Efficacy and Safety of Dapagliflozin by Baseline Insulin Regimen and Dose: Post Hoc Analyses From DECLARE-TIMI 58
Bibliographic record
Abstract
OBJECTIVE: The cardiorenal benefits of adding sodium-glucose cotransporter 2 (SGLT2) inhibitor therapy for patients on insulin, particularly those on intensive regimens that include short-acting (SA) insulin, have not been explored. RESEARCH DESIGN AND METHODS: In Dapagliflozin Effect on Cardiovascular Events trial (DECLARE-TIMI 58), 17,160 patients with type 2 diabetes were randomized to dapagliflozin or placebo for a median follow-up of 4.2 years. Cardiovascular (CV), renal, metabolic, and safety outcomes with dapagliflozin versus placebo by insulin dose and regimen were studied with Cox regression models. RESULTS: The study included 7,013 insulin users at baseline, with 4,650 (66.3%) patients on regimens including SA insulin. Insulin doses varied, with 2,443 (34.8%) patients receiving <0.5 IU/kg, 2,795 (39.9%) 0.5 to ≤1 IU/kg, and 1,339 (19.1%) >1 IU/kg. Dapagliflozin reduced CV death/hospitalization for heart failure among overall insulin users (hazard ratio [HR] 0.82 [95% CI 0.69-0.97]) and consistently in patients on insulin regimens with or without SA insulin (0.83 [0.67-1.03] and 0.78 [0.57-1.07], respectively, Pinteraction = 0.75). No heterogeneity was observed by insulin dose (Pinteraction = 0.43). The HR for major adverse CV events with dapagliflozin among insulin users (0.84 [0.74-0.97]) was similar irrespective of regimen or dose (Pinteraction = 0.75 and 0.07). Dapagliflozin reduced the rate of adverse renal outcomes overall and consistently across subgroups of insulin users. Decreases in HbA1c, weight, and systolic blood pressure with dapagliflozin were seen regardless of insulin dose or regimen. The known safety profile of dapagliflozin was unchanged in patients on intensive insulin regimens. CONCLUSIONS: The benefits and safety of dapagliflozin were maintained in high-risk patients receiving high-dose or intensive insulin regimens including SA insulin.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".