Can the cardiovascular risk reductions observed with empagliflozin in the EMPA‐REG OUTCOME trial be explained by concomitant changes seen in conventional cardiovascular risk factor levels?
Bibliographic record
Abstract
AIM: To perform post-hoc analyses of the EMPA-REG OUTCOME trial examining the degree to which empagliflozin-induced changes in conventional cardiovascular (CV) risk factors might explain the observed CV benefits. MATERIALS AND METHODS: We estimated 3-year EMPA-REG OUTCOME CV event rates using a type 2 diabetes-specific clinical outcomes simulation model applied to annual patient-level data. Variables included were atrial fibrillation, smoking, albuminuria, HDL cholesterol, LDL cholesterol, systolic blood pressure, glycated haemoglobin, heart rate, white cell count, haemoglobin, estimated glomerular filtration rate, and histories of ischaemic heart disease, heart failure, amputation, blindness, renal failure, stroke, myocardial infarction or diabetic ulcer. Multiple simulations were performed for each participant to minimize uncertainty and optimize confidence interval precision around CV risk point estimates. Observed and simulated cardiovascular relative risk reductions were compared. RESULTS: Model-predicted relative risk reductions were smaller than those observed in the trial, with empagliflozin-associated changes in conventional CV risk factor values appearing to explain only 12% of the observed relative risk reduction for all-cause death (4% of 32%), 7% for CV death (3% of 39%) and 15% for heart failure (4% of 29%). CONCLUSIONS: Empagliflozin-associated changes in conventional CV risk factors in EMPA-REG OUTCOME appear to explain only a small proportion of the CV and all-cause death reductions observed. Alternative risk-reduction mechanisms need to be explored to determine if the observed CV risk changes can be explained by other factors, or possibly by a direct drug-specific effect.
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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 | 0.037 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".