Cardiac Reverse Remodeling as a Mechanism for the Cardioprotective Benefits Offered by Sodium-Glucose Cotransporter 2 Inhibitors
Bibliographic record
Abstract
Sodium-glucose cotransporter 2 inhibitors (SGLT2i) have demonstrated a marked ability to reduce the occurrence of adverse cardiovascular and heart failure events across a wide variety of patient populations. These benefits have been reported in patients with pre-existing heart failure, as well as in patients without prior documented heart failure who have risk factors. The profound clinical benefits associated with SGLT2i have sparked significant interest in elucidating the underlying mechanism(s) responsible for how SGLT2i delay the development and progression of heart failure. One postulated mechanism with merit involves the SGLT2i-mediated reversal of adverse cardiac remodeling. Within the current thesis, we aimed to investigate the mechanism responsible for the cardiac benefits associated with SGLT2i, to understand whether 6-month treatment with empagliflozin promoted cardiac reverse remodeling in patients without prevalent heart failure but who had risk factors. We also aimed to assess underlying factors that could influence the degree of benefit derived from SGLT2i treatment in relation to cardiac reverse remodeling. All assessments of cardiac structure and function in our studies were performed using gold-standard cardiac magnetic resonance imaging. We reported that (1) in patients with diabetes and coronary artery disease, those with an increased left ventricular mass indexed to body surface area (LVMi) at baseline experienced greater LVMi regression with empagliflozin over 6 months compared to patients with lower baseline LVMi. (2) Among individuals without diabetes or significant HF but with risk factors for adverse cardiac remodeling, as represented by an increased baseline LVM, SGLT2 inhibition with empagliflozin did not result in a significant reduction in LVMi after 6 months. This thesis presents novel insights suggesting that the cardiac reverse remodeling benefits derived from SGLT2i treatment may vary depending on the baseline demographics of the patient population, including the baseline severity of cardiac remodeling, baseline cardiovascular risk, and other currently unknown factors that may influence reverse remodeling. Overall, cardiac reverse remodeling may not be the only mechanism, but rather one of several mechanisms working simultaneously to improve outcomes in individuals treated with SGLT2i.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".