Elevated Glucagon-Like Peptide-1-(7–36)-Amide, but Not Glucose, Associated with Hyperinsulinemic Compensation for Fat Feeding
Bibliographic record
Abstract
We previously developed a canine model of central obesity and insulin resistance by supplementing the normal chow diet with 2 g cooked bacon grease/kg body weight. Dogs fed this fatty diet maintained glucose tolerance with compensatory hyperinsulinemia. The signal(s) responsible for this up-regulation of plasma insulin is unknown. We hypothesized that meal-derived factors such as glucose, fatty acids, or incretin hormones may signal beta-cell compensation in the fat-fed dog. We fed the same fat-supplemented diet for 12 wk to six dogs and compared metabolic responses with seven control dogs fed a normal diet. Fasting and stimulated fatty acid and glucose-dependent insulinotropic peptide concentrations were not increased by fat feeding, whereas glucose was paradoxically decreased, ruling out those three factors as signals for compensatory hyperinsulinemia. Fasting plasma glucagon-like peptide-1 (GLP-1) concentration was 2.5-fold higher in the fat-fed animals, compared with controls, and 3.4-fold higher after a mixed meal. Additionally, expression of the GLP-1 receptor in whole pancreas was increased 2.3-fold in the fat-fed dogs. The increase in both circulating GLP-1 and its target receptor may have increased beta-cell responsiveness to lower glucose. Glucose is not the primary cause of hyperinsulinemia in the fat-fed dog. Corequisite meal-related signals may be permissive for development of hyperinsulinemia.
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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 | 0.001 | 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) | 0.001 | 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".