Open Access

Drug-induced gingival hyperplasia: a retrospective study using spontaneous reporting system databases

  • Haruna Hatahira1,
  • Junko Abe1, 2,
  • Yuuki Hane1,
  • Toshinobu Matsui1,
  • Sayaka Sasaoka1,
  • Yumi Motooka1,
  • Shiori Hasegawa1,
  • Akiho Fukuda1,
  • Misa Naganuma1,
  • Tomofumi Ohmori1, 3,
  • Yasutomi Kinosada4 and
  • Mitsuhiro Nakamura1Email authorView ORCID ID profile
Journal of Pharmaceutical Health Care and SciencesThe official journal of the Japanese Society of Pharmaceutical Health Care and Sciences20173:19

https://doi.org/10.1186/s40780-017-0088-5

Received: 27 March 2017

Accepted: 9 July 2017

Published: 19 July 2017

Abstract

Background

Drug-induced gingival hyperplasia (DIGH) causes problems with chewing, aesthetics, and pronunciation, and leads to the deterioration of the patient’s quality of life (QOL). Thus, the aim of this study was to evaluate the incidence of DIGH using spontaneous reporting system (SRS) databases.

Methods

We analyzed reports of DIGH from SRS databases and calculated the reporting odds ratios (RORs) of suspected drugs (immunosuppressants, calcium channel blockers, and anticonvulsants). The SRS databases used were the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and the Japanese Adverse Drug Event Report (JADER) database. With the data, we evaluated the time-to-onset profile and the hazard type using the Weibull shape parameter (WSP). Furthermore, we used the association rule mining technique to discover undetected relationships such as possible risk factors.

Results

The FAERS contained 5,821,716 reports. The RORs (95% confidence interval: CI) for cyclosporine, everolimus, sirolimus, mycophenolate mofetil, amlodipine, nifedipine, carbamazepine, clobazam, levetiracetam, phenobarbital, phenytoin, primidone, topiramate, and valproic acid, were 39.4 (95% CI: 30.3–51.2), 4.2 (1.7–10.0), 6.6 (2.5–17.7), 13.1 (7.2–23.2), 94.8 (80.0–112.9), 57.9 (35.7–94.0), 15.1 (10.3–22.3), 65.4 (33.8–126.7), 6.5 (3.6–11.8), 19.7 (8.8–44.0), 65.4 (52.4–82.9), 56.5 (21.1–151.7), 2.9 (1.1–7.7), and 17.5 (12.6–24.4), respectively. The JADER database contained 430,587 reports. The median time-to-onset of gingival hyperplasia values for immunosuppressants, calcium channel blockers, and anticonvulsants use were 71, 262, and 37 days, respectively. Furthermore, the 95% CI of the WSP β for anticonvulsants was over and excluded 1, which meant that they were wear-out failure type.

Conclusions

Our results suggest that DIGH monitoring of patients administered immunosuppressants, calcium channel blockers, or anticonvulsants is important. We demonstrated the potential risk of DIGH following the long-term use of calcium channel blocker over approximately 260 days. Based on the results of the association rule mining approach, patients with intellectual disability who are administered phenytoin should be monitored carefully. We recommend that patients who experience symptoms related to DIGH should be closely monitored.

Keywords

Drug-induced gingival hyperplasia (DIGH) JADER FAERS Time-to-onset analysis Association rule mining technique Spontaneous reporting system

Background

Drug-induced gingival hyperplasia (DIGH) is a periodontal side effects of certain drugs, causing swelling, bleeding, and problems with chewing, aesthetics, and pronunciation. In more severe cases, it can cause high mobility and detachment of the teeth due to alveolar bone absorption. All of these effects lead to the deterioration of the patient’s quality of life (QOL). More than 20 drugs are associated with DIGH [1], principal among them are immunosuppressants, calcium channel blockers, and anticonvulsants [1]. Dongali-Bagtzoglow [1] reported that >70, 6–15, and 50% of DIGH incidences were observed with cyclosporine (CsA; an immunosuppressant), nifedipine (a calcium channel blocker), and phenytoin (an anticonvulsant), respectively.

Since DIGH is a rare adverse event, epidemiologic research is difficult to perform. Spontaneous reporting systems (SRSs) are useful for the detection of rare adverse events and have been recognized as primary tools for pharmacovigilance that reflect the realities of clinical practice. The main aim of regulatory authorities is to collect and store safety reports for monitoring community health. Several pharmacovigilance indexes including the reporting odds ratio (ROR), were developed to evaluate drug-associated adverse events determined though SRS data. The concept of disproportionate analysis of ROR is common in the conventional analysis of SRSs, which attempts to quantify the degree of “unexpectedness” of a drug to adverse event association.

Recently, analysis of time-to-onset data has been proposed as a new method to detect signals for adverse events in SRS. To the best of our knowledge, analyses of the time-to-onset for DIGH using the Japanese Adverse Drug Event Report (JADER) database are rare. Association rule mining has been proposed as an analytical approach in order to study rare adverse drug events, and is a well-established method for discovering undetected relationships such as possible risk factors between variables in huge databases [24]. We examined DIGH using both analytical methods, and adjusted for the influence of demography and polypharmacy. This is the first study to evaluate the association between drugs and DIGH using ROR, time-to-onset analysis, and association rule mining. The aims of the study were to obtain new information of risk comparison on drugs or undetected several clinical factor combination, and onset profiles of DIGH for prescription drugs in the real world.

Methods

Data sources

The US Food and Drug Administration (FDA) adverse event reporting system (FAERS) is an SRS and the largest and best-known database worldwide. The regulatory authority in Japan, the Pharmaceuticals and Medical Devices Agency (PMDA), controls the SRS of the JADER database. Adverse events recorded in the FAERS database from January 2004 to June 2014 were downloaded from the FDA website (http://www.fda.gov). Relevant information from the JADER database from April 2004 to November 2016 was downloaded from the PMDA website (http://www.pmda.go.jp). We constructed a database that integrated each FAERS and JADER dataset using the FileMaker Pro 13 (FileMaker Inc.). For duplicate entries, we followed the FDA recommendation (http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Surveillance/AdverseDrugEffects), and adopted the most recent case number to identify duplicate patient reports and excluded them from the analysis.

We analyzed four immunosuppressants (CsA, everolimus, sirolimus, and mycophenolate mofetil), four calcium channel blockers (amlodipine, benidipine, nicardipine and nifedipine), and 11 anticonvulsants (carbamazepine, clobazam, diazepam, gabapentin, levetiracetam, phenobarbital, phenytoin, primidone, topiramate, valproic acid, and zonisamide). For drug definitions, we used both the general and brand names based on the DrugBank 3.0 and 4.0 (Table 1). Drugs in the FAERS were classified into four categories: Primary Suspect drug (PS), Secondary Suspect drug (SS), Concomitant (C), and Interacting (I); according to their anticipated degree of involvement in adverse events. The analysis was restricted to reports where drugs were recorded as PS and SS in the FAERS database. In the “drug information” table of the JADER database, each drug was assigned a code according to its association with adverse drug reactions: “suspected drug,” “concomitant drug,” or “interacting drug.” The analysis was restricted to reports where drugs were recorded as “suspected drugs” in the JADER database.
Table 1

Brand names of drugs

Generic namea

Number of brand name

Brand name

Immunosuppressants

 

 Cyclosporine

6

Gengraf, Neoral, etc.

 Everolimus

1

Certican

 Sirolimus

1

Rapamune

 Mycophenolate mofetil

2

Cellcept, Mucoloc

Calcium Channel Blockers

 

 Amlodipine

7

Amlocard, Amlodis, etc.

 Benidipineb

-

-

 Nicardipine

3

Cardene, Cardene IV, etc.

 Nifedipine

106

Adalat, Adalat 10, etc.

Anticonvulsants

 

 Carbamazepine

29

Apo-Carbamazepine, Atretol, etc.

 Clobazam

6

Chlorepin, Clorepin, etc.

 Diazepam

116

Alboral, Aliseum, etc.

 Gabapentin

3

Aclonium, Neurontin, etc.

 Levetiracetam

1

Keppra

 Phenobarbital

138

Adonal, Aephenal, etc.

 Phenytoin

130

Aleviatin, Antisacer, etc.

 Primidone

36

Apo-Primidone, Cyral, etc.

 Topiramate

2

Topamax, Topamax Sprinkle, etc.

 Valproic acid

25

Alti-Valproic, Avugane, etc.

 Zonisamide

4

Excegran, Exegram, etc.

a Generic name and brand name were used in this analysis b Benidipine exists only generic name

Definition of DIGH

The adverse event definitions used in FAERS were those provided by the Medical Dictionary for Regulatory Activities (MedDRA) version 17.1. For the extraction of cases from the FAERS database, we used two preferred terms (PTs), gingival hyperplasia (PT code: 10018283) and gingival hypertrophy (PT code: 10018284). The adverse event definitions used in JADER were those provided by MedDRA version 19.0. In the MedDRA 19.0, the two PTs related to DIGH were combined into “gingival hypertrophy (PT code: 10018284).” Thus, for the extraction of cases from the JADER database, we used the PT gingival hypertrophy (PT code: 10018284).

Data mining

ROR

For the detection of DIGH, we calculated the ROR as the ratio of the odds of reporting a DIGH adverse event versus all other events for a given drug, compared to the reporting odds for all other drugs. We detected the signals when the ROR estimated and lower limits of the corresponding 95% confidence interval (CI) were greater than 1, and at least 2 cases were required to define the signal [5, 6].

Time-to-onset analysis

Median, quartile, and Weibull shape parameter (WSP) tests were used to evaluate the time-to-onset analysis [710]. We analyzed the time the specific adverse event occurred from when the prescription of specific drugs commenced by using the Weibull distribution parameter. We excluded reports that did not have complete adverse event occurrence and prescription start times. The scale parameter α determined the scale of the distribution function while the shape parameter β determined the shape of the distribution function. In the analysis of the SRSs, the shape parameter β of the Weibull distribution was used to indicate the hazard without reference populations as follows: If the 95% CI of β included 1, the hazard was estimated to be constant over time (random failure type). If the lower limit of the 95% CI of β was greater than 1, the hazard was considered to increase over time (wear-out failure type). If the upper limit of the 95% CI of β was less than 1, the hazard was considered to decrease over time (initial failure type) [11]. The time-to-onset analysis was performed using JMP version 11.0 software (SAS Institute, Cary, NC, USA).

Association rule mining

The association rule mining approach attempts to evaluate frequent items in databases. Given a set of transactions T, an association rule can be expressed as X → Y, where X and Y are mutually exclusive sets of items [1214]. The rule’s statistical significance and strength are measured as support and confidence. The support is defined as the percentage of transactions in the data that contain all items in both the antecedent (left hand side) and the consequent (right hand side) of the rule [1214]. The support indicates how frequently the rule occurs in the transaction and has the following formula:
$$ Support=\mathrm{P}\left(\mathrm{X}\cap \mathrm{Y}\right)=\left\{\mathrm{X}\cap \mathrm{Y}\right\}/\left\{\mathrm{D}\right\} $$
where D is total number of transactions in the database. The confidence corresponds to the conditional probability P (Y|X). It is important for a rule to have a high confidence because it provides an accurate prediction of the association of the items in the rule. The formula for calculating confidence is as follows:
$$ Confidence=\mathrm{P}\left(\mathrm{X}\cap \mathrm{Y}\right)/\mathrm{P}\left(\mathrm{X}\right) $$
Lift is the probability of X and Y occurring together divided by the multiple of the two individual probabilities for X and Y; that is,
$$ Lift=\mathrm{P}\ \left(\mathrm{X}\cap \mathrm{Y}\right)/\mathrm{P}\left(\mathrm{X}\right)\mathrm{P}\left(\mathrm{Y}\right) $$

Since P (Y) appears in the denominator of the lift equation, the lift can be considered to be the confidence divided by P (Y). The lift can be evaluated as follows: lift = 1, > 1, and <1 if X and Y are independent, positively correlated, and negatively correlated, respectively. We performed these analyses using the apriori function of the arules library in the arules package R version 3.3.2 software [15].

Results

The FAERS database contained 5,821,716 reports that were submitted between January 2004 and June 2014. After deleting the duplicate reports, 4,551,642 reports were analyzed. The number of case reports and the RORs are summarized in Table 2. The RORs for cases involving CsA, everolimus, sirolimus, mycophenolate mofetil, amlodipine, nifedipine, carbamazepine, clobazam, levetiracetam, phenobarbital, phenytoin, primidone, topiramate, and valproic acid as PS or SS were 39.4 (95% CI: 30.3–51.2), 4.2 (1.7–10.0), 6.6 (2.5–17.7), 13.1 (7.2–23.2), 94.8 (80.0–112.9), 57.9 (35.7–94.0), 15.1 (10.3–22.3), 65.4 (33.8–126.7), 6.5 (3.6–11.8), 19.7 (8.8–44.0), 65.4 (52.4–82.9), 56.5 (21.1–151.7), 2.9 (1.1–7.7), and 17.5 (12.6–24.4), respectively.
Table 2

Number of reports and the reporting odds ratio for gingival hyperplasia by drugs

Drug

 

Total

Suspected Druga, b

  

Total

Case

ROR

(95% CI)

Total

Case

ROR

(95% CI)

FAERS

 

4,551,642

628

      

 Immunosuppressants

         

  Cyclosporine

 

20,578

66

25.9

(20.1–33.5)

12,693

62

39.4

(30.3–51.2)

  Everolimus

 

9292

6

4.7

(2.1–10.5)

8756

5

4.2

(1.7–10.0)

  Sirolimus

 

5607

5

6.5

(2.7–15.7)

4415

4

6.6

(2.5–17.7)

  Mycophenolate mofetil

 

20,212

30

11.3

(7.8–16.3)

6213

11

13.1

(7.2–23.2)

 Calcium Channel Blockers

         

  Amlodipine

 

96,153

195

20.9

(17.7–24.8)

18,509

174

94.8

(80.0–112.9)

  Benidipine

 

423

0

29

0

  Nicardipine

 

1838

1

c

 

674

1

c

 

  Nifedipine

 

18,542

23

9.3

(6.1–14.1)

2202

17

57.9

(35.7–94.0)

 Anticonvulsants

         

  Carbamazepine

 

24,644

31

9.6

(6.7–13.7)

13,494

27

15.1

(10.3–22.3)

  Clobazam

 

3155

11

25.8

(14.2–46.9)

1020

9

65.4

(33.8–126.7)

  Diazepam

 

36,751

9

1.8

(0.9–3.5)

11,581

3

1.9

(0.6–5.9)

  Gabapentin

 

71,069

11

1.1

(0.6–2.0)

17,372

2

0.8

(0.2–3.3)

  Levetiracetam

 

2630

14

39.6

(23.3–67.4)

12,437

11

6.5

(3.6–11.8)

  Phenobarbital

 

7717

18

17.4

(10.9–27.8)

2235

6

19.7

(8.8–44.0)

  Phenytoin

 

20,522

95

39.5

(31.8–49.2)

10,865

85

65.4

(52.4–82.9)

  Primidone

 

2644

10

27.9

(14.9–52.3)

520

4

56.5

(21.1–151.7)

  Topiramate

 

23,635

7

2.2

(1.0–4.6)

10,066

4

2.9

(1.1–7.7)

  Valproic acid

 

34,487

43

9.6

(7.1–13.1)

16,258

37

17.5

(12.6–24.4)

  Zonisamide

 

4138

4

7.1

(2.6–18.9)

1916

2

1.3

(0.7–2.5)

JADER

 

430,587

80

      

 Immunosuppressants

         

  Cyclosporine

 

8890

14

10.1

(5.7–17.9)

5711

14

15.8

(8.9–28.2)

  Mycophenolate mofetil

 

5320

3

3.1

(1.0–9.9)

3060

1

c

 

 Calcium Channel Blockers

         

  Amlodipine

 

30,451

18

3.8

(2.3–6.5)

3025

18

41.3

(24.4–69.8)

  Benidipine

 

3655

1

c

 

310

1

c

 

  Nicardipine

 

1755

5

16.3

(6.6–40.4)

302

5

96.6

(38.8–240.5)

  Nifedipine

 

10,350

8

4.5

(2.2–9.4)

954

6

36.7

(15.9–84.6)

 Anticonvulsants

         

  Carbamazepine

 

7411

18

16.6

(9.8–28.1)

5068

16

21.1

(12.2–36.4)

  Clobazam

 

898

11

77.2

(40.7–146.4)

239

10

268.4

(136.6–527.3)

  Diazepam

 

3831

10

16.0

(8.2–31.0)

733

6

47.9

(20.8–110.5)

  Gabapentin

 

1222

2

9.0

(2.2–36.8)

404

1

c

 

  Levetiracetam

 

1801

6

19.4

(8.4–44.6)

1177

5

24.4

(9.9–60.5)

  Phenobarbital

 

2622

12

28.9

(15.6–53.5)

1027

9

53.5

(26.7–107.3)

  Phenytoin

 

3712

27

59.0

(37.1–93.9)

1667

22

98.9

(60.4–161.9)

  Primidone

 

100

4

236.0

(84.7–657.8)

25

4

1078.9

(361.8–3217.5)

  Topiramate

 

536

1

c

 

258

1

c

 

  Valproic acid

 

8185

26

24.9

(15.6–39.8)

2618

20

54.9

(33.0–91.2)

  Zonisamide

 

2492

12

30.5

(16.5–56.3)

1073

8

44.8

(21.5–93.2)

a For FAERS, “Primary Suspect Drug” and “Secondary Suspect Drug” were analyzed

b For JADER, “Higiyaku” was analyzed

c Number of cases <2

The JADER contained 430,587 reports submitted between April 2004 and November 2016. The lower limits of the ROR 95% CI for CsA, amlodipine, nicardipine, nifedipine, carbamazepine, clobazam, diazepam, levetiracetam, phenobarbital, phenytoin, primidone, valproic acid, and zonisamide as suspected drug were all greater than one.

Time-to-onset

We evaluated data from the JADER database using time-to-onset analysis. The time-to-onset data and WSP are summarized in Fig. 1. The medians and quartile ranges for the onset day of DIGH after treatment with immunosuppressants, calcium channel blockers, and anticonvulsants were 71 (interquartile ranges: 22–120), 262 (76–442), and 37 (37–77) days, respectively. This time-to-onset profile shows that over 50% of DIGH were observed after 37–120 days. The WSP β and 95% CI of immunosuppressants, calcium channel blockers, and anticonvulsants were 1.41 (0.31–3.82), 1.70 (0.84–2.97), and 1.79 (1.23–2.44), respectively (Fig. 1). The WSP β and 95% CI lower limit of anticonvulsants exceeded 1, which describes a wear-out failure type, indicating a significant association between anticonvulsants and DIGH.
Fig. 1

Box-chart of time-to-onset analysis for immunosuppressants, calcium channel blockers, and anticonvulsants (the JADER database from April 2004 to November 2016 (n = 430,587))

Association rule mining

We analyzed the JADER database using an association rule mining technique. Association rule mining was applied to the DIGH data using demographic data including age (22 items: < 10 y.o., child, adolescent, etc.), patient history in the all reported cases (8141 items: intellectual disability, cerebral palsy, epilepsy, etc.), administered drugs listed in the Table 1 (19 items: cyclosporine, everolimus, silorimus, etc.), and adverse event [1 item: gingival hypertrophy (PT code: 10018284)]. The apriori algorithm efficiently extracts sets of adverse events that occur more frequently than the minimum support threshold (defined as 0.00001 in this study), and generates sets of adverse events with the minimum confidence threshold (defined as 0.01 in this study). Furthermore, the maximum size of mined frequent itemsets (maxlen: a parameter in the arules package) was restricted to 3. The result of the mining algorithm was a set of 44 rules (Tables 3 and 4). The support, confidence, and lift for each association rule are summarized in Tables 3 and 4; the association rules in descending order of the support are shown in Table 3, and in descending order of the lift are shown in Table 4. Anticonvulsants, especially phenytoin, demonstrated a high support value (Table 3, Fig. 2). The lift aspect of the association rules strength for anticonvulsants, especially phenytoin, carbamazepine, clobazam, and diazepam were high. The association rule of {phenytoin, intellectual disability} → {gingival hypertrophy} with high scores for lift and support were demonstrated (Table 3 (id [18]), Table 4 (id [4]), Fig. 2). For this rule, the values for support, confidence, and lift were 0.000021, 0.07, and 375.08, respectively. The association rule of {clobazam, diazepam} → {gingival hypertrophy} demonstrated high scores for lift (Table 4 (id [3]), Fig. 2). The association rule of {phenytoin, cerebral palsy} → {gingival hypertrophy} and {carbamazepine, cerebral palsy} → {gingival hypertrophy} also demonstrated high scores for lift (Table 4 (id [1, 2]), Fig. 2).
Table 3

Association parameters of rules (sort by support)

id

lhs (left hand side, antecedent)

 

rhs (right hand side, consequent)

support

confidence

lift

[1]

{epilepsy, phenytoin}

{gingival hypertrophy}

0.000044

0.02

85.21

[2]

{valproic acid, phenytoin}

{gingival hypertrophy}

0.000044

0.02

125.83

[3]

{carbamazepine, phenytoin}

{gingival hypertrophy}

0.000030

0.02

119.21

[4]

{cerebral palsy}

{gingival hypertrophy}

0.000028

0.02

82.79

[5]

{epilepsy, cerebral palsy}

{gingival hypertrophy}

0.000028

0.05

233.00

[6]

{carbamazepine, valproic acid}

{gingival hypertrophy}

0.000028

0.01

55.27

[7]

{clobazam}

{gingival hypertrophy}

0.000026

0.01

62.79

[8]

{intellectual disability}

{gingival hypertrophy}

0.000023

0.01

60.38

[9]

{valproic acid, cerebral palsy}

{gingival hypertrophy}

0.000023

0.05

244.10

[10]

{epilepsy, intellectual disability}

{gingival hypertrophy}

0.000023

0.03

130.43

[11]

{valproic acid, intellectual disability}

{gingival hypertrophy}

0.000023

0.03

152.11

[12]

{zonisamide, phenobarbital}

{gingival hypertrophy}

0.000023

0.04

183.73

[13]

{epilepsy, phenobarbital}

{gingival hypertrophy}

0.000023

0.01

73.54

[14]

{valproic acid, phenobarbital}

{gingival hypertrophy}

0.000023

0.02

78.38

[15]

{<10 y.o., phenobarbital}

{gingival hypertrophy}

0.000023

0.02

115.45

[16]

{zonisamide, valproic acid}

{gingival hypertrophy}

0.000023

0.01

70.41

[17]

{diazepam, valproic acid}

{gingival hypertrophy}

0.000023

0.02

123.52

[18]

{phenytoin, intellectual disability}

{gingival hypertrophy}

0.000021

0.07

375.08

[19]

{<10 y.o., zonisamide}

{gingival hypertrophy}

0.000021

0.03

172.14

[20]

{diazepam, epilepsy}

{gingival hypertrophy}

0.000021

0.03

137.71

[21]

{carbamazepine, diazepam}

{gingival hypertrophy}

0.000021

0.03

142.39

[22]

{carbamazepine, cerebral palsy}

{gingival hypertrophy}

0.000019

0.08

394.31

[23]

{clobazam, valproic acid}

{gingival hypertrophy}

0.000019

0.01

76.22

[24]

{diazepam, phenytoin}

{gingival hypertrophy}

0.000019

0.04

206.07

[25]

{10–19 years of age, epilepsy}

{gingival hypertrophy}

0.000019

0.01

52.51

[26]

{<10 y.o., epilepsy}

{gingival hypertrophy}

0.000019

0.01

59.78

[27]

{<10 y.o., valproic acid}

{gingival hypertrophy}

0.000019

0.01

58.17

[28]

{phenytoin, cerebral palsy}

{gingival hypertrophy}

0.000016

0.12

618.66

[29]

{clobazam, phenytoin}

{gingival hypertrophy}

0.000016

0.03

153.34

[30]

{clobazam, diazepam}

{gingival hypertrophy}

0.000016

0.07

377.71

[31]

{phenytoin, phenobarbital}

{gingival hypertrophy}

0.000016

0.01

75.70

[32]

{10–19 years of age, phenytoin}

{gingival hypertrophy}

0.000016

0.04

199.35

[33]

{periodontitis}

{gingival hypertrophy}

0.000014

0.01

71.69

[34]

{zonisamide, intellectual disability}

{gingival hypertrophy}

0.000014

0.05

256.30

[35]

{clobazam, zonisamide}

{gingival hypertrophy}

0.000014

0.02

106.42

[36]

{clobazam, epilepsy}

{gingival hypertrophy}

0.000014

0.01

61.88

[37]

{carbamazepine, clobazam}

{gingival hypertrophy}

0.000014

0.02

97.02

[38]

{carbamazepine, phenobarbital}

{gingival hypertrophy}

0.000014

0.02

79.89

[39]

{zonisamide, phenytoin}

{gingival hypertrophy}

0.000014

0.02

89.67

[40]

{phenobarbital, cerebral palsy}

{gingival hypertrophy}

0.000012

0.04

194.17

[41]

{<10 y.o., cerebral palsy}

{gingival hypertrophy}

0.000012

0.02

107.69

[42]

{clobazam, phenobarbital}

{gingival hypertrophy}

0.000012

0.03

141.60

[43]

{<10 y.o., clobazam}

{gingival hypertrophy}

0.000012

0.02

111.44

[44]

{clonazepam, phenytoin}

{gingival hypertrophy}

0.000012

0.02

114.93

Table 4

Association parameters of rules (sort by lift)

id

lhs (left hand side, antecedent)

 

rhs (right hand side, consequent)

support

confidence

lift

[1]

{phenytoin, cerebral palsy}

{gingival hypertrophy}

0.000016

0.12

618.66

[2]

{carbamazepine, cerebral palsy}

{gingival hypertrophy}

0.000019

0.08

394.31

[3]

{clobazam, diazepam}

{gingival hypertrophy}

0.000016

0.07

377.71

[4]

{phenytoin, intellectual disability}

{gingival hypertrophy}

0.000021

0.07

375.08

[5]

{zonisamide, intellectual disability}

{gingival hypertrophy}

0.000014

0.05

256.30

[6]

{valproic acid, cerebral palsy}

{gingival hypertrophy}

0.000023

0.05

244.10

[7]

{epilepsy, cerebral palsy}

{gingival hypertrophy}

0.000028

0.05

233.00

[8]

{diazepam, phenytoin}

{gingival hypertrophy}

0.000019

0.04

206.07

[9]

{10–19 years of age, phenytoin}

{gingival hypertrophy}

0.000016

0.04

199.35

[10]

{phenobarbital, cerebral palsy}

{gingival hypertrophy}

0.000012

0.04

194.17

[11]

{zonisamide, phenobarbital}

{gingival hypertrophy}

0.000023

0.04

183.73

[12]

{<10 y.o., zonisamide}

{gingival hypertrophy}

0.000021

0.03

172.14

[13]

{clobazam, phenytoin}

{gingival hypertrophy}

0.000016

0.03

153.34

[14]

{valproic acid, intellectual disability}

{gingival hypertrophy}

0.000023

0.03

152.11

[15]

{carbamazepine, diazepam}

{gingival hypertrophy}

0.000021

0.03

142.39

[16]

{clobazam, phenobarbital}

{gingival hypertrophy}

0.000012

0.03

141.60

[17]

{diazepam, epilepsy}

{gingival hypertrophy}

0.000021

0.03

137.71

[18]

{epilepsy, intellectual disability}

{gingival hypertrophy}

0.000023

0.03

130.43

[19]

{valproic acid, phenytoin}

{gingival hypertrophy}

0.000044

0.02

125.83

[20]

{diazepam, valproic acid}

{gingival hypertrophy}

0.000023

0.02

123.52

[21]

{carbamazepine, phenytoin}

{gingival hypertrophy}

0.000030

0.02

119.21

[22]

{<10 y.o., phenobarbital}

{gingival hypertrophy}

0.000023

0.02

115.45

[23]

{clonazepam, phenytoin}

{gingival hypertrophy}

0.000012

0.02

114.93

[24]

{<10 y.o., clobazam}

{gingival hypertrophy}

0.000012

0.02

111.44

[25]

{<10 y.o., cerebral palsy}

{gingival hypertrophy}

0.000012

0.02

107.69

[26]

{clobazam, zonisamide}

{gingival hypertrophy}

0.000014

0.02

106.42

[27]

{carbamazepine, clobazam}

{gingival hypertrophy}

0.000014

0.02

97.02

[28]

{zonisamide, phenytoin}

{gingival hypertrophy}

0.000014

0.02

89.67

[29]

{epilepsy, phenytoin}

{gingival hypertrophy}

0.000044

0.02

85.21

[30]

{cerebral palsy}

{gingival hypertrophy}

0.000028

0.02

82.79

[31]

{carbamazepine, phenobarbital}

{gingival hypertrophy}

0.000014

0.02

79.89

[32]

{valproic acid, phenobarbital}

{gingival hypertrophy}

0.000023

0.02

78.38

[33]

{clobazam, valproic acid}

{gingival hypertrophy}

0.000019

0.01

76.22

[34]

{phenytoin, phenobarbital}

{gingival hypertrophy}

0.000016

0.01

75.70

[35]

{epilepsy, phenobarbital}

{gingival hypertrophy}

0.000023

0.01

73.54

[36]

{periodontitis}

{gingival hypertrophy}

0.000014

0.01

71.69

[37]

{zonisamide, valproic acid}

{gingival hypertrophy}

0.000023

0.01

70.41

[38]

{clobazam}

{gingival hypertrophy}

0.000026

0.01

62.79

[39]

{clobazam, epilepsy}

{gingival hypertrophy}

0.000014

0.01

61.88

[40]

{intellectual disability}

{gingival hypertrophy}

0.000023

0.01

60.38

[41]

{<10 y.o., epilepsy}

{gingival hypertrophy}

0.000019

0.01

59.78

[42]

{<10 y.o., valproic acid}

{gingival hypertrophy}

0.000019

0.01

58.17

[43]

{carbamazepine, valproic acid}

{gingival hypertrophy}

0.000028

0.01

55.27

[44]

{10–19 years of age, epilepsy}

{gingival hypertrophy}

0.000019

0.01

52.51

Fig. 2

Association rules for gingival hyperplasia (the JADER database from April 2004 to November 2016 (n = 430,587)). The plot represents items and rules as vertices connected with directed edges. Relation parameters are typically added to the plot as labels on the edges or by varying the color or width of the arrows indicating the edges

Discussion

Our results suggest that adverse-event signals of DIGH were detected for several drugs in the FAERS and JADER databases. The risk of DIGH is stated in the package inserts of amlodipine, phenytoin, and zonisamide in the US, and CsA, mycophenolate mofetil, amlodipine, benidipine, nifedipine, levetiracetam, phenytoin, topiramate, and valproic acid in Japan, which agrees with our results. Furthermore, we detected signals of DIGH for several drugs such as nicardipine, carbamazepine, clobazam, diazepam, phenobarbital, primidone, and zonisamide that have no adverse-event warning stated in their package inserts in Japan. A more detailed analysis focusing on these drugs should be the subject of future investigation.

Our study had some limitations that should be noted. SRSs are subject to numerous biases and confounders. Since the SRSs did not contain control populations, the ROR does not provide sufficient evidence on causality and should be considered exploratory in the context of signal detection [57, 1621]. The time-to-onset analysis using the WSP method allowed the detection of potential adverse events without requiring a control population [22, 23]. For this reason, we examined the time-to-onset of DIGH using the WSP test.

To the best of our knowledge, no time-to-onset analyses of DIGH have been addressed using SRSs. The aim of the time-to-onset analysis was to obtain new information and compare the risks and onset profiles of DIGH for prescription drugs in the real world. The medians of the times-to-onset values for immunosuppressants and anticonvulsants were 71 and 37 days. The WSP β of anticonvulsants was 1.79 (1.23–2.44) and, so, the hazard was considered to increase over time (Fig. 1). DIGH induced by anticonvulsants was likely to be wear-out failure type. According to a report by Seymour et al. [24], phenytoin-induced gingival hyperplasia can occur within 3 months of drug use, which agrees with our results. These results also corresponded with those of previous reports. To alleviate DIGH, early countermeasures must be initiated. The effective treatments for DIGH are drug substitution or withdrawal, good oral hygiene practices such as plaque control [1], and surgical treatment. DIGH induced by anticonvulsants is clinically important because the number of therapies available for epilepsy has increased. Seizure control is the primary goal of epilepsy treatment [25] and therefore it is difficult to withdraw anticonvulsant drugs. Costa et al. [26] have reported that appropriate plaque control and early detection of periodontal disease is difficult, and that care and periodontal disease tend to worsen easily in patients with refractory epilepsy. The analysis results suggest that early monitoring of the gingival tissue following the observation of gingival hyperplasia in patients administered anticonvulsant agents is required to prevent aggravation of the condition.

The median onset of DIGH by calcium channel blockers was 262 days, which differed from those of anticonvulsants and immunosuppressants. Special attention should be paid to the possibility of DIGH occurrence with these drugs, and careful observation is recommended from 2 to 14 months.

The mechanism mediating the pathogenesis of medication-triggered connective tissue responses in the gingiva is still poorly understood. Some hypotheses have suggested the role of factors such as 1) fibroblasts [2732], 2) inflammatory cytokines [30, 3336], and 3) matrix metalloproteinase (MMP) synthesis [31]. CsA, nifedipine, and phenytoin promote the modeling of periodontal fibroblasts through the synthesis of gingival fibroblasts or inhibition of the decomposition of gingival fibroblasts [2731]. Phenytoin may increase the level of translatable collagen mRNA in human gingival fibroblast [32], while CsA, nifedipine, and phenytoin enhance the synthesis of collagenous proteins in vitro [30, 3336]. In the case of human gingival fibroblasts simultaneously exposed to nifedipine and interleukin-1β [33], an enhancement of collagenous protein synthesis was observed [33]. CsA may cause a decline in the secretion of MMP-1 and an accumulation of collagenous proteins [31]. The differences in these mechanisms may have affected the ROR value or time-to-onset profiles of each drug.

In the association rule mining approach, since the lift values of two combined items, {phenytoin, intellectual disability} were high, patients with intellectual disability have a potential risk of DIGH following treatment with anticonvulsants. Therefore, patients with intellectual disabilities should be monitored carefully. The lift values of two combined items {diazepam, clobazam}, which were greater than that of one value were also high enough to suggests a strong association. The support value was low, and these data suggest that each association was strong, although the expression rates were low. This information suggests that polypharmacy with anticonvulsants may increase the risk of DIGH. Since Harpaz et al. addressed the issue of confounding factors when applying the association rule mining approach, our obtained association rules might be tabulated independently in the future to evaluate the confounding factors related to DIGH [3].

Patients who were administered calcium channel blockers such as nifedipine or amlodipine demonstrated high drug levels in their gingival crevicular fluid and were likely to be exposed to high levels of these drugs [37, 38]. DIGH was observed with CsA treatment in 25–30% and ≥70% of adults and pediatric patients, respectively [1]. Co-administration of medications with CsA increased the risk of CsA-induced gingival hyperplasia [39], although, the relationship between the dosage, duration of therapy, age, and sex is still not clear. Unfortunately, cases reported in the SRS database do not always contain sufficient information on patient background, drug dosage, drug-drug interactions and mode of administration to allow proper evaluation. Considering the causality constraints of the current analysis, further epidemiological studies are recommended.

Conclusions

This study was the first to evaluate the incidences of DIGH using SRSs. Despite the limitations inherent to SRS, we identified the risk of DIGH induced by anticonvulsants, immunosuppressants, and calcium channel blockers. We demonstrated the potential risk of DIGH following the long-term use of calcium channel blocker for approximately 260 days. The association rule mining results suggest that patients with intellectual disabilities administered phenytoin, should be monitored carefully. We recommend that patients who experience symptoms related to gingival hyperplasia should be closely monitored and advised to adhere to an appropriate care plan for oral hygiene. Finally, it is our hope that these data will update the information available to clinicians and be potentially useful for improving the management of DIGH.

Abbreviations

C: 

Concomitant

CI: 

Confidence Interval

CsA: 

Cyclosporine

DIGH: 

Drug-induced gingival hyperplasia

FAERS: 

US Food and Drug Administration Adverse Event Reporting System

FDA: 

Food and Drug Administration

I: 

Interacting

JADER: 

Japanese Adverse Drug Event Report

MedDRA: 

Medical Dictionary for Regulatory Activities

MMP: 

Matrix metalloproteinase

PMDA: 

The Pharmaceuticals and Medical Devices Agency

PS: 

Primary Suspect

PT: 

Preferred Term

QOL: 

Quality of life

ROR: 

Reporting Odds Ratio

SRS: 

Spontaneous Reporting System

SS: 

Secondary Suspect

WSP: 

Weibull Shape Parameter

Declarations

Acknowledgements

Not applicable.

Funding

This research was partially supported by JSPS KAKENHI Grant number, 24360126 and 17K08452.

Availability of data and materials

The dataset supporting the conclusions of this article is included within the article.

Authors’ contributions

HH and MN conceived of the study, participated in its design and coordination, and drafted the manuscript. JA conceived of the study, participated in its design, helped the statistical analysis, and drafted the manuscript. YH, TM, SS, YM performed the statistical analysis. SH, AF, and MN helped to interpret of data. YK and TO helped to interpretation of data, and participated in its design of the study. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

JA is an employee of Medical Database Co., Ltd. TO is an employee of Ace Pharmacy, Seiyou Trading Co., Ltd. The other authors have no conflict of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratory of Drug Informatics, Gifu Pharmaceutical University
(2)
Medical Database Co., Ltd
(3)
Ace Pharmacy, Seiyou Trading Co., Ltd
(4)
United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University

References

  1. Dongari-Bagtzoglou A. Informational paper: drug-associated gingival enlargement. J Periodontol. 2004;75:1424–31.View ArticlePubMedGoogle Scholar
  2. Fujiwara M, Kawasaki Y, Yamada H. A Pharmacovigilance approach for post-Marketing in Japan Using the Japanese adverse drug event report (JADER) database and association analysis. Plos One. 2016;11:e0154425.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Harpaz R, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics. 2010;11:S7.View ArticlePubMedPubMed CentralGoogle Scholar
  4. Yildirim P. Association patterns in open data to explore ciprofloxacin adverse events. Appl Clin Inform. 2015;6:728–47.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Hashiguchi M, Imai S, Uehara K, Maruyama J, Shimizu M, Mochizuki M. Factors affecting the timing of signal detection of adverse drug reactions. Plos One. 2015;10:e0144263.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Poluzzi E, Raschi E, Piccinni C, De Ponti F. Data mining techniques in Pharmacovigilance: analysis of the publicly accessible FDA adverse event reporting system (AERS). INTECH. 2012:265–302. doi:10.5772/50095.
  7. Nakamura M, Umetsu R, Abe J, Matsui T, Ueda N, Kato Y, et al. Analysis of the time-to-onset of osteonecrosis of jaw with bisphosphonate treatment using the data from a spontaneous reporting system of adverse drug events. J Pharm Health Care Sci. 2015;1:34.View ArticlePubMedPubMed CentralGoogle Scholar
  8. Abe J, Umetsu R, Mataki K, Kato Y, Ueda N, Nakayama Y, et al. Analysis of Stevens-Johnson syndrome and toxic epidermal necrolysis using the Japanese adverse drug event report database. J Pharm Health Care Sci. 2016;2:14.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Sasaoka S, Matsui T, Hane Y, Abe J, Ueda N, Motooka Y, et al. Time-to-onset analysis of drug-induced long QT syndrome based on a spontaneous reporting system for adverse drug events. Plos One. 2016;11:e0164309.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Sasaoka S, Matsui T, Hane Y, Abe J, Ueda N, Motooka Y, et al. Evaluation of the association of hand-foot syndrome with anticancer drugs using the US Food and Drug Administration adverse event reporting system (FAERS) and Japanese adverse drug event report (JADER) databases. Yakugaku Zasshi. 2016;136:507–15.View ArticlePubMedGoogle Scholar
  11. Leroy F, Dauxois JY, Théophile H, Haramburu F, Tubert-Bitter P. Estimating time-to-onset of adverse drug reactions from spontaneous reporting databases. BMC Med Res Methodol. 2014;14:17.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Agrawal R, Srikant R. Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases. 1994;7:478–99.Google Scholar
  13. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. Special Interenst Group on Management of Data. 1993;22:207–16.Google Scholar
  14. Zhu AL, Li J, Leong TY. Automated knowledge extraction for decision model construction: a data mining approach. AMIA Ann Symp Proc. 2003;2003:758–76.Google Scholar
  15. Hahsler M, Grün B, Hornik K. A computational environment for mining association rules and frequent item sets. J Stat Soft. 2005;14:15.View ArticleGoogle Scholar
  16. Suzuki Y, Suzuki H, Umetsu R, Uranishi H, Abe J, Nishibata Y, et al. Analysis of the interaction between clopidogrel, aspirin, and proton pump inhibitors using the FDA adverse event reporting system database. Biol Pharm Bull. 2015;38:680–6.View ArticlePubMedGoogle Scholar
  17. Abe J, Umetsu R, Kato Y, Ueda N, Nakayama Y, Suzuki Y, et al. Evaluation of Dabigatran- and Warfarin-associated hemorrhagic events using the FDA-adverse event reporting system database stratified by age. Int J Med Sci. 2015;12:312–21.View ArticlePubMedPubMed CentralGoogle Scholar
  18. Ueda N, Umetsu R, Abe J, Kato Y, Nakayama Y, Kato Z, et al. Analysis of neuropsychiatric adverse events in patients treated with Oseltamivir in spontaneous adverse event reports. Biol Pharm Bull. 2015;38:1638–44.View ArticlePubMedGoogle Scholar
  19. Umetsu R, Nishibata Y, Abe J, Suzuki Y, Hara H, Nagasawa H, et al. Evaluation of the association between the use of oral anti-hyperglycemic agents and hypoglycemia in Japan by data mining of the Japanese adverse drug event report (JADER) database. Yakugaku Zasshi. 2014;134:299–304.View ArticlePubMedGoogle Scholar
  20. Kato Y, Umetsu R, Hosoya N, Ueda N, Abe J, Nakayama Y, et al. Analysis of licorice-induced pseudoaldosteronism in the Japanese adverse drug event report database. Tradit Kampo Med. 2016;3:63–70.View ArticleGoogle Scholar
  21. Kato Y, Umetsu R, Abe J, Ueda N, Nakayama Y, Kinosada Y, et al. Hyperglycemic adverse events following antipsychotic drug administration in spontaneous adverse event reports. J Pharm Health Care Sci. 2015;1:15.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Yamada M, Handa J. Comparison of the onset time profile among the interferon formulations in adverse drug reaction of suicide- or diabetes related. Jpn J Pharmacoepidemiol. 2014;19:23–30.View ArticleGoogle Scholar
  23. Sauzet O, Carvajal A, Escudero A, Molokhia M, Cornelius VR. Illustration of the weibull shape parameter signal detection tool using electronic healthcare record data. Drug Saf. 2013;36:995–1006.View ArticlePubMedGoogle Scholar
  24. Seymour RA, Ellis JS, Thomason JM. Risk factors for drug-induced gingival overgrowth. J Clin Periodontol. 2000;27:217–23.View ArticlePubMedGoogle Scholar
  25. Johnson EK, Jones JE, Seidenberg M, Hermann BP. The relative impact of anxiety, depression, and clinical seizure features on health-related quality of life in epilepsy. Epilepsia. 2004;45:544–50.View ArticlePubMedGoogle Scholar
  26. Costa AL, Yasuda CL, Shibasaki W, Nahás-Scocate AC, de Freitas CF, Carvalho PE, et al. The association between periodontal disease and seizure severity in refractory epilepsy patients. Seizure. 2014;23:227–30.View ArticlePubMedGoogle Scholar
  27. Hassell TM, Page RC, Narayanan AS, Cooper CG. Diphenylhydantoin (Dilantin) gingival hyperplasia: drug-induced abnormality of connective tissue. Proc Natl Acad Sci. 1976;73:2909–12.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Chung Y, Fu E. Crosstalk between Shh and TGF-β signaling in cyclosporine-enhanced cell proliferation in human gingival fibroblasts. Plos One. 2013;8:e70128.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Montebugnoli L, Bernardi F. Cyclosporin a effects on extra oral connective tissue in heart transplanted patients. J Int Acad Periodontol. 1999;1:91–4.PubMedGoogle Scholar
  30. Morton RS, Dongari-Bagtzoglou AI. Regulation of gingival fibroblast interleukin-6 secretion by cyclosporine a. J Periodontol. 1999;70:1464–71.View ArticlePubMedGoogle Scholar
  31. Bolzani G, Coletta RD, Junior HM, Almeida OP, Graner E. Cyclosporin a inhibits production and activity of matrix metalloproteinases by gingival fibroblasts. J Periodontal Res. 2000;35:51–8.View ArticlePubMedGoogle Scholar
  32. Deshmukh SN, Dive AM, Moharil R, Munde P. Enigmatic insight into collagen. J Oral Maxillofac Pathol. 2016;20:276–83.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Johnson RB, Zebrowski EJ, Dai X. Synergistic enhancement of collagenous protein synthesis by human gingival fibroblasts exposed to nifedipine and interleukin-1-beta in vitro. J Oral Pathol Med. 2000;29:8–12.View ArticlePubMedGoogle Scholar
  34. Williamson MS, Miller EK, Plemons J, Rees T, Iacopino AM. Cyclosporine a upregulates interleukin-6 gene expression in human gingiva: possible mechanism for gingival overgrowth. J Periodontol. 1994;65:895–903.View ArticlePubMedGoogle Scholar
  35. Duncan MR, Berman B. Stimulation of collagen and Glycosaminoglycan production in cultured human adult dermal fibroblasts by recombinant human interleukin 6. J Invest Dermatol. 1991;97:686–92.View ArticlePubMedGoogle Scholar
  36. Myrillas TT, Linden GJ, Marley JJ, Irwin CR. Cyclosporin a regulates interleukin-1beta and interleukin-6 expression in gingiva: implications for gingival overgrowth. J Periodontol. 1999;70:294–300.View ArticlePubMedGoogle Scholar
  37. Seymour RA, Preshaw PM, Thomason JM, Ellis JS, Steele JG. Cardiovascular diseases and periodontology. J Clin Periodontol. 2003;30:279–92.View ArticlePubMedGoogle Scholar
  38. Ellis JS, Seymour RA, Monkman SC, Idle JR. Gingival sequestration of nifedipine in nifedipine-induced gingival overgrowth. Lancet. 1992;339:1382–3.View ArticlePubMedGoogle Scholar
  39. Bartoli F, Castronovo G, Stabile A. Risk factors conditioning the incidence and severity of cyclosporine A-induced gingival overgrowth and methods of prevention. Minerva Stomatol. 2004;53:165–70.PubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement