Sodium–Glucose Cotransporter 2 Inhibitors and the Short-term Risk of Bladder Cancer: An International Multisite Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether sodium-glucose cotransporter 2 (SGLT2) inhibitors, compared with glucagon-like peptide 1 receptor agonists (GLP-1RAs) or dipeptidyl peptidase 4 (DPP-4) inhibitors, are associated with an increased risk of early bladder cancer events. RESEARCH DESIGN AND METHODS: We conducted a multisite, population-based, new-user, active comparator cohort study using the U.K. Clinical Practice Research Datalink, Medicare fee-for-service, Optum's de-identifed Clinformatics Data Mart Database (CDM), and MarketScan Health databases from January 2013 through December 2020. We assembled two cohorts of adults with type 2 diabetes initiating 1) SGLT2 inhibitors or GLP-1RAs and 2) SGLT2 inhibitors or DPP-4 inhibitors. Cox proportional hazards models were fit to estimate hazard ratios (HRs) and 95% CIs of incident bladder cancer. The models were weighted using propensity score fine stratification. Site-specific HRs were pooled using random-effects models. RESULTS: SGLT2 inhibitor (n = 453,560) and GLP-1RA (n = 375,997) users had a median follow-up ranging from 1.5 to 2.2 years. Overall, SGLT2 inhibitors were not associated with an increased risk of bladder cancer compared with GLP-1RAs (HR 0.90, 95% CI 0.81-1.00). Similarly, when compared with DPP-4 inhibitors (n = 853,186), SGLT2 inhibitors (n = 347,059) were not associated with an increased risk of bladder cancer (HR 0.99, 95% CI 0.91-1.09) over a median follow-up ranging from 1.6 to 2.6 years. Results were consistent across sensitivity analyses. CONCLUSIONS: Contrary to previous randomized controlled trials, these findings indicate that the use of SGLT2 inhibitors is not associated with an increased risk of bladder cancer compared with GLP-1RAs or DPP-4 inhibitors. This should provide reassurance on the short-term effects of SGLT2 inhibitors on bladder cancer incidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".