Are the cardiovascular and kidney benefits of empagliflozin influenced by baseline glucose‐lowering therapy?
Bibliographic record
Abstract
Abstract Aims In the EMPA‐REG OUTCOME® trial, the sodium‐glucose cotransporter 2 inhibitor empagliflozin when given in addition to standard care improved cardiovascular (CV) and renal outcomes, and reduced mortality. Trial participants were on a variety of glucose‐lowering therapies at baseline, some of which could potentially affect CV risk. This analysis investigated whether the use of background diabetes therapy affected the risk of CV death, hospitalizations for heart failure, and progression of chronic kidney disease, among patients treated with empagliflozin. Materials and methods Patients meeting inclusion and exclusion criteria were randomized to placebo, empagliflozin 10 mg or empagliflozin 25 mg; glucose‐lowering therapy was to remain unchanged for 12 weeks and then adjusted to achieve glycaemic control according to local guidelines. Differences in risk of cardio‐renal outcomes between empagliflozin and placebo by baseline use of metformin, sulphonylurea (SU) and insulin were assessed using a Cox proportional hazards model. Results Of 7020 eligible patients, 74% were receiving metformin, 43% SU and 48% insulin at baseline (each alone or in combination); the most common regimens were metformin plus SU (20%) and metformin plus insulin (20%). Empagliflozin reduced the risk of CV death irrespective of the use of: metformin [with: hazard ratio (HR) 0.71 (95% confidence interval, CI, 0.54–0.94); without: 0.46 (0.32–0.68); P interaction = 0.07]; SU [with: HR 0.64 (0.44–0.92); without: 0.61 (0.46–0.81); P interaction = 0.85]; or insulin [with: HR 0.63 (0.46–0.85); without: 0.61 (0.44–0.85); P interaction = 0.92]. Reductions in three‐point major adverse CV events, hospitalizations for heart failure, and all‐cause mortality were consistent across subgroups of baseline therapies. Empagliflozin reduced the risks of incident or worsening nephropathy versus placebo irrespective of the use of SU or insulin at baseline ( P interaction > 0.05), but there was a greater reduction in this risk for patients not using metformin [HR 0.47 (95% CI 0.37–0.59)] versus those using metformin [HR 0.68 (95% CI 0.58–0.79)] at baseline ( P interaction = 0.01). Conclusions The addition of empagliflozin to antihyperglycaemic regimens of patients with type 2 diabetes and CV disease consistently reduced their risks of adverse CV outcomes and mortality irrespective of baseline use of metformin, SU or insulin. For chronic kidney disease progression, there may be a larger benefit from empagliflozin in those patients who are not using metformin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".