The Effect of Sodium-Glucose Cotransporter-2 (SGLT2) Inhibitors on Cardiovascular Outcomes in Cancer Patients With Type 2 Diabetes Mellitus: A Systematic Review
Bibliographic record
Abstract
While the cardiovascular benefits of sodium-glucose cotransporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM) are well-established, their role in cancer patients with T2DM - a population at heightened risk of cardiovascular complications due to both malignancy and cardiotoxic therapies - remains unclear. This systematic review aimed to synthesize the existing evidence on the effects of SGLT2is on cardiovascular outcomes in this high-risk group. We conducted a comprehensive literature search across PubMed/MEDLINE, Embase, Scopus, and Web of Science following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Observational cohort studies and randomized controlled trials (RCTs) evaluating SGLT2is in adult cancer patients with T2DM were included. Data extraction and quality assessment (using the Newcastle-Ottawa Scale for cohort studies) were performed independently by two reviewers. Due to clinical and methodological heterogeneity, findings were synthesized narratively, with effect estimates [hazard ratios (HRs), odds ratios (ORs)] and 95% confidence intervals (CIs) reported. Nine studies (n = 162,605 participants) were included, comprising retrospective cohorts (n = 7) and population-based studies (n = 2). SGLT2i use was consistently associated with reduced all-cause mortality and heart failure (HF)-related hospitalizations. Benefits were particularly pronounced in patients exposed to anthracyclines or with pre-existing cardiovascular risk. Subgroup analyses suggested a dose-dependent survival advantage, while safety outcomes were comparable to non-users. Study quality was generally high, though heterogeneity precluded meta-analysis. SGLT2 inhibitors appear to confer significant cardiovascular protection in cancer patients with T2DM, particularly against mortality and HF. These findings support their cautious integration into cardio-oncology practice, though randomized trials are needed to confirm causality and optimize protocols. Clinicians should weigh individual risks, especially in immunocompromised patients, while researchers should prioritize prospective studies to clarify mechanisms and long-term effects.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".