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

Abstract

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.

Plain language summary

When everyday medicines affect the kidneys: Insights from safety reports

Nephrotic syndrome is a kidney condition caused by damage to the glomeruli, often leading to proteinuria, swelling, and declining kidney function. In addition to underlying diseases, some medications may also trigger or worsen this condition, but large-scale real-world studies remain limited.

In this study, we analyzed adverse drug event reports from the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) between 2004 and 2024. After rigorous data processing and statistical evaluation, we identified 3,990 cases of medication-related nephrotic syndrome involving 54 drugs. High-risk signals were mainly observed for anticancer drugs, anti-inflammatory agents, and certain gastrointestinal drugs. Further analysis showed that the onset time of nephrotic syndrome varied by drug type: anticancer drugs tended to cause earlier onset (around 4 months), whereas anti-inflammatory drugs were associated with later onset.

To our knowledge, this is the first study to systematically evaluate high-risk medications associated with nephrotic syndrome using the FAERS database. These findings provide important insights for clinicians and pharmacists, supporting earlier identification and intervention to reduce medication-related kidney damage and improve patient safety.

Keywords

adverse drug events FAERS medication-induced risk medication induction time nephrotic syndrome

Introduction

Nephrotic syndrome (NS) is a clinical condition characterized by massive proteinuria, hypoalbuminemia, edema, and hyperlipidemia, often accompanied by hypertension.¹ It is commonly seen in both children and adults and is associated with significant morbidity and mortality. With the global aging population, the incidence of NS has been rising, and it is expected that by 2030, the global burden of kidney disease will substantially increase, affecting millions of individuals’ quality of life.² The etiology of NS is complex and can be classified as primary or secondary. Secondary factors of NS include infections, autoimmune diseases, metabolic disorders, and medication-induced causes.^3
–5

NS is influenced by multiple genetic, immunological, and environmental factors, while medications represent an important—though not the most common—inducer of secondary NS (secondary cause refers to NS triggered by identifiable external factors, including drugs).⁶ Early intervention can help reduce the incidence of medication-related nephrotic syndrome (MRNS). With the increasing use of medications, the risk of medication-induced nephrotoxicity has become more prominent, not only affecting patients’ quality of life but also adding to the healthcare burden on society.^7,8 Epidemiological data show that medication-induced nephrotoxicity is responsible for 60% of acute kidney injury cases in hospitalized patients and is linked to higher morbidity and mortality rates in both adults and children.⁹ Therefore, strengthening pharmacovigilance, identifying high-risk medications, and optimizing treatment strategies are crucial for improving the safety and effectiveness of NS management. However, systematic research on MRNS remains limited, making it a significant challenge in clinical practice.

Based on a comprehensive review of the existing literature, this study represents the first systematic evaluation of potential adverse reactions associated with medications used in the treatment of NS using the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database.¹⁰ By leveraging large-scale real-world pharmacovigilance data, we aimed to characterize the patterns and frequencies of NS-related adverse drug reactions (ADRs) and to assess the safety profiles of commonly used therapies, particularly those administered long term. The findings of this study are expected to provide evidence-based support for clinicians in identifying and managing medication-related risks, thereby facilitating more individualized and safer treatment strategies for patients with NS. Furthermore, it may provide supplementary insights into potential ADRs associated with MRNS that are not explicitly mentioned on some medication labels.

Methods

Data source

FAERS is a publicly accessible pharmacovigilance database that collects spontaneous reports of adverse events (AEs), medication errors, and medication-related product quality complaints, with the aim of providing post-marketing medication safety surveillance. The database is primarily composed of seven datasets: Patient Demographics and Management Information (DEMO), Drug and Biological Information (DRUG), Adverse Events (REAC), Patient Outcomes (OUTC), Report Source (RPSR), Drug Therapy Start and End Dates (THER), and Drug Usage and Diagnostic Indications (INDI).¹¹ For further details, please refer to previous studies. FAERS is updated quarterly and has accumulated over 229 million reports to date. For the present study, we retrieved all quarterly releases from January 1, 2004 (Q1) to September 30, 2024 (Q3). These datasets were downloaded from the FDA public portal (https://www.fda.gov/drugs/drug-approvals-and-databases/fda-adverse-event-reporting-system-faers-database) and imported into a unified analytical framework for preprocessing and analysis. This is a retrospective study.

The raw dataset initially contained 21,838,627 reports. To ensure data quality and avoid overcounting, duplicate entries were removed according to the FDA’s recommended deduplication criteria, which retain only the most recent version of reports sharing the same primary ID and CASE number. This process resulted in the removal of 1,294,466 duplicate entries, yielding 20,544,161 unique reports.

NS-related AEs were identified by screening the standardized Medical Dictionary for Regulatory Activities (MedDRA) Preferred Terms within the REAC dataset, resulting in 4079 NS-associated AE reports corresponding to 3990 unique patients and 1132 medication products. Drug names from the DRUG dataset were subsequently standardized using the DrugBank database to reconcile spelling variations, synonyms, and brand-generic name inconsistencies. To enhance the specificity and stability of the safety signal analyses, medications associated with fewer than three NS-related AEs were excluded, and products with different brand names that represented the same generic substance were consolidated. After these cleaning and harmonization steps, 325 medications were retained for downstream analyses (Figure 1). Our study is a pharmacovigilance-based investigation.

Figure 1.

Flow chart of the FAERS database for medication -associated nephrotic syndrome.

Identification of adverse medication reactions

This study focused on the analysis of MRNS. Patients with NS may exhibit varying clinical manifestations due to medication-induced nephrotoxicity. All ADRs were coded according to the MedDRA and categorized using standardized terminology.¹² In the FAERS database, MRNS is coded as “medication-related nephrotic syndrome” (PT Preferred term = 10029164). Therefore, this study exclusively retrieved all AE reports associated with this perform term.

Statistical analysis

We used four disproportionality analysis methods to evaluate the relationship between medications and adverse effects, including the Reporting Odds Ratio (ROR),¹³ the Proportional Reporting Ratio (PRR),¹⁴ the Multi-Item Gamma Poisson Shrinker (MGPS),¹⁵ and Bayesian Confidence Propagation Neural Network (BCPNN).¹⁶ To strictly limit its true power, we used the intersection of the four disproportionality analysis algorithms and restricted the informants to medical professionals such as doctors and pharmacists.^17
–20 The specific calculation methods can be found in Tables S1 and S2. The main software packages used in R (version 4.4.1, R Foundation for Statistical Computing) data analysis included ggplot2 (version 3.5.1), dplyr (version 1.1.4), and readxl (version 1.4.3). The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.²¹

Results

Baseline subject information

From 2004 to 2024, a total of 3990 subjects in the FAERS database reported cases of MRNS adverse reactions (Table 1). The age of the subjects was mainly concentrated around 50.26 ± 23.15 years, with males accounting for the majority (56.23%). The age distribution of males with MRNS was concentrated in the 60–65 age range, while females were mainly concentrated in the 65–70 age range (Figure 2(a)). Furthermore, we observed a gradual increase in the number of reported MRNS cases over the years, reaching a peak in 2018 and 2019, with the annual incidence rate in males significantly higher than in females (Figure 2(b)). The main outcomes for these subjects were concentrated in “Hospitalization—Initial or Prolonged” (44.60%) and “Death” (7.15%) (Figure 2(c)). The main routes of administration were oral (43.14%) and intravenous (18.74%) (Figure 2(d)). The majority of reports came from Japan (29.58%) and the United States (14.36%) (Figure 2(e)).

Table 1.

Baseline information of the patients with the medication-related nephrotic syndrome.

Variable	Total
Age	50.26 ± 23.15
Weight	67.92 ± 25.61
Sex
Male	1963 (56.23)
Female	1528 (43.77)
Reported country
Japan	1178 (29.58)
United States	572 (14.36)
France	398 (9.99)
Germany	225 (5.65)
United Kingdom	190 (4.77)
Canada	132 (3.31)
Italy	124 (3.11)
Spain	95 (2.39)
Other countries	1069 (26.84)
Route
Oral	1455 (43.14)
Intravenous	632 (18.74)
Subcutaneous	347 (10.29)
Intramuscular	30 (0.89)
Other routes	25 (0.74)
Intraocular	11 (0.33)
Transdermal	4 (0.12)
Unknown	854 (25.32)
Other	15 (0.44)
Outcome
Hospitalization: initial or prolonged	1733 (44.60)
Death	278 (7.15)
Life-threatening	167 (4.30)
Disability	45 (1.16)
Required intervention to prevent permanent impairment/damage	12 (0.31)
Congenital anomaly	2 (0.05)
Other serious (important medical event)	1649 (42.43)
Indication
Product used for an unknown indication	280 (7.55)
Nephrotic syndrome	246 (6.63)
Rheumatoid arthritis	168 (4.53)
Renal cell carcinoma	99 (2.67)
Hypertension	86 (2.32)
Hepatocellular carcinoma	63 (1.70)
Hepatitis C	59 (1.59)
HIV infection	57 (1.54)
Other indications	2652 (71.48)

Figure 2.

Distribution of baseline data for subjects with MRNS in the FAERS database. (a) Pyramid plot of age distribution in subjects with MRNS. (b) Annual distribution of MRNS. (c) Distribution of adverse reaction outcomes in subjects with MRNS. (d) Distribution of medication intake methods in subjects with MRNS. (e) Country distribution of MRNS patient reports.

Distribution of medications causing medication-related nephrotic syndrome

We first conducted a disproportionality analysis on a dataset comprising 325 medications associated with NS-related adverse reactions and identified 75 medications with positive signal values (Table 2). Subsequently, we excluded medications with potential reverse causality and merged medications with the same generic name but different brand names, totaling 21 medications. Finally, among the remaining 54 medications with positive signals, there were 26 antineoplastic medications (48.15%), 19 other medications (35.19%), 7 anti-inflammatory medications (12.96%), and 2 digestive system medications (3.70%) (Figure 3).

Table 2.

Disproportionality analysis results of medication-related nephropathy syndrome.

Medication name	Categorization	ROR (95% CI)	PRR (95% CI)	MGPS (95% CI)	BCPNN (95% CI)	PRR (χ²)	p Value
Sulfasalazine	Anti-inflammatory medication	24.74 (9.26–66.1)	24.62 (23.64–25.6)	24.6 (10.81–55.99)	4.62 (2.95–6.29)	24.62 (90.58)	<0.001
Rifampin	Anti-inflammatory medication	7.51 (5.33–10.58)	7.5 (7.16–7.84)	7.45 (5.59–9.92)	2.9 (1.23–4.56)	7.5 (184.5)	<0.001
Celecoxib	Anti-inflammatory medication	6.16 (1.99–19.14)	6.16 (5.03–7.29)	6.15 (2.39–15.88)	2.62 (0.95–4.29)	6.16 (12.96)	0.004
Mesalazine	Anti-inflammatory medication	5.74 (3.56–9.25)	5.74 (5.26–6.21)	5.72 (3.84–8.52)	2.52 (0.85–4.18)	5.74 (66.21)	<0.001
Meloxicam	Anti-inflammatory medication	5.03 (2.61–9.68)	5.02 (4.37–5.68)	5.02 (2.9–8.67)	2.33 (0.66–3.99)	5.02 (28.96)	<0.001
Diclofenac	Anti-inflammatory medication	4.52 (3.5–5.84)	4.52 (4.26–4.77)	4.47 (3.61–5.53)	2.16 (0.49–3.83)	4.52 (162.05)	<0.001
Naproxen	Anti-inflammatory medication	3.78 (2.14–6.66)	3.78 (3.21–4.35)	3.77 (2.35–6.06)	1.91 (0.25–3.58)	3.78 (24.45)	<0.001
Ramucirumab	Antineoplastic agents medication	33.59 (19.84–56.86)	33.37 (32.85–33.89)	33.26 (21.41–51.67)	5.06 (3.39–6.72)	33.37 (438.2)	<0.001
Asciminib	Antineoplastic agents medication	16.82 (5.41–52.26)	16.76 (15.63–17.89)	16.75 (6.49–43.26)	4.07 (2.4–5.74)	16.76 (44.44)	<0.001
Pamidronic acid	Antineoplastic agents medication	13.82 (7.42–25.72)	13.78 (13.16–14.4)	13.75 (8.17–23.13)	3.78 (2.11–5.45)	13.78 (118.27)	<0.001
Lenvatinib	Antineoplastic agents medication	13.09 (11.02–15.54)	13.06 (12.89–13.23)	12.66 (10.96–14.62)	3.66 (2–5.33)	13.06 (1453.71)	<0.001
Axitinib	Antineoplastic agents medication	11.63 (9.11–14.83)	11.6 (11.36–11.84)	11.43 (9.32–14.01)	3.51 (1.85–5.18)	11.6 (629.13)	<0.001
Oxaliplatin	Antineoplastic agents medication	10.3 (3.86–27.47)	10.28 (9.3–11.26)	10.27 (4.52–23.34)	3.36 (1.69–5.03)	10.28 (33.47)	<0.001
Pazopanib	Antineoplastic agents medication	10.08 (7.76–13.1)	10.06 (9.8–10.32)	9.94 (7.98–12.37)	3.31 (1.65–4.98)	10.06 (458.83)	<0.001
Bicalutamide	Antineoplastic agents medication	9.83 (5.11–18.92)	9.82 (9.16–10.47)	9.8 (5.66–16.94)	3.29 (1.63–4.96)	9.82 (71.12)	<0.001
Lorlatinib	Antineoplastic agents medication	8.02 (2.58–24.88)	8 (6.87–9.14)	8 (3.1–20.64)	3 (1.33–4.67)	8 (18.38)	0.001
Bevacizumab	Antineoplastic agents medication	7.97 (7.08–8.97)	7.96 (7.84–8.08)	7.46 (6.75–8.23)	2.9 (1.23–4.56)	7.96 (1665.56)	<0.001
Cabozantinib	Antineoplastic agents medication	7.07 (5.25–9.52)	7.06 (6.76–7.36)	6.99 (5.45–8.97)	2.81 (1.14–4.47)	7.06 (226.45)	<0.001
Denosumab Injection	Antineoplastic agents medication	5.97 (1.92–18.52)	5.96 (4.83–7.09)	5.96 (2.31–15.37)	2.57 (0.91–4.24)	5.96 (12.37)	0.005
Nintedanib	Antineoplastic agents medication	5.88 (3.7–9.35)	5.88 (5.42–6.34)	5.86 (3.97–8.63)	2.55 (0.88–4.22)	5.88 (72.57)	<0.001
Ibandronate	Antineoplastic agents medication	5.87 (1.89–18.22)	5.86 (4.73–7)	5.86 (2.27–15.12)	2.55 (0.88–4.22)	5.86 (12.1)	0.005
Atezolizumab	Antineoplastic agents medication	5.3 (4.18–6.73)	5.3 (5.06–5.53)	5.22 (4.28–6.38)	2.39 (0.72–4.05)	5.3 (236.47)	<0.001
Gemcitabine	Antineoplastic agents medication	5.26 (3.72–7.45)	5.26 (4.91–5.61)	5.22 (3.9–6.99)	2.39 (0.72–4.05)	5.26 (109.48)	<0.001
Dasatinib	Antineoplastic agents medication	4.58 (3.28–6.38)	4.57 (4.24–4.91)	4.54 (3.44–6)	2.18 (0.52–3.85)	4.57 (96.87)	<0.001
Sorafenib	Antineoplastic agents medication	4.37 (3.12–6.12)	4.36 (4.03–4.7)	4.34 (3.27–5.75)	2.12 (0.45–3.78)	4.36 (87.47)	<0.001
Regorafenib	Antineoplastic agents medication	4.31 (2.71–6.85)	4.31 (3.85–4.77)	4.3 (2.92–6.33)	2.1 (0.44–3.77)	4.31 (45.57)	<0.001
Busulfan	Antineoplastic agents medication	4.19 (1.88–9.33)	4.18 (3.38–4.99)	4.18 (2.14–8.17)	2.06 (0.4–3.73)	4.18 (14.53)	0.001
Everolimus	Antineoplastic agents medication	3.98 (3–5.3)	3.98 (3.7–4.27)	3.95 (3.11–5.01)	1.98 (0.31–3.65)	3.98 (105.94)	<0.001
Nivolumab	Antineoplastic agents medication	3.77 (3.11–4.59)	3.77 (3.58–3.97)	3.7 (3.15–4.36)	1.89 (0.22–3.55)	3.77 (206.53)	<0.001
Sunitinib	Antineoplastic agents medication	3.76 (3–4.72)	3.76 (3.54–3.99)	3.71 (3.07–4.49)	1.89 (0.23–3.56)	3.76 (151.33)	<0.001
Ipilimumab	Antineoplastic agents medication	3.72 (2.2–6.28)	3.71 (3.19–4.24)	3.7 (2.39–5.75)	1.89 (0.22–3.56)	3.71 (27.67)	<0.001
Interferon	Antineoplastic agents medication	3.56 (2.59–4.9)	3.56 (3.24–3.88)	3.53 (2.7–4.62)	1.82 (0.15–3.49)	3.56 (69.21)	<0.001
Imatinib	Antineoplastic agents medication	3.15 (1.86–5.32)	3.14 (2.62–3.67)	3.14 (2.02–4.87)	1.65 (−0.02–3.32)	3.14 (20.41)	<0.001
Famotidine	Digestive system medication	29.28 (12.15–70.56)	29.11 (28.24–29.99)	29.08 (13.93–60.71)	4.86 (3.19–6.53)	29.11 (135.6)	<0.001
Lansoprazole	Digestive system medication	24.1 (14.5–40.06)	23.99 (23.49–24.5)	23.91 (15.63–36.58)	4.58 (2.91–6.25)	23.99 (329.36)	<0.001
Phentermine	Other medication	41.76 (13.4–130.15)	41.43 (40.3–42.55)	41.4 (15.99–107.16)	5.37 (3.7–7.04)	41.43 (118.29)	<0.001
Penicillamine	Other medication	35.32 (11.34–110)	35.08 (33.95–36.21)	35.06 (13.55–90.69)	5.13 (3.46–6.8)	35.08 (99.28)	<0.001
Anti-inhibitor coagulant complex	Other medication	20.74 (6.67–64.47)	20.66 (19.53–21.79)	20.64 (7.99–53.33)	4.37 (2.7–6.04)	20.66 (56.08)	<0.001
Doxepin	Other medication	19.45 (8.08–46.83)	19.38 (18.5–20.25)	19.36 (9.28–40.38)	4.27 (2.61–5.94)	19.38 (87.06)	<0.001
Omalizumab	Other medication	16 (5.99–42.72)	15.95 (14.97–16.93)	15.94 (7.01–36.25)	3.99 (2.33–5.66)	15.95 (56.02)	<0.001
Aflibercept	Other medication	15.82 (6.57–38.09)	15.78 (14.9–16.65)	15.76 (7.56–32.87)	3.98 (2.31–5.65)	15.78 (69.13)	<0.001
Foscarnet	Other medication	13.27 (4.27–41.23)	13.24 (12.11–14.37)	13.23 (5.12–34.16)	3.73 (2.06–5.39)	13.24 (33.93)	<0.001
Tenofovir	Other medication	8.32 (5.01–13.82)	8.31 (7.8–8.81)	8.28 (5.42–12.66)	3.05 (1.38–4.72)	8.31 (96.06)	<0.001
Lithium carbonate	Other medication	7.72 (4.56–13.05)	7.71 (7.18–8.23)	7.68 (4.95–11.92)	2.94 (1.27–4.61)	7.71 (81.43)	<0.001
Eftrenonacog alfa	Other medication	7.39 (2.77–19.72)	7.38 (6.4–8.36)	7.38 (3.25–16.77)	2.88 (1.22–4.55)	7.38 (22.06)	<0.001
Isoniazid	Other medication	6.23 (2.34–16.63)	6.23 (5.25–7.21)	6.22 (2.74–14.14)	2.64 (0.97–4.3)	6.23 (17.54)	<0.001
Genvoya	Other medication	6.07 (2.28–16.2)	6.07 (5.09–7.05)	6.06 (2.67–13.78)	2.6 (0.93–4.27)	6.07 (16.92)	<0.001
Peginterferon	Other medication	5.76 (4.15–7.99)	5.75 (5.42–6.08)	5.71 (4.34–7.52)	2.51 (0.85–4.18)	5.75 (140.12)	<0.001
Voriconazole	Other medication	5.73 (2.86–11.48)	5.73 (5.03–6.42)	5.72 (3.2–10.22)	2.52 (0.85–4.18)	5.73 (31.16)	<0.001
Inotersen	Other medication	5.37 (2.41–11.97)	5.37 (4.57–6.17)	5.36 (2.74–10.48)	2.42 (0.76–4.09)	5.37 (21.31)	<0.001
Rosuvastatin	Other medication	4.62 (3.06–6.96)	4.61 (4.2–5.02)	4.59 (3.26–6.47)	2.2 (0.53–3.87)	4.61 (64.72)	<0.001
Somatotropin	Other medication	4.53 (2.85–7.21)	4.53 (4.07–4.99)	4.52 (3.06–6.65)	2.17 (0.51–3.84)	4.53 (49.33)	<0.001
Doxycycline	Other medication	4.48 (2.01–9.97)	4.47 (3.67–5.27)	4.47 (2.29–8.73)	2.16 (0.49–3.83)	4.47 (16.16)	<0.001
Ribavirin	Other medication	3.75 (2.59–5.44)	3.75 (3.38–4.12)	3.73 (2.73–5.09)	1.9 (0.23–3.57)	3.75 (56.12)	<0.001

BCPNN, Bayesian Confidence Propagation Neural Network; MGPS, Multi-Item Gamma Poisson Shrinker; PRR, proportional reporting ratio; ROR, reporting odds ratio.

Figure 3.

Classification of medications causing medication-related nephrotic syndrome according to different mechanisms.

Medication risk and medication-induced time of MRNS

Among the medications associated with MRNS, the top three anti-inflammatory medications ranked by ROR were sulfasalazine (ROR = 24.74), rifampin (ROR = 7.51), and celecoxib (ROR = 6.16). For antineoplastic agents medication, the top three were ramucirumab (ROR = 33.59), asciminib (ROR = 16.82), and pamidronic acid (ROR = 13.82). In the category of digestive system medication, the medications were famotidine (ROR = 29.28) and lansoprazole (ROR = 24.10). Among other medications, the highest ROR values were phentermine (ROR = 41.76), penicillamine (ROR = 35.32), and anti-inhibitor coagulant complex (ROR = 20.74) (Figure 4 and Table 2).

Figure 4.

Forest plot and signal value heatmap of the disproportionation analysis method for five medications causing medication-related nephrotic syndrome.

Risk values of medications associated with MRNS

Among the 54 medications, 20 were classified as high risk (37%) and 34 as medium risk (63%) based on BCPNN. The top three medications with the highest risk level were phentermine (BCPNN = 5.37), penicillamine (BCPNN = 5.13), and ramucirumab (BCPNN = 5.06). The top three medications with the lowest risk of stroke were lorlatinib (BCPNN = 3.00), lithium carbonate (BCPNN = 2.94), and bevacizumab (BCPNN = 2.90) (Figure 5).

Figure 5.

Bar chart of medication risk stratification for medications causing medication-related nephrotic syndrome.

Comparison of medication-induced onset time among different categories of medications

We divided medications into four categories based on their action mechanisms, including anti-inflammatory medication, antineoplastic agents medication, digestive system medication, and other medication, to assess the differences in the onset time of MRNS among these categories.

When categorizing medications based on mechanisms of action, the cumulative risk curve results showed non-significant differences in medication-induced onset time among different categories (p = 0.41). Furthermore, the results of a one-way ANOVA analysis indicated that the antineoplastic agent medication had the shortest onset time (Mean = 120.98 days). While the anti-inflammatory medication had the longest onset time (Mean = 165.12 days), there was a significant difference in medication-induced onset time between antineoplastic agents and anti-inflammatory medications (p < 0.001) (Figure 6).

Figure 6.

The cumulative risk curve and violin plot of drugs with different mechanism categories were used to compare the time to onset of medication-related nephrotic syndrome. (a) Cumulative hazard curves for time to onset. (b) Comparison of time to onset, with letter labels indicating statistical differences.

Discussion

Pharmacovigilance activities span the entire lifecycle of medications—including development, production, marketing, clinical use, and regulatory oversight—with ADR databases playing a pivotal role. As a critical component of the U.S. pharmacovigilance system, FAERS compiles extensive real-world AE reports, providing valuable data for medication safety research and risk assessment. This study systematically analyzed MRNS cases reported in FAERS from January 2004 to September 2024. A total of 54 medications were identified to exhibit significant positive signals for NS, predominantly belonging to anti-inflammatory medication, antineoplastic agents medication, digestive system medication, and other medication categories. This study represents the first large-scale evaluation of MRNS risk based on real-world FAERS data. The findings not only delineate the risk profiles of specific drug classes but also provide important insights into their potential impact on patients with NS, thereby supporting strategies aimed at reducing MRNS risk at its source.

The number of reported cases of MRNS has shown an increasing trend over the years, reaching its peak between 2018 and 2019. This rise may be associated with the increased use of medications and the gradual improvement of AE monitoring systems. In terms of gender distribution, the proportion of reported cases in male patients was higher than in females, with the primary onset age for males concentrated between 60 and 65 years, while for females, it was between 65 and 70 years. This suggested that gender and age may play a role in the occurrence of MRNS. Clinical outcome analysis revealed that hospitalization and death were the most common outcomes, indicating a significant impact of MRNS on patients’ health and survival. Regarding the route of administration, oral and intravenous administration were the most frequently reported, suggesting that different administration methods may influence the risk of medication-induced kidney injury. In addition, MRNS cases were predominantly concentrated in Japan and the United States, reflecting differences in the emphasis placed on pharmacovigilance and data collection across countries. Pharmacovigilance is better in high-income countries.²² The United States was among the first 10 member states to join PIDM in 1968, while Japan initiated pharmacovigilance activities as early as 1967 and formally joined PIDM in 1972.^23
–25

In clinical practice, preventing MRNS is often more effective than treating it after it has onset. Effective prevention not only reduces morbidity but also conserves medical resources and reduces the economic burden on patients. Recent pharmacovigilance evidence further highlights the importance of early safety monitoring. For example, Javed et al. identified a statistically significant association between clindamycin and acute renal failure based on a FAERS disproportionality analysis, with the signal remaining robust even after excluding concomitant medications, underscoring the need for further causality assessment.²⁶ Moreover, NS can occur as a paraneoplastic manifestation of various cancers, suggesting that underlying systemic diseases may also contribute to renal vulnerability.²⁷ Antineoplastic medications (48%) and anti-inflammatory medications (12%) account for a large proportion of MRNS. Bevacizumab and ramucirumab, both vascular endothelial growth factor (VEGF) inhibitors, are the most common medication-related causes of renal complications during immunotherapy in cancer patients.²⁸ NS is a severe and rapidly progressing AE following ramucirumab therapy.²⁹ In the phase III RAISE study of ramucirumab, Tabernero et al. reported that among 529 AEs, proteinuria occurred in 74 patients (14%) and renal failure in 11 patients (2%).³⁰ Abnormal lipid metabolism is a common complication in patients with NS.⁴ Rosuvastatin is a commonly used medication for treating hyperlipidemia.³¹ Research has shown that statins have both renoprotective and nephrotoxic effects.³² When used at high doses, they may increase the risk of renal dysfunction.^33,34

Determining the risk levels of various medications is crucial for the early assessment of MRNS.³⁵

Phentermine, an amphetamine analog, is commonly used for the short-term treatment of obesity in adolescents aged 16 years and older. In this study, phentermine was identified as one of the medications with the highest risk of inducing MRNS. The prevalence of obesity is closely associated with the increasing incidence of chronic kidney disease worldwide.³⁶ Renal fat accumulation can lead to structural and functional changes in glomerular and renal tubular epithelial cells, thereby contributing to the development of obesity-related kidney diseases.^37,38 Theoretically, phentermine, as a weight-loss agent, could help reduce the risk of NS. However, Shao et al. reported a case of a 43-year-old Caucasian female who developed acute interstitial nephritis after taking phentermine for 9 months.³⁹ Despite this, research on phentermine’s nephrotoxicity remains limited, suggesting that it may be a potential pathogenic factor for MRNS, with its underlying mechanisms still needing further investigation.

Penicillamine poses the second highest risk of inducing MRNS, following phentermine. Although its precise mechanism remains unclear, multiple case reports support its association with MRNS.^40,41 For instance, Salman et al. reported a case of a 31-year-old female patient with Wilson’s disease who developed acute-onset NS after receiving D-penicillamine therapy. Remarkably, her condition significantly improved and achieved complete remission within months after simply discontinuing D-penicillamine, without any immunosuppressive treatment.⁴² This suggested that penicillamine may have potential nephrotoxic effects. Another study described a 27-year-old male patient with Wilson’s disease who was diagnosed with NELL-1-associated membranous nephropathy after prolonged penicillamine use.⁴³ Despite switching from penicillamine to trientine, his condition persisted, eventually achieving complete remission after 21 months of corticosteroid and mycophenolate mofetil therapy. This case suggests that penicillamine may induce MN through immune-mediated mechanisms. Collectively, these cases further support the potential association between penicillamine and the development of MRNS. However, its exact mechanism remains to be elucidated.

Determining the onset time of medication-induced conditions is crucial for the prevention and management of medication-related diseases. A key innovation of this study is the analysis of the time characteristics of MRNS induced by different medication categories. The results showed that antineoplastic agents had the shortest onset time, whereas anti-inflammatory medications had the longest, with a statistically significant difference between the two. This phenomenon may be associated with the high toxicity and cumulative effects of antineoplastic medications, making them more likely to induce renal injury within a short period. By contrast, although widely used, anti-inflammatory medications may induce renal damage more gradually, resulting in a longer onset time. For example, diclofenac effectively alleviates pain and inflammation.⁴⁴ However, it is also a potent nephrotoxic agent that may cause renal damage through extensive genomic DNA fragmentation and apoptosis mediated by oxidative stress.⁴⁵ Sulfasalazine has the highest risk of MRNS among anti-inflammatory medications. A 28-year-old male patient with an 18-month history of ulcerative colitis developed NS 2 weeks after receiving sulfasalazine treatment. Kidney biopsy suggested minimal change disease, possibly induced by the medication. Mesalazine is considered a relatively medium-risk medication for MRNS. Mesalazine can induce focal segmental glomerulosclerosis in patients with ulcerative colitis.⁴⁶ Ulcerative colitis patients treated with sulfasalazine or mesalazine may develop NS, possibly related to 5-aminosalicylic acid.⁴⁷ These findings highlight the importance of tailoring follow-up plans based on medication categories to ensure the early detection and management of nephrotoxicity.

Limitations

Although FAERS provides valuable insights, it has inherent limitations. First, the FAERS database relies on a voluntary reporting system, leading to underreporting and reporting errors, which compromise data quality and completeness. Second, the majority of reports are from developed regions, mainly Europe and the United States, which may not reflect the overall occurrence of adverse medication reactions around the globe. In addition, the strength of signals only represents the relative magnitude of risks and does not quantify the absolute risk. Further studies are needed to establish the underlying causal associations.

Conclusion

This study utilized a large real-world database to evaluate potential risk medications associated with MRNS, providing crucial data support for clinical pharmacovigilance assessments. By identifying high-risk medications and the onset time of medication-related AE of specific medication categories, this study establishes a data foundation for optimizing clinical medication guidance and treatment strategies. Future research should further integrate genetic, clinical, and pharmacological data to gain deeper insights into individual susceptibility to medication-induced nephrotoxicity. Moreover, strengthening global medication safety monitoring systems is essential for mitigating the burden of MRNS and improving patient outcomes.

Supplemental Material

sj-docx-1-taw-10.1177_20420986261446488 – Supplemental material for Medication-related nephrotic syndrome: a real-world study from 2004 to 2024 based on the Food and Drug Administration Adverse Event Reporting System

Supplemental material, sj-docx-1-taw-10.1177_20420986261446488 for Medication-related nephrotic syndrome: a real-world study from 2004 to 2024 based on the Food and Drug Administration Adverse Event Reporting System by Yan Zheng, Tingfen Han, Yanjun Wu, Tingting Wang and Jinbo Wang in Therapeutic Advances in Drug Safety

Supplemental Material

sj-docx-2-taw-10.1177_20420986261446488 – Supplemental material for Medication-related nephrotic syndrome: a real-world study from 2004 to 2024 based on the Food and Drug Administration Adverse Event Reporting System

Supplemental material, sj-docx-2-taw-10.1177_20420986261446488 for Medication-related nephrotic syndrome: a real-world study from 2004 to 2024 based on the Food and Drug Administration Adverse Event Reporting System by Yan Zheng, Tingfen Han, Yanjun Wu, Tingting Wang and Jinbo Wang in Therapeutic Advances in Drug Safety

Footnotes

Acknowledgements

None.

Declarations

ORCID iD

Jinbo Wang

Supplemental material

Supplemental material for this article is available online.

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