Diabetic cardiomyopathy: the need for adjusting experimental models to meet clinical reality
Bibliographic record
Abstract
Diabetic cardiomyopathy (CM), occurring in the absence of hypertension, coronary artery disease, and valvular or congenital heart disease, is now recognized as a distinct, multifactorial disease leading to ventricular hypertrophy and abnormal myocardial contractility that correlates with an array of complex molecular and cellular changes. Animal models provide the unique opportunity to investigate mechanistic aspects of diabetic CM, but important caveats exist when extrapolating findings obtained from preclinical models of diabetes to humans. Indeed, animal models do not recapitulate the complexity of environmental factors, most notably the duration of the exposure to insulin resistance that may play a crucial role in the development of diabetic CM. Moreover, most preclinical studies are performed in animals with uncontrolled or poorly controlled diabetes, whereas patients tend to undergo therapeutic intervention. Finally, whilst type 2 diabetes mellitus prevalence trajectory mainly increases at 40- < 75 years (with a currently alarming increase at younger ages, however), it is a legitimate concern how closely rodent models employing young animals recapitulate the disease developing in old people. The aim of this review is to identify the current limitations of rodent models and to discuss how future mechanistic and preclinical studies should integrate key confounding factors to better mimic the diabetic CM phenotype.
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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 | 0.049 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| 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".