Left ventricular mass predicts cardiac reverse remodelling in patients treated with empagliflozin
Bibliographic record
Abstract
Abstract Background The cardiovascular (CV) benefits of sodium-glucose transport protein 2 inhibitors have been attributed, in part, to cardiac reverse remodelling. The EMPA-HEART CardioLink-6 study reported that sodium-glucose cotransporter-2 inhibition for 6 months with empagliflozin was associated with a significant reduction in left ventricular mass indexed to body surface area (LVMi). In this sub-analysis, we evaluated whether baseline LVMi may influence how empagliflozin affects cardiac reverse remodelling. Methods A total of 97 patients with type 2 diabetes and coronary artery disease were randomized to empagliflozin (10 mg/d) or matching placebo for 6 months. The study cohort was divided into those whose baseline LVMi was ≤ 60 g/m 2 and those who had a baseline LVMi > 60 g/m 2 . Subgroup comparisons were conducted using a linear regression model adjusted for baseline values (ANCOVA) that included an interaction term between LVMi subgroup and treatment. Results Baseline LVMi was 53.3 g/m 2 (49.2–57.2) and 69.7 g/m 2 (64.2–76.1) for those with baseline ≤ 60 g/m 2 (n = 54) and LVMi > 60 g/m 2 (n = 43) respectively. The adjusted difference of LVMi regression between those randomized to empagliflozin and placebo were − 0.46 g/m 2 (95% CI: −3.44, 2.52, p = 0.76) in the baseline LVMi ≤ 60 g/m 2 subgroup and − 7.26 g/m 2 (95% CI: −11.40, −3.12, p = 0.0011) in the baseline LVMi > 60 g/m 2 subgroup ( p -for-interaction = 0.007). No significant associations were found between baseline LVMi and 6-month change in LV end systolic volume-indexed ( p -for-interaction = 0.086), LV end diastolic volume-indexed ( p -for-interaction = 0.34), or LV ejection fraction ( p -for-interaction = 0.15). Conclusions Patients with higher LVMi at baseline experienced greater LVM regression with empagliflozin.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".