Exercise‐Induced Increase in Pro‐ and Macroglycogen Content in Hearts from Type 1 Diabetic Mice
Bibliographic record
Abstract
Diabetes adversely affects glucose metabolism, resulting in elevated levels of heart glycogen. Exercise training increases glycogen synthesis activity in diabetes; therefore the aim of this study was to examine the effects of exercise on glycogen content in hearts from streptozotocin‐induced (insulin‐deficient for duration of study) type 1 diabetic mice (T1D) mice compared to CD‐1 controls. Mice were exercised by running for 60 min/d, 5 d/wk for 6 wk at low‐moderate intensity. Hearts from T1D and control mice at 12 wk of age were subjected to a 60 min working heart perfusion with glucose and palmitate, prior to glycogen measurements (μmol/g dw) as total glycogen (G t ), proglycogen (PG) and macroglycogen (MG). G t for T1D sedentary (SED) hearts was not significantly different from SED or exercised (EX) non‐diabetic, CD1 controls. In T1D SED hearts, PG contributed 60% of total glycogen, which was significantly elevated compared to EX controls but not to SED controls. EX T1D hearts showed a nearly 2‐fold increase in G t compared to SED diabetic hearts (935±86 vs 584±20) with PG and MG contributing 558±21 and 436±80 glucosyl units respectively. There are intriguing aspects of this data: i) there was no difference in G t between SED T1D and controls implying glycogen synthesis is not impaired in T1D hearts. (ii) Elevated PG levels in T1D hearts reflects a distinct selective process in glycogen formation compared to controls, suggesting T1D hearts form new granules at the expense of MG to maintain G t. (iii) An exercise‐induced improvement in glucose disposal may account for elevated PG and MG levels in EX T1D perfused hearts. Supported by CIHR (DS) and Genome Alberta (JS).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".