Sodium glucose cotransporter 2 inhibitors and risk of genital mycotic and urinary tract infection: A population‐based study of older women and men with diabetes
Bibliographic record
Abstract
AIMS: The objective of the study was to quantify the association between SGLT2 inhibitors and genital mycotic infection and between SGLT2 inhibitors and urinary tract infection (UTI) within 30 days of drug initiation among older women and men. MATERIALS AND METHODS: This was a retrospective cohort study using linked administrative databases of women and men with diabetes, aged 66 years or older, in Ontario, Canada. We compared the incidence of genital mycotic infection or UTI within 30 days between new users of an SGLT2 inhibitor and of a dipeptidyl-peptidase-4 (DPP4) inhibitor. RESULTS: We identified 21 444 incident users of SGLT2 inhibitor and 22 463 incident users of DPP4 inhibitor. Among SGLT2 inhibitor users, there were 8848 (41%) women and the mean age at index was 71.8 ± 5 (SD) years. After adjusting for propensity score, age, sex and recent UTI, there was a 2.47-fold increased risk of genital mycotic infection with incident use of SGLT2 inhibitors (adjusted hazard ratio (HR), 2.47; 95% confidence interval (CI), 2.08-2.92; P < 0.001) within 30 days compared to incident use of DPP4 inhibitors. For UTI, the adjusted HR was 0.89 (95% CI, 0.78-1.00; P = 0.05). CONCLUSIONS: Incident use of SGLT2 inhibitors among older women and men is associated with increased risk of genital mycotic infections within 30 days; there is no associated increased risk of UTI. These findings from a real-world setting provide evidence of the potential harms of SGLT2 inhibitors.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".