PPARδ activation attenuates hepatic steatosis in Ldlr mice by enhanced fat oxidation, reduced lipogenesis, and improved insulin sensitivity
Bibliographic record
Abstract
PPARδ regulates systemic lipid homeostasis and inflammation, but its role in hepatic lipid metabolism remains unclear. Here, we examine whether intervening with a selective PPARδ agonist corrects hepatic steatosis induced by a high-fat, cholesterol-containing (HFHC) diet. Ldlr(-/-) mice were fed a chow or HFHC diet (42% fat, 0.2% cholesterol) for 4 weeks. For an additional 8 weeks, the HFHC group was fed HFHC or HFHC plus GW1516 (3 mg/kg/day). GW1516-intervention significantly attenuated liver TG accumulation by induction of FA β-oxidation and attenuation of FA synthesis. In primary mouse hepatocytes, GW1516 treatment stimulated AMP-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) phosphorylation in WT hepatocytes, but not AMPKβ1(-/-) hepatocytes. However, FA oxidation was only partially reduced in AMPKβ1(-/-) hepatocytes, suggesting an AMPK-independent contribution to the GW1516 effect. Similarly, PPARδ-mediated attenuation of FA synthesis was partially due to AMPK activation, as GW1516 reduced lipogenesis in WT hepatocytes but not AMPKβ1(-/-) hepatocytes. HFHC-fed animals were hyperinsulinemic and exhibited selective hepatic insulin resistance, which contributed to elevated fasting FA synthesis and hyperglycemia. GW1516 intervention normalized fasting hyperinsulinemia and selective hepatic insulin resistance and attenuated fasting FA synthesis and hyperglycemia. The HFHC diet polarized the liver toward a proinflammatory M1 state, which was reversed by GW1516 intervention. Thus, PPARδ agonist treatment inhibits the progression of preestablished hepatic steatosis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".