Cardiac substrate metabolism in type 2 diabetes
Bibliographic record
Abstract
As the most metabolically demanding organ on a per gram basis, substrate metabolism in the heart is intricately linked to cardiac function. Virtually all major cardiovascular pathologies are associated with perturbations in cardiac substrate metabolism, and increasing evidence supports that these perturbations in substrate metabolism can directly contribute to cardiac dysfunction. Furthermore, type 2 diabetes (T2D) is a major risk factor for increased cardiovascular disease burden, while also being characterized by a very distinct metabolic profile in the heart. This includes increases in cardiac fatty oxidation rates and a robust reduction in cardiac glucose oxidation rates. Herein, we will describe the primary mechanisms responsible for the increase in cardiac fatty acid oxidation and decrease in cardiac glucose oxidation during T2D, while also detailing perturbations in cardiac ketone and amino acid metabolism. In addition, we will interrogate preclinical studies that have addressed whether correcting perturbations in cardiac substrate metabolism may have clinical utility against ischemic heart disease, diabetic cardiomyopathy, or heart failure associated with T2D. Lastly, we will consider the translational potential of such an approach to manage cardiovascular disease in people living with T2D.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".