Sodium‐glucose cotransporter 2 inhibitors and the risk of venous thromboembolism: A population‐based cohort study
Bibliographic record
Abstract
AIMS: The cardiovascular benefits of sodium-glucose cotransporter 2 inhibitors (SGLT2Is) result from their complex impact on coronary and arterial vessels. However, their effect on veins and the risk of venous thromboembolism (VTE) remains unclear. Meta-analysis of trials has suggested no significant change in risk, but observational studies on the topic are scarce. Our objective was to determine if the use of SGLT2Is, compared to the use of dipeptidyl peptidase 4 inhibitors (DPP-4Is), is associated with the risk of VTE among patients with type 2 diabetes. METHODS: Using the Clinical Practice Research Datalink linked to hospitalization and vital statistics databases, we conducted a retrospective cohort study using a prevalent new-user design. SGLT2Is were matched to DPP-4I users on calendar time, diabetes treatment intensity, duration of previous DPP-4I use and time-conditional high-dimensional propensity score. Cox proportional hazard models estimated the hazard ratio (HR) for VTE with SGLT2Is versus DPP-4Is. RESULTS: SGLT2I use was not associated with an increased risk of VTE (HR 0.65, 95% confidence interval [CI] 0.34 to 1.25). This finding was consistent among prevalent (HR 0.47, 95% CI 0.16 to 1.42) and incident (HR 0.75, 95% CI 0.33 to 1.72) new users. CONCLUSIONS: We found that SGLT2Is were not associated with an increased risk of VTE compared to DPP-4Is. Although we observed a numerically decreased risk of VTE with SGLT2Is, estimates were accompanied by wide 95% CIs. Nonetheless, given the morbidity associated with VTE, our results provide some reassurance regarding the safety of SGLT2Is with respect to VTE.
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 | 0.003 | 0.003 |
| 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 | 0.000 |
| 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 | 0.000 |
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".