Integrating Pharmacovigilance Data Mining and Mendelian Randomization to Identify Risk Profiles and Causal Targets of Opioid-Induced Delirium.

BACKGROUND

Opioid-induced delirium represents a major clinical challenge with substantial implications for patient safety and outcomes. An integrated evaluation combining pharmacovigilance and genetic evidence remains limited. This study aimed to systematically assess delirium risk across different opioids and to elucidate underlying molecular mechanisms by integrating real-world safety data with Mendelian randomization (MR) analysis.

METHODS

Using data from the US Food and Drug Administration Adverse Event Reporting System (FAERS) spanning 2004-2025, disproportionality analyses (reporting odds ratio, proportional reporting ratio, and empirical Bayes geometric mean) and multivariable logistic regression were conducted to quantify delirium risk associated with 12 commonly used opioids. Network pharmacology was subsequently applied to identify candidate pathways and targets, followed by MR analyses based on FinnGen data to investigate potential causal relationships between key target genes and delirium.

RESULTS

Substantial heterogeneity in delirium risk was observed across opioids. Methadone showed the strongest association with delirium (EBGM 25.98) and the highest independent risk after adjustment (adjusted odds ratio 29.79), followed by oxycodone. Stronger risk signals were consistently observed among older individuals (> 55 years) and females. Mechanistic analyses highlighted pathways related to xenobiotic metabolism and neuroinflammation, particularly the PI3K-Akt signaling pathway. MR analyses identified COMT as a risk-associated gene for delirium (OR 1.25), whereas MYD88 and CYP1B1 demonstrated potential protective effects.

CONCLUSIONS

Marked differences in delirium risk exist among opioids. COMT, MYD88, and CYP1B1 were identified as key candidate genes underlying opioid-associated delirium. These findings provide a genetic and mechanistic framework to support clinical risk stratification and the development of precision prevention and intervention strategies.