Peroxisome Proliferator–Activated Receptor-α Agonist Treatment in a Transgenic Model of Type 2 Diabetes Reverses the Lipotoxic State and Improves Glucose Homeostasis
Bibliographic record
Abstract
Abnormalities in insulin action are the characteristics of type 2 diabetes. Dominant-negative muscle-specific IGF-I receptor (MKR) mice exhibit elevated lipid levels at an early age and eventually develop type 2 diabetes. To evaluate the role of elevated lipids in the progression of the diabetic state, MKR mice were treated with WY14,643, a peroxisome proliferator-activated receptor (PPAR)-alpha agonist. WY14,643 treatment markedly reduced serum fatty acid and triglyceride levels within a few days, as well as muscle triglyceride levels, and subsequently normalized glucose and insulin levels in MKR mice. Hyperinsulinemic-euglycemic clamp analysis showed that WY14,643 treatment enhanced muscle and adipose tissue glucose uptake by improving whole-body insulin sensitivity. Insulin suppression of endogenous glucose production by the liver of MKR mice was also improved. The expression of genes involved in fatty acid oxidation was increased in liver and skeletal muscle, whereas gene expression levels of hepatic gluconeogenic enzymes were decreased in WY14,643-treated MKR mice. WY14,643 treatment also improved the pattern of glucose-stimulated insulin secretion from the perfused pancreata of MKR mice and reduced the beta-cell mass. Taken together, these findings suggest that the reduction in circulating or intracellular lipids by activation of PPAR-alpha improved insulin sensitivity and the diabetic condition of MKR mice.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".