---
title: "SGLT2 Inhibitors and Rheumatoid Arthritis Risk in Type 2 Diabetes: Summary of Reported Emulated Ta"
id: "frontiers-in-immunology-19-the-association-between-sglt2-inhibitors-and-rheumatoid-arthritis-risk-in-type"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-19-the-association-between-sglt2-inhibitors-and-rheumatoid-arthritis-risk-in-type"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1867117"
published_at: "2026-08-04T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# SGLT2 Inhibitors and Rheumatoid Arthritis Risk in Type 2 Diabetes: Summary of Reported Emulated Ta
## Provenance & Clinical Metadata
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- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1867117)
- **Published At:** 2026-08-04T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The source article title reports an investigation of the association between **SGLT2 inhibitors** and **rheumatoid arthritis** risk in people with **type 2 diabetes**, using large-scale **emulated target trials**. - The published entry is on Frontiers in Immunology, but the provided source text contains only the journal navigation and metadata, not the article content itself. - Key study elements such as objectives, population characteristics, exposure definitions, comparator treatments, follow-up duration, statistical methods, effect estimates, and p values were not available in the provided source content. - Because outcome data and methodological details were not reported in the supplied content, no study results, relative risks, hazard ratios, or conclusions can be extracted or summarized from this source alone. - The title implies large-scale observational emulation of randomized trials to study a potential immunologic outcome (RA) linked to a class of glucose-lowering drugs, reflecting growing interest in cardiovascular and immune effects of diabetes therapies. - Readers should consult the full article for raw results, design specifics (data sources, inclusion/exclusion, confounding control, sensitivity analyses), and authors’ conclusions before applying findings to clinical practice. - Any clinical or prescribing implications cannot be drawn from the supplied text; the original article must be accessed for validated results and interpretation.
## Clinical Analysis & Structured Key Points
Frontiers | The association between SGLT2 inhibitors and rheumatoid arthritis risk in type 2 diabetes: findings from large-scale emulated target trials ORIGINAL RESEARCH article Front. Immunol. , 04 August 2026 Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1867117 Published in Frontiers in Immunology Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders 7 impact factor 11.3 citescore Part of a Research Topic Real-world advances in inflammatory diseases: from population data to immunological mechanisms and clinical impact Submission open 6593 views 8 articles Editor & Reviewers Edited by P C Philip Curman Reviewed by H H Hai-Anh Ha S J Sultana Jahan Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Table 1 Baseline characteristics of study participants: SGLT2i vs. Sulfonylureas (before and after matching). View in article Table 2 Rheumatoid arthritis risks (1 day to 1 year). View in article ORIGINAL RESEARCH article Front. Immunol. , 04 August 2026 Sec. Autoimmune and Autoinflammatory Disorders : Autoimmune Disorders Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1867117 The association between SGLT2 inhibitors and rheumatoid arthritis risk in type 2 diabetes: findings from large-scale emulated target trials F Y Fu-Shun Yen 1 † S W Shiow-Ing Wang 2,3 † C H Chih-Cheng Hsu 4,5,6,7 S H Sung Huang Laurent Tsai 8,9,10,11 C H Chii-Min Hwu 12,13 * J C James Cheng-Chung Wei 3,14,15 * 1. Dr. Yen’s Clinic, Taoyuan, Taiwan 2. Center for Health Data Science, Department of Medical Research, Chung Shan Medical University Hospital, Taichung, Taiwan 3. Department of Health Policy and Management, College of Health Care and Management, Chung Shan Medical University, Taichung, Taiwan 4. Institute of Population Health Sciences, National Health Research Institutes, Miaoli, Taiwan 5. Department of Health Services Administration, China Medical University, Taichung, Taiwan 6. Department of Family Medicine, Min-Sheng General Hospital, Taoyuan, Taiwan 7. National Center for Geriatrics and Welfare Research, National Health Research Institutes, Huwei, Yunlin, Taiwan 8. Department of Orthopedics, Taipei Medical University Hospital, Taipei, Taiwan 9. Department of Orthopaedics, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 10. Department of Biomedical Engineering, National Taiwan University, Taipei, Taiwan 11. Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan 12. Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan 13. Faculty of Medicine, National Yang Ming Chiao Tung University School of Medicine, Taipei, Taiwan 14. Department of Allergy, Immunology & Rheumatology, Chung Shan Medical University Hospital, Taichung, Taiwan 15. Graduate Institute of Integrated Medicine, China Medical University, Taichung, Taiwan See more Article metrics View details Abstract Introduction: Globally, the prevalence of rheumatoid arthritis (RA) is on the rise. Certain antidiabetic medications have been reported to lower the risk of RA. In this study, we conducted multicenter emulated target trials to compare the risk of RA development between sodium-glucose cotransporter-2 inhibitors (SGLT2i) and non-SGLT2i treatment options in patients with type 2 diabetes mellitus (T2DM). Methods: We identified 4,991,988 patients with T2DM from the TriNetX network between January 1, 2016, to June 30, 2023. From this cohort, we selected 310,507, 206,069, and 80,846 propensity score-matched pairs of patients using SGLT2i versus sulfonylureas, dipeptidyl peptidase-4 inhibitors (DPP-4i), and pioglitazone, respectively. The Kaplan-Meier method was used to determine the relative hazards of developing RA among these groups. Subgroup analyses were conducted based on sex, age, race, obesity status, HbA1C levels, and eGFR, with an additional sensitivity analysis using the intention-to-treat approach. Results: SGLT2i use was associated with a significantly lower risk of incident RA compared to sulfonylurea use (HR: 0.899, 95% CI: 0.835–0.968). However, there was no significant difference in RA risk when comparing SGLT2i with DPP-4 inhibitors (HR: 0.914, 95% CI: 0.831–1.005) or pioglitazone (HR: 0.922, 95% CI: 0.796–1.068). Kaplan-Meier curves demonstrated that SGLT2i users had a significantly lower likelihood of developing RA than sulfonylurea users (log-rank p = 0.004). Subgroup analyses further indicated that SGLT2i use was consistently associated with a lower risk of RA across all analyzed patient subgroups compared to sulfonylureas. Additionally, sensitivity analyses confirmed the robustness of these findings, aligning with the results from the emulated target trial comparing SGLT2i to sulfonylureas. Discussion: This study found that SGLT2i use was associated with a lower risk of incident RA compared with sulfonylurea use among patients with T2DM. However, because of the observational study design, these findings should be interpreted as associations rather than evidence of causality. 1 Introduction Rheumatoid arthritis (RA) is the most common chronic inflammatory disease worldwide, often leading to musculoskeletal impairment, disability, and premature death ( 1 , 2 ).Between 1990 and 2020, the global age-standardized prevalence rate rose by approximately 14.1% ( 3 ). In the United States, the prevalence of RA is estimated to be between 0.5% and 1%, affecting over 1.3 million adults. The annual incidence is around 40 per 100, 000 individuals, with age- and sex-adjusted rates of 41 per 100, 000 (53 per 100, 000 in women and 29 per 100, 000 in men) between 2005 and 2014 ( 4 ). Clearly, the prevalence of RA has been increasing globally. RA may contribute to insulin resistance and type 2 diabetes mellitus (T2DM) due to the frequent use of corticosteroids and high levels of systemic inflammation ( 5 , 6 ). Conversely, individuals with T2DM, may have an increased risk of RA, possibly due to obesity-related metabolic syndrome and chronic low-grade inflammation ( 7 ). Some antidiabetic medications have been reported to reduce the risk of RA through their effects on improving insulin resistance, lowering blood glucose levels, and reducing inflammation ( 8 – 12 ). A nationwide cohort study found that sulfonylureas and biguanides were associated with a lower risk of developing RA than non-sulfonylurea and non-biguanide treatments in patients with T2DM ( 8 ). Moreover, a systematic review and meta-analysis of ten clinical trials concluded that dipeptidyl peptidase-4 inhibitors (DPP-4i) do not increase the risk of RA ( 9 ). However, another meta-analysis of four cohort studies found that the use of DPP-4i was associated with a 28% lower risk of developing RA compared with non-use ( 10 ). A retrospective cohort study demonstrated that patients using thiazolidinediones (TZDs) had a lower risk of developing RA compared to those using alpha glucosidase inhibitors (AGIs) ( 11 ). Additionally, a case-control study indicated that among patients with T2DM, those taking TZDs showed a trend toward reduced rheumatoid arthritis risk compared to non-TZD users, though this reduction did not reach statistical significance ( 12 ). With respect to newer glucose-lowering therapies, evidence regarding the association between sodium–glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and the risk of RA remains limited. SGLT2is exert their glucose-lowering effect by enhancing renal glucose excretion. Beyond glycemic control, these agents have been shown to attenuate oxidative stress and exert anti-inflammatory properties.¹³ Experimental studies suggest additional immunometabolic effects of SGLT2is, including enhanced lipid utilization, increased ketone body production, suppression of NLRP3 inflammasome activation, and reduced secretion of interleukin (IL)-1β.¹ 4 Canagliflozin has been demonstrated to inhibit T-cell activation and proliferation through downregulation of mammalian target of rapamycin complex 1 (mTORC1) in CD4 + T cells isolated from patients with systemic lupus erythematosus and RA ( 15 ). Furthermore, experiments using synovial fluid mononuclear cells from patients with RA have shown that canagliflozin reduces IL-17 expression ( 15 , 16 ). Despite these mechanistic insights, clinical evidence evaluating the impact of SGLT2i on the incidence of RA is still scarce. We therefore hypothesized that initiation of SGLT2i therapy would be associated with a lower incidence of RA. we conducted multicenter emulated target trials ( 17 ) to compare the incidence of RA among patients with T2DM receiving SGLT2is versus those treated with alternative glucose-lowering agents. 2 Patients and methods 2.1 Study design and data source This study was conducted as a retrospective cohort analysis using data from TriNetX, the world’s largest real-world data and evidence ecosystem in the life sciences and healthcare sector. The platform has been widely used for high-quality research ( 18 , 19 ). TriNetX integrates de-identified electronic health records from over 250 million individuals across more than 120 global healthcare organizations (HCOs). These records undergo processing through data warehouses and research repositories before being incorporated into the TriNetX platform. The dataset includes structured information, such as demographics, diagnoses, procedures, medications, laboratory test results, and vital signs. TriNetX captures both inpatient and outpatient data, with most HCOs contributing comprehensive datasets. To ensure data quality, TriNetX employs a standardized framework that evaluates three key metrics: conformance, completeness, and plausibility ( 20 ). The data and analysis for this study were conducted in January 2025. We utilized the US Collaborative Network, a subset of the TriNetX platform, which included 69 healthcare organizations. In accordance with our study objectives, we restricted the study period to data collected between January 1, 2016, and December 31, 2024. 2.2 Ethics statement The TriNetX platform complies with the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). The Western Institutional Review Board (WIRB) has granted TriNetX a waiver, as the database provides aggregated counts and statistical summaries of de-identified data. Additionally, the use of TriNetX for this study was approved by the Institutional Review Board of Chung Shan Medical University Hospital (CSMUH Approval No: CS2-21176). 2.3 Target trials We applied the framework proposed by Hernán and Robins (2016) ( 15 ) to emulate clinical trials comparing SGLT2i treatment with non-SGLT2i treatment (DPP-4i, pioglitazone, or sulfonylureas) in patients with T2DM using an unblinded design. First, we established the target trial emulation framework to align with our research objectives, incorporating key elements, such as eligibility criteria, treatment strategies, allocation methods, follow-up procedures, outcomes of interest, causal comparisons, and the analysis plan ( Supplementary Table 1 ) ( 21 ). Next, we defined the approach for leveraging observational data to replicate these protocol components and conduct the corresponding analyses ( Supplementary Table 2 ). 2.4 Study eligibility criteria Individuals eligible for this study were those with at least two documented diagnoses of T2DM, identified using the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes ( Supplementary Table 3 ). We excluded individuals under 20 years of age and those with a history of type 1 diabetes mellitus. Additionally, to focus on recently diagnosed cases and assess treatment outcomes in patients with newly diagnosed T2DM, we excluded individuals diagnosed with T2DM before December 31, 2015. Furthermore, patients were excluded from both cohorts if they had a history of dialysis or kidney transplantation, as these conditions could introduce confounding factors related to comorbidities that might influence the study outcomes. 2.5 Assigned treatment strategies and groups In the first target trial, individuals classified as SGLT2i users were those who received an SGLT2i prescription following their initial diagnosis of T2DM. SGLT2i use was identified using the Anatomical Therapeutic Chemical (ATC) code A10BK, with the index date defined as the date of the first SGLT2i prescription. Similarly, the sulfonylurea cohort consisted of individuals who were prescribed sulfonylureas (ATC code A10BB) after their first T2DM diagnosis, with the index date set as the date of their first sulfonylurea prescription. The second target trial compared the initiation of SGLT2i to DPP-4i at baseline, while the third target trial compared the initiation of SGLT2i to pioglitazone at baseline. In all scenarios, the initiation date (index date) was defined as the date of filling the first prescription for the respective medication. Each emulated target trial was constructed independently from the original eligible T2DM source population. Patients were re-selected and propensity score matched separately for each treatment comparison. Therefore, treatment assignment was specific to each target trial and was determined solely by the index medication. Within each comparison, the two comparator-defining medications were mutually exclusive at baseline, whereas other glucose-lowering medications could be present as baseline comedications and were included as covariates in the propensity score matching. To preserve the intention-to-treat design, individuals were not excluded if they switched treatment during the study period. However, patients in both cohorts were excluded if they had a prior diagnosis of RA, or had died on or before the index date. 2.6 Study outcomes and follow-up The primary outcome was incident rheumatoid arthritis, identified using ICD-10-CM codes M05 and M06 recorded after the index date. Individuals with any diagnosis of RA before cohort entry were excluded to improve the specificity of incident case ascertainment. The secondary outcomes included all-cause mortality, and medical utilization (defined as hospital inpatient services, emergency department visits, and critical care services). To assess the risk of the specified outcomes, each participant was followed from the initiation of the assigned treatment, for up to one year. 2.7 Emulation of target trials To simulate randomization, propensity score matching (PSM) was employed to estimate the probability (propensity score) of treatment assignment (e.g., initiation of SGLT2i, sulfonylureas, DPP-4i, or pioglitazone) based on observed baseline characteristics. SGLT2i initiators were matched 1:1 with contemporaneous initiators of sulfonylureas, DPP-4i, or pioglitazone. The matching process utilized the TriNetX propensity score generation tool, applying 1:1 greedy nearest neighbor matching with a caliper width of 0.1 pooled standard deviations for the matching variables. The balance between cohorts, before and after matching, was assessed using standardized mean differences (SMDs), with SMDs below 0.1 indicating well-balanced groups. To account for potential confounding, variables were analyzed during the one year prior to the index date. The variables were included as follows. 1. Demographic and socioeconomic factors (age at the index date, sex, race, and socioeconomic status). 2. Lifestyle factors, such as smoking status (tobacco use, nicotine dependence, personal history of nicotine dependence, and alcohol-related disorders). 3. Diabetes severity indicators (T2DM complications including hyperosmolarity, ketoacidosis, kidney complications, ophthalmic complications, neurological complications, circulatory complications, other specified complications, unspecified complications, and T2DM without complications). 4. Medical utilization (office or other outpatient services, emergency services, inpatient services, and preventive care services). 5. Comorbidities (defined by ICD-10 Codes and categorized as present or absent) including hypertensive diseases, ischemic heart disease, heart failure, cerebrovascular diseases, diseases of the arteries, arterioles, and capillaries, overweight and obesity, dyslipidemia, chronic kidney disease, unspecified kidney failure, unspecified proteinuria, chronic lower respiratory diseases, depressive episodes, recurrent major depressive disorder, liver diseases, viral hepatitis, psoriasis, systemic lupus erythematosus, and ankylosing spondylitis. 6. Medication use, such as metformin, thiazolidinediones, DPP-4i, GLP-1 RAs, insulin and analogs, statins, beta-blockers, diuretics, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers, calcium channel blockers, aspirin, corticosteroids for systemic use, proton pump inhibitors, non-steroidal anti-inflammatory drugs (NSAIDs), and alpha-adrenoreceptor antagonists. 7. Laboratory data: hemoglobin A1C (HbA1c), estimated glomerular filtration rate (eGFR; calculated using the Modification of Diet in Renal Disease [MDRD] formula), body mass index (BMI), and rheumatoid factor levels. By integrating these variables into the propensity score matching process, we aimed to reduce confounding and ensure comparability between treatment groups, thus, enhancing the validity of our target trial emulation. 2.8 Analysis plan and statistical methods The primary causal estimands in this study were the intention-to-treat (ITT) effects of the assigned treatment strategies. To estimate hazard ratios (HRs) and confidence intervals (CIs) and assess the proportionality of outcome risks, we utilized the Survival package (version 3.2-3) in R. The proportional hazards assumption was assessed using the proportionality test provided within the TriNetX platform together with visual inspection of Kaplan–Meier survival curves. Kaplan-Meier analysis was performed to estimate the cumulative probability of developing RA within the emulated target trials. Differences between survival curves for the treatment groups were assessed using the log-rank test. To explore variations in RA risk, we conducted the following subgroup analyses: sex (male, female); age groups (20–44 years, 45–64 years, ≥65 years); race (White, African-American, Asian); obesity status (with and without obesity); HbA1c levels (<7%, ≥9%); and eGFR categories (<60, 60-90, ≥90 ml/min/1.73 m²). To assess the robustness of our findings, we performed additional sensitivity analyses. First, using different sets of matching variables while maintaining the same study design. Second, adjusting the study design by excluding participants who switched treatments during follow-up. Third, conducting the same study design within a different TriNetX network to evaluate consistency across datasets. Fourth, we performed additional analyses using longer maximum follow-up durations of 3, 5, and 7 years. 3 Results 3.1 Characteristics of study subjects In this study, we initially identified 4, 991, 988 patients with T2DM. After excluding ineligible individuals, the cohort included 458, 428 users of SGLT2i and 484, 205 users of sulfonylureas. Following propensity score matching (PSM), 310, 507 patients were classified as SGLT2i users, with an equal number in the sulfonylurea group ( Figure 1 ). Baseline characteristics of the study population, before and after matching, are presented in Table 1 . Prior to matching, significant differences were observed between SGLT2i and sulfonylurea users in terms of age, diabetic kidney complications, healthcare utilization, comorbidities, concomitant medications, and laboratory findings. After PSM, covariate balance was substantially improved. Most baseline characteristics achieved an SMD <0.1; however,
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