Feline Diabetes Is Associated with Deficits in Markers of Insulin Signaling in Peripheral Tissues
Bibliographic record
Abstract
Like humans, cats have a strong relationship between decreasing insulin sensitivity and the development of diabetes with obesity. However, the underlying molecular mechanisms of impaired insulin secretion and signaling in cats remain largely unknown. A total of 54 client-owned nondiabetic lean (n = 15), overweight (n = 15), and diabetic (n = 24) cats were included in the study. The pancreas, liver, and skeletal muscle were quantified for mRNA and protein abundances of insulin and incretin signaling markers. Diabetic cats showed increased liver and muscle adiposity. The pancreas of diabetic cats had decreased transcript abundances of insulin, insulin receptor, insulin-receptor substrate (IRS)-1, glucose transporters (GLUT), and protein abundance of mitogen-activated protein kinase. In treated diabetics, protein abundance of glucagon-like peptide-1 and glucose-dependent insulinotropic peptide receptors, total and phosphorylated Akt, and GLUT-1 were increased in the pancreas, whereas untreated diabetics had downregulation of markers of insulin and incretin signaling. In the muscle and liver, diabetic cats had reduced mRNA abundances of insulin receptor, IRS-1/2, and phosphatidylinositol-3-kinase, and reduced protein abundances of GLUT-4 and phosphatidylinositol-3-kinase-p85α in muscle. We demonstrate that feline diabetes is associated with ectopic lipid deposition in the liver and skeletal muscle, deficits in insulin synthesis and incretin signaling in the pancreas, and impaired insulin signaling in the muscle and liver. These findings have implications for understanding the pathophysiological mechanisms of obesity and diabetes in humans and pets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".