Glucagon‐Like Peptide 1 Receptor Agonists, Sodium‐Glucose Cotransporter 2 Inhibitors, and Risk of Autoimmune Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 inhibitors (SGLT2i) facilitate weight loss and exhibit immunomodulatory effects, but their impact on the risk of developing autoimmune rheumatic diseases (ARDs) is unclear. We compared ARD incidence following initiation of GLP-1RAs or SGLT2i versus a weight-neutral comparator (dipeptidyl peptidase 4 inhibitors [DPP4i]). METHODS: We performed a population-based cohort study using administrative health data from a Canadian province with universal health care. We included adults with type 2 diabetes (T2D) and no prior ARD initiating a GLP-1RA, SGLT2i, or DPP4i between January 1, 2014, and December 31, 2022. Incident ARD cases, including rheumatoid arthritis, psoriatic disease, axial spondyloarthritis, and systemic ARDs (SARDs; systemic lupus erythematosus, systemic sclerosis, Sjögren disease, idiopathic inflammatory myopathies, and systemic vasculitides), were identified using validated algorithms. Propensity score (PS) weighting was used to balance cohorts at treatment initiation. Then hazard ratios (HRs) were estimated using Cox regression. RESULTS: Among 229,300 adults, 49,514 initiated GLP-1RAs, 101,925 initiated SGLT2i, and 77,861 initiated DPP4i. After PS weighting, ARD incidence per 10,000 person-years was 29.1 (95% confidence interval [CI] 23.5-35.5) with GLP-1RAs, 24.4 (95% CI 19.8-29.7) with SGLT2i, and 27.3 (95% CI 22.1-33.4) with DPP4i. Mean follow-up was 1.3 to 1.6 years. Relative to DPP4i, adjusted HRs (aHRs) of ARD were 1.04 (95% CI 0.81-1.33) with GLP-1RAs and 0.93 (95% CI 0.75-1.16) with SGLT2i. Risk of SARDs, but not other diseases, was lower with SGLT2i versus DPP4i (aHR 0.51 [95% CI 0.31-0.84]). CONCLUSION: Neither GLP-1RA nor SGLT2i treatment was associated with increased or decreased ARD risk versus DPP4i in adults with T2D; however, SGLT2i use was associated with significantly lower risk of SARDs, warranting further study.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".