Impact of empagliflozin on right ventricular parameters and function among patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Background Sodium-glucose cotransporter 2 (SGLT2) inhibition reduces cardiovascular events in type 2 diabetes (T2DM) and is associated with a reduction in left ventricular (LV) mass index. However, the impact on right ventricular (RV) remodeling is unknown. Accordingly, the objective of this study was to assess the impact of SGLT2 inhibition on RV parameters and function in T2DM and coronary artery disease (CAD). Methods In EMPA-HEART CardioLink-6, 97 patients with T2DM and CAD were randomly assigned to empagliflozin 10 mg (n = 49) once daily or placebo (n = 48). Cardiac magnetic resonance imaging was performed at baseline and after 6 months. RV mass index (RVMi), RV end-diastolic and end-systolic volume index (RVEDVi, RVESVi) and RV ejection fraction (RVEF) were assessed in blinded fashion. Results At baseline, mean RVMi (± SD) (11.8 ± 2.4 g/m2), RVEF (53.5 ± 4.8%), RVEDVi (64.3 ± 13.2 mL/m2) and RVESVi (29.9 ± 6.9 mL/m2) were within normal limits and were similar between the empagliflozin and placebo groups. Over 6 months, there were no significant differences in RVMi (− 0.11 g/m2, [95% CI − 0.81 to 0.60], p = 0.76), RVEF (0.54%, [95% CI − 1.4 to 2.4], p = 0.58), RVEDVi (− 1.2 mL/m2, [95% CI − 4.1 to 1.7], p = 0.41) and RVESVi (− 0.81 mL/m2, [95% CI − 2.5 to 0.90], p = 0.35) in the empaglifozin group as compared with the placebo group. In both groups, there was no significant correlation between RVMi and LVMi changes from baseline to 6 months. Conclusions In this post-hoc analysis, SGLT2 inhibition with empagliflozin had no impact on RVMi and RV volumes in patients with T2DM and CAD. The potentially differential effect of empagliflozin on the LV and RV warrants further investigation. Clinical Trial Registration: URL: https://www.clinicaltrials.gov/ct2/show/NCT02998970?cond=NCT02998970&draw=2&rank=1 . Unique identifier: NCT02998970.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".