SGLT2i and Primary Prevention of Cancer Therapy–Related Cardiac Dysfunction in Patients With Diabetes
Bibliographic record
Abstract
Background: Specific cancer treatments can lead to cancer therapy-related cardiac dysfunction (CTRCD). Sodium glucose cotransporter-2 inhibitors (SGLT2is) can potentially prevent these cardiotoxic effects. Objectives: This study sought to determine whether SGLT2i use is associated with a lower incidence of CTRCD in patients with type 2 diabetes mellitus (T2DM) and cancer, exposed to potentially cardiotoxic antineoplastic agents, and without a prior documented history of cardiomyopathy or heart failure. Methods: We conducted a retrospective analysis of patients aged ≥18 years within the TriNetX database with T2DM, cancer, exposure to cardiotoxic therapies, and no prior documented history of cardiomyopathy or heart failure. Patients were categorized by SGLT2i use. After propensity score matching, outcomes were compared over 12 months using Cox proportional HRs. Subgroup analyses focusing on different cancer therapy classes were performed. Results: The study included 8,675 propensity-matched patients in each cohort (mean age = ∼65 years, 42% females, 71% White, ∼19% gastrointestinal malignancy, and ∼25% anthracyclines). Patients prescribed SGLT2is had a lower risk of developing CTRCD (HR: 0.76: 95% CI: 0.69-0.84). SGLT2is also reduced heart failure exacerbations (HR: 0.81; 95% CI: 0.72-0.90), all-cause mortality (HR: 0.67; 95% CI: 0.61-0.74), and all-cause hospitalizations/emergency department visits (HR: 0.93; 95% CI: 0.89-0.97). Subgroup analyses also demonstrated reduced CTRCD risk across various classes of cancer therapies in patients prescribed SGLT2is. Conclusions: SGLT2i administration was associated with a significantly decreased risk of developing CTRCD in patients with T2DM and cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".