Mechanisms of action of SGLT2 inhibitors and their beneficial effects on the cardiorenal axis
Bibliographic record
Abstract
Large clinical studies conducted with sodium-glucose co-transporter 2 inhibitors (SGLT2i) in patients with type 2 diabetes and heart failure with reduced ejection fraction have demonstrated their ability to achieve both cardiac and kidney benefits. Although there is huge evidence on SGLT2i-mediated clinical benefits both in diabetic and non-diabetic patients, the pathophysiological mechanisms underlying their efficacy are still poorly understood. Some favorable mechanisms are likely due to the prompt glycosuric action which is associated with natriuretic effects leading to hemodynamic benefits as well as a reduction in glomerular hyperfiltration and renin-angiotensin-aldosterone system activation. In addition to the renal mechanisms, SGLT2i may play a relevant role in cardiorenal axis protection by improving the cardiomyocyte metabolism, by exerting anti-fibrotic and anti-inflammatory actions, and by increasing cardioprotective adipokine expression. New studies will be needed to better understand the specific molecular mechanisms that mediate the SGLT2i favorable effects in patients suffering diabetes. Our aim is to first discuss about the molecular mechanisms underlying the cardiovascular benefits of SGLT2i in each of the main organs involved in the cardiorenal axis. Furthermore, we update on the most recent clinical trials evaluating the beneficial effects of SGLT2i in treatment of both diabetic and non-diabetic patients suffering heart failure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.003 | 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".