Sodium-Glucose Cotransporter-2 Inhibitors and Urinary Tract Infections: A Propensity Score–matched Population-based Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Sodium-glucose cotransporter-2 (SGLT2) inhibitor-induced glycosuria is hypothesized to increase the risk of urinary tract infections (UTIs). We assessed the risk of UTIs associated with SGLT2 inhibitor initiation in type 2 diabetes. METHODS: We conducted a population-based cohort study using primary care data from the United Kingdom's Clinical Practice Research Datalink (CPRD) and administrative health-care data from Alberta, Canada. From a base cohort of new metformin users, we constructed 5 comparative cohorts, wherein the exposure contrast was defined as new use of SGLT2 inhibitors or 1 of 5 active comparators: dipeptidylpeptidase-4 (DPP-4) inhibitors, sulfonylureas (SU), glucagon-like peptide-1 receptor agonists (GLP-1 RA), thiazolidinediones (TZD) and insulin. We defined a composite UTI outcome based on hospitalizations or physician visit records. For each comparative cohort, we used high-dimensional propensity score matching to adjust for confounding and Cox proportional hazards regression to estimate the hazard ratios (HRs) in each database. We meta-analyzed estimates using a random-effects model. RESULTS: SGLT2 inhibitor use was not associated with a higher risk of UTI compared with DPP-4 inhibitors (pooled HR, 1.08; 95% confidence interval [CI], 0.89 to 1.30), SU (pooled HR, 1.08; 95% CI, 0.90 to 1.30), GLP-1 RA (pooled HR, 0.81; 95% CI, 0.61 to 1.09) or TZD (pooled HR, 0.81; 95% CI, 0.55 to 1.19). The risk of UTI was lower compared with insulin (pooled HR, 0.74; 95% CI, 0.63 to 0.87). The risk of UTI did not differ based on the SGLT2 inhibitor agent or dose. Last, SGLT2 inhibitor initiation was not associated with an increased risk of UTI recurrence. CONCLUSION: SGLT2 inhibitor use is not associated with an increased risk of UTIs, compared with other antidiabetic agents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".