Mortality and major adverse cardiovascular events after glucagon-like peptide-1 receptor agonist initiation in patients with immune-mediated inflammatory diseases and type 2 diabetes: A population-based study
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of all-cause mortality and major adverse cardiovascular events (MACE) in patients with immune-mediated inflammatory diseases (IMIDs) and type 2 diabetes newly initiating glucagon-like peptide-1 receptor agonists (GLP-1-RAs) versus dipeptidyl peptidase-4 inhibitors (DPP-4is). METHODS: We performed a population-based cohort study using administrative health data from British Columbia. Patients with an IMID (i.e., rheumatoid arthritis, psoriatic disease, ankylosing spondylitis, inflammatory bowel disease, or a systemic autoimmune rheumatic disease) and type 2 diabetes who newly initiated a GLP-1-RA or DPP-4i between January 1, 2010, and December 31, 2021 were identified using ICD-9/10 codes. The primary outcome was all-cause mortality. Secondary outcomes included MACE and its components (i.e., cardiovascular death, myocardial infarction, and ischemic stroke). Cox proportional hazard regressions were used with propensity score overlap weighting. The analysis was repeated in age- and sex-matched adults without IMIDs. RESULTS: We identified 10,855 adults with IMIDs and type 2 diabetes who newly initiated a GLP-1-RA or DPP-4i. All-cause mortality rate was lower among initiators of GLP-1-RAs compared to initiators of DPP-4is, with a weighted hazard ratio (HR) of 0.48 (95% confidence interval [CI], 0.31-0.75) and rate difference (RD) of -9.4 (95% CI, -16.0 to -2.7) per 1000 person-years. Rate of MACE was also lower with GLP-1-RA exposure (HR 0.66 [0.50-0.88], RD -10.5 [-20.4 to -0.8]). Effect sizes were similar in adults without IMIDs. CONCLUSION: In patients with IMIDs and type 2 diabetes, GLP-1-RA exposure is associated with a lower risk of all-cause mortality and MACE compared to a cardioneutral active comparator.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".