to bring new hope for the treatment of these diseases through blood metabolites.Investigating the causal link between blood metabolites and IS and CRSWD.A genome-wide association study (GWAS) for 486 metabolites was used as the exposure, whereas two different GWAS datasets for sleep disorders were the outcome, and all datasets were obtained from publicly available databases. We employed the standard inverse variance weighting (IVW) method for causal analysis, supported by the MR-Egger method, weighted median (WM) method, and MR-PRESSO method for sensitivity analysis to mitigate the impact of pleiotropy. Genetic correlation between IS, CRSWD, and blood metabolites was explored through linkage disequilibrium analysis (LDSC), while Multivariable MR analysis (MVMR) elucidated whether these metabolites exhibit a direct association with IS and CRSWD. Further, we conducted metabolic pathway analysis to identify the specific metabolites driving these relationships.Employing meticulous MVMR analysis, we have identified specific metabolites that independently influence IS, including 2-hydroxypalmitate (OR 2.95, 95%CI 1.05–8.31 P = 0.040), X-11786-Methylcysteine (OR = 0.25, 95%CI 0.08–0.76 P = 0.014), and salicylate (OR 0.89, 95%CI 0.83–0.95 P = 9 × 10–4). In the context of CRSWD, our findings reveal direct associations with metabolites such as carnitine (OR 0.02, 95%CI: 0.00–0.20, P = 0.002), levulinate (OR 0.06, 95%CI: 0.01–0.64, P = 0.020), p-cresol sulfate (OR 0.25, 95% CI: 0.09–0.67, P = 0.006), and X-14208-Phenylalanylserine (OR 0.36, 95% CI: 0.16–0.81, P = 0.014). These discoveries contribute to a nuanced understanding of the distinct metabolic signatures underlying IS and CRSWD.