Bullous pemphigoid (BP) is a rare autoimmune blistering skin disease. Although previous case reports and two disproportionality analyses of European populations have suggested associations between dipeptideyl peptidase 4 (DPP-4) inhibitor use and BP (13), the involvement of race, antidiabetes drugs, sex, age, and other risk factors in BP induced by DPP-4 inhibitors has not yet been evaluated. We conducted disproportionality analysis using the Japanese Adverse Drug Event Report (JADER) database, which contains all pharmacovigilance data that have been spontaneously reported to the Pharmaceuticals and Medical Devices Agency (PMDA) since April 2004.

As of June 2017, the database contained 454,027 reports of adverse drug reactions. For this study, data were downloaded from the PMDA website (http://www.pmda.go.jp). Potential signals of DPP-4 inhibitors or other drugs with BP were assessed with categories using the reporting odds ratio (ROR), which is an established parameter in pharmacovigilance research. An ROR was calculated using a two-by-two contingency table (4). We examined DPP-4 inhibitors (alogliptin, anagliptin, linagliptin, omarigliptin, saxagliptin, sitagliptin, trelagliptin, teneligliptin, and vildagliptin), other antihyperglycemia drug classes (sulfonylureas, biguanides, α-glucosidase inhibitors [α-GIs], glinides, glucagon-like peptide 1 receptor agonists [GLP-1 RAs], sodium–glucose cotransporter 2 inhibitors [SGLT2is], thiazolidines, and any kind of insulins), other drugs that are reported as causal reagents for BP, and negative controls (2,5). An ROR is considered significant when the lower bound of the two-sided 95% CI for the risk of BP was larger than 1. The data were analyzed using the R software (version 3.4.0; The R Foundation for Statistical Computing, Vienna, Austria).

In the database, 546 BP (referred to as pemphigoid in the database) case subjects were found. The total number of reports of BP associated with DPP-4 inhibitor use was 392. Although the RORs (95% CI) for acetaminophen were 0.25 (0.09–0.66), the RORs (95% CI) for the DPP-4 inhibitors were wholly significant and as follows; alogliptin, 8.02 (4.87–13.22); anagliptin, 10.84 (3.46–33.96); sitagliptin, 12.59 (9.86–16.06); trelagliptin, 13.77 (3.40–55.85); saxagliptin, 15.85 (5.87–42.79); linagliptin, 28.96 (21.38–39.23); omarigliptin, 43.79 (5.85–327.70); teneligliptin, 58.52 (42.75–80.10); and vildagliptin, 105.33 (88.54–125.30) (Table 1). Because most patients with diabetes receive combinations of medications, we excluded case subjects who received DPP-4 inhibitors. After such exclusion, significant RORs disappeared for case subjects receiving all the other antihyperglycemia drugs. Conversely, case subjects receiving DPP-4 inhibitors showed significant RORs even after exclusion of subjects who were receiving any other antihyperglycemia or candidate drugs, indicating that co-prescription bias was excluded. We stratified data by time trends of report and confirmed that the RORs of DPP-4 inhibitors were consistently significant over the period of 2012–2016. An analysis of ROR by therapeutic area with subjects who used antihyperglycemia drugs showed the RORs (95% CI) as follows: linagliptin, 2.67 (1.96–3.64); teneligliptin, 5.52 (4.01–7.60); vildagliptin, 12.09 (9.88–14.79); and DPP-4 inhibitors, 69.49 (34.50–139.99). Meanwhile, statistically significant associations disappeared for sitagliptin, saxagliptin, and alogliptin, implying a drug-specific effect. Vildagliptin, teneligliptin, and linagliptin, which show higher RORs than other DPP-4 inhibitors, possess lower substrate selectivity for DPP-4 or higher volume of distribution. It is possible that the inhibition of DPP-8 or DPP-9, but not that of DPP-4, in the skin evokes immunopathogenic reactions that result in the blister formation in BP. The number of males with BP taking DPP-4 inhibitors was significantly higher than the number of females with BP (male, 8,131 out of 222,567; female, 4,911 out of 215,460; χ2 = 715.4; P < 2.2e-16). There was no significant difference in the frequency of BP after adjustment for DPP-4 inhibitor use (with DPP-4 inhibitor use, 238 males out of 8,131, 147 females out of 4,911, χ2 = 0.05, P = 0.83; without DPP-4 inhibitor use, 71 males out of 214,436, 76 females out of 210,549, χ2 = 0.27, P = 0.60). Thus, the higher number of males with DPP-4 inhibitor–related BP was presumably because DPP-4 inhibitors were used more often in males than in females. Most of the case subjects with BP were elderly people older than 60 years old. In 2015, NDB (National Database of Health Insurance Claims and Specific Health Checkups of Japan) Open Data Japan, a comprehensive prescription database, indicated that the distribution of DPP-4 inhibitor prescription in each 10-year age-group (from <40 to ≥90 years) was consistent with the adverse events induced by DPP-4 inhibitors in the JADER database. This suggests that advanced age is a risk factor for BP induced by DPP-4 inhibitor use, which is concordant with several previous reports, mainly from Europe, that showed BP was predominantly encountered in elderly people (2,3).

Table 1

Exposure to target drugs that had significant ROR values and numbers of BP case subjects

With BP (n)Without BP (n)ROR95% CI
All drugs 546 453,481   
All antihyperglycemia drugs 402 38,473 30.11 (24.89–36.44) 
 All antihyperglycemia drugs without DPP-4 inhibitors 10 25,672 0.31 (0.17–0.58) 
 DPP-4 inhibitors 392 12,811 87.56 (72.61–105.59) 
  Alogliptin 16 1,700 8.02 (4.87–13.22) 
  Anagliptin 231 10.84 (3.46–33.96) 
  Sitagliptin 76 5,752 12.59 (9.86–16.06) 
  Trelagliptin 121 13.77 (NA) (3.40–55.85) 
  Saxagliptin 211 15.85 (5.87–42.79) 
  Linagliptin 47 1,470 28.96 (21.38–39.23) 
  Omarigliptin 19 43.79 (NA) (5.85–327.70) 
  Teneligliptin 45 695 58.52 (42.75–80.10) 
  Vildagliptin 229 3,089 105.33 (88.54–125.30) 
  Without biguanides 305 9,790 57.36 (48.38–67.99) 
  Without glinides 359 11,988 70.70 (59.19–84.45) 
  Without sulfonylureas 295 8,517 61.40 (51.82–72.76) 
  Without thiazolidines 365 11,422 78.05 (65.25–93.36) 
  Without GLP-1 RAs 384 12,732 82.06 (68.23–98.68) 
  Without α-GIs 327 10,520 62.87 (52.92–74.69) 
  Without insulins 322 11,168 56.93 (47.96–67.59) 
  Without SGLT2is 379 11,529 87.00 (72.45–104.47) 
 Biguanides 88 6,589 13.03 (10.36–16.39) 
 Glinides 35 2,827 10.92 (7.74–15.41) 
 Sulfonylureas 100 13,814 7.14 (5.74–8.87) 
 Thiazolidines 28 4,866 4.98 (3.40–7.30) 
 GLP-1 RAs 848 7.94 (3.94–16.00) 
 α-GIs 70 9,306 7.02 (5.46–9.03) 
 Insulins 73 11,643 5.86 (4.57–7.50) 
 SGLT2is 13 2,183 5.04 (2.90–8.76) 
 Biguanides without DPP-4 inhibitors 3,568 NA (0.03–1.65) 
 Glinides without DPP-4 inhibitors 2,004 NA (0.21–3.32) 
 Sulfonylureas without DPP-4 inhibitors 9,520 0.26 (0.08–0.80) 
 Thiazolidines without DPP-4 inhibitors 3,477 NA (0.03–1.69) 
 GLP-1 RAs without DPP-4 inhibitors 769 NA — 
 α-GIs without DPP-4 inhibitors 7,015 0.59 (0.24–1.42) 
 Insulins without DPP-4 inhibitors 10,003 0.24 (0.08–0.76) 
 SGLT2is without DPP-4 inhibitors 901 NA — 
Other drugs     
 Psoralens 27 NA (4.18–227.18) 
 Ustekinumab 300 8.35 (2.67–26.11) 
 Galantamine hydrobromide 814 4.10 (1.53–11.00) 
 Hydrochlorothiazide 13 3,309 3.32 (1.91–5.76) 
 Nifedipine 36 10,680 2.93 (2.09–4.11) 
 Terbinafine 1,888 2.66 (1.19–5.95) 
 Amlodipine 88 31,732 2.55 (2.03–3.21) 
 Furosemide 67 24,344 2.47 (1.91–3.19) 
 Aspirin 73 28,778 2.28 (1.78–2.92) 
 Tiobutarit 2,769 2.11 (1.00–4.46) 
 Losartan 15 6,144 2.06 (1.23–3.44) 
 Psoralens without DPP-4 inhibitors 27 NA (4.18–227.18)* 
 Ustekinumab without DPP-4 inhibitors 292 8.57 (2.74–26.82)* 
 Galantamine hydrobromide without DPP-4 inhibitors 760 NA — 
 Hydrochlorothiazide without DPP-4 inhibitors 2,876 NA — 
 Nifedipine without DPP-4 inhibitors 9,869 0.50 (0.22–1.12) 
 Terbinafine without DPP-4 inhibitors 1,848 1.80 (0.67–4.83) 
 Amlodipine without DPP-4 inhibitors 14 28,860 0.39 (0.23–0.66) 
 Furosemide without DPP-4 inhibitors 30 22,941 1.09 (0.75–1.58) 
 Aspirin without DPP-4 inhibitors 26,638 0.24 (0.12–0.48) 
 Tiobutarit without DPP-4 inhibitors 2,711 2.16 (1.02–4.56)* 
 Losartan without DPP-4 inhibitors 5,680 0.44 (0.14–1.36) 
Negative control     
 Acetaminophen 13,098 0.25 (0.09–0.66) 
  Without DPP-4 inhibitors 12,605 NA — 
With BP (n)Without BP (n)ROR95% CI
All drugs 546 453,481   
All antihyperglycemia drugs 402 38,473 30.11 (24.89–36.44) 
 All antihyperglycemia drugs without DPP-4 inhibitors 10 25,672 0.31 (0.17–0.58) 
 DPP-4 inhibitors 392 12,811 87.56 (72.61–105.59) 
  Alogliptin 16 1,700 8.02 (4.87–13.22) 
  Anagliptin 231 10.84 (3.46–33.96) 
  Sitagliptin 76 5,752 12.59 (9.86–16.06) 
  Trelagliptin 121 13.77 (NA) (3.40–55.85) 
  Saxagliptin 211 15.85 (5.87–42.79) 
  Linagliptin 47 1,470 28.96 (21.38–39.23) 
  Omarigliptin 19 43.79 (NA) (5.85–327.70) 
  Teneligliptin 45 695 58.52 (42.75–80.10) 
  Vildagliptin 229 3,089 105.33 (88.54–125.30) 
  Without biguanides 305 9,790 57.36 (48.38–67.99) 
  Without glinides 359 11,988 70.70 (59.19–84.45) 
  Without sulfonylureas 295 8,517 61.40 (51.82–72.76) 
  Without thiazolidines 365 11,422 78.05 (65.25–93.36) 
  Without GLP-1 RAs 384 12,732 82.06 (68.23–98.68) 
  Without α-GIs 327 10,520 62.87 (52.92–74.69) 
  Without insulins 322 11,168 56.93 (47.96–67.59) 
  Without SGLT2is 379 11,529 87.00 (72.45–104.47) 
 Biguanides 88 6,589 13.03 (10.36–16.39) 
 Glinides 35 2,827 10.92 (7.74–15.41) 
 Sulfonylureas 100 13,814 7.14 (5.74–8.87) 
 Thiazolidines 28 4,866 4.98 (3.40–7.30) 
 GLP-1 RAs 848 7.94 (3.94–16.00) 
 α-GIs 70 9,306 7.02 (5.46–9.03) 
 Insulins 73 11,643 5.86 (4.57–7.50) 
 SGLT2is 13 2,183 5.04 (2.90–8.76) 
 Biguanides without DPP-4 inhibitors 3,568 NA (0.03–1.65) 
 Glinides without DPP-4 inhibitors 2,004 NA (0.21–3.32) 
 Sulfonylureas without DPP-4 inhibitors 9,520 0.26 (0.08–0.80) 
 Thiazolidines without DPP-4 inhibitors 3,477 NA (0.03–1.69) 
 GLP-1 RAs without DPP-4 inhibitors 769 NA — 
 α-GIs without DPP-4 inhibitors 7,015 0.59 (0.24–1.42) 
 Insulins without DPP-4 inhibitors 10,003 0.24 (0.08–0.76) 
 SGLT2is without DPP-4 inhibitors 901 NA — 
Other drugs     
 Psoralens 27 NA (4.18–227.18) 
 Ustekinumab 300 8.35 (2.67–26.11) 
 Galantamine hydrobromide 814 4.10 (1.53–11.00) 
 Hydrochlorothiazide 13 3,309 3.32 (1.91–5.76) 
 Nifedipine 36 10,680 2.93 (2.09–4.11) 
 Terbinafine 1,888 2.66 (1.19–5.95) 
 Amlodipine 88 31,732 2.55 (2.03–3.21) 
 Furosemide 67 24,344 2.47 (1.91–3.19) 
 Aspirin 73 28,778 2.28 (1.78–2.92) 
 Tiobutarit 2,769 2.11 (1.00–4.46) 
 Losartan 15 6,144 2.06 (1.23–3.44) 
 Psoralens without DPP-4 inhibitors 27 NA (4.18–227.18)* 
 Ustekinumab without DPP-4 inhibitors 292 8.57 (2.74–26.82)* 
 Galantamine hydrobromide without DPP-4 inhibitors 760 NA — 
 Hydrochlorothiazide without DPP-4 inhibitors 2,876 NA — 
 Nifedipine without DPP-4 inhibitors 9,869 0.50 (0.22–1.12) 
 Terbinafine without DPP-4 inhibitors 1,848 1.80 (0.67–4.83) 
 Amlodipine without DPP-4 inhibitors 14 28,860 0.39 (0.23–0.66) 
 Furosemide without DPP-4 inhibitors 30 22,941 1.09 (0.75–1.58) 
 Aspirin without DPP-4 inhibitors 26,638 0.24 (0.12–0.48) 
 Tiobutarit without DPP-4 inhibitors 2,711 2.16 (1.02–4.56)* 
 Losartan without DPP-4 inhibitors 5,680 0.44 (0.14–1.36) 
Negative control     
 Acetaminophen 13,098 0.25 (0.09–0.66) 
  Without DPP-4 inhibitors 12,605 NA — 

A total of 42 drugs did not have significant ROR and were omitted here. NA, not applicable due to the low number of case subjects (<3).

*

Significant ROR even without DPP-4 inhibitors.

As the JADER database is a spontaneous reporting system, there are several limitations, such as underreporting, overreporting, missing data, mistakes in data entry, and a lack of control data. Especially among these, strong selection bias, so-called notoriety bias, must always be taken into consideration. In this study, we did not investigate the contribution of coexisting illnesses, drug dose, or the period of exposure. Further clinical monitoring and analytical observational studies that can evaluate the association are needed.

Acknowledgments. The authors acknowledge all the contributors of JADER database. The authors thank Misa Katayama (Yokohama City University) for her excellent secretarial assistance.

Funding. This work was partly supported by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan [Grants-in-Aid for Scientific Research (B) 16H05329] (to Y.T.).

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. M.A. and J.S. initially conceived the concept of this study and drafted the manuscript. M.A. was responsible for data collection and performed the statistical analysis. H.K., N.S., and Y.T. made substantial contributions to the interpretation of data and revised the manuscript for important intellectual content. All authors approved submission of the manuscript. J.S. and Y.T. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

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