Potential Underlying Mechanisms Explaining the Cardiorenal Benefits of Sodium–Glucose Cotransporter 2 Inhibitors
Bibliographic record
Abstract
There is a bidirectional pathophysiological interaction between the heart and the kidneys, and prolonged physiological stress to the heart and/or the kidneys can cause adverse cardiorenal complications, including but not limited to subclinical cardiomyopathy, heart failure and chronic kidney disease. Whilst more common in individuals with Type 2 diabetes, cardiorenal complications also occur in the absence of diabetes. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) were initially approved to reduce hyperglycaemia in patients with Type 2 diabetes. Recently, these agents have been shown to significantly improve cardiovascular and renal outcomes in patients with and without Type 2 diabetes, demonstrating a robust reduction in hospitalisation for heart failure and reduced risk of progression of chronic kidney disease, thus gaining approval for use in treatment of heart failure and chronic kidney disease. Numerous potential mechanisms have been proposed to explain the cardiorenal effects of SGLT2i. This review provides a simplified summary of key potential cardiac and renal mechanisms underlying the cardiorenal benefits of SGT2i and explains these mechanisms in the clinical context. Key mechanisms related to the clinical effects of SGLT2i on the heart and kidneys explained in this publication include their impact on (1) tissue oxygen delivery, hypoxia and resultant ischaemic injury, (2) vascular health and function, (3) substrate utilisation and metabolic health and (4) cardiac remodelling. Knowing the mechanisms responsible for SGLT2i-imparted cardiorenal benefits in the clinical outcomes will help healthcare practitioners to identify more patients that can benefit from the use of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".