High glucose uptake in growing rats adapted to a low-protein, high-carbohydrate diet determines low fasting glycemia even with high hepatic gluconeogenesis
Bibliographic record
Abstract
The our objective was to investigate the adaptations induced by a low-protein, high-carbohydrate (LPHC) diet in growing rats, which by comparison with the rats fed a control (C) diet at displayed lower fasting glycemia and similar fasting insulinemia, despite impairment in insulin signaling in adipose tissues. In the insulin tolerance test the LPHC rats showed higher rates of glucose disappearance (30%) and higher tolerance to overload of glucose than C rats. The glucose uptake by the soleus muscle, evaluated in vivo by administration of 2-deoxy-[(14)C]glucose, increased by 81%. The phosphoenolpyruvate carboxykinase content and the incorporation of [1-(14)C]pyruvate into glucose was also higher in the slices of liver from the LPHC rats than in those from C rats. The LPHC rats showed increases in l-lactate as well as in other gluconeogenic precursors in the blood. These rats also had a higher hepatic production of glucose, evaluated by in situ perfusion. The data obtained indicate that the main substrates for gluconeogenesis in the LPHC rats are l-lactate and glycerol. Thus, we concluded that the fasting glycemia in the LPHC animals was maintained mainly by increases in the hepatic gluconeogenesis from glycerol and l-lactate, compensating, at least in part, for the higher glucose uptake by the tissues.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".