Assessment of Drug Induced Thyroid Dysfunction Risk Using the United States Food and Drug Administration Adverse Event Reporting System.

OBJECTIVE

To quantify drug-specific associations with thyroid dysfunction (TD) using data from FDA Adverse Event Reporting System and to identify agents with significant disproportionality signals.

METHODS

Using data from FDA Adverse Event Reporting System (Q1 2004 to Q1 2025), disproportionality analysis (reporting odds ratio; Bayesian confidence propagation neural network) was employed to assess signals of drug-induced TD. Univariate analysis, least absolute shrinkage and selection operator (LASSO) regression, and multivariate logistic regression were applied to identify independent risk factors. Time-to-onset analysis was used to evaluate the timing of TD following drug initiation.

RESULTS

A total of 44 317 reports associated with TD were identified, with a higher proportion involving female patients (60.3%). The median patient age was 59 years (interquartile range [IQR]: 45-69 years). Signal detection identified 172 suspect drugs significantly associated with TD. Following disproportionality screening, drug candidates with a reporting odds ratio whose 95% confidence interval lower bound exceeded 1, cell count a > 100, and P-adjust < 0.01 were advanced to LASSO regression. LASSO identified 43 drugs associated with TD. In the multivariable logistic regression model, female sex, the 45-59-year age group, and the 43 drugs were independent risk factors for drug-associated TD. Furthermore, the median time-to-onset for drug-induced TD was 34 days (IQR: 7-105 days), with approximately 75% of cases occurring within the first 100 days of drug initiation.

CONCLUSION

This study identified multiple drugs significantly associated with TD and highlighted sex, age, and specific medications as independent risk factors. These findings support improved pharmacovigilance and clinical awareness of drug-induced TD.