A low-protein diet combined with low-dose endotoxin leads to changes in glucose homeostasis in weanling rats
Bibliographic record
Abstract
Severe malnutrition is a leading cause of global childhood mortality, and infection and hypoglycemia or hyperglycemia are commonly present. The etiology behind the changes in glucose homeostasis is poorly understood. Here, we generated an animal model of severe malnutrition with and without low-grade inflammation to investigate the effects on glucose homeostasis. Immediately after weaning, rats were fed diets containing 5 [low-protein diet (LP)] or 20% protein [control diet (CTRL)], with or without repeated low-dose intraperitoneal lipopolysaccharide (LPS; 2 mg/kg), to mimic inflammation resulting from infections. After 4 wk on the diets, hyperglycemic clamps or euglycemic hyperinsulinemic clamps were performed with infusion of [U-(13)C6]glucose and [2-(13)C]glycerol to assess insulin secretion, action, and hepatic glucose metabolism. In separate studies, pancreatic islets were isolated for further analyses of insulin secretion and islet morphometry. Glucose clearance was reduced significantly by LP feeding alone (16%) and by LP feeding with LPS administration (43.8%) compared with control during the hyperglycemic clamps. This was associated with a strongly reduced insulin secretion in LP-fed rats in vivo as well as ex vivo in islets but signficantly enhanced whole body insulin sensitivity. Gluconeogenesis rates were unaffected by LP feeding, but glycogenolysis was higher after LP feeding. A protein-deficient diet in young rats leads to a susceptibility to low-dose endotoxin-induced impairment in glucose clearance with a decrease in the islet insulin secretory pathway. A protein-deficient diet is associated with enhanced peripheral insulin sensitivity but impaired insulin-mediated suppression of hepatic glycogenolysis.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".