The Impact of Antidiabetic Therapies on Diastolic Dysfunction and Diabetic Cardiomyopathy
Bibliographic record
Abstract
Diabetic cardiomyopathy is more prevalent in people with type 2 diabetes mellitus (T2DM) than previously recognized, while often being characterized by diastolic dysfunction in the absence of systolic dysfunction. This likely contributes to why heart failure with preserved ejection fraction is enriched in people with T2DM vs. heart failure with reduced ejection fraction. Due to revised mandates from major health regulatory agencies, all therapies being developed for the treatment of T2DM must now undergo rigorous assessment of their cardiovascular risk profiles prior to approval. As such, we now have data from tens of thousands of subjects with T2DM demonstrating the impact of major therapies including the sodium-glucose co-transporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor (GLP-1R) agonists, and dipeptidyl peptidase 4 (DPP-4) inhibitors on cardiovascular outcomes. Evidence to date suggests that both SGLT2 inhibitors and GLP-1R agonists improve cardiovascular outcomes, whereas DPP-4 inhibitors appear to be cardiovascular neutral, though evidence is lacking to determine the overall utility of these therapies on diastolic dysfunction or diabetic cardiomyopathy in subjects with T2DM. We herein will review the overall impact SLGT2 inhibitors, GLP-1R agonists, and DPP-4 inhibitors have on major parameters of diastolic function, while also highlighting the potential mechanisms of action responsible. A more complete understanding of how these therapies influence diastolic dysfunction will undoubtedly play a major role in how we manage cardiovascular disease in subjects with T2DM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".