α-Linolenic acid supplementation and exercise training reveal independent and additive responses on hepatic lipid accumulation in obese rats
Bibliographic record
Abstract
α-Linolenic acid (ALA) supplementation or exercise training can independently prevent hepatic lipid accumulation and reduced insulin signaling; however, this may occur through different mechanisms of action. In the current study, obese Zucker rats displayed decreased phospholipid (PL) content in association with hepatic lipid abundance, and therefore, we examined whether ALA and exercise training would prevent these abnormalities differently to reveal additive effects on the liver. To achieve this aim, obese Zucker rats were fed control diet alone or supplemented with ALA and were sedentary or exercise trained for 4 wk (C-Sed, ALA-Sed, C-Ex, and ALA-Ex). ALA-Sed rats had increased microsomal-triglyceride transfer protein (MTTP), a protein required for lipoprotein assembly/secretion, as well as modestly increased PL content in the absence of improvements in mitochondrial content, lipid accumulation, or insulin sensitivity. In contrast, C-Ex rats had increased mitochondrial content and insulin sensitivity; however, this corresponded with minimal improvements in PL content and hepatic lipid accumulation. Importantly, ALA-Ex rats demonstrated additive improvements in PL content and hepatic steatosis, which corresponded with increased mitochondrial content, MTTP and apolipoprotein B100 content, greater serum triacylglyceride, and insulin sensitivity. Overall, these data demonstrate additive effects of ALA and exercise training on hepatic lipid accumulation, as exercise training preferentially increased mitochondrial content, while ALA promoted an environment conducive for lipid secretion. These data highlight the potential for combination therapy to mitigate liver disease progression.
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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) | 0.001 | 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".