Influence of Long-Term Diabetes on Renal Glycogen Metabolism in the Rat
Bibliographic record
Abstract
BACKGROUND/AIMS: The effects of acute insulin deficiency on the kidney have been investigated in animal models of experimental diabetes; however, the impact of long-term diabetes has not been determined. METHODS: We measured renal glycogen contents in streptozotocin (STZ)-diabetic rats 3 weeks (n = 12) or 9 months (n = 12) after the induction of diabetes, and in 2 groups of control rats of similar age (n = 16 and n = 12, respectively), in the fed state and after a 24-hour fast. RESULTS: Diabetic rats had high glucose levels, low insulin but normal glucagon concentrations in portal blood. In the fasting state, kidney glycogen content was very low in both young control and young diabetic rats (54 +/- 15 and 189 +/- 26 microg/g, respectively, mean +/- SD); in contrast, glycogen levels were markedly elevated in rats with long-standing diabetes as compared to old nondiabetic animals (2,628 +/- 1,023 +/- and 1,968 +/- 989 microg/g of diabetic rat, fasting and fed, respectively, p < 0.001 vs. 0 +/- 0 and 4 +/- 6 microg/g of control rats). On electron microscopy, large glycogen clusters were localized to the renal tubules. Kidney phosphorylase activity was higher, and synthase activity lower in diabetic than control rats (p < 0.05 for both), whereas kidney glycogen was strongly related to plasma glucose levels, suggesting that the enzyme changes were secondary to glycogen accumulation itself. Renal hexosephosphates and fructose-2,6-bisphosphate contents were both increased in long-term diabetic rats (p < 0.05), implying enhanced fluxes through both glycolysis and gluconeogenesis. CONCLUSION: In chronic, untreated diabetes glycogen accumulates in the renal tubules; prolonged hyperglycemia is the sole driving force for this phenomenon.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".