Mortality Reduction in EMPA-REG OUTCOME Trial: Beyond the Antidiabetes Effect
Bibliographic record
Abstract
Two recent large-scale cardiovascular outcome trials, a now common tool in assessing the safety of pharmacological treatments for type 2 diabetes, reported significant reductions in all-cause mortality. In EMPA-REG OUTCOME [BI 10773 (Empagliflozin) Cardiovascular Outcome Event Trial in Type 2 Diabetes Mellitus Patients], patients who received the SGLT2 inhibitor empagliflozin had a notable reduction of 9.2 deaths per 1,000 per year, while LEADER (Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results—A Long Term Evaluation) found that the patients receiving the GLP-1 receptor agonist liraglutide had a reduction of 3.7 deaths per 1,000 per year. The hypotheses to explain the sizable mortality reduction in EMPA-REG OUTCOME have mainly focused on the potential cardiovascular mechanisms of empagliflozin, but none considered its expected antidiabetes effects. I estimated the portion of the reduction in mortality observed in EMPA-REG OUTCOME expected to be a result of its antidiabetes effects, as measured by glycemic control and the need for additional antidiabetes medication, and contrasted it with LEADER. With use of the mean 0.45% reduction in HbA1c with empagliflozin compared with placebo in EMPA-REG OUTCOME, the rate reduction of 9.2 deaths per 1,000 per year would be expected to be at most 4.5 deaths per 1,000 per year, leaving 4.7 deaths per 1,000 per year otherwise explained. On the other hand, LEADER’s rate reduction of 3.7 deaths per 1,000 per year with liraglutide would be expected to be 3.5 by virtue of its effect on HbA1c, leaving 0.2 deaths per 1,000 per year explained otherwise. Similar results were found using the need for additional antidiabetes treatment during follow-up to measure the antidiabetes impact. In conclusion, the expected antidiabetes effects of empagliflozin and liraglutide on the reduction in mortality are important. However, empagliflozin appears to have significant additional effects on survival, possibly due to specific cardiovascular mechanisms, which merit further investigation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".