Association between anti-tumor necrosis factor alpha exposure and new-onset autoimmune diseases in inflammatory bowel disease: a nationwide case-control study in Korea

Shin Ju Oh; Ji Eun Kim; Su Jin Jeong; Chang Kyun Lee; the Big Data Research Group (BDRG) of the Korean Society of Gastroenterology

doi:10.5217/ir.2025.00195

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Original Article Association between anti-tumor necrosis factor alpha exposure and new-onset autoimmune diseases in inflammatory bowel disease: a nationwide case-control study in Korea: Shin Ju Oh¹, Ji Eun Kim¹, Su Jin Jeong², Chang Kyun Lee¹, the Big Data Research Group (BDRG) of the Korean Society of Gastroenterology; DOI: https://doi.org/10.5217/ir.2025.00195
Published online: February 10, 2026

¹Department of Gastroenterology, Center for Crohn’s and Colitis, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, Seoul, Korea

²Department of Statistics Support, Medical Science Research Institute, Kyung Hee University Medical Center, Seoul, Korea

Correspondence to Chang Kyun Lee, Department of Gastroenterology, Center for Crohn’s and Colitis, Kyung Hee University Hospital, College of Medicine, Kyung Hee University, 23 Kyungheedae-ro, Dongdaemun-gu, Seoul 02447, Korea. E-mail: changkyun.lee@khu.ac.kr

• Received: September 2, 2025 • Revised: November 21, 2025 • Accepted: December 3, 2025

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

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Abstract
Graphical abstract
INTRODUCTION
METHODS
RESULTS
DISCUSSION
NOTES
Supplementary Material
REFERENCES

Abstract

Background/Aims
Autoimmune-related events following anti-tumor necrosis factor alpha (anti-TNF-α) therapy are increasingly reported, but population-level data on new-onset autoimmune disease in inflammatory bowel disease (IBD) remain limited. We evaluated whether anti-TNF-α exposure is associated with autoimmune disease development in IBD.
Methods
We conducted a nationwide population-based case-control study using data from the Korean National Health Insurance Service database (2004–2018). Patients with IBD who developed new-onset autoimmune diseases, including psoriasis, interstitial lung disease (ILD), systemic lupus erythematosus, systemic vasculitis, and central nervous system disorders, were matched 1:1 to controls by age, sex, diagnosis year, and IBD subtype. Logistic regression with propensity score matching and spline modeling was used to assess associations, including subgroup and sensitivity analyses.
Results
Among 8,586 matched pairs, anti-TNF-α therapy was associated with increased risks of autoimmune disease (adjusted odds ratio [aOR], 1.65), particularly psoriasis (aOR, 1.58) and ILD (aOR, 1.88). A non-linear dose–response relationship was observed: the risk rose sharply at early exposure, plateaued at approximately 30 prescriptions, and gradually declined beyond 64. This association remained regardless of prior immunosuppressant use and was attenuated but significant in immunosuppressant users with psoriasis. No significant associations were found for systemic lupus erythematosus, central nervous system disorders, or vasculitis. Patients receiving prolonged concomitant therapy ( ≥ 90 days) showed increased risk (aOR, 1.43). Monotherapy showed a non-significant trend (aOR, 1.19).
Conclusions
Anti-TNF therapy for IBD is associated with an increased risk of developing new-onset autoimmune diseases, especially psoriasis and ILD. Careful monitoring is warranted, particularly in patients receiving prolonged or combined immunosuppressive therapies.
Keywords: Anti-TNF-α agent; Inflammatory bowel diseases; Autoimmune diseases; Psoriasis; Interstitial lung diseases

Graphical abstract

INTRODUCTION

Tumor necrosis factor alpha (TNF-α) is a pro-inflammatory cytokine that plays a pivotal role in the inflammatory cascade underlying chronic immune-mediated inflammatory diseases (IMIDs) [1]. Since their introduction in 1991, synthetic anti-TNF-α antibodies have markedly improved clinical outcomes in conditions such as rheumatoid arthritis, ankylosing spondylitis, psoriasis, and inflammatory bowel disease (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC) [2]. Despite the emergence of novel therapies targeting specific cytokines or molecular pathways, anti-TNF-α agents remain a cornerstone of IBD treatment in routine clinical practice [3,4]. However, as highlighted in recent guidelines, the optimal use of these agents is an area of ongoing investigation. Recent real-world evidence suggests that certain subgroups of patients with IBD—such as those with concomitant ankylosing spondylitis—require anti-TNF-α therapy more frequently owing to increased disease burden and severity [5].

While the therapeutic efficacy of anti-TNF-α agents is well established, their use is associated with an increased risk of adverse events, notable infections and malignancies, necessitating ongoing surveillance during treatment [6-8]. More recently, paradoxical immune-mediated adverse events—where anti-TNF-α therapy appears to induce immune-mediated diseases rather than suppress them—have been increasingly recognized [9]. These adverse events include psoriasis, interstitial lung disease (ILD) [10-12], central nervous system (CNS) demyelinating disorder [13,14], lupus and systemic vasculitis [15,16]. Notably, most of these events have been documented in patients with rheumatic arthritis [17,18].

With the growing global prevalence of IBD and expanding use of anti-TNF-α agents, concerns about these paradoxical autoimmune phenomena have grown [19,20]. These events challenge the conventional understanding of TNF-α inhibition by suggesting that blocking one arm of the inflammatory pathway may inadvertently activate alternative immune responses or trigger autoantibody production. Importantly, these autoimmune conditions are conceptually distinct from the classical extraintestinal manifestations (EIMs) of IBD. Unlike EIMs, which are generally linked to underlying IBD activity, these conditions are believed to represent paradoxical, treatment-related autoimmune phenomena associated with TNF-α blockade. Distinguishing between these entities is essential for accurately interpreting the pathophysiological context of anti-TNF-associated autoimmune events. However, the underlying etiopathogenesis remains poorly understood, and population-level data specifically addressing autoimmune disease risk in IBD patients are limited. Understanding whether anti-TNF-α exposure contributes to the development of new-onset autoimmune diseases in patients with IBD is critical for personalized risk stratification and long-term management. Therefore, herein, we conducted a large-scale, nationwide, population-based case-control study to evaluate the association between anti-TNF-α therapy and incident autoimmune disease in patients with IBD.

METHODS

1. Ethics Approval Statement

This study was approved by the Institutional Review Board of Kyung Hee University Hospital (IRB No. 2019-12-040-003). The data used in this study were approved and provided in a de-identified form by the Korean National Health Insurance Service (NHIS; NHIS-2021-4-004). The requirement for written informed consent was waived by the IRB.

2. Data Source

We used data from the extensive health claims database of the Korean NHIS, a mandatory nationwide health insurance system covering approximately 97% of the Korean population. The NHIS database provides a comprehensive overview of the nation’s health landscape, mirroring the real-world medical setting of Korea [21,22]. The database encompasses demographic information, diagnoses based on the International Classification of Diseases, 10th Revision (ICD-10), and all health-related information on medical procedures and treatments. Additionally, the NHIS conducts a biennial general health screening program that collects data on individual health behaviors (e.g., smoking status, alcohol consumption, and physical activity) and physiological parameters such as body mass index (BMI).

3. Study Population and Study Design

We identified all patients with IBD using ICD-10 codes K50 (CD) and K51 (UC) between January 1, 2004, and December 31, 2018. To establish a clear baseline, we excluded patients with any IBD-related diagnostic codes between 2002 and 2004 to eliminate potential preexisting cases. The initial extraction yielded a total of 614,204 individuals. To accurately define IBD cases, we applied a validated diagnostic algorithm that required both (1) a newly assigned ICD-10 code for CD or UC and (2) at least one healthcare encounter involving a prescription of an IBD-specific medication [23]. Patients with overlapping or ambiguous diagnostic codes were categorized as having IBD-unclassified. The diagnosis date was defined as the date of the first IBD-related diagnostic code. Individuals who did not receive IBD-specific medications or those with a prior diagnosis of any autoimmune disease of interest were excluded from the study.

Cases were defined as patients with IBD who developed new-onset autoimmune disease after IBD diagnosis. The autoimmune diseases of interest include psoriasis, ILD, systemic lupus erythematosus (SLE), systemic vasculitis, and CNS demyelinating or non-demyelinating diseases unrelated to infections or malignancies. Each autoimmune disease was identified using the corresponding ICD-10 code (Supplementary Table 1). Controls were defined as patients with IBD who did not develop any of the aforementioned autoimmune diseases during the study period. Controls were matched to cases in a 1:1 ratio based on age, sex, IBD diagnosis year, and IBD subtype (CD or UC). The index date for the cases was defined as the date of autoimmune disease diagnosis, and matched controls were assigned the same index date as their corresponding cases. The study also examined outcomes based on specific types of autoimmune diseases and provided a detailed stratification.

4. Ascertainment of Exposure

We identified all prescription records related to anti-TNF-α agent exposure for cases and controls prior to their outcome event or index date. Information about the type of anti-TNF agents (infliximab, adalimumab, golimumab), cumulative frequency of anti-TNF-α exposure, and time of first anti-TNF-α exposure after IBD diagnosis were also collected.

5. Statistical Analysis

We conducted a logistic regression analysis to evaluate the association between anti-TNF exposure and the development of autoimmune diseases. Propensity score matching was used to ensure comparability between cases and controls. Crude odds ratios (ORs) and 95% confidence intervals (95% CIs) were calculated before and after matching. Multivariable-adjusted ORs were derived by adjusting for potential confounders. The multivariable model was adjusted for sex, age, IBD subtype (CD or UC), IBD diagnosis year, BMI, smoking status, Charlson Comorbidity Index scores, and frequency of hospitalization due to IBD and IBD-related surgeries as proxies for the presence of active disease. In addition, patients with concomitant IMIDs, including rheumatoid arthritis (ICD-10 codes M05, M06, M08/71209, 71219, 71229, and 71239) and ankylosing spondylitis (ICD-10 code M45), were included in the multivariable analysis. BMI and smoking status were determined based on the closest available data from the National Health Screening program within 3 years of the index date. If unavailable, the imputed values based on age-matched and sex-matched population means were used. IBD-related hospitalization was defined as inpatient admission to the gastroenterology or general surgery department for more than 3 days with a primary diagnosis code of K50 or K51. IBD-related surgeries were identified using surgical procedure codes including those related to strictures, stomas, perianal disease, and bowel resection (Supplementary Table 2). To evaluate the dose-dependent relationship between anti-TNF-α exposure and the risk of autoimmune disease, we conducted a frequency-based analysis by using cumulative number of anti-TNF-α prescriptions as a continuous variable. A smoothing spline regression model was employed to assess the potential non-linear relationship between the dose of anti-TNF-α use and autoimmune disease development.

To explore the potential influence of immunosuppressant use on the risk of autoimmune disease development, we conducted subgroup and sensitivity analyses using distinct methodological approaches. In the subgroup analysis, patients were stratified according to any history of immunosuppressant use, regardless of whether the use overlapped with the course of anti-TNF-α therapy. This analysis aimed to evaluate whether the use of immunosuppressants at any point during the disease course—irrespective of timing relative to anti-TNF-α initiation—was associated with an altered risk of autoimmune disease development compared to that in non-users. In contrast, the sensitivity analysis focused on assessing the impact of concomitant use of anti-TNF-α agents and immunosuppressants. Here, concomitant therapy was defined as the administration of immunosuppressants for at least 90 days during the course of anti-TNF-α treatment. Patients were categorized into 3 groups for comparison: (1) anti-TNF-α monotherapy, (2) no anti-TNF-α exposure, and (3) concurrent anti-TNF-α and immunosuppressant therapy. This approach allowed the evaluation of whether the simultaneous use of these immunomodulatory agents modulated the risk of paradoxical autoimmune events, thereby providing a robustness check for the main analysis.

All analyses were performed using SAS Enterprise Guide 7.1 (SAS Institute Inc., Cary, NC, USA).

RESULTS

1. Baseline Characteristics and Risk of Autoimmune Disease

In total, 119,739 patients with IBD were included in the study after applying the exclusion criteria. Patients with only a diagnostic code and no prescription for IBD-specific medication (n =488,530) and those with a prior diagnosis of an autoimmune disease before their IBD diagnosis (n=5,935) were excluded from the initial dataset. Among the included patients, 8,586 were subsequently diagnosed with autoimmune diseases, such as psoriasis, ILD, SLE, systemic vasculitis, or CNS disease. These patients comprised the final case group (Fig. 1). The remaining 111,153 patients with IBD who did not develop any autoimmune diseases during the study period served as the control group. Baseline characteristics of the study population are shown in Table 1. The median age of the patients in the case group was 47 years, and 50.9% were men. The median duration from IBD diagnosis to the onset of autoimmune disease in the case group was 4.17 years (interquartile range, 1.55–7.68 years). Of the total patients, 11,294 (10.2%) in the control group and 1,183 (13.8%) in the case group were treated with anti-TNF agents. Demographic and baseline characteristics of the case and control groups were balanced using 1:1 matching. However, even after matching, the case group had higher burdens of comorbidity, more frequent hospitalizations, and greater use of immunosuppressants and anti-TNF-α therapies. In addition, 1,029 cases (12.0%) and 620 (7.2%) controls received combination therapy, which was defined as the simultaneous use of conventional therapy and anti-TNF-α agents for at least 90 days.

Exposure to anti-TNF-α agents among patients with IBD was associated with a significantly increased risk of developing autoimmune diseases. Detailed analysis revealed an OR of 1.52 in the multivariable analysis, which was adjusted for several confounding factors, indicating a 52% higher risk in patients with IBD exposed to anti-TNF-α agents than in those not exposed to anti-TNF-α agents. This association remained consistent across the various statistical models. In the propensity score matched analysis, the risk remained elevated, with an OR of 1.65 (95% CI, 1.44–1.89; P<0.001), reinforcing the strength and reliability of the observed relationship. When individual autoimmune diseases were analyzed separately, certain conditions showed a particularly strong association with anti-TNF-α exposure. Specifically, psoriasis was associated with a 58% increase in risk (OR, 1.58; 95% CI, 1.34–1.86; P<0.001); ILD demonstrated an even higher risk, with an OR of 1.88 (95% CI, 1.33–2.66; P<0.001), indicating nearly double the risk as that for non-exposed individuals. In contrast, other autoimmune conditions, including CNS disease, SLE, and systemic vasculitis, did not show a statistically significant association with anti-TNF-α agent exposure in either the adjusted or matched models (Table 2).

We explored the dose-dependent association between anti-TNF-α exposure and the development of autoimmune disease by analyzing the cumulative number of anti-TNF-α prescriptions as a continuous variable (Fig. 2). Using a smoothing spline regression model, we observed a non-linear relationship, showing that the estimated risk increased sharply with initial exposure (mean risk, 1.52; 95% CI 1.33–1.71 for <30 prescriptions) and plateaued around 30 prescriptions (mean risk, 1.82; 95% CI, 1.64–2.01 for 30–64 prescriptions). The risk reached its highest point at approximately 64 prescriptions (risk, 1.83; 95% CI, 1.57–2.13) and showed a gradual decline beyond this point (mean risk, 1.69; 95% CI, 1.50–1.87 for >64 prescriptions), although the risk remained elevated compared to that at the lowest exposure levels.

2. Stratified Risk by Immunosuppressant Exposure and Concomitant Therapy

Table 3 presents subgroup analyses stratified by any history of immunosuppressant use, irrespective of timing relative to anti-TNF-α initiation. In both strata, anti-TNF-α exposure was significantly associated with an increased risk of overall autoimmune disease, although the effect size was modestly attenuated among patients with a history of immunosuppressant use compared with that among non-users. For specific autoimmune conditions, this association was statistically significant only for psoriasis, and the relationship persisted in immunosuppressant users with a slightly reduced magnitude. No significant associations were observed between ILD, CNS disease, lupus, and vasculitis in either subgroup.

Table 4 presents the sensitivity analysis focusing on concomitant therapy, defined as ≥90 days of overlapping anti-TNF-α and immunosuppressant use. In multivariable logistic regression, patients receiving concomitant therapy demonstrated a significantly elevated risk of overall autoimmune disease compared with those without anti-TNF-α exposure (OR, 1.51; 95% CI, 1.22–1.87; P<0.001), and this association remained robust after propensity score matching (OR, 1.43; 95% CI, 1.04–1.98; P=0.029). Anti-TNF-α monotherapy also showed a trend toward increased risk relative to the no-exposure group but did not meet the conventional significance threshold (OR, 1.19; 95% CI, 0.99–1.42; P=0.050) (data not shown).

DISCUSSION

This nationwide study explores the complex relationship between anti-TNF-α therapy and new-onset autoimmune diseases in patients with IBD. While anti-TNF-α agents are cornerstone treatments for IBD, providing substantial relief from inflammation and other debilitating symptoms, our findings highlight a significant association of anti-TNF-α therapy with new-onset autoimmune diseases. Patients exposed to anti-TNF-α agents had a 65% higher likelihood of developing an autoimmune disease compared to those with no such exposure, even after adjusting for multiple confounding factors. This risk was notably higher for specific conditions such as psoriasis and ILD. Stratified analyses showed that the increased risk of autoimmune disease with anti-TNF-α therapy was consistent regardless of prior immunosuppressant use and was further elevated with ≥90 days of concomitant use, suggesting a synergistic effect.

The occurrence of new-onset autoimmune disease following anti-TNF-α therapy has been documented across various populations and healthcare settings. According to Pérez-De-Lis et al. [9] from the BIOGEAS Registry, autoimmune phenomena have been increasingly reported with the widespread use of biological agents, particularly TNF-α inhibitors. Although these agents effectively target key immune pathways in diseases such as rheumatic disorders, cancer, and IBD, they also paradoxically induce autoimmune responses. Commonly reported conditions include psoriasis, CNS demyelination, ILD, and lupus. These findings are supported by large population-based studies reporting elevated risks of rheumatoid arthritis, psoriasis, and hidradenitis suppurativa after anti-TNF-α therapy in patients with IBD. A pooled hazard ratio of 1.76 was observed, indicating a 76% increased risk. Furthermore, an active comparator analysis against azathioprine monotherapy reaffirmed the heightened autoimmune risk linked to anti-TNF-α agents [24].

Our dose-response analysis revealed a non-linear association between cumulative anti-TNF-α exposure and the risk of autoimmune disease. The risk increased steeply with early exposure and plateaued after approximately 30 prescriptions, with a modest decline after 64. This pattern suggests that the emergence of paradoxical immune phenomena is more closely linked to early-phase immune dysregulation triggered by TNF-α blockade, rather than simply being a function of the cumulative dose. One possible explanation is that early exposure to anti-TNF-α agents may trigger immune dysregulation or unmask latent autoimmunity in genetically or immunologically susceptible individuals. Conversely, patients who continue long-term therapy without developing autoimmune complications may represent a subgroup with a more stable immune tolerance or less inherent susceptibility. Additionally, clinical selection bias may play a role, as patients who experience adverse immune reactions early in treatment are more likely to discontinue therapy, thereby enriching the long-term exposure group with individuals who are less prone to such effects.

In our study, anti-TNF-α exposure was significantly associated with psoriasis and ILD, with psoriasis showing a 58% increased risk, and ILD showing nearly twice the risk, compared to that in unexposed patients. Prior studies consistently report a link between anti-TNF-α therapy and psoriatic conditions in IBD and other IMIDs, but the risk varies by psoriasis subtype and patient characteristics [25]. A systematic review and metaanalysis by Xie et al. [26] reported an overall incidence of psoriasis and/or psoriasiform lesions at 6.0% (95% CI, 5.0%–7.0%). Bae et al. [27] also indicated a higher risk of psoriasis, particularly in younger male patients, which is somewhat different from the demographic characteristics highlighted by Xie et al. [26] However, the consistent increase in the risk of psoriatic disease across different populations underlines a common pharmacological effect, regardless of regional or demographic variations. Despite the evidence that patients on anti-TNF-α therapy may experience exacerbations of existing pulmonary conditions or develop new-onset ILD [12,28-30], the current incidence is likely underestimated owing to the rarity of disease, the short observation periods in randomized controlled trials, and the diagnostic methods used to identify anti-TNF-α-associated ILD. However, by utilizing a nationwide registry that provides a comprehensive and high-quality dataset representative of the Korean population, we were able to obtain significant evidence of an association.

Our study found no statistically significant association between anti-TNF-α exposure and CNS disease, lupus, or vasculitis. This aligns with data from the British Society for Rheumatology Biologics Registry in Rheumatoid Arthritis, which reported only a marginal, non-significant increase in CNS demyelinating events among anti-TNF-α users—an effect that disappeared in sensitivity analyses [16]. However, given the rarity of these events and diagnostic challenges, as highlighted in studies such as those by Kunchok et al. [13], who observed increased CNS risk in broader autoimmune population, underestimation remains possible in observational settings. These findings underscore the need for larger targeted studies and continued clinical vigilance, particularly for neurological and vascular adverse events, despite the absence of strong statistical signals.

The mechanistic pathways through which anti-TNF-α agents induce autoimmune diseases are not fully understood but are believed to involve the disruption of cytokine networks and immune homeostasis. Several hypotheses have been proposed to explain these mechanistic pathways. These include cytokine remodulation, which results in the overexpression of certain inflammatory molecules such as interferon and the interleukin (IL)-17/IL-23 axis [31]. This overexpression can initiate an inflammatory process, facilitating the activation and proliferation of pathogenic T cells, particularly in individuals genetically predisposed to such reactions. Additionally, the immunogenic nature of most TNF-α inhibitors often leads to the development of anti-drug antibodies and complex immune responses [32,33].

In our study, we evaluated whether the risk of autoimmune disease associated with anti-TNF-α therapy is influenced by the use of other medications, specifically immunosuppressants. When patients were stratified simply by the presence or absence of immunosuppressant use, both groups showed a significant increase in risk with anti-TNF-α therapy. Although the risk was slightly lower in the immunosuppressant group, this difference was not statistically significant, suggesting no clear protective or interactive effects. This finding implies that the small differences observed between the groups may be driven more by the underlying patient characteristics than by the pharmacological interaction of immunosuppressants. To examine this relationship more precisely, we performed an additional analysis categorizing patients into 3 groups: anti- TNF-α monotherapy, no anti-TNF-α exposure, and concomitant therapy. In this analysis, patients receiving concomitant therapy had a higher risk of autoimmune disease than those receiving anti-TNF-α monotherapy. This contrasts with some prior studies reporting a potential protective effect of combination therapy with anti-TNF-α agents and traditional immunomodulators [34]. This apparent discrepancy suggests the importance of treatment timing, cumulative exposure, and potential paradoxical immune reactions. Historical immunosuppressant use may reflect milder disease activity or an independent immunomodulatory effect; however, prolonged concurrent exposure to 2 potent immunosuppressive agents may synergistically enhance immune dysregulation, thereby increasing the risk of autoimmune events. These findings emphasize the need for careful consideration of combination therapy in clinical practice and suggest that the autoimmune risk profile differs depending on the nature and duration of immunosuppressant exposure. Our analysis focused on treatment exposures occurring prior to the onset of autoimmune disease, allowing us to more clearly evaluate the temporality between anti-TNF-α therapy and autoimmune outcomes. Further studies that systematically assess treatment modifications after autoimmune disease onset—such as discontinuation of anti-TNF-α therapy or switching to alternative agents—would strengthen the clinical interpretation of these findings. Incorporating post-outcome treatment trajectories will also be important to clarify how therapeutic decisions evolve after autoimmune complications and how they influence long-term outcomes.

Despite these insights, this study had certain limitations that must be acknowledged. First, despite the use of robust propensity score matching, the observational nature of this study limits our ability to infer definitive causal relationships. Second, this study relied on claims data, which, although comprehensive, may be prone to diagnostic misclassification or incomplete coding. In particular, the diagnoses of autoimmune diseases are based on ICD-10 codes without confirmation by histopathology or specialist assessment, raising the possibility of misclassification. Third, although we adjusted for multiple potential confounders, residual confounding from unmeasured variables, such as family history, genetic susceptibility, and prior immune-related events, cannot be fully ruled out. Additionally, in this study, EIMs commonly associated with IBD, such as arthritis and skin involvement, were intentionally excluded to focus on the risk of other new-onset immune-mediated diseases. While this approach allowed for clearer attribution of autoimmune outcomes to anti-TNF-α exposure, it may underestimate the full spectrum of immune-related adverse events in clinical practice. Furthermore, during the study period, reimbursement for newer biologics and small-molecule agents in Korea began only near the end or after our observation window, preventing meaningful comparison across different biologic classes. This reimbursement context also limited our ability to distinguish biologic-naive from biologic-experienced patients. Because nearly all anti-TNF-α users in our study initiated treatment before other agents became reimbursed or were routinely used in clinical practice, they can be reasonably regarded as biologic-naive at the time of initial exposure. Rare non-reimbursed or trial-based use of other biologics could not be identified in claims data, but such cases are expected to be exceedingly uncommon and unlikely to influence our findings. We acknowledge that some patients may have become biologic-experienced by switching from one anti-TNF-α agent to another, which could theoretically influence risk profiles. Our study did not stratify analyses based on anti-TNF-α switching status. Although our dose-response analysis indicates that the highest risk occurs during the earliest phase of anti-TNF exposure, we were unable to determine whether switching confers additional risk beyond initial exposure. Future studies with detailed longitudinal tracking of biologic sequencing, including anti-TNF-α switching and later transitions to newer biologic agents, will be important to clarify risk differences between biologic-naive and biologic-experienced patients.

In conclusion, our nationwide analysis demonstrated that anti-TNF-α therapy is significantly associated with an increased risk of new-onset autoimmune diseases in patients with IBD, particularly psoriasis and ILD. This risk persists regardless of immunosuppressant use and is further elevated with prolonged combination therapy. These findings highlight the need for individualized risk-benefit assessments and close monitoring of paradoxical immune responses, especially during the early phase of treatment. Future prospective studies incorporating integrative multi-omics approaches are needed to validate these associations and better elucidate the temporal dynamics and individual susceptibility underlying these paradoxical immune responses.

NOTES

Funding Source

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023- KH135855). This study was conducted as collaborative research project between the Korean Society of Gastroenterology and the National Health Insurance Service in Korea, with support from both institutions.

Conflict of Interest

No potential conflict of interest relevant to this article was reported.

Data Availability Statement

The findings of this study are based on secondary, processed, nationwide data that could not be stored or publicly archived due to their size and resource limitations. The Korean National Health Insurance Service provided only the analysis results. The raw data can be accessed through the Korean National Health Insurance Sharing Service (https://nhiss/nhis/or/kr), subject to applicable requirements and associated fees.

Author Contributions

Conceptualization; Lee CK, Oh SJ. Data curation: Oh SJ, Jeong SJ. Data interpretation: Oh SJ, Jeong SJ. Formal analysis: Jeong SJ. Investigation: all authors. Methodology; Project administration; Lee CK, Oh SJ, Jeong SJ. Resources; Software; Supervision; Lee CK, Jeong SJ. Validation; Visualization: Oh SJ, Jeong SJ. Writing–original draft: all authors. Writing–review & editing: all authors. Approval of final manuscript: all authors.

Supplementary Material

Supplementary materials are available at the Intestinal Research website (https://www.irjournal.org).

Supplementary Table 1.

ICD-10 Codes Used to Define Autoimmune Diseases Included in the Study

ir-2025-00195-Supplementary-Table-1.pdf

Supplementary Table 2.

Inflammatory Bowel Disease-Related Surgery ICD-10 Codes

ir-2025-00195-Supplementary-Table-2.pdf

Fig. 1.

Study flow for the inclusion of cases and controls. ICD-10, International Classification of Diseases, 10th Revision; IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease.

Fig. 2.

Dose-response relationship between anti-tumor necrosis factor alpha (anti-TNF-α) use and autoimmune disease risk.

Table 1.

Baseline Characteristics of Cases and Controls in Study Population

Variable	Unmatched			1:1 Matched
Variable	Case (n=8,586)	Control (n=111,153)	P-value	Case (n=8,586)	Control (n=8,586)	P-value
Age at index date (yr)	47 (33–60)	40 (27–54)	<0.001	47 (33–60)	47 (33–60)	0.824
Age at index date (yr)			<0.001			0.970
≤ 19	622 (7.2)	11,193 (10.1)		622 (7.1)	606 (7.1)
20–39	2,418 (28.2)	42,804 (38.5)		2,418 (28.2)	2,431 (28.3)
40–59	3,328 (38.8)	37,412 (33.7)		3,328 (38.8)	3,333 (38.8)
≥ 60	2,218 (25.8)	19,744 (17.8)		2,218 (25.8)	2,216 (25.8)
Subtype			<0.001			0.848
Ulcerative colitis	5,917 (68.9)	79,054 (71.1)		5,917 (68.9)	5,951 (69.3)
Crohn’s disease	1,529 (17.8)	18,951 (17.1)		1,529 (17.8)	1,513 (17.6)
Unclassified	1,140 (13.3)	13,148 (11.8)		1,140 (13.3)	1,121 (13.1)
Sex			<0.001			0.951
Male	4,366 (50.9)	64,393 (57.9)		4,366 (50.9)	4,362 (50.8)
Female	4,220 (49.2)	46,760 (42.1)		4,220 (49.2)	4,224 (49.2)
BMI (kg/m²)	24.2 (22.1–26.4)	24.1 (21.9–26.3)	0.003	24.2 (22.1–26.4)	24.1 (21.9–26.3)	0.091
Charlson Comorbidity Index score	4 (2–6)	2 (1–4)	<0.001	3 (2–5)	4 (2–6)	<0.001
Disease duration of IBD (yr)	4.17 (1.55–7.68)	–		4.17 (1.55–7.68)	–
Frequency of hospitalizations due to IBD	9.96±42.91	6.73±27.20		9.96±42.91	7.28±27.68	<0.001
Immunosuppressant used	4,742 (55.2)	33,996 (30.6)	<0.001	4,742 (55.2)	2,880 (33.5)	<0.001
Anti-TNF-α used	1,183 (13.8)	11,294 (10.2)	<0.001	1,183 (13.8)	735 (8.6)	<0.001
Infliximab	782 (9.1)	8,029 (7.2)		782 (9.1)	527 (6.1)
Adalimumab	580 (6.8)	4,569 (4.1)		580 (6.8)	313 (3.7)
Golimumab	89 (1.0)	613 (0.6)		89 (1.0)	46 (0.5)
Cumulative duration of anti-TNF-α (yr)	3.02 (1.14–7.68)	2.54 (1.00–4.84)	<0.001	3.02 (1.14–5.66)	2.84 (1.02–5.61)	0.587

Values are presented as median (interquartile range), number (%), or mean±standard deviation.

BMI, body mass index; IBD, inflammatory bowel disease; TNF, tumor necrosis factor.

Table 2.

Risk for Autoimmune Disease According to Anti-TNF-α Agent Exposure in Patients with IBD

	Simple		Multivariable^a		Matching^b
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Overall
Case	1.41 (1.33–1.51)	<0.001	1.52 (1.39–1.66)	<0.001	1.65 (1.44–1.89)	<0.001
Control	1.00		1.00		1.00
CNS disorders
Case	1.38 (1.11–1.72)	0.004	1.19 (0.87–1.61)	0.271	0.94 (0.57–1.54)	0.796
Control	1.00		1.00		1.00
ILD
Case	1.15 (0.97–1.36)	0.101	2.10 (1.70–2.59)	<0.001	1.88 (1.33–2.66)	<0.001
Control	1.00		1.00		1.00
Psoriasis
Case	1.55 (1.43–1.67)	<0.001	1.54 (1.39–1.71)	<0.001	1.58 (1.34–1.86)	<0.001
Control	1.00		1.00		1.00
Lupus
Case	1.13 (0.97–1.31)	0.118	1.14 (0.94–1.38)	0.182	1.32 (0.93–1.88)	0.120
Control	1.00		1.00		1.00
Vasculitis
Case	1.34 (0.94–1.90)	0.107	1.23 (0.76–1.99)	0.401	1.48 (0.65–3.38)	0.351
Control	1.00		1.00		1.00

^a Estimates were adjusted for age, sex, IBD subtype, IBD diagnosis year, body mass index, smoking, frequency of hospitalization due to IBD, IBD-related surgeries, Charlson Comorbidity Index, and presence of concomitant immune-mediated inflammatory diseases (e.g., rheumatoid arthritis and ankylosing spondylitis).

^b Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; IBD, inflammatory bowel disease; OR, odds ratio; CI, confidence interval; CNS, central nervous system; ILD, interstitial lung disease.

Table 3.

Risk of Autoimmune Disease Associated with Anti-TNF-α Agents, Stratified by Immunosuppressant Use

	Multivariable^a			Matching^b
	Immunosuppressants not use	Immunosuppressant use	Interaction P-value	Immunosuppressants not use	Immunosuppressant use	Interaction P-value
	OR (95% CI)	OR (95% CI)	Interaction P-value	OR (95% CI)	OR (95% CI)	Interaction P-value
Overall	1.32 (1.02–1.69)	1.12 (1.02–1.23)	0.003	1.38 (0.96–2.00)	1.26 (1.08–1.48)	0.244
CNS disorders	1.14 (0.47–2.74)	1.03 (0.74–1.45)	0.571	1.06 (0.24–4.81)	0.74 (0.42–1.31)	0.527
ILD	1.70 (0.84–3.44)	1.21 (0.96–1.51)	0.144	1.05 (0.40–2.71)	1.19 (0.08–1.77)	0.970
Psoriasis	1.60 (1.19–2.15)	1.17 (1.04–1.32)	0.014	1.64 (1.05–2.56)	1.26 (1.04–1.52)	0.246
Lupus	0.52 (0.27–0.99)	0.89 (0.73–1.08)	0.271	0.63 (0.21–1.91)	1.04 (0.71–1.53)	0.597
Vasculitis	<0.01 (0.00–1,000)	1.03 (0.63–1.68)	0.970	<0.01 (0.00–1,000)	1.27 (0.47–3.40)	0.968

^b Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; OR, odds ratio; CI, confidence interval; CNS, central nervous system; ILD, interstitial lung disease; IBD, inflammatory bowel disease.

Table 4.

Difference in risk of Autoimmune Disease Associated with Anti-TNF-α Agents in Subgroup Analysis

	Simple		Multivariable^a		Matching^b
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Overall
Anti-TNF-α monotherapy	Reference		Reference		Reference
Anti-TNF-α no use	0.85 (0.72–1.00)	0.050	0.93 (0.76–1.13)	0.460	0.82 (0.61–1.11)	0.193
Concomitant use of immunosuppressants	1.23 (1.03–1.47)	0.020	1.51 (1.22–1.87)	<0.001	1.43 (1.04–1.98)	0.029

^b Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; OR, odds ratio; CI, confidence interval; IBD, inflammatory bowel disease.

REFERENCES

1. Schett G, McInnes IB, Neurath MF. Reframing immune-mediated inflammatory diseases through signature cytokine hubs. N Engl J Med 2021;385:628–639.Article PubMed
2. Nielsen OH, Ainsworth MA. Tumor necrosis factor inhibitors for inflammatory bowel disease. N Engl J Med 2013;369:754–762.Article PubMed
3. Na SY, Choi CH, Song EM, et al. Korean clinical practice guidelines on biologics and small molecules for moderate-to-severe ulcerative colitis. Intest Res 2023;21:61–87.Article PubMed PDF
4. Koh SJ, Hong SN, Park SK, et al. Korean clinical practice guidelines on biologics for moderate to severe Crohn’s disease. Intest Res 2023;21:43–60.Article PubMed PDF
5. Jun YK, Yoon H, Koh SJ, et al. Concomitant ankylosing spondylitis can increase the risk of biologics or small molecule therapies to control inflammatory bowel disease. Intest Res 2023;21:244–251.Article PubMed PDF
6. Click B, Regueiro M. A practical guide to the safety and monitoring of new IBD therapies. Inflamm Bowel Dis 2019;25:831–842.Article PubMed PMC
7. Lichtenstein GR, Feagan BG, Cohen RD, et al. Drug therapies and the risk of malignancy in Crohn’s disease: results from the TREAT™ registry. Am J Gastroenterol 2014;109:212–223.Article PubMed PDF
8. Lichtenstein GR, Feagan BG, Cohen RD, et al. Serious infection and mortality in patients with Crohn’s disease: more than 5 years of follow-up in the TREAT™ registry. Am J Gastroenterol 2012;107:1409–1422.Article PubMed PMC PDF
9. Pérez-De-Lis M, Retamozo S, Flores-Chávez A, et al. Autoimmune diseases induced by biological agents: a review of 12,731 cases (BIOGEAS Registry). Expert Opin Drug Saf 2017;16:1255–1271.Article PubMed
10. Caccaro R, Savarino E, D’Incà R, Sturniolo GC. Noninfectious interstitial lung disease during infliximab therapy: case report and literature review. World J Gastroenterol 2013;19:5377–5380.Article PubMed PMC
11. Eliadou E, Moleiro J, Ribaldone DG, et al. Interstitial and granulomatous lung disease in inflammatory bowel disease patients. J Crohns Colitis 2020;14:480–489.Article PubMed PDF
12. Herrinton LJ, Harrold LR, Liu L, et al. Association between anti-TNF-α therapy and interstitial lung disease. Pharmacoepidemiol Drug Saf 2013;22:394–402.Article PubMed PMC
13. Kunchok A, Aksamit AJ, Davis JM, et al. Association between tumor necrosis factor inhibitor exposure and inflammatory central nervous system events. JAMA Neurol 2020;77:937–946.Article PubMed PMC
14. Oh SJ, Kwon YN, Lee CK, Lee JS. Anti-NMDAR encephalitis in Crohn’s disease undergoing long-term infliximab treatment: a case report. Front Immunol 2022;13:957575.Article PubMed PMC
15. Beigel F, Schnitzler F, Paul Laubender R, et al. Formation of antinuclear and double-strand DNA antibodies and frequency of lupus-like syndrome in anti-TNF-α antibody-treated patients with inflammatory bowel disease. Inflamm Bowel Dis 2011;17:91–98.Article PubMed
16. Jani M, Dixon WG, Kersley-Fleet L, et al. Drug-specific risk and characteristics of lupus and vasculitis-like events in patients with rheumatoid arthritis treated with TNFi: results from BSRBR-RA. RMD Open 2017;3:e000314.Article PubMed PMC
17. Nakashita T, Ando K, Kaneko N, Takahashi K, Motojima S. Potential risk of TNF inhibitors on the progression of interstitial lung disease in patients with rheumatoid arthritis. BMJ Open 2014;4:e005615.Article PubMed PMC
18. Harrison MJ, Dixon WG, Watson KD, et al. Rates of new-onset psoriasis in patients with rheumatoid arthritis receiving antitumour necrosis factor alpha therapy: results from the British Society for Rheumatology Biologics Register. Ann Rheum Dis 2009;68:209–215.PubMed
19. Kwak MS, Cha JM, Lee HH, et al. Emerging trends of inflammatory bowel disease in South Korea: a nationwide population-based study. J Gastroenterol Hepatol 2019;34:1018–1026.Article PubMed PDF
20. Kim JW, Lee CK, Lee JK, et al. Long-term evolution of direct healthcare costs for inflammatory bowel diseases: a population-based study (2006-2015). Scand J Gastroenterol 2019;54:419–426.Article PubMed
21. Park JS, Lee CH. Clinical study using healthcare claims database. J Rheum Dis 2021;28:119–125.Article PubMed PMC
22. National Health Insurance Service (NHIS). National Health Insurance & Long-Term Care Insurance System in Republic of Korea. Wonju: NHIS, 2021.
23. Lee CK, Ha HJ, Oh SJ, et al. Nationwide validation study of diagnostic algorithms for inflammatory bowel disease in Korean National Health Insurance Service database. J Gastroenterol Hepatol 2020;35:760–768.Article PubMed PDF
24. Ward D, Nyboe Andersen N, Gørtz S, et al. Tumor necrosis factor inhibitors in inflammatory bowel disease and risk of immune mediated inflammatory diseases. Clin Gastroenterol Hepatol 2024;22:135–143.e8.Article PubMed
25. Thein D, Egeberg A, Skov L, Loft N. Absolute and relative risk of new-onset psoriasis associated with tumor necrosis factor-α inhibitor treatment in patients with immune-mediated inflammatory diseases: a Danish nationwide cohort study. JAMA Dermatol 2022;158:997–1004.Article PubMed PMC
26. Xie W, Xiao S, Huang H, Zhang Z. Incidence of and risk factors for paradoxical psoriasis or psoriasiform lesions in inflammatory bowel disease patients receiving Anti-TNF therapy: systematic review with meta-analysis. Front Immunol 2022;13:847160.Article PubMed PMC
27. Bae JM, Lee HH, Lee BI, et al. Incidence of psoriasiform diseases secondary to tumour necrosis factor antagonists in patients with inflammatory bowel disease: a nationwide population-based cohort study. Aliment Pharmacol Ther 2018;48:196–205.Article PubMed PDF
28. Ostor AJ, Crisp AJ, Somerville MF, Scott DG. Fatal exacerbation of rheumatoid arthritis associated fibrosing alveolitis in patients given infliximab. BMJ 2004;329:1266.Article PubMed PMC
29. Perez-Alvarez R, Perez-de-Lis M, Diaz-Lagares C, et al. Interstitial lung disease induced or exacerbated by TNF-targeted therapies: analysis of 122 cases. Semin Arthritis Rheum 2011;41:256–264.Article PubMed
30. Curtis JR, Sarsour K, Napalkov P, Costa LA, Schulman KL. Incidence and complications of interstitial lung disease in users of tocilizumab, rituximab, abatacept and anti-tumor necrosis factor α agents, a retrospective cohort study. Arthritis Res Ther 2015;17:319.Article PubMed PMC
31. Collamer AN, Battafarano DF. Psoriatic skin lesions induced by tumor necrosis factor antagonist therapy: clinical features and possible immunopathogenesis. Semin Arthritis Rheum 2010;40:233–240.Article PubMed
32. Jani M, Dixon WG, Chinoy H. Drug safety and immunogenicity of tumour necrosis factor inhibitors: the story so far. Rheumatology (Oxford) 2018;57:1896–1907.Article PubMed PMC
33. Mohan N, Edwards ET, Cupps TR, et al. Leukocytoclastic vasculitis associated with tumor necrosis factor-alpha blocking agents. J Rheumatol 2004;31:1955–1958.PubMed
34. Qiu Y, Mao R, Chen BL, et al. Effects of combination therapy with immunomodulators on trough levels and antibodies against tumor necrosis factor antagonists in patients with inflammatory bowel disease: a meta-analysis. Clin Gastroenterol Hepatol 2017;15:1359–1372.e6.Article PubMed

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Association between anti-tumor necrosis factor alpha exposure and new-onset autoimmune diseases in inflammatory bowel disease: a nationwide case-control study in Korea

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Association between anti-tumor necrosis factor alpha exposure and new-onset autoimmune diseases in inflammatory bowel disease: a nationwide case-control study in Korea

Fig. 1. Study flow for the inclusion of cases and controls. ICD-10, International Classification of Diseases, 10th Revision; IBD, inflammatory bowel disease; UC, ulcerative colitis; CD, Crohn’s disease.

Fig. 2. Dose-response relationship between anti-tumor necrosis factor alpha (anti-TNF-α) use and autoimmune disease risk.

Graphical abstract

Fig. 1.

Fig. 2.

Graphical abstract

Association between anti-tumor necrosis factor alpha exposure and new-onset autoimmune diseases in inflammatory bowel disease: a nationwide case-control study in Korea

Variable	Unmatched			1:1 Matched
Variable	Case (n=8,586)	Control (n=111,153)	P-value	Case (n=8,586)	Control (n=8,586)	P-value
Age at index date (yr)	47 (33–60)	40 (27–54)	<0.001	47 (33–60)	47 (33–60)	0.824
Age at index date (yr)			<0.001			0.970
≤ 19	622 (7.2)	11,193 (10.1)		622 (7.1)	606 (7.1)
20–39	2,418 (28.2)	42,804 (38.5)		2,418 (28.2)	2,431 (28.3)
40–59	3,328 (38.8)	37,412 (33.7)		3,328 (38.8)	3,333 (38.8)
≥ 60	2,218 (25.8)	19,744 (17.8)		2,218 (25.8)	2,216 (25.8)
Subtype			<0.001			0.848
Ulcerative colitis	5,917 (68.9)	79,054 (71.1)		5,917 (68.9)	5,951 (69.3)
Crohn’s disease	1,529 (17.8)	18,951 (17.1)		1,529 (17.8)	1,513 (17.6)
Unclassified	1,140 (13.3)	13,148 (11.8)		1,140 (13.3)	1,121 (13.1)
Sex			<0.001			0.951
Male	4,366 (50.9)	64,393 (57.9)		4,366 (50.9)	4,362 (50.8)
Female	4,220 (49.2)	46,760 (42.1)		4,220 (49.2)	4,224 (49.2)
BMI (kg/m²)	24.2 (22.1–26.4)	24.1 (21.9–26.3)	0.003	24.2 (22.1–26.4)	24.1 (21.9–26.3)	0.091
Charlson Comorbidity Index score	4 (2–6)	2 (1–4)	<0.001	3 (2–5)	4 (2–6)	<0.001
Disease duration of IBD (yr)	4.17 (1.55–7.68)	–		4.17 (1.55–7.68)	–
Frequency of hospitalizations due to IBD	9.96±42.91	6.73±27.20		9.96±42.91	7.28±27.68	<0.001
Immunosuppressant used	4,742 (55.2)	33,996 (30.6)	<0.001	4,742 (55.2)	2,880 (33.5)	<0.001
Anti-TNF-α used	1,183 (13.8)	11,294 (10.2)	<0.001	1,183 (13.8)	735 (8.6)	<0.001
Infliximab	782 (9.1)	8,029 (7.2)		782 (9.1)	527 (6.1)
Adalimumab	580 (6.8)	4,569 (4.1)		580 (6.8)	313 (3.7)
Golimumab	89 (1.0)	613 (0.6)		89 (1.0)	46 (0.5)
Cumulative duration of anti-TNF-α (yr)	3.02 (1.14–7.68)	2.54 (1.00–4.84)	<0.001	3.02 (1.14–5.66)	2.84 (1.02–5.61)	0.587

	Simple		Multivariable^a		Matching^b
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Overall
Case	1.41 (1.33–1.51)	<0.001	1.52 (1.39–1.66)	<0.001	1.65 (1.44–1.89)	<0.001
Control	1.00		1.00		1.00
CNS disorders
Case	1.38 (1.11–1.72)	0.004	1.19 (0.87–1.61)	0.271	0.94 (0.57–1.54)	0.796
Control	1.00		1.00		1.00
ILD
Case	1.15 (0.97–1.36)	0.101	2.10 (1.70–2.59)	<0.001	1.88 (1.33–2.66)	<0.001
Control	1.00		1.00		1.00
Psoriasis
Case	1.55 (1.43–1.67)	<0.001	1.54 (1.39–1.71)	<0.001	1.58 (1.34–1.86)	<0.001
Control	1.00		1.00		1.00
Lupus
Case	1.13 (0.97–1.31)	0.118	1.14 (0.94–1.38)	0.182	1.32 (0.93–1.88)	0.120
Control	1.00		1.00		1.00
Vasculitis
Case	1.34 (0.94–1.90)	0.107	1.23 (0.76–1.99)	0.401	1.48 (0.65–3.38)	0.351
Control	1.00		1.00		1.00

	Multivariable^a			Matching^b
	Immunosuppressants not use	Immunosuppressant use	Interaction P-value	Immunosuppressants not use	Immunosuppressant use	Interaction P-value
	OR (95% CI)	OR (95% CI)	Interaction P-value	OR (95% CI)	OR (95% CI)	Interaction P-value
Overall	1.32 (1.02–1.69)	1.12 (1.02–1.23)	0.003	1.38 (0.96–2.00)	1.26 (1.08–1.48)	0.244
CNS disorders	1.14 (0.47–2.74)	1.03 (0.74–1.45)	0.571	1.06 (0.24–4.81)	0.74 (0.42–1.31)	0.527
ILD	1.70 (0.84–3.44)	1.21 (0.96–1.51)	0.144	1.05 (0.40–2.71)	1.19 (0.08–1.77)	0.970
Psoriasis	1.60 (1.19–2.15)	1.17 (1.04–1.32)	0.014	1.64 (1.05–2.56)	1.26 (1.04–1.52)	0.246
Lupus	0.52 (0.27–0.99)	0.89 (0.73–1.08)	0.271	0.63 (0.21–1.91)	1.04 (0.71–1.53)	0.597
Vasculitis	<0.01 (0.00–1,000)	1.03 (0.63–1.68)	0.970	<0.01 (0.00–1,000)	1.27 (0.47–3.40)	0.968

	Simple		Multivariable^a		Matching^b
	OR (95% CI)	P-value	OR (95% CI)	P-value	OR (95% CI)	P-value
Overall
Anti-TNF-α monotherapy	Reference		Reference		Reference
Anti-TNF-α no use	0.85 (0.72–1.00)	0.050	0.93 (0.76–1.13)	0.460	0.82 (0.61–1.11)	0.193
Concomitant use of immunosuppressants	1.23 (1.03–1.47)	0.020	1.51 (1.22–1.87)	<0.001	1.43 (1.04–1.98)	0.029

Table 1. Baseline Characteristics of Cases and Controls in Study Population

Values are presented as median (interquartile range), number (%), or mean±standard deviation.

BMI, body mass index; IBD, inflammatory bowel disease; TNF, tumor necrosis factor.

Table 2. Risk for Autoimmune Disease According to Anti-TNF-α Agent Exposure in Patients with IBD

Estimates were adjusted for age, sex, IBD subtype, IBD diagnosis year, body mass index, smoking, frequency of hospitalization due to IBD, IBD-related surgeries, Charlson Comorbidity Index, and presence of concomitant immune-mediated inflammatory diseases (e.g., rheumatoid arthritis and ankylosing spondylitis).

Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; IBD, inflammatory bowel disease; OR, odds ratio; CI, confidence interval; CNS, central nervous system; ILD, interstitial lung disease.

Table 3. Risk of Autoimmune Disease Associated with Anti-TNF-α Agents, Stratified by Immunosuppressant Use

Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; OR, odds ratio; CI, confidence interval; CNS, central nervous system; ILD, interstitial lung disease; IBD, inflammatory bowel disease.

Table 4. Difference in risk of Autoimmune Disease Associated with Anti-TNF-α Agents in Subgroup Analysis

Estimates were derived from propensity score matched analysis followed by multivariable adjustment using the same covariates.

TNF, tumor necrosis factor; OR, odds ratio; CI, confidence interval; IBD, inflammatory bowel disease.