Medication-related nephrotic syndrome: a real-world study from 2004 to 2024 based on the Food and Drug Administration Adverse Event Reporting System

Background: Nephrotic syndrome is a clinical syndrome caused by glomerular injury. Medication-related nephrotic syndrome (MRNS) has become an important focus of pharmacovigilance. However, there is currently a lack of real-world studies on MRNS in large populations. Objectives: To systematically evaluate adverse drug events associated with MRNS using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and to identify high-risk medications and their onset characteristics. Design: A retrospective pharmacovigilance study based on disproportionality analysis and Bayesian signal detection. Methods: This is a retrospective pharmacovigilance study. FAERS reports from 2004 to 2024 were extracted, cleaned, and standardized to identify MRNS cases. The association between medications and MRNS was evaluated using four methods: Reporting Odds Ratio (ROR), Proportional Reporting Ratio, Multi-Item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). In addition, medication risk classification and cumulative risk curve of medication-induced onset time were conducted. Results: A total of 3990 MRNS cases were identified, including 1963 males (56.23%) and 1528 females (43.77%). Fifty-four medications demonstrated significant positive signals, including 26 antineoplastic medications (ramucirumab, ROR = 33.59), 7 anti-inflammatory medications (sulfasalazine, ROR = 24.74), 2 digestive system medications (famotidine, ROR = 29.28), and 19 other medications (phentermine, ROR = 41.76). BCPNN values indicated that phentermine (5.37), penicillamine (5.13), and ramucirumab (5.06) posed the highest risk. Onset-time analysis showed the shortest average onset for anticancer agents (120.98 days) and the longest for anti-inflammatory medications (165.12 days). Conclusion: This study provides the first large-scale evaluation of MRNS using FAERS. By identifying high-risk medications and characterizing onset-time patterns, these findings offer valuable evidence for early risk recognition and may support improved pharmacovigilance strategies to reduce medication-related kidney injury.