Carnitine supplementation alleviates lipid metabolism derangements and protects against oxidative stress in non-obese hereditary hypertriglyceridemic rats
Bibliographic record
Abstract
The aim of this study was to estimate the effect of carnitine supplementation on lipid disorders and peripheral tissue insulin sensitivity in a non-obese animal model of insulin resistance, the hereditary hypertriglyceridemic (HHTg) rat. Male HHTg rats were fed a standard diet, and half of them received daily doses of carnitine (500 mg·kg(-1) body weight) for 8 weeks. Rats of the original Wistar strain were used for comparison. HHTg rats exhibited increased urinary excretion of free carnitine and reduced carnitine content in the liver and blood. Carnitine supplementation compensated for this shortage and promoted urinary excretion of acetylcarnitine without any signs of (acyl)carnitine accumulation in skeletal muscle. Compared with their untreated littermates, carnitine-treated HHTg rats exhibited lower weight gain, reduced liver steatosis, lower fasting triglyceridemia, and greater reduction of serum free fatty acid content after glucose load. Carnitine treatment was associated with increased mitochondrial biogenesis and oxidative capacity for fatty acids, amelioration of oxidative stress, and restored substrate switching in the liver. In skeletal muscle (diaphragm), carnitine supplementation was associated with significantly higher palmitate oxidation and a more favorable complete to incomplete oxidation products ratio. Carnitine supplementation further enhanced insulin sensitivity ex vivo. No effects on whole-body glucose tolerance were observed. Our data suggest that some metabolic syndrome-related disorders, particularly fatty acid oxidation, steatosis, and oxidative stress in the liver, could be attenuated by carnitine supplementation. The effect of carnitine could be explained, at least partly, by enhanced substrate oxidation and increased fatty acid transport from tissues in the form of short-chain acylcarnitines.
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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".