New Therapeutic Options for Type 2 Diabetes Mellitus and Their Impact Against Ischemic Heart Disease
Bibliographic record
Abstract
Type 2 diabetes mellitus (T2DM) greatly increases risk for cardiovascular disease, including ischemic heart disease and myocardial infarction. With the completion of several cardiovascular outcomes trials (CVOTs) for new glucose-lowering therapies, including the sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor (GLP-1R) agonists, we now have strong evidence alluding to the cardioprotective nature of these agents in people with T2DM. These agents have frequently been observed to reduce rates for 3-point major adverse cardiovascular events, which encompass death from cardiovascular causes, nonfatal myocardial infarction, or nonfatal stroke. Herein we will provide an overview on whether reductions in nonfatal myocardial infarction and ischemic heart disease status are a key component of the improved cardiovascular outcomes in people with T2DM treated with either SGLT2 inhibitors or GLP-1R agonists. Observations from preclinical studies will be compared to their clinical counterparts, while being further interrogated to define potential mechanisms that may account for SGLT2 inhibitor or GLP-1R agonist-induced cardioprotection against ischemic heart disease. A better understanding of the role these agents have in impacting the progression of ischemic heart disease in individuals with T2DM will have a substantial impact in our management of this patient population.
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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 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".