Serum Lipid, Amino Acid and Acylcarnitine Profiles of Obese Cats Supplemented with Dietary Choline and Fed to Maintenance Energy Requirements
Bibliographic record
Abstract
Obesity is a health concern for domestic cats. Obesity and severe energy restriction predispose cats to feline hepatic lipidosis. As choline is linked to lipid metabolism, we hypothesized that dietary choline supplementation would assist in reducing hepatic fat through increased lipoprotein transport and fatty acid oxidation. Twelve obese cats (body condition score [BCS] ≥ 8/9) were split into two groups. Cats were fed a control (n = 6; 4587 mg choline/kg dry matter [DM]) or a high choline diet (n = 6; 18,957 mg choline/kg DM) for 5 weeks, for adult maintenance. On days 0 and 35, fasted blood was collected, and the body composition was assessed. Serum lipoprotein and biochemistry profiles, plasma amino acids and plasma acylcarnitines were analyzed. The body weight, BCS and body composition were unaffected (p > 0.05). Choline increased the serum cholesterol, triacylglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very low-density lipoprotein cholesterol and plasma methionine (p < 0.05) and decreased the serum blood urea nitrogen and alkaline phosphatase (p < 0.05). Choline also reduced the plasma acylcarnitine to free carnitine ratio (p = 0.006). Choline may assist in eliminating hepatic fat through increased fat mobilization and enhanced methionine recycling.
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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) | 0.000 | 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".