Assessment of drug induced hyperuricemia and gout risk using the FDA adverse event reporting system

Hyperuricemia, the key pathological basis of gout, is increasingly prevalent worldwide. While lifestyle factors contribute, various medications also play a role. However, their specific risks and mechanisms remain inadequately studied. Disproportionality analysis (ROR, PRR, BCPNN) was used to assess drug-induced hyperuricemia and gout reports (Q1 2004-Q3 2023). Univariate analysis, LASSO, XGBoost, and multivariate regression identified independent risk factors. Time-to-onset analysis evaluated the occurrence timing post-drug initiation. A total of 18,531 reports related to hyperuricemia and gout were identified. Reports involving male patients were significantly more frequent than those involving female patients for both hyperuricemia and gout. The mean ages of patients were relatively high, at 58.7 years (standard deviation [SD] 18.6 years) for hyperuricemia and 64.6 years (SD 13.5 years) for gout. Signal detection identified 131 drugs associated with hyperuricemia and 177 drugs associated with gout. Among the hyperuricemia-related reports, telaprevir was the most frequently implicated drug, whereas lenalidomide ranked highest in the gout-related reports. Subsequent multivariate analysis following machine learning-based screening identified male sex and older age as independent risk factors for drug-induced hyperuricemia and gout. Specifically, peginterferon alfa-2b was found to be an independent risk factor for drug-induced hyperuricemia, while 20 drugs-including pegloticase, febuxostat, allopurinol, rofecoxib, and furosemide-were identified as independent risk factors for drug-induced gout. Furthermore, the median time to onset (TTO) of drug-induced hyperuricemia and gout was 11 days (interquartile range [IQR]: 2-63 days) and 31 days (IQR: 1-269 days), respectively. Notably, over 50% of cases occurred within the first 30 days after initiation of the implicated drug. By leveraging FAERS-based signal detection, this study systematically elucidated significant associations between various drugs and the risks of hyperuricemia and gout. Furthermore, key independent risk factors-including sex, age, and specific drugs-were identified through machine learning and multivariate analysis. These findings provide valuable insights for pharmacovigilance and clinical medication management.