SGLT2 Inhibitors in Cancer Patients: A Comprehensive Review of Clinical, Biochemical, and Therapeutic Implications in Cardio-Oncology
Bibliographic record
Abstract
Patients with active cancer and cancer survivors are at a markedly increased risk for developing cardiovascular comorbidities, including heart failure, coronary artery disease, and renal dysfunction, which are often compounded by the cardiotoxic effects of cancer therapies. This heightened cardiovascular vulnerability underscores the urgent need for effective, safe, and evidence-based cardioprotective strategies to reduce both cardiovascular morbidity and mortality. Sodium-glucose cotransporter 2 inhibitors (SGLT2is), a class of drugs originally developed for the treatment of type 2 diabetes, have demonstrated significant cardiovascular and renal benefits in high-risk populations, independent of glycemic control. Among the currently available SGLT2i, such as empagliflozin, canagliflozin, dapagliflozin, and sotagliflozin, there is growing evidence supporting their role in reducing major adverse cardiovascular events (MACEs), hospitalization for heart failure, and the progression of chronic kidney disease. Recent preclinical and clinical data suggest that SGLT2is exert cardioprotective effects through multiple mechanisms, including the modulation of inflammasome activity, specifically by reducing NLRP3 inflammasome activation and MyD88-dependent signaling, which are critical drivers of cardiac inflammation and fibrosis. Moreover, SGLT2is have been shown to enhance mitochondrial viability in cardiac cells, promoting improved cellular energy metabolism and function, thus mitigating cardiotoxicity. This narrative review critically evaluates the emerging evidence on the cardiorenal protective mechanisms of SGLT2is, with a particular focus on their potential role in cardio-oncology. We explore the common pathophysiological pathways between cardiovascular dysfunction and cancer, the molecular rationale for the use of SGLT2is in cancer patients, and the potential benefits in both primary and secondary prevention of cardiovascular toxicity related to oncological treatments. The aim is to propose a therapeutic paradigm utilizing SGLT2is to reduce cardiovascular mortality, MACE, and the burden of cardiotoxicity in high-risk oncology patients, fostering an integrated approach to cardio-oncology care.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".