The relationship between genetic prediction of 486 blood metabolites and the risk of COPD: mendelian randomization study.

Metabolic disorders are an important feature of chronic lung disease. Patients diagnosed with chronic obstructive pulmonary disease (COPD) have been found to experience metabolic disorders. Nonetheless, evidence on the causal role of circulating metabolites in promoting or preventing COPD is still lacking. Conducting a methodical examination on the causal connection between blood metabolites and COPD can aid in identifying fresh objectives for the screening and prevention of COPD. Therefore, we performed a two-sample Mendelian randomization (MR) analysis to evaluate the causal association between COPD and 486 blood metabolites.We used two-sample MR techniques and genome-wide association study (GWAS) data to evaluate the correlation between COPD and 486 serum metabolites. To evaluate the causal impact of serum metabolites on the risk of COPD, we predominantly employed inverse variance weighting (IVW) methodology. The MR-Egger regression test was employed to assess multiple validity, while the presence of heterogeneity was examined using the Cochran's Q test. To ensure the reliability of the findings, a leave-one-out analysis was conducted. The Bonferroni correction is used to adjust for multiple comparisons, ensuring rigorous validation of our results.After filtering by IVW and sensitivity analysis, we identified 10 known metabolites including fructose, margarate (17:0), guanosine, 2-stearoylglycerophosphocholine, hexadecanedioate, lactate, 5-oxoproline, paraxanthine, phenyllactate (PLA) and N-acetylglycine. Of these, fructose, margarate (17:0), guanosine, 2-stearoylglycerophosphocholine and hexadecanedioate are risk metabolites, and additionally, lactate, 5-oxoproline, paraxanthine phenyllactate(PLA) and N-acetylglycine are protective metabolites. In addition, the study identified five currently unknown chemical structures. Cochran's Q-test showed no significant heterogeneity, and MR Egger's intercept analysis confirmed the absence of horizontal multidirectionality. Leave-one-out analysis also proved the reliability of the MR analysis.We identified seven COPD-related risks and eight protective human serum metabolites. By combining genomics and metabolism, it provides new insights into the underlying mechanisms of COPD, with important implications for COPD screening and prevention.