Effect of empagliflozin on cardiorenal outcomes and mortality according to body mass index: A subgroup analysis of the <scp>EMPA‐REG OUTCOME</scp> trial with a focus on Asia
Bibliographic record
Abstract
Abstract Aim To investigate whether the cardiorenal benefits of the sodium‐glucose co‐transporter‐2 inhibitor empagliflozin are affected by body mass index (BMI) in type 2 diabetes patients with established cardiovascular (CV) disease, including Asians. Methods In this exploratory analysis of the EMPA‐REG OUTCOME trial, we used Cox regression to evaluate the effects of empagliflozin on all‐cause mortality, hospitalization for heart failure (HHF) or CV death, and incident or worsening nephropathy by baseline BMI category. Results Of the 7020 participants (1517 Asians [21.6%]), 934 (13.3%), 2465 (35.1%) and 3621 (51.6%) had a BMI of less than 25, 25 to less than 30, and 30 kg/m 2 or higher, respectively. Overall, hazard ratios for empagliflozin versus placebo for all‐cause mortality, HHF or CV death, and incident or worsening nephropathy were 0.68 (95% CI 0.57, 0.82), 0.66 (0.55, 0.79) and 0.61 (0.53, 0.70), respectively, and were consistent across BMI categories ( P values for interaction between treatment and BMI were .6772, .3087 and .6265, respectively). Results were similar in Asians using these BMI categories and categories of less than 24, 24 to less than 28, and 28 kg/m 2 or higher. Conclusion Empagliflozin reduced cardiorenal and mortality risk regardless of BMI at baseline, including in Asians with a lower BMI.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.011 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".