Use of Sodium–Glucose Co-Transporter-2 Inhibitors in Patients with and without Type 2 Diabetes: Implications for Incident and Prevalent Heart Failure
Bibliographic record
Abstract
Type 2 diabetes (T2D) is associated with an increased risk of heart failure (HF), with recent reports indicating that HF with preserved ejection fraction (HFpEF) may be more common than HF with reduced ejection fraction (HFrEF) in patients with T2D. T2D and HF result in worse outcomes than either disease alone. Sodium-glucose co-transporter-2 inhibitors (SGLT-2is) have significantly improved HF outcomes in patients with T2D and may represent a new therapeutic alternative for patients with T2D at risk for or with HF. Current guidelines recommend prevention of HF through risk factor management. Once developed, treatment of HFrEF should include neurohormonal and haemodynamic modulations; however, there are no specific treatments available for HFpEF. SGLT-2is are the first class of glucose-lowering therapy to prevent HF in clinical trials and real-world studies in patients with T2D (with or without established cardiovascular disease and with or without baseline HF). Mechanistic studies suggest that SGLT-2is have beneficial effects on both systolic and diastolic function and additional systemic effects that could benefit HF outcomes. In patients with HFrEF, SGLT-2i treatment as add-on to standard HF therapy has had beneficial effects on HF outcomes, irrespective of T2D status. These results and those of ongoing outcomes trials with SGLT-2is may help establish this drug class as a treatment for HF in patients with HFrEF and HFpEF, as well as HF in patients without T2D.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".