Salmon peptides limit obesity‐associated metabolic disorders by modulating a gut‐liver axis in vitamin D‐deficient mice
Bibliographic record
Abstract
Abstract Objective This study investigated the effects of a low‐dose salmon peptide fraction (SPF) and vitamin D3 (VitD3) in obese and VitD3‐deficient mice at risk of metabolic syndrome (MetS). Methods Obese and VitD3‐deficient low‐density lipoprotein receptor (LDLr)−/−/apolipoprotein B100 (ApoB)100/100 mice were treated with high‐fat high‐sucrose diets, with 25% of dietary proteins replaced by SPF or a nonfish protein mix (MP). The SPF and MP groups received a VitD3‐deficient diet or a supplementation of 15,000 IU of VitD3 per kilogram of diet. Glucose homeostasis, atherosclerosis, nonalcoholic fatty liver disease, and gut health were assessed. Results VitD3 supplementation increased plasma 25‐hydroxyvitamin D to optimal status whereas the VitD3‐deficient diet maintained moderate deficiency. SPF‐treated groups spent more energy and accumulated less visceral fat in association with an improved adipokine profile. SPF lowered homeostatic model assessment of insulin resistance compared with MP, suggesting that SPF can improve insulin sensitivity. SPF alone blunted hepatic and colonic inflammation, whereas VitD3 supplementation attenuated ileal inflammation. These effects were associated with changes in gut microbiota such as increased Mogibacterium and Muribaculaceae. Conclusions SPF treatment improves MetS by modulating hepatic and gut inflammation along with gut microbiota, suggesting that SPF operates through a gut‐liver axis. VitD3 supplementation has limited influence on MetS in this model.
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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) | 0.000 | 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 | 0.001 | 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".