Exercise training does not correct abnormal cardiac glycogen accumulation in the<i>db/db</i>mouse model of type 2 diabetes
Bibliographic record
Abstract
Substrate imbalance is a well-recognized feature of diabetic cardiomyopathy. Insulin resistance effectively limits carbohydrate oxidation, resulting in abnormal cardiac glycogen accumulation. Aims of the present study were to 1) characterize the role of glycogen-associated proteins involved in excessive glycogen accumulation in type 2 diabetic hearts and 2) determine if exercise training can attenuate abnormal cardiac glycogen accumulation. Control (db(+)) and genetically diabetic (db/db) C57BL/KsJ-lepr(db)/lepr(db) mice were subjected to sedentary or treadmill exercise regimens. Exercise training consisted of high-intensity/short-duration (10 days) and low-intensity/long-duration (6 wk) protocols. Glycogen levels were elevated by 35-50% in db/db hearts. Exercise training further increased (2- to 3-fold) glycogen levels in db/db hearts. Analysis of soluble and insoluble glycogen pools revealed no differential accumulation of one glycogen subspecies. Phosphorylation (Ser(640)) of glycogen synthase, an indicator of enzymatic fractional activity, was greater in db/db mice subjected to sedentary and exercise regimens. Elevated glycogen levels were accompanied by decreased phosphorylation (Thr(172)) of 5'-AMP-activated kinase and phosphorylation (Ser(79)) of its downstream substrate acetyl-CoA carboxylase. Glycogen concentration was not associated with increases in other glycogen-associated proteins, including malin and laforin. Novel observations show that exercise training does not correct diabetes-induced elevations in cardiac glycogen but, rather, precipitates further accumulation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".