CAUSAL ASSOCIATION BETWEEN MEDICATION USE AND SEPSIS: A TWO-SAMPLE MENDELIAN RANDOMIZATION STUDY.

Objective

This study aimed to explore the causal association between genetically predicted medication use and sepsis.

Methods

Utilizing summary data from genome-wide association studies (GWAS) on selected medications use and sepsis, a two-sample Mendelian randomization (MR) design was employed. Analysis was conducted using the Inverse Variance Weighted (IVW), weighted median, weighted mode, and MR-Egger regression methods. Sensitivity analysis included MR-Egger, MR-PRESSO, Cochran's Q, and leave-one-out methods.

Results

The IVW method suggested that genetically predicted medication use, including Drugs used in diabetes (OR = 1.08, 95%CI: 1.02-1.15, P = 0.015), diuretics (OR = 1.08, 95%CI: 1.01-1.15, P = 0.034), thyroid preparations (OR = 1.08, 95%CI: 1.03-1.13, P = 0.001), and adrenergics (OR = 1.09, 95%CI: 1.01-1.18, P = 0.037), might be causally associated with a higher risk of sepsis. Datasets for drugs in diabetes, antithrombotic agents, renin-angiotensin system agents, anilides, and glucocorticoids may exhibit heterogeneity. MR-Egger regression results indicated minimal influence of horizontal pleiotropy on all associations except possibly for antithrombotic agents and sepsis. MR-PRESSO test found no outliers. Leave-one-out analysis confirmed associations were not driven by any single factor. After removing confounders, the IVW method suggested that genetically predicted diuretics use (OR = 1.17, 95%CI: 1.03-1.34, P = 0.018) might be causally associated with a higher risk of sepsis, while drugs used in diabetes, thyroid preparations, and inhaled adrenergics were not. No heterogeneity was observed. MR-Egger regression results indicated the minimal influence of horizontal pleiotropy on all associations. MR-PRESSO test found no outliers. Leave-one-out analysis confirmed any single factor did not drive associations.

Conclusions

This study found that prior use of diuretics may increase sepsis risk, with inconclusive evidence for other medications.