The Causal Relationship Between Opioid Use and Obstructive Sleep Apnea: A Bidirectional Mendelian Randomization Study.

Background: Opioid medications are widely used for pain management, but their impact on obstructive sleep apnea (OSA) remains controversial. Given the high prevalence of OSA and the increasing use of opioids, understanding the causal relationship between the condition and this type of medication is critical.

Objectives: This study aims to investigate the causal relationship between opioid use and OSA using a bidirectional 2-sample Mendelian randomization (MR) analysis. Specifically, the study seeks to determine whether exposure to opioid use increases the risk of developing OSA and whether OSA influences the likelihood of opioid use.

Study design: The study employed a bidirectional 2-sample MR analysis to explore the causal relationship between opioid use and OSA. Genetic variants from large-scale genome-wide association studies (GWAS) were used as instrumental variables to ensure robust causal inference.

Setting: The study utilized data from 2 large-scale GWAS datasets. Opioid use data were obtained from the UK Biobank, while OSA data were sourced from the FinnGen study. Both datasets predominantly included patients of European ancestry with similar demographic characteristics.

Methods: This study employed a 2-sample bidirectional Mendelian randomization (MR) approach to investigate the causal relationship between opioid use and obstructive sleep apnea (OSA). Genetic instruments for opioid use and OSA were selected from large-scale genome-wide association studies (GWAS) conducted in European populations, ensuring consistency in genetic backgrounds. The inverse variance-weighting (IVW) method was used as the primary analysis to estimate causal effects, supplemented by the weighted median, MR-Egger, simple mode, and weighted mode methods to ensure robustness. Sensitivity analyses, including MR-Egger regression, leave-one-out analysis, and MR-PRESSO, were conducted to assess pleiotropy, heterogeneity, and the influence of individual SNPs on the results.

Results: The IVW method demonstrated a significant causal effect of opioid use on the risk of developing OSA, with a causal effect size of 0.28 (OR = 1.32, 95% CI = 0.09 to 0.46, P-value = 0.004). This association was supported by the weighted median method, though the MR-Egger, simple mode, and weighted mode methods did not achieve statistical significance but showed a consistent direction of effect. Conversely, no significant causal relationship was observed between OSA and opioid use across all methods, suggesting that OSA did not significantly influence opioid use.

Limitations: The primary limitations of this study include the use of binary phenotypes for opioid use and OSA, which precludes the assessment of dose-response relationships. Additionally, the genetic data were derived predominantly from European populations, limiting the generalizability of the findings to other ethnic groups. Potential pleiotropy and unmeasured confounders, although addressed through various sensitivity analyses, may still introduce bias into the results.

Conclusion: This study provides strong evidence of a unidirectional causal relationship in which opioid use increases the risk of developing OSA. These findings underscore the importance of monitoring patients who use opioids for potential respiratory complications, particularly OSA.