Plasma carnosine, but not muscle carnosine, attenuates high-fat diet-induced metabolic stress
Bibliographic record
Abstract
There is growing in vivo evidence that the dipeptide carnosine has protective effects in metabolic diseases. A critical unanswered question is whether its site of action is tissues or plasma. This was investigated using oral carnosine versus β-alanine supplementation in a high-fat diet rat model. Thirty-six male Sprague-Dawley rats received a control diet (CON), a high-fat diet (HF; 60% of energy from fat), the HF diet with 1.8% carnosine (HFcar), or the HF diet with 1% β-alanine (HFba), as β-alanine can increase muscle carnosine without increasing plasma carnosine. Insulin sensitivity, inflammatory signaling, and lipoxidative stress were determined in skeletal muscle and blood. In a pilot study, urine was collected. The 3 HF groups were significantly heavier than the CON group. Muscle carnosine concentrations increased equally in the HFcar and HFba groups, while elevated plasma carnosine levels and carnosine-4-hydroxy-2-nonenal adducts were detected only in the HFcar group. Elevated plasma and urine N(ε)-(carboxymethyl)lysine in HF rats was reduced by ∼50% in the HFcar group but not in the HFba group. Likewise, inducible nitric oxide synthase mRNA was decreased by 47% (p < 0.05) in the HFcar group, but not in the HFba group, compared with HF rats. We conclude that plasma carnosine, but not muscle carnosine, is involved in preventing early-stage lipoxidation in the circulation and inflammatory signaling in the muscle of rats.
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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 | 0.000 | 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".