Open Access

Hyperglycemic adverse events following antipsychotic drug administration in spontaneous adverse event reports

  • Yamato Kato1,
  • Ryogo Umetsu1,
  • Junko Abe1, 2,
  • Natsumi Ueda1,
  • Yoko Nakayama1,
  • Yasutomi Kinosada3 and
  • Mitsuhiro Nakamura1Email author
Journal of Pharmaceutical Health Care and Sciences20151:15

https://doi.org/10.1186/s40780-015-0015-6

Received: 16 December 2014

Accepted: 23 March 2015

Published: 16 April 2015

Abstract

Background

Antipsychotics are potent dopamine antagonists used to treat schizophrenia and bipolar disorder. The aim of this study was to evaluate the relationship between antipsychotic drugs and adverse hyperglycemic events using the FDA Adverse Event Reporting System (FAERS) database. In particular, we focused on adverse hyperglycemic events associated with atypical antipsychotic use, which are major concerns.

Findings

We analyzed reports of adverse hyperglycemic events associated with 26 antipsychotic drugs in the FAERS database from January 2004 to March 2013. The Standardized Medical Dictionary for Regulatory Activities Queries (SMQ) preferred terms (PTs) was used to identify adverse hyperglycemic events. The number of adverse hyperglycemic reports for the top eight antipsychotic drugs, quetiapine, olanzapine, risperidone, aripiprazole, haloperidol, clozapine, prochlorperazine, and chlorpromazine was 12,471 (28.9%), 8,423 (37.9%), 5,968 (27.0%), 4,045 (23.7%), 3,445 (31.5%), 2,614 (14.3%), 1,800 (19.8%), and 1,003 (35.7%), respectively. The reporting ratio increased with co-administration of multiple antipsychotic drugs. For example, adverse hyperglycemic events represented 21.6% of reports for quetiapine monotherapy, 39.9% for two-drug polypharmacy, and 66.3% for three-drug polypharmacy.

Conclusion

Antipsychotic drug polypharmacy may influence signal strength, and may be associated with hyperglycemia. After considering the causality restraints of the current analysis, further robust epidemiological studies are recommended.

Keywords

Antipsychotic drugs Hyperglycemic adverse events Adverse event reporting system Antipsychotic polypharmacy Antipsychotic monotherapy

Findings

Background

Antipsychotics are potent dopamine antagonists used to treat schizophrenia and bipolar disorder [1]. Antipsychotics are categorized as first-generation antipsychotics (typical) and second-generation antipsychotics (atypical). Several studies have reported abnormal glucose metabolism during antipsychotic drug treatment [2-4]. In 2002, diabetic ketoacidosis and coma were reported after olanzapine and quetiapine treatment in Japan [5]. Furthermore, the Food and Drug Administration (FDA) asked manufacturers of atypical antipsychotic (AAP) drugs to add a warning to drug labels regarding the increased risk of hyperglycemia and diabetes in 2004 [6]. Thus, hyperglycemia due to antipsychotic drug administration is a serious clinical problem.

According to clinical practice guidelines, AAPs should be used as the first and second line of treatment following the first schizophrenic episode [7-10]. However, treatment resistance and poor efficacy continue to be a significant clinical problem [2,11,12]. Since antipsychotic polypharmacy is suggested after failure of antipsychotic monotherapy [7,9,10], multiple antipsychotic drugs have been frequently prescribed [2,11,13]. A case-control study indicated that the administration of multiple antipsychotics increases the risk of diabetes mellitus when using AAPs [1]. Several studies also demonstrated the effect of antipsychotic polypharmacy on the adverse events; however, the effect of antipsychotic polypharmacy on hyperglycemia remains unclear [11-14].

The FDA Adverse Event Reporting System (FAERS) is a spontaneous reporting system for adverse events. It is the largest and best-known database worldwide, and reflects the realities of clinical practice. Therefore, the FAERS database is one of the primary tools used in pharmacovigilance. The aim of this study was to evaluate the relationship between antipsychotic drugs and adverse hyperglycemic events using the FAERS database. To our knowledge, this study is the first to evaluate the effect of antipsychotic polypharmacy on adverse hyperglycemic events using the FAERS database.

Methods

Data from the FAERS database from January 2004 to March 2013 were obtained from the FDA website. The FAERS database structure complies with the International Conference on Harmonization (ICH) E2B. We analyzed 26 antipsychotic drugs associated with hyperglycemia (Table 1). Since drug names in the FAERS database are registered arbitrarily, DrugBank, a reliable drug database, was utilized as a dictionary for the batch conversion and compilation of drug names (Table 2). We followed the FDA’s recommendation to adopt the most recent case number in order to identify duplicate reports from the same patient and excluded these from analysis.
Table 1

Characteristics of antipsychotics in the FDA adverse event reporting system database

Drugs

Total

Cases *

Reporting ratio (%)

ROR (95%CI)

Atypical

96841

21151

21.8

2.5

(2.4-2.5)

Aripiprazole

17093

4045

23.7

2.6

(2.5-2.7)

Clozapine

18217

2614

14.3

1.4

(1.3-1.5)

Olanzapine

22200

8423

37.9

5.3

(5.1-5.4)

Quetiapine

43169

12471

28.9

3.5

(3.4-3.6)

Perospirone

83

26

31.3

3.8

(2.4-6.1)

Risperidone

22121

5968

27.0

3.1

(3.0-3.2)

Zotepine

134

31

23.1

2.5

(1.7-3.8)

Typical

19569

3948

20.2

2.1

(2.1-2.2)

Bromperidol

48

11

22.9

2.5

(1.3-4.9)

Chlorpromazine

2812

1003

35.7

4.6

(4.3-5.0)

Fluphenazine

923

234

25.4

2.8

(2.4-3.3)

Haloperidol

10922

3445

31.5

3.9

(3.7-4.0)

Levomepromazine

799

166

20.8

2.2

(1.8-2.6)

Moperone

0

0

-

-

 

Nemonapride

4

1

25.0

2.8

(0.3-26.8)

Perphenazine

911

341

37.4

5.0

(4.4-5.7)

Pimozide

246

65

26.4

3.0

(2.3-4.0)

Pipamperone

207

26

12.6

1.2

(0.8-1.8)

Prochlorperazine

9103

1800

19.8

2.1

(2.0-2.2)

Propericiazine

190

45

23.7

2.6

(1.9-3.6)

Spiperone

1

0

-

-

 

Sulpiride

1809

331

18.3

1.9

(1.7-2.1)

Sultopride

97

11

11.3

1.1

(0.6-2.0)

Thioridazine

574

160

27.9

3.2

(2.7-3.9)

Tiapride

336

81

24.1

2.7

(2.1-3.4)

Timiperone

15

4

26.7

3.0

(1.0-9.5)

Trifluoperazine

619

274

44.3

6.6

(5.7-7.8)

*With adverse events of interest.

Table 2

Generic names and brand names of antipsychotics in the DrugBank

 

Generic name

Brand name

Atypical

  
 

Aripiprazole

Abilify, Aripiprazole

 

Clozapine

Clozapin, Clozapine, Clozaril, Fazaclo odt, Leponex

 

Olanzapine

Olansek, Olanzapine, Symbyax, Zydis, Zyprexa, Zyprexa intramuscular, Zyprexa zydis

 

Quetiapine

Quetiapine, Quetiapine fumarate, Seroquel, Seroquel xr

 

Risperdone

Risperdal, Risperdal consta, Risperdal m-tab, Risperdone, Risperidona, Risperidone, Risperidonum, Risperin, Rispolept

Typical

  
 

Chlorpromazine

Chlorpromanyl, Chlorpromazine, Largactil, Thorazine

 

Haloperidole

Aloperidin, Aloperidol, Aloperidolo, Apo-haloperidol, Haldol, Haldol la, Haldol solutab, Haloperidol, Haloperidol decanoate, Haloperidol lactate, Halopidol, Halosten, Keselan, Linton, Novo-peridol, Peridol, Serenace

 

Prochloroperazine

Buccastem, Chlorperazine, Combid, Compazine, Compro, Emetiral, Novamin, Pasotomin, Prochloroperazine, Prochlorpemazine, Prochlorperazin, Prochlorperazine, Prochlorperazine edisylate, Prochlorperazine maleate, Prochlorpromazine, Procloperazine, Proclorperazine, Stemetil, Stemzine, Vertigon

Adverse events in the FAERS database are coded according to the terminology preferred by the Medical Dictionary for Regulatory Activities (MedDRA). The Standardized MedDRA Queries (SMQ) index is widely accepted and utilized in the analysis of the FAERS database [15]. We utilized the SMQ for hyperglycemia/new onset diabetes mellitus events (SMQ code: 20000041). We selected 93 Preferred Terms (PTs), which are summarized in Table 3.
Table 3

Preferred terms associated with adverse hyperglycemia in the Standardized MedDRA Queries (SMQ; 20000041)

Preferred terms

Code

Total

Atypical

Typical

   

Cases *

Reporting ratio (%)

Cases *

Reporting ratio (%)

Total

 

241478

21151

8.8

3948

1.6

Abnormal loss of weight

10000159

532

28

5.3

9

1.7

Abnormal weight gain

10000188

134

33

24.6

0

0

Acidosis

10000486

1956

102

5.2

44

2.2

Altered state of consciousness

10001854

3306

303

9.2

111

3.4

Anti-GAD antibody positive

10059728

23

2

8.7

0

0

Anti-insulin antibody increased

10053815

51

0

0

0

0

Anti-insulin antibody positive

10053814

115

0

0

0

0

Anti-insulin receptor antibody increased

10068226

0

0

0

0

0

Anti-insulin receptor antibody positive

10068225

3

0

0

0

0

Anti-islet cell antibody positive

10049439

4

1

25

0

0

Blood 1,5-anhydroglucitol decreased

10065367

0

0

0

0

0

Blood cholesterol increased

10005425

10887

1648

15.1

63

0.6

Blood glucose abnormal

10005554

1547

116

7.5

12

0.8

Blood glucose fluctuation

10049803

2267

76

3.4

6

0.3

Blood glucose increased

10005557

35838

1398

3.9

241

0.7

Blood insulin abnormal

10005606

7

0

0

0

0

Blood insulin decreased

10005613

23

1

4.3

1

4.3

Blood lactic acid increased

10005635

826

47

5.7

6

0.7

Blood osmolarity increased

10005697

112

16

14.3

3

2.7

Blood triglycerides increased

10005839

5404

1199

22.2

35

0.6

Body mass index decreased

10005895

59

14

23.7

0

0

Body mass index increased

10005897

112

29

25.9

0

0

Central obesity

10065941

81

7

8.6

1

1.2

Coma

10010071

10703

1018

9.5

253

2.4

Dehydration

10012174

27804

1067

3.8

1025

3.7

Depressed level of consciousness

10012373

10200

819

8

333

3.3

Diabetes complicating pregnancy

10012596

3

1

33.3

0

0

Diabetes mellitus

10012601

15780

5523

35

98

0.6

Diabetes mellitus inadequate control

10012607

3689

825

22.4

25

0.7

Diabetes with hyperosmolarity

10012631

27

8

29.6

0

0

Diabetic coma

10012650

1045

551

52.7

1

0.1

Diabetic hepatopathy

10071265

0

0

0

0

0

Diabetic hyperglycaemic coma

10012668

80

7

8.8

1

1.3

Diabetic hyperosmolar coma

10012669

170

66

38.8

7

4.1

Diabetic ketoacidosis

10012671

2725

1090

40

26

1

Diabetic ketoacidotic hyperglycaemic coma

10012672

32

6

18.8

0

0

Fructosamine increased

10017395

5

0

0

0

0

Gestational diabetes

10018209

594

140

23.6

15

2.5

Glucose tolerance decreased

10018428

13

0

0

0

0

Glucose tolerance impaired

10018429

1058

260

24.6

6

0.6

Glucose tolerance impaired in pregnancy

10018430

3

1

33.3

0

0

Glucose tolerance test abnormal

10018433

36

3

8.3

0

0

Glucose urine present

10018478

318

23

7.2

15

4.7

Glycosuria

10018473

384

140

36.5

5

1.3

Glycosuria during pregnancy

10018475

1

0

0

0

0

Glycosylated haemoglobin increased

10018484

2569

171

6.7

11

0.4

Hunger

10020466

1575

142

9

8

0.5

Hypercholesterolaemia

10020603

2210

256

11.6

26

1.2

Hyperglycaemia

10020635

7844

1382

17.6

129

1.6

Hyperglycaemic hyperosmolar nonketotic syndrome

10063554

184

98

53.3

7

3.8

Hyperglycaemic seizure

10071394

5

0

0

0

0

Hyperglycaemic unconsciousness

10071286

10

0

0

0

0

Hyperlactacidaemia

10020660

333

13

3.9

5

1.5

Hyperlipidaemia

10062060

4585

747

16.3

45

1

Hyperosmolar state

10020697

113

24

21.2

3

2.7

Hyperphagia

10020710

632

157

24.8

4

0.6

Hypertriglyceridaemia

10020869

1127

154

13.7

14

1.2

Hypoglycaemia

10020993

10839

672

6.2

99

0.9

Hypoinsulinaemia

10070070

1

0

0

0

0

Impaired fasting glucose

10056997

67

22

32.8

0

0

Impaired insulin secretion

10052341

21

0

0

0

0

Increased appetite

10021654

2646

494

18.7

21

0.8

Increased insulin requirement

10021664

31

2

6.5

0

0

Insulin autoimmune syndrome

10022472

23

0

0

0

0

Insulin resistance

10022489

297

75

25.3

0

0

Insulin resistance syndrome

10022490

18

6

33.3

0

0

Insulin resistant diabetes

10022491

27

8

29.6

0

0

Insulin tolerance test abnormal

10022494

3

0

0

0

0

Insulin-requiring type 2 diabetes mellitus

10053247

122

60

49.2

0

0

Ketoacidosis

10023379

640

250

39.1

3

0.5

Ketonuria

10023388

188

63

33.5

5

2.7

Ketosis

10023391

100

13

13

3

3

Lactic acidosis

10023676

4561

119

2.6

61

1.3

Latent autoimmune diabetes in adults

10066389

16

0

0

0

0

Lipids increased

10024592

368

57

15.5

1

0.3

Loss of consciousness

10024855

28249

1750

6.2

355

1.3

Metabolic acidosis

10027417

5512

253

4.6

121

2.2

Metabolic syndrome

10052066

392

197

50.3

2

0.5

Neonatal diabetes mellitus

10028933

3

0

0

1

33.3

Obesity

10029883

2787

1211

43.5

23

0.8

Overweight

10033307

442

114

25.8

3

0.7

Pancreatogenous diabetes

10033660

6

2

33.3

0

0

Polydipsia

10036067

1026

271

26.4

16

1.6

Polyuria

10036142

1444

197

13.6

27

1.9

Slow response to stimuli

10041045

161

37

23

7

4.3

Thirst

10043458

2595

224

8.6

40

1.5

Type 1 diabetes mellitus

10067584

1252

590

47.1

7

0.6

Type 2 diabetes mellitus

10067585

5272

2862

54.3

16

0.3

Underweight

10048828

111

8

7.2

2

1.8

Unresponsive to stimuli

10045555

5657

442

7.8

123

2.2

Urine ketone body present

10057597

304

31

10.2

13

4.3

Weight decreased

10047895

42275

1765

4.2

466

1.1

Weight increased

10047899

30417

5070

16.7

867

2.9

*With adverse events of interest.

For signal detection, we calculated the reporting odds ratio (ROR), an established pharmacovigilance index, using a disproportionality analysis. The ROR is calculated as a*d/b*c (Figure 1). The ROR is the ratio of the odds of reporting a specific adverse event versus all other adverse events for a given drug (antipsychotics), compared to the reporting odds for all other drugs present in the database. RORs were expressed as point estimates with 95% confidence intervals (CI). The detection of a signal was dependent on the signal indices exceeding a predefined threshold. Safety signals were considered significant when the ROR estimates and the lower limits of the 95% CI were greater than 2 [16]. We analyzed the effects of monotherapy, two-drug polypharmacy, and three-drug polypharmacy. Data analyses were performed using JMP 9.0 (SAS Institute Inc., Cary, NC, USA).
Figure 1

Two by two contingency table for analysis.

Results

The FAERS database contains 4,746,890 reports from January 2004 to March 2013. After excluding duplicates according to the FDA’s recommendation and extracting the reports with complete age and gender information, 2,257,902 reports were analyzed. Using the SMQ “hyperglycemia/new onset diabetes mellitus” (SMQ20000041), we identified 241,478 adverse hyperglycemic events. The reporting ratios and RORs (95% CI) for adverse hyperglycemic events are summarized in Table 1. The reporting ratios of adverse hyperglycemic events in AAPs and typical antipsychotics (TAPs) were 21.8% (21151/96841) and 20.2% (3948/19569), respectively. The number of adverse hyperglycemic events among the top eight reported drugs, quetiapine, olanzapine, risperidone, aripiprazole, haloperidol, clozapine, prochlorperazine, and chlorpromazine, was 12,471 (28.9%), 8,423 (37.9%), 5,968 (27.0%), 4,045 (23.7%), 3,445 (31.5%), 2,614 (14.3%), 1,800 (19.8%), and 1,003 (35.7%), respectively. Each reporting ratio and ROR was analyzed based on administration (monotherapy, two-drug combination, and three-drug combination; Table 4). The RORs (95% CI) for monotherapy with quetiapine, olanzapine, risperidone, aripiprazole, haloperidol, clozapine, prochlorperazine, and chlorpromazine were 2.3 (95% CI: 2.3-2.4), 3.7 (95% CI: 3.6-3.8), 1.5 (95% CI: 1.5-1.6), 1.4 (95% CI: 1.3-1.5), 2.8 (95% CI: 2.7-3.0), 1.1 (95% CI: 1.0-1.1), 2.0 (95% CI: 1.9-2.1), and 1.6 (95% CI: 1.3-1.8), respectively. In contrast, the RORs (95% CI) for three-drug combination therapy were 16.5 (95% CI: 15.1-18.0), 12.0 (95% CI: 11.0-13.2), 12.0 (95% CI: 10.9-13.1), 10.3 (95%: CI 9.1-11.6), 5.9 (95% CI: 5.3-6.7), 2.3 (95% CI: 2.0-2.8), 6.0 (95% CI: 3.6-10.0), and 5.6 (95% CI: 4.5-6.9), respectively.
Table 4

Reporting ratio and ROR for antipsychotic polypharmacy

 

Drugs *

Total

Cases **

Reporting ratio (%)

ROR (95%CI)

Atypical

     
 

Aripiprazole

    
 

mono

11457

1645

14.4

1.4(1.3-1.5)

 

two

3499

927

26.5

3.0(2.8-3.3)

 

three

1099

606

55.1

10.3(9.1-11.6)

 

Clozapine

    
 

mono

13466

1515

11.3

1.1(1.0-1.1)

 

two

3486

584

16.8

1.7(1.5-1.8)

 

three

750

164

21.9

2.3(2.0-2.8)

 

Olanzapine

    
 

mono

13935

4226

30.3

3.7(3.6-3.8)

 

two

4862

1908

39.2

5.4(5.1-5.8)

 

three

1904

1121

58.9

12.0(11.0-13.2)

 

Quetiapine

    
 

mono

32942

7114

21.6

2.3(2.3-2.4)

 

two

6413

2556

39.9

5.6(5.3-5.9)

 

three

2175

1441

66.3

16.5(15.1-18.0)

 

Risperidone

    
 

mono

13820

2154

15.6

1.5(1.5-1.6)

 

two

4860

1476

30.4

3.7(3.4-3.9)

 

three

1917

1128

58.8

12.0(10.9-13.1)

Typical

     
 

Chlorpromazine

    
 

mono

1117

175

15.7

1.6(1.3-1.8)

 

two

724

179

24.7

2.7(2.3-3.2)

 

three

355

142

40.0

5.6(4.5-6.9)

 

Haloperidol

    
 

mono

5604

1420

25.3

2.8(2.7-3.0)

 

two

3102

704

22.7

2.5(2.3-2.7)

 

three

1079

448

41.5

5.9(5.3-6.7)

 

Prochlorperazine

    
 

mono

8514

1634

19.2

2.0(1.9-2.1)

 

two

487

111

22.8

2.5(2.0-3.0)

 

three

62

26

41.9

6.0(3.6-10.0)

*Monotherapy and polypharmacy of each antipsychotic.

**With adverse events of interest.

Discussion

Our results suggest that several antipsychotics increase adverse hyperglycemic events, and that antipsychotic polypharmacy may influence these events using the FAERS database.

In a previous cohort study, olanzapine and clozapine were associated with increased risk for type 2 diabetes [1,2,17]. Citrome et al. suggested that exposure to multiple AAPs significantly increased the risk of treatment-emergent diabetes mellitus, as compared to TAPs [1]. However, they discussed that their study design does not permit the quantification of differences between AAPs and the risk of emergent diabetes [1]. Another research group reported that AAP administration results in a small increase, as compared to TAP administration [18]. In our study, the reporting ratio of adverse hyperglycemic events in AAPs (21.8% [21151/96841]) and TAPs (20.2% [3948/19569]) were similar. Thus, we could not obtain meaningful results regarding the difference between AAP administration and TAP administration using the reporting ratio of hyperglycemic adverse events.

The lower limits of the ROR 95% CI for olanzapine, quetiapine, and haloperidol monotherapy were greater than 2 (Table 4). Baker et al. reported that olanzapine (AAP), clozapine (AAP), and risperidone (AAP) were associated with hyperglycemic adverse events, whereas aripiprazole (AAP), haloperidol (TAP), and ziprasidone (AAP) had a low association in the FAERS database. We do not have a conclusive explanation for the differences in reporting ratio between the previous report [19] and our findings. One plausible reason could be differences in the terms selected for adverse hyperglycemic events in the MedDRA database. Our study used 93 PTs, whereas Baker et al. used 24. Additionally, different datasets were used for the analyses. Baker et al. performed their analysis using cumulative subsets from 1968 to 2006, whereas our group utilized datasets from 2004 to 2013.

In this study, each reporting ratio and ROR increased with increasing number of drugs administered (Table 4). The ROR of the three-drug polypharmacy had the highest value for every antipsychotic. Therefore, antipsychotic-induced adverse hyperglycemic events may be influenced by the number of drugs administered. However, the lower limit of the clozapine ROR 95% CI was less than 2. Since the administration of clozapine is not recommended as a first-line treatment [20], physicians may be unlikely to use clozapine in diabetic patients. Therefore, the signal for adverse hyperglycemic events following clozapine might be not detected. Antipsychotic monotherapy and polypharmacy to treat schizophrenia and bipolar disorder has been compared to understand its risk-benefit profile [11,14]. In general, polypharmacy using antipsychotics is not recommended [7-9]. Baker et al. evaluated the adverse events signals for each AAP. However, they did not evaluate the effect of antipsychotic polypharmacy on hyperglycemia. Our results suggest that antipsychotic polypharmacy may influence adverse hyperglycemic events. Therefore, clinician should comply with guidelines [7-10] and monitor for adverse polypharmacy-induced hyperglycemic events.

The mechanism by which antipsychotics induce adverse hyperglycemic events remains unclear. AAPs are associated with clinically significant weight gain, and have raised significant concerns regarding possible association with hyperglycemia and type 2 diabetes [1,11,18,19]. Obesity or diabetes may be confounders for adverse hyperglycemic events. However, detailed information, including patient background and diagnosis, is not included in the FAERS database. Therefore, it is difficult to define and stratify the patients investigated.

The FAERS database is subject to various biases, including the exclusion of healthy individuals, the lack of denominator, and confounding factors [21]. Because of these deficits within the spontaneous reporting, ROR do not allow for risk quantification. Rather, the RORs offer a rough indication of the signal strength [21]. Therefore, special attention has to be paid to the interpretation of results from the FAERS database. Other epidemiological studies are required to determine the true risk of adverse hyperglycemic events.

Despite the limitations inherent to spontanesous reporting, we obtained reasonable results in the context of the reported literature. The reporting ratio and ROR suggested an association between antipsychotic drugs and hyperglycemic adverse events, and the reporting ratio was increased with an increase in the number of co-administered antipsychotic drugs. Our study indicates the importance of comparing drug safety profiles using post-marketing real-world data. This information could be useful to improve schizophrenia and bipolar disorder management.

Abbreviations

FDA: 

The Food and Drug Administration

AAP: 

Atypical antipsychotic

FAERS: 

The FDA adverse event reporting system

ICH: 

The International Conference on Harmonization

MedDRA: 

The medical dictionary for regulatory activities

SMQ: 

The Standardized MedDRA queries

PT: 

Preferred terms

ROR: 

Reporting odds ratio

CI: 

Confidence intervals

TAP: 

Typical antipsychotic

Declarations

Acknowledgements

This research was partially supported by JSPS KAKENHI Grant Number, 24390126.

Authors’ Affiliations

(1)
Laboratory of Drug Informatics, Gifu Pharmaceutical University
(2)
Medical Database Co., LTD
(3)
Department of Biomedical Informatics, Gifu University Graduate School of Medicine

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© Kato et al.; licensee BioMed Central. 2015

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

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