Linoleic and α‐Linolenic Acid Prevent Insulin Resistance in Obese Zucker Rats but have Different Impacts on Skeletal Muscle Metabolism
Bibliographic record
Abstract
The use of long‐chain polyunsaturated fatty acids (LC‐PUFAs) to prevent the development of insulin resistance (IR) has recently gained considerable interest. However, the roles of essential PUFAs (i.e., linoleic acid, LA; α‐linolenic acid, ALA) remain poorly understood. We investigated the efficacy of diets enriched with either LA or ALA on preventing IR in obese Zucker rats. After the 12‐wk intervention, both LA‐ and ALA‐enriched diets protected against IR compared to obese control animals. The maintenance of whole‐body glucose homeostasis was linked to the preservation of muscle‐specific insulin sensitivity. LA and ALA differentially regulated markers of skeletal muscle mitochondrial content, respiratory function and H 2 O 2 emission. However, both LA and ALA prevented increases in 4‐HNE content (i.e., a marker of oxidative stress) compared to obese control animals. Collectively, the protective effects of LA and ALA appear independent of mitochondrial bioenergetics, but may involve improvements in oxidative stress. Subsequent lipid profiling in skeletal muscle by gas chromatography revealed that LA and ALA increased omega‐6 and omega‐3 PUFA content, as expected. However, diacylglycerol (DAG) and ceramide accumulation was similar between obese control, LA and ALA groups; suggesting that preservation of skeletal muscle insulin sensitivity occurred independent of changes in reactive lipid content. Overall, LA and ALA are efficacious in preventing obesity‐related IR, but further work is necessary to elucidate the mechanism(s) of action. Grant Funding Source : Supported by OMAFRA
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".