Identification of Drug Associated Factors for Gingival Disorders: A Real-World Pharmacovigilance Study

Abstract

Objective

This study aimed to evaluate the reports of drug adverse events associated with gingival disorders and explore potential drug associated factors.

Methods

Data were extracted from the Food and Drug Administration Adverse Event Reporting System (FAERS) database from the first quarter of 2004 to the third quarter of 2024. Reporting odds ratio (ROR) were calculated using disproportionality analysis to identify signals of potential associations. For drugs that met the criteria, the least absolute shrinkage and selection operator (LASSO) regression, and multifactor logistic regression were performed to further explore the potential associated factors.

Results

A total of 34,354 relevant reports were collected, with 62.1% of the cases being female. Forty-three drugs were identified as associated factors for gingival disorders, including antineoplastic agents, immunosuppressants, and antithrombotic drugs. The median time from drug use to the onset of gingival disorders was 35 days, with variations observed among different drugs.

Conclusions

This study is the first comprehensive correlative associations of drugs related to gingival disorders using the Food and Drug Administration Adverse Event Reporting System database, providing preliminary safety signals for the rational monitoring of gingival disorders in clinical practice. Further research is required to validate these potential associations.

Introduction

Gingival disorders refer to a category of diseases that occur in the gingival tissues. These disorders are typically characterised by symptoms such as redness, swelling, bleeding, and hyperplasia of the gums. They not only affect oral health but may also lead to a range of oral functional impairments and, in some cases, impact overall systemic health. The common types of gingival disorders include gingivitis, periodontitis, and drug-induced gingival hyperplasia. Gingival disorders can lead to infections of the periodontal tissues and progressive loss of alveolar bone, ultimately resulting in tooth loss in severe cases.¹ There are many factors that can cause gingival disorders, such as poor lifestyle habits, immune system diseases, and the effects of certain medications.² Previous research has already revealed that the long-term use of certain substances, such as cannabis, is a risk factor for periodontal disease.³ It is worth noting that an increasing number of studies have shown that many medications may be associated with the occurrence and development of gingival disorders.^4,5

The mechanisms underlying drug-induced gingival disorders are not fully clear. Researchers speculate that the development of gingival disorders may be associated with cytokines.⁶ For instance, studies have shown that certain drugs, such as cyclosporine A, can promote gingival overgrowth by upregulating the expression of pro-inflammatory cytokines like IL-6 and TNF-α.⁷ Also, an imbalance in collagen synthesis and degradation and the activation of pathways like the Wnt/β-catenin pathway play a role in drug-induced gingival overgrowth.⁸ In addition, there is already some evidence indicating that antiresorptive medications may impede the healing process of the oral mucosa, leading to delayed wound closure.⁹ Moreover, these medications can also cause the rare but serious complication of osteonecrosis of the jaw (ONJ), especially in patients receiving high-dose intravenous bisphosphonates for cancer treatment.⁵ Despite some reports on drug-induced gingival disorders, there is still a lack of large-scale studies on the association between gingival disorders and drug use.

The Food and Drug Administration Adverse Event Reporting System (FAERS) database is a spontaneous reporting system that collects a vast number of drug adverse event reports from around the world. It provides a rich data resource for studying drug safety and adverse reactions.¹⁰ The FAERS database is a valuable resource for identifying potential drug adverse reactions and has been widely applied in high-quality studies to monitor drug safety.¹¹ The present study aims to comprehensively evaluate adverse event reports related to gingival disorders in the FAERS database and explore potential drug associated factors, thereby providing valuable references for clinical practice.

Methods

Study design and data sources

In this retrospective pharmacovigilance study, adverse events were extracted from the FAERS database spanning from the first quarter of 2004 to the third quarter of 2024. The FAERS database comprises 6 data sets in ASCII format: demographic and administrative information (DEMO), drug information (DRUG), therapy start dates and end dates for reported drugs (THER), coded for the adverse events (REAC), patient outcomes for the event (OUTC), and indications for use/diagnosis (INDI).

Initially, we followed to FDA guidelines to eliminate duplicate data. In DEMO, the FDA assigned CASEID, FDA_DT (date of FDA acceptance of the report), PRIMARYID (report unique identification number) for each report. When CASEID were identical in the DEMO data table, we retained cases with the most recent FDA_DT. Furthermore, when both CASEID and FDA_DT were identical, we selected cases with the most recent PRIMARYID. In the DRUG, relevant drugs are assigned different roles, such as primary suspect, secondary suspect, concomitant, and interacting. In this study, only drugs listed as 'primary suspect' were included. According to the MedDRA system, adverse events in the REAC are coded using Preferred Terms (PT). Additionally, all PTs that represent symptoms, signs, investigations or diagnoses with potential relevance can be grouped using Standardised MedDRA Queries (SMQs). SMQs are sets of terms related to specific medical conditions or areas of interest and can be used to facilitate case identification. In this study, a narrow definition of gingival disorders was adopted, which included 26 PTs from the MedDRA dictionary (Supplementary Table S1). The inclusion of these 26 PTs was strictly based on SMQs to ensure a high level of sensitivity and consistency in identifying all potential drug-related gingival injuries, as per international pharmacovigilance standards. This approach allows for a comprehensive assessment of the overall clinical burden on gingival tissues across diverse drug classes.

Disproportionality Analysis

For the detected drug adverse events reports, disproportionality analysis was employed for pharmacovigilance analysis. A 2 × 2 contingency table was constructed for the calculation of the reporting odds ratio (ROR) (Supplementary Table S2). The ROR value and its 95% confidence interval calculation method are shown in Supplementary Table S3. If the number of reports for a certain drug is greater than 3 and the lower limit of the 95% confidence interval of ROR is greater than 1, it is considered a positive signal, indicating that the drug may be associated with gingival disorders.

Regression analysis

In our logistic regression model, the dependent variable is the occurrence of a gingival disorder event (1 = Case, 0 = Non-case). The Cases are defined as reports containing any of the 26 PTs identified via the SMQ for gingival disorders. The Non-cases comprise all other adverse event reports within the FAERS database during the same study period. Adverse event reports containing basic demographic information (gender, age and weight) were extracted from both groups. Reports with missing demographic data and outliers (age > 120 gt; 120 years or weight > 400 gt; 400 kg) were excluded prior to analysis. For drugs with a lower limit of the 95% confidence interval of the ROR greater than 1 and case number exceeding 100, single-factor analysis was conducted. Subsequently, drugs with P value less than .01 in the single-factor analysis were subjected to the least absolute shrinkage and selection operator (LASSO) regression. Thereafter, a multifactor logistic regression analysis was performed on the drugs screened by LASSO regression to identify the drug associated factors associated with gingival disorders. This two-step process was designed to ensure sufficient statistical power and to reduce computational noise before performing the multifactor logistic regression.

Analysis of gender differences in drug associated factors

For the drug associated factors identified by multifactor logistic regression, we used the ROR method and Fisher's exact test to examine differences between genders.¹² We collected 2 separate datasets, one containing only females and the other containing only males, and identified the corresponding drug associated factors. These 2 datasets were then matched based on drug names. The criteria in adverse event signals using the ROR value and Fisher's exact test are provided in Supplementary Table S4 and Supplementary Table S5.

Statistical analysis

Descriptive analysis was conducted to summarise the clinical characteristics of the patients. The Bonferroni correction method was applied to adjust the P values for multiple comparisons, with P < .05 considered statistically significant. All statistical analyses and visualisations were performed using R software (version 4.2.1).

Results

Baseline characteristics

From the first quarter of 2004 to the third quarter of 2024, a total of 34,354 drug adverse event reports related to gingival disorders were collected. As shown in Table 1 and Figure 1, a total of 21,330 (62.1%) reports were from females, and 10,475 (37.9%) from males. The median age was 60.0 years (IQR 59.0-84.7 years). The number of reports increased with age group (Figure 1A). The median weight was 70.3 kg (IQR 59.0-84.7 kg). The primary reporters were consumers (15,418, 44.9%), followed by physicians (8,063, 23.5%). The top 3 countries with the highest number of reports were the United States (19,279, 56.1%), the United Kingdom (2,204, 6.4%) and Canada (1,886, 5.5%). The most common outcomes of drug-related gingival disorders were hospitalisation or prolonged hospitalisation (Figure 1B). Additionally, the number of reports showed an increasing trend over time, with the highest number of reports in 2015 (Figure 1C).

Table 1.

Basic characteristics of patient.

Characteristics	Drug-related GD (N = 34354)
Age (years old)
Median (Q1, Q3)	60.0 (47.0, 70.0)
Unknown	10,983 (32.0%)
Gender
Female	21,330 (62.1%)
Male	10,475 (30.5%)
Unknown	2549 (7.4%)
Weight (kg)
Median (Q1, Q3)	70.3 (59.0, 84.7)
Unknown	22,746 (66.2%)
Occupation of the reporter
Physician	8063 (23.5%)
Pharmacist	1604 (4.7%)
Other health-professional	4595 (13.4%)
Consumer	15,418 (44.9%)
Lawyer	215 (0.6%)
others	2583 (7.5%)
Unknown	1876 (5.5%)
Country of the reporter
United States	19,279 (56.1%)
United Kingdom	2204 (6.4%)
Canada	1886 (5.5%)
Other country or not specified	10,985 (32.0%)

Q1, first Quartile; Q3, third Quartile.

Fig. 1.

Relevant information from various drug-related gingival disorders reports. (A) Number of cases of drug-related gingival disorders by patient age; (B) outcome with drug-related gingival disorders; (C) Drug-related gingival disorders reports by year.

Gingival disorders related drugs

Using the ROR method, Table 2 showed the top 50 drugs most frequently reported in association with gingival disorders, ranked by the number of reports. The 3 most frequently reported drugs were zoledronic acid, denosumab and alendronic acid. The volcano plot illustrated the association between all suspected drugs and gingival disorders (Figure 2). In the volcano plot, the x-axis represented the logarithm of the ROR value, the y-axis represented the negative logarithm of the P-value after Fisher's exact test and Bonferroni correction, and the colour intensity of the points indicated the logarithm of the number of reports. Thus, points located in the upper right corner of the volcano plot indicated a more significant signal difference and stronger signal intensity. Interestingly, in addition to several drugs for the treatment of bone disease showing strong signal intensity and differences, amlodipine also exhibited strong signal intensity and differences.

Table 2.

The top 50 of drug-related gingival disorders report in the FAERS database.

Drug	Case	ROR	RORL	RORU	P value	P_adjust
Zoledronic acid	1780	17.39	16.57	18.26	<.001	<.001
Denosumab	1409	5.08	4.82	5.36	<.001	<.001
Alendronic acid	1318	21.43	20.26	22.66	<.001	<.001
Adalimumab	1276	1.12	1.06	1.19	<.001	.087
Amlodipine	1156	13.25	12.49	14.06	<.001	<.001
Rivaroxaban	856	3.86	3.61	4.13	<.001	<.001
Lenalidomide	698	1.09	1.01	1.17	.035	1
Nicotine	495	4.80	4.39	5.25	<.001	<.001
Apixaban	417	1.67	1.52	1.84	<.001	<.001
Pamidronic acid	406	60.97	54.99	67.6	<.001	<.001
Sunitinib	397	5.85	5.30	6.46	<.001	<.001
Infliximab	364	1.12	1.01	1.24	.036	1
Methotrexate	361	1.51	1.36	1.68	<.001	<.001
Palbociclib	343	2.43	2.19	2.71	<.001	<.001
Cabozantinib	315	5.03	4.50	5.62	<.001	<.001
Dabigatran	307	2.47	2.21	2.76	<.001	<.001
Varenicline	293	1.96	1.75	2.20	<.001	<.001
Phenytoin	282	9.98	8.87	11.23	<.001	<.001
Ciclosporin	263	2.97	2.63	3.36	<.001	<.001
Teriparatide	261	1.28	1.14	1.45	<.001	.081
Rituximab	248	1.40	1.23	1.58	<.001	<.001
Ibrutinib	243	1.99	1.75	2.26	<.001	<.001
Bevacizumab	223	1.76	1.54	2.01	<.001	<.001
Warfarin	220	2.76	2.42	3.15	<.001	<.001
Peginterferon alfa-2a	214	3.66	3.20	4.19	<.001	<.001
Everolimus	213	3.09	2.70	3.53	<.001	<.001
Gabapentin	210	1.75	1.53	2.01	<.001	<.001
Ibandronic acid	206	5.37	4.68	6.17	<.001	<.001
Eltrombopag	189	4.79	4.15	5.53	<.001	<.001
Clopidogrel	179	2.46	2.12	2.85	<.001	<.001
Tocilizumab	175	1.68	1.44	1.94	<.001	<.001
Acetylsalicylic acid	174	1.91	1.65	2.22	<.001	<.001
Ruxolitinib	169	1.52	1.31	1.77	<.001	<.001
Mycophenolic acid	157	1.45	1.24	1.70	<.001	.004
Lamotrigine	151	1.56	1.33	1.83	<.001	<.001
Niraparib	151	4.08	3.48	4.79	<.001	<.001
Telaprevir	145	2.99	2.54	3.52	<.001	<.001
Risedronic acid	139	11.52	9.74	13.63	<.001	<.001
Ocrelizumab	137	1.49	1.26	1.76	<.001	.004
Finasteride	126	5.37	4.5	6.40	<.001	<.001
Octocog alfa	126	6.80	5.71	8.11	<.001	<.001
Alteplase	125	5.68	4.76	6.78	<.001	<.001
Ciprofloxacin	125	1.78	1.5	2.13	<.001	<.001
Valproic acid	125	1.96	1.64	2.33	<.001	<.001
Sorafenib	122	3.49	2.92	4.17	<.001	<.001
Buprenorphine; naloxone	120	3.31	2.76	3.96	<.001	<.001
Topiramate	104	2.49	2.05	3.02	<.001	<.001
Alemtuzumab	102	4.89	4.03	5.95	<.001	<.001
Lenvatinib	97	2.42	1.98	2.95	<.001	<.001
Nilotinib	85	1.77	1.43	2.19	<.001	<.001

ROR, reporting odds ratio; RORL, lower of 95% confidence interval; RORU, upper of 95% confidence interval; P-adjust, P-value after Bonferroni correction.

Fig. 2.

Volcano plots of gingival disorders related drugs. ROR, reporting odds ratio; P-adjust, P-value after Bonferroni correction.

Associated factors for drug-related gingival disorders

LASSO regression was performed on suspected drugs with more than 100 cases and P < .01 in the single-factor, resulting in the identification of 44 drugs (Figure 3). Multifactor logistic regression was then conducted, incorporating patient basic information (age, weight and gender). The results indicated that female, zoledronic acid, denosumab, pamidronic acid, alendronic acid, alteplase, amlodipine, eltrombopag, sunitinib and 35 other drugs were significantly associated with gingival disorders (Figure 4). The accuracy of the multifactor logistic regression prediction model for gingival disorders, as measured by the ROC-AUC, was 0.701 (Supplementary Fig. S1). The identified associated factors included 9 antineoplastic agents, 6 immunosuppressants, 6 antithrombotic agents, 6 drugs for the treatment of bone diseases, 5 antiepileptics and 11 other drugs (Supplementary Table S6).

Fig. 3.

Results of the LASSO regression analysis. LASSO, least absolute shrinkage and selection operator.

Fig. 4.

Results of the multifactor logistic regression analysis. CI, confidence interval; OR, odds ratio.

Time interval between drug use and the onset of gingival disorders

The time interval between drug use and the occurrence of gingival disorders was assessed using the dates of adverse event reports (FDA_DT) and the therapy start dates (START_DT) from the THER file. Date-quality filtering rules were applied as follows: (1) Reports where the event onset date preceded the therapy start date were excluded; (2) Only records with complete Year-Month-Day information were used; (3) Duplicates were rigorously removed following FDA guidelines using CASE_ID and FDA_DT prior to time analysis. After applying these filters, 10,613 valid reports remained to support the median onset estimate. The median number of days between drug use and the onset of gingival disorders was 35 days (IQR 2-284 days), with the majority of adverse events occurring within 300 days (Supplementary Figure S2). Box plots were created to illustrate the time intervals for the 43 drugs identified by multifactor logistic regression. As shown in Figure 5, drugs with larger median time include pamidronic acid, risedronic acid and zoledronic acid.

Fig. 5.

Box plot of time interval between drug use and the onset of gingival disorders for the 43 drugs.

Difference of drug-related gingival disorders between female and male

Figure 6 showed the gender differences in the proportion of gingival disorders caused by different drugs relative to their overall adverse events. Compared to females, males had a higher proportion of gingival disorders caused by zoledronic acid (ROR: 0.65, 95% CI: 0.59-0.72, P < .001), amlodipine (ROR: 0.86, 95% CI: 0.76-0.97, P = .017), ciclosporin (ROR: 0.54, 95% CI: 0.42-0.69, P < .001) and alteplase (ROR: 0.65, 95% CI: 0.44-0.96, P = .037). Conversely, denosumab (ROR: 1.36, 95% CI: 1.14-1.63, P = .001) and alendronic acid (ROR: 1.32, 95% CI: 1.06-1.64, P = .011) and other 15 drugs were more likely to cause gingival disorders in famales. Supplementary Figure S3 illustrated the gender differences in drug associated factors that met the criteria.

Fig. 6.

Difference of drug-related gingival disorders between female and male.

Discussion

The etiology of gingival disorders is multifactorial, typically encompassing genetic/developmental disorders, specific infections and inflammatory conditions and others.13, 14, 15 However, drug-related gingival disorders are usually overlooked. Although the gums and oral cavity represent a localised environment, they can also be affected by systemically acting drugs. Until now, researchers had identified several drugs that impact the gums, such as phenytoin and cyclosporine.¹⁶ However, these studies were limited and lacked a comprehensive investigation into drug-related gingival disorders.

The FAERS database is the largest global database for reporting adverse drug events and can be used to investigate the association between suspected drugs and gingival disorders. This pharmacovigilance study found that the median age of patients with drug-related gingival disorders was 60 years. This may be related to the metabolic status of elderly patients. Studies showed that periodontitis was more common with increasing age,¹⁷ and older individuals usually take multiple medications with decreased liver and kidney function, which increased the likelihood of adverse drug events. This study also found that the majority of patients with drug-related gingival disorders were female (62.1%), and the results of the multifactor logistic regression analysis indicated that females were more likely to experience drug-related gingival disorders. This may be related to the more common use of osteoporosis treatment drugs among elderly women. Previous studies have also shown that there are gender differences in the occurrence of gingival diseases.¹⁸ However, further clinical evidence is needed to explain the impact of potential confounding factors in gender on gingival disorders. Additionally, while hospitalisation was identified as the most common serious outcome, this finding requires cautious interpretation. Patients treated with antineoplastic or antiresorptive agents often have complex systemic comorbidities that may independently increase the likelihood of hospitalisation, regardless of the gingival adverse event itself.

In this study, we validated 43 drugs as associated factors for gingival disorders through multifactor logistic regression analysis, with the most common categories being antineoplastic agents, immunosuppressants, antithrombotic agents, drugs for treatment of bone diseases, antiepileptics and several other drugs. Notably, drugs for the treatment of bone diseases accounted for the highest number of reports related to gingival disorders.

In this study, the antineoplastic agents identified as associated factors for gingival disorders included tyrosine kinase inhibitors (sunitinib, cabozantinib, sorafenib, and ibrutinib), cyclin-dependent kinase inhibitors (palbociclib), poly-ADP ribose polymerase inhibitors (niraparib), methotrexate, bevacizumab and alemtuzumab. Gingival disorders are a common adverse event associated with the use of antineoplastic agents.¹⁹ Tyrosine kinase inhibitors were found to cause gingival disorders through several mechanisms. One proposed mechanism was related to their antiangiogenic effects, which could reduce blood supply to the gingival tissues.²⁰ Additionally, research showed that tyrosine kinases play a role in the function of gingival fibroblasts.²¹ Inhibition of these kinases could disrupt intracellular signalling pathways, thereby affecting the proliferation and differentiation of gingival fibroblasts. This disruption could lead to changes in the extracellular matrix and contribute to the development of gingival overgrowth. Our results indicated that several antineoplastic agents were associated factors for gingival disorders. Additionally, other factors such as poor oral hygiene and genetic predisposition can exacerbate these drug-induced gingival issues. Therefore, our findings will help identify the often-overlooked gingival disorders associated with antineoplastic agents.

In addition, we identified 6 immunosuppressants as associated factors for gingival disorders. Immunosuppressants are a class of drugs used to inhibit the body's immune response and are widely applied in the management of postorgan transplant rejection and autoimmune diseases.²² Long-term use of immunosuppressants can suppress the immune system, leading to a decreased ability of the oral mucosa and gingival tissues to defend against bacterial infections, thereby contributing to the development of gingival disorders. Cyclosporine-induced gingival hyperplasia is a well-documented side effect of this immunosuppressive agent. The mechanism underlying cyclosporine-induced gingival hyperplasia involves the drug's ability to increase the levels of phosphoinositide 3-kinase (PI3K) and protein kinase B (Akt) in gingival fibroblasts, which in turn promotes cell proliferation and extracellular matrix production.²³ Cyclosporine can also induce gingival overgrowth by increasing the expression of galectin-8 (Gal-8), which in turn activates the TGF-β pathway, leading to collagen accumulation and subsequent gingival hyperplasia.⁸ In addition, immunosuppressants may also reduce the expression of matrix metalloproteinases (MMPs), thereby decreasing collagen degradation and contributing to gingival overgrowth.²⁴ Therefore, clinicians should remain vigilant regarding gingival health in patients on long-term immunosuppressant therapy.

Antithrombotic agents, including rivaroxaban and warfarin, were identified as factors associated with gingival disorder reports. Antithrombotic agents may reduce the coagulation ability of patients, leading to an increased risk of bleeding during daily toothbrushing and dental surgery.²⁵ However, it is clinically plausible that these signals primarily reflect an exacerbated bleeding tendency in the gingival sulcus during daily oral hygiene or dental procedures, rather than a drug-induced structural alteration of the gingiva. Therefore, for patients with cardiovascular diseases or thromboembolic diseases who require anticoagulation therapy, attention should be paid to the risk of gingival bleeding.

Drugs for the treatment of bone diseases are significant associated factors for gingival disorders. These drugs include bisphosphonates and denosumab. These medications inhibit the activity of osteoclasts, reduce bone resorption, thereby increase bone density, and lower the risk of fractures, and are thus used in the treatment of osteoporosis.²⁶ Postmenopausal bone loss is the primary cause of osteoporosis, which is why osteoporosis is more common in elderly women.²⁷ This is consistent with the gender distribution differences observed in the baseline information of this study. Previous studies have confirmed that medication-related osteonecrosis of the jaw is one of the serious adverse events associated with the use of bisphosphonates and denosumab. Although the exact mechanism of MRONJ has not been fully elucidated, it is likely related to the inhibition of bone resorption, effects on bone metabolism and local immune responses caused by these medications.²⁸ Animal models have demonstrated that zoledronic acid and denosumab can inhibit the secretion of IFNγ in the gingiva, thereby impairing the function of immune cells.²⁹ Zoledronic acid inhibited the migration of oral epithelial cells and may also alter the behaviour of oral bacteria, thereby increasing the risk of bacterial infections.³⁰ Drugs for the treatment of osteoporosis often require long-term use, which may increase the risk of gingival disorders. Clinicians may consider heightened awareness of gingival health in patients using antiosteoporosis medications, particularly when invasive dental procedures are planned. In particular, when undergoing invasive procedures involving the jawbone, such as tooth extraction, it is important to promptly inform the physician to be vigilant about the occurrence of osteonecrosis of the jaw.

Our study also found that antiepileptic drugs, such as phenytoin, gabapentin, lamotrigine, valproic acid and topiramate, are associated factors for gingival disorders. Phenytoin-induced gingival overgrowth is a common adverse event, and its mechanism is associated with gingival fibroblasts. In vitro experiments have shown that phenytoin can inhibit the apoptosis of gingival fibroblasts.³¹ Other studies have suggested that phenytoin-induced gingival overgrowth may be associated with the upregulation of periostin in gingival fibroblasts in a TGFβ-dependent manner.³² Additionally, studies have reported that Gabapentin induces increased production of the extracellular matrix and decreased degradation, which may lead to gingival overgrowth.³³ Other antiepileptic drugs such as Valproic acid have also been linked to gingival overgrowth, although the prevalence is relatively lower compared to Phenytoin. Case reports have also documented that lamotrigine, valproic acid and topiramate can induce gingival overgrowth,34, 35, 36 although the precise mechanisms remain to be fully elucidated. Therefore, particular attention should be paid to the occurrence of gingival overgrowth when using these antiepileptic drugs.

The present study also found that the median time to onset of drug-induced gingival disorders was 35 days. However, bisphosphonates such as pamidronic acid and risedronic acid exhibited longer and more variable onset times, which is consistent with the literature regarding their long-term impact on bone metabolism and cumulative dosing effects in osteonecrosis-related pathologies.³⁷ The observed differences in time-to-onset across different drug classes may be related to their respective pharmacological mechanisms—from immediate-type reactions like gingival bleeding (antithrombotic agents) to delayed-type reactions like drug-induced gingival hyperplasia or osteonecrosis-related changes (bisphosphonates). In addition, we conducted a gender analysis of 43 drug associated factors associated with gingival disorders. We found that zoledronic acid, amlodipine, ciclosporin and alteplase were more likely to cause gingival disorders in men than in women. However, the reasons or mechanisms underlying this difference remain unclear. Further investigation into the causes of this disparity will be an important area of focus in our future research.

To our knowledge, this study was the first to comprehensively investigate the suspected drugs that may cause gingival disorders using the FAERS database. However, several limitations existed in our study. First, as a spontaneous reporting database, the FAERS is prone to underreporting, duplicate reporting and inaccurate reporting. Second, our study lacked sufficient patient information, such as concomitant medications and underlying diseases, which mean that we cannot rule out the interference of specific confounding factors. Finally, the predictive performance of our multifactor logistic regression model should be interpreted as modest. This underscores the multifactorial nature of gingival disorders, where unmeasured confounders—such as smoking habits, individual oral hygiene and genetic factors—play a substantial role. Since FAERS data lack these granular clinical details, our model captures only a segment of the risk profile. Therefore, the results should be interpreted as potential associated factors identified within the structured data available, and further clinical studies with more granular patient records are essential to validate these findings across broader populations. Despite these limitations, the FAERS database remains an important reference for pharmacovigilance research.

Conclusion

In the present study, we identified several medications that are associated with the occurrence of gingival disorders. Patients using any of the 43 drugs investigated showed statistically significant associations with gingival disorder reports. Our findings provide preliminary hypotheses and potential safety references for the awareness of gingival disorders. These statistical associations should be interpreted as a call for focused clinical observation. Further clinical studies are needed to confirm or refute our observations.

Conflict of interest

None disclosed.