Impact of α-lipoic acid on liver peroxisome proliferator-activated receptor-α, vascular remodeling, and oxidative stress in insulin-resistant rats
Bibliographic record
Abstract
This study sought to determine the impact of α-lipoic acid (LA) on superoxide anion (O(2)(•-)) production and peroxisome proliferator-activated receptor-α (PPARα) expression in liver tissue, plasma free fatty acids (FFA), and aortic remodeling in a rat model of insulin resistance. Sprague-Dawley rats (50-75 g) were given either tap water or a drinking solution containing 10% D-glucose for 14 weeks, combined with a diet with or without LA supplement. O(2)(•-) production was measured by lucigenin chemiluminescence, and PPAR-α expression by Western blotting. Cross-sectional area (CSA) of the aortic media and lumen and number of smooth muscle cells (SMC) were determined histologically. Glucose increased systolic blood pressure (SBP), plasma levels of glucose and insulin, and insulin resistance (HOMA index). All of these effects were attenuated by LA. Whereas glucose had no effect on liver PPAR-α protein level, it decreased plasma FFA. LA decreased the aortic and liver O(2)(•-) production, body weight, and plasma FFA levels in control and glucose-treated rats. Liver PPAR-α protein levels were increased by LA, and negatively correlated with plasma FFA. Medial CSA was reduced in all glucose-treated rats, and positively correlated with plasma FFA but not with SBP or aortic O(2)(•-) production. Glucose also reduced aortic lumen area, so that the media-to-lumen ratio remained unchanged. The ability of LA to lower plasma FFA appears to be mediated, in part, by increased hepatic PPAR-α expression, which may positively affect insulin resistance. Glucose-fed rats may serve as a unique model of aortic atrophic remodeling in hypertension and early metabolic syndrome.
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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 | 0.000 |
| 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) | 0.001 | 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".