Sodium–Glucose Cotransporter 2 Inhibitors and the Risk of Below-Knee Amputation: A Multicenter Observational Study
Bibliographic record
Abstract
OBJECTIVE: Reports of amputations associated with sodium-glucose cotransporter 2 (SGLT2) inhibitors have been inconsistent. We aimed to compare the risk of below-knee amputation with SGLT2 inhibitors versus dipeptidyl peptidase 4 (DPP-4) inhibitors among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: This multicenter observational study used administrative health care databases from seven Canadian provinces and the U.K. Incident SGLT2 inhibitor users were matched to DPP-4 inhibitor users using a prevalent new-user design and time-conditional propensity scores. Cox proportional hazards models were used to estimate site-specific adjusted hazard ratios (HR) and corresponding 95% CIs of incident below-knee amputation for SGLT2 inhibitor versus DPP-4 inhibitor users. Random effects meta-analyses were used to pool the site-specific results. RESULTS: The study cohort included 207,817 incident SGLT2 inhibitor users matched to 207,817 DPP-4 inhibitor users. During a mean exposed follow-up time of 11 months, the amputation rate was 1.3 per 1,000 person-years among SGLT2 inhibitor users and 1.5 per 1,000 person-years among DPP-4 inhibitor users. The adjusted HR of below-knee amputations associated with SGLT2 inhibitor use compared with DPP-4 inhibitor use was 0.88 (95% CI 0.71-1.09). Similar results were obtained in stratified analyses by specific SGLT2 inhibitor molecule. CONCLUSIONS: In this large multicenter observational study, there was no association between SGLT2 inhibitor use and incident below-knee amputations among patients with type 2 diabetes compared with DPP-4 inhibitor use. While these findings provide some reassurance, studies with a longer duration of follow-up are needed to assess potential long-term effects.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".