Edited by: Dimitrios Vassilopoulos, National and Kapodistrian University of Athens, Greece
Reviewed by: Apostolos Kontzias, Stony Brook University, United States; Mauro Waldemar Keiserman, Hospital São Lucas da PUCRS, Brazil
This article was submitted to Rheumatology, a section of the journal Frontiers in Medicine
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Disease-modifying antirheumatic drugs (DMARDs) are a group of drugs defined by their effects of slowing down disease progression, which can be divided into biologic DMARDs and targeted synthetic DMARDs (tsDMARDs) and conventional synthetic DMARDs (csDMARDs). As conventional antirheumatic agents, csDMARDs are widely used for their efficacy and low cost. Hydroxychloroquine (HCQ), methotrexate (MTX), azathioprine (AZA), and cyclophosphamide (CTX) are commonly prescribed csDMARDs, particularly for systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and psoriasis (PsO) or psoriatic arthritis (PsA) patients, of which the usages have also been suggested in treatment guidelines of corresponding diseases (
In consideration of the relative long-term usage of csDMARDs of patients with rheumatic diseases (RD), and the relationship of predisposition of malignancy to races and regions, based on a population-based database of health insurance research in Taiwan, we aimed here to compare the medication differences between RD patients without malignancy and RD patients with malignancy after exposure to csDMARDs targeting East Asians.
We conducted a population-based nested case–control study by retrieving all patients newly diagnosed with RA, SLE, and PsA or PsO from the 2000 Longitudinal Health Insurance Database (LHID 2000) in Taiwan. This study was approved by the Ethics Review Board of Chung Shan Medical University. Patient informed consent was not required, as the NHIRD data files contain only de-identified secondary data.
The data used in this study came from LHID 2000, a subset of Taiwan's National Health Insurance Research Database (NHIRD). The NHIRD database consists of all inpatient and outpatient visits, procedure codes, catastrophic illness files, and drug prescription data of the 23.5 million insured residents, whereas LHID 2000 contains all the original claim data for one million beneficiaries randomly sampled from the 2000 Registry for Beneficiaries of the National Health Insurance program. In the LHID 2000 database, the diagnosis and medication of patients are recorded via the diagnostic codes in the format of the International Classification of Diseases, Revision 9 (ICD-9), and medication code in the format of Anatomical Therapeutic Chemical (ATC) code, respectively.
All patients diagnosed with RA (ICD-9 code: 714.0), SLE (ICD-9 code: 710.0), and PsA (ICD-9 code: 696.0) or PsO (ICD-9 code: 696.1) from 1997 to 2013 were retrieved from LHID 2000, and their usage of hydroxychloroquine (ATC code: P01BA02), methotrexate (ATC code: L04AX03), azathioprine (ATC code: L04AX01), and cyclophosphamide (ATC code: L01AA01) was retrieved at the same time.
Patients from 1997 to 2013 who had ever diagnosed with rheumatic diseases (RDs, include SLE, RA, PsA, and PsO) by rheumatologists were included (
The selected patients were divided into neoplasm group and neoplasm-free (control) group, respectively. The patients, who had previous diagnosis of neoplasm (ICD9 codes: 140-208) in the neoplasm group, were also excluded (
Comorbidities were potential confounders and were identified with ICD-9 codes. Thyroid disorders (ICD-9 codes: 240, 241, 242, 244.9, 245.0, 245.1, 245.2), viral hepatitis (ICD-9 code: 070), infectious mononucleosis (ICD-9 code: 075), hypertension (ICD-9 codes: 401–405), diabetes mellitus (ICD-9 code: 250), hyperlipidemia (ICD-9 code: 272), coronary artery disease (ICD-9 codes: 410–414), CKD (ICD-9 code: 585), asthma (ICD-9 code: 493), COPD (ICD-9 codes: 490–496), esophageal disease (ICD-9 codes: 530.0–530.9), gastrointestinal ulcer (ICD-9 codes: 531–534), regional enteritis (including Crohn's disease) and ulcerative colitis (ICD-9 code: 556), and chronic liver disease (ICD-9 code: 571) were identified as comorbidities in this study.
Composition of characteristic indices, comorbidities, and medication between neoplasm and neoplasm-free (control) groups were compared with Chi-square tests. Differences of days of prescription of different medications were presented as Median ± Interquartile range (IQR) of each group and compared using the Wilcoxon rank-sum test. A two-tailed
Between 1997 and 2013, 8,219 patients ever diagnosed with rheumatic diseases (RD) were selected from the 2000 Longitudinal Health Insurance Database (LHID 2000). Two thousand three hundred seventy-nine participants were excluded for their diagnosis before 2002; 5099 RD participants without neoplasms and 741 RD participants with neoplasms were remained. After exclusion of 432 participants with neoplasms diagnosed before enrollment, we made a 1:1 match for the neoplasm cases with neoplasm-free cases based on the criteria listed in Methods. Finally, both the neoplasm-free (control) group and the neoplasm group consisted of 261 cases, as shown in
Study design and flow.
The ratios of types of rheumatic diseases, age at index date, urbanization state, and length of hospital stay within 2 years before index date are similar between the neoplasm and control groups, as shown in
Characteristics among groups.
Rheumatic diseases | 0.1260 | ||
Only SLE | 36 (13.79%) | 43 (16.48%) | |
Only RA | 186 (71.26%) | 165 (63.22%) | |
Only PsA | 3 (1.15%) | 7 (2.68%) | |
Only PsO | 20 (7.66%) | 21 (8.05%) | |
Combined 2 diseases | 14 (5.36%) | 25 (9.58%) | |
Combined ≥3 diseases | 2 (0.77%) | 0 (0.00%) | |
Age at index date | 1.0000 | ||
<60 | 129 (49.43%) | 129 (49.43%) | |
≥60 | 132 (50.57%) | 132 (50.57%) | |
Sex | 1.0000 | ||
Female | 184 (70.50%) | 184 (70.50%) | |
Male | 77 (29.50%) | 77 (29.50%) | |
Urbanization | 0.368 | ||
Urban | 156 (59.77%) | 166 (63.60%) | |
Suburban | 105 (40.23%) | 95 (36.40%) | |
Length of hospital stay within 2 years before index date | 0.1482 | ||
0 | 171 (65.52%) | 146 (55.94%) | |
1–6 | 33 (12.64%) | 39 (14.94%) | |
7–13 | 26 (9.96%) | 32 (12.26%) | |
≥14 | 31 (11.88%) | 44 (16.86%) | |
Comorbidities (within 2 year before index date) | |||
Thyroid disorders | 14 (5.36%) | 25 (9.58%) | 0.0671 |
Viral hepatitis | 17 (6.51%) | 41 (15.71%) | 0.0008 |
Infectious mononucleosis | 0 (0%) | 0 (0%) | |
Hypertension | 107 (41.00%) | 125 (47.89%) | 0.1129 |
Diabetes mellitus | 48 (18.39%) | 69 (26.44%) | 0.0275 |
Hyperlipidemia | 62 (23.75%) | 74 (28.35%) | 0.2315 |
Coronary artery disease | 50 (19.16%) | 51 (19.54%) | 0.9118 |
CKD | 11 (4.21%) | 24 (9.20%) | 0.0229 |
Asthma | 24 (9.2%) | 28 (10.73%) | 0.5588 |
COPD | 44 (16.86%) | 54 (20.69%) | 0.2624 |
Esophageal disease | 46 (17.62%) | 49 (18.77%) | 0.7336 |
Gastrointestinal ulcer | 86 (32.95%) | 100 (38.31%) | 0.2007 |
Regional enteritis and ulcerative colitis | 0 (0%) | 0 (0%) | |
Chronic liver disease | 51 (19.54%) | 69 (26.44%) | 0.0611 |
Similar comparable durations of the control and neoplasm groups are shown with an interquartile range in
Time interval (month) of comparable duration.
Time interval | ||
Min | 0 | 0 |
Q1 | 12 | 12 |
Median | 32 | 32 |
Q3 | 62 | 62 |
Max | 137 | 136 |
Medication between groups from enrollment to index date.
Hydroxychloroquine | 133 (50.96%) | 128 (49.04%) | 0.6616 |
Days of prescription | 189 ± 613 | 250 ± 700 | 0.6393 |
Methotrexate | 70 (26.82%) | 72 (27.59%) | 0.8441 |
Days of prescription | 297.5 ± 777 | 332.5 ± 562.5 | 0.6630 |
Azathioprine | 9 (3.45%) | 8 (3.07%) | 0.8052 |
Days of prescription | 154 ± 448 | 234.5 ± 1743 | 0.7052 |
Cyclophosphamide | 3 (1.15%) | 6 (2.30%) | 0.3131 |
Days of prescription | 935 ± 910 | 212 ± 291 | 0.7086 |
Medication between groups within 3 years.
Hydroxychloroquine | 40 (33.06%) | 36 (30.25%) | 0.6404 |
Days of prescription | 479 ± 848.5 | 588 ± 644 | 0.5108 |
Methotrexate | 25 (20.66%) | 30 (25.21%) | 0.4018 |
Days of prescription | 592 ± 714 | 399 ± 770 | 0.5850 |
Azathioprine | 3 (2.48%) | 3 (2.52%) | 0.9835 |
Days of prescription | 532 ± 588 | 791 ± 499 | 0.4227 |
Cyclophosphamide | 1 (0.83%) | 1 (0.84%) | 0.9906 |
Days of prescription | 448 ± 0 | 182 ± 0 | – |
Subgroup analysis for medication among groups from enrollment to index date.
Hydroxychloroquine | 19 (52.78%) | 20 (46.51%) | 0.5790 |
Days of prescription | 231 ± 715 | 141 ± 636 | 0.9332 |
Methotrexate | 2 (5.56%) | 2 (4.65%) | 0.8551 |
Days of prescription | 525 ± 434 | 175 ± 210 | 0.3293 |
Azathioprine | 2 (5.56%) | 3 (6.98%) | 0.7961 |
Days of prescription | 1,215 ± 2,262 | 2,219 ± 2,207 | 0.7872 |
Cyclophosphamide | 2 (5.56%) | 4 (9.30%) | 0.5313 |
Days of prescription | 518 ± 910 | 211.5 ± 642 | 1.0000 |
Hydroxychloroquine | 105 (56.45%) | 93 (56.36%) | 0.9868 |
Days of prescription | 182 ± 536 | 287 ± 700 | 0.3733 |
Methotrexate | 51 (27.42%) | 53 (32.12%) | 0.3356 |
Days of prescription | 335 ± 861 | 385 ± 679 | 0.3496 |
Azathioprine | 4 (2.15%) | 2 (1.21%) | 0.4984 |
Days of prescription | 476 ± 364 | 91 ± 154 | 0.2994 |
Cyclophosphamide | 1 (0.54%) | 1 (0.61%) | 0.9323 |
Days of prescription | 935 ± 0 | 214 ± 0 | – |
According to our knowledge, this is the first population-based, nested case–control study targeting East Asians, which investigated the carcinogenetic effects of commonly used csDMARDs (including HCQ, MTX, AZA, and CTX) on patients suffering from rheumatic diseases. For this purpose, we compare the difference of ratio and medication time (via days of prescription) in the usage of different csDMARDs, which was based on a 1:1 match between neoplasm and neoplasm-free patients. Our study showed no differences in the indexes described above, which meant the usage of these four nbDMARDs had no correlation with carcinogenesis.
As the backbone of treatment to rheumatic diseases, csDMARDs can help attenuate the disease activity and slow down the progression, while avoiding the severe side effects resulting from long-term usage of steroids in high dose. The relative lower cost of csDMARDs also makes them easier to be accepted by most patients. According to the mechanism of HCQ, MTX, AZA, and CTX, the actions resulting from these csDMARDs (including immunosuppression, cytotoxicity, and etc.) have the potential to contribute to the pathogenesis of neoplasm with long-term usage (
Several investigations had focused on the incidences of neoplasms in some RDs under exposure to csDMARDs. However, the conclusions from these studies were controversial. For RA, investigation from Jin et al. targeting 13,210 Chinese RA patients identified malignancy as one of the major comorbidities of RA with a prevalence of 0.6% at baseline, and MTX usage was negatively associated with malignancy (HR = 0.57,
As described above, several studies had observed an association between elevated risk of malignancy in some systems and some rheumatic diseases, like RA, SLE, and PsO. In a review of Klein et al., they indicated that the association between lymphoma risk and RA might be explained mainly by three theories: genetic predisposition, persistence of long-standing disease activity with continued immune stimulation, and the role of anti-RA therapy given (
Our study had also limitations. Firstly, this study was based on a claim-based health insurance database, and although the length of hospital stay of cases was well-documented, and also regarded as a marker for disease severity in this study, we could not evaluate their activity and severity in details, which may introduce the bias into the final results. Secondly, contribution of lifestyle to cancers, such as smoking and alcohol, is lacking in our database. However, we did match life style-related diseases such as diabetes, COPD, and liver disease to reduce the confounding. We also excluded patients with prior cancer history to enhance baseline comparability between cases and controls. Thirdly, it is difficult to ascertain from “days of prescription” the duration of exposure and adherence to treatment directly (or very precisely), but the days of prescription can reflect the requirements of specific medication of patients, which can indicate the real usage of that medication indirectly. Lastly, although this is a nested case–control study, and the compositions of cases of different diseases were similar between different groups after matching, the composition of different diseases varied significantly (from around 1 to 70%). Therefore, clinical trials with a higher level and larger scale should be made in the future, to give a more precise and detailed answer to this important problem in clinical practice, and basic researches should also be made to investigate the precise carcinogenetic effects of these csDMARDs in rheumatic patients at the level of mechanism.
In summary, we compared the medication differences of some commonly used csDMARDs between rheumatic diseases patients with and without neoplasms, which showed no differences and indicated no correlation between csDMARD usage and neoplasm risk in patients with rheumatic diseases.
The raw data supporting the conclusions of this article will be available upon reviewers' request. Requests to access the datasets should be directed to jccwei@gmail.com.
This study was approved by the Ethics Review Board of Chung Shan Medical University.
JW, LD, and SC participated in the design of this study. SC wrote this manuscript. JH and J-YC made the statistical analyses. W-TP provided very useful advices. All authors contributed to the article and approved the submitted version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.