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.