Effects of isoflavones on body fat accumulation in neutered male and female dogs
Bibliographic record
Abstract
Increased incidence of overweight and obesity has been observed in neutered male and female dogs. This study investigated whether soy isoflavones reduced body fat accumulation, and a combination of isoflavones, L‐carnitine and conjugated linoleic acid (CLA) had synergistic effects on body fat accumulation in neutered male and female dogs. Neutered Labrador Retrievers with normal body weight were randomized into three groups: control, isoflavone, and blend. The dogs were fed 25% more than their maintenance energy requirements for 12 months. Body composition, thyroid function and blood biochemistry parameters were measured at the baseline and every three months after initiation of the feeding trial. Compared with the corresponding control dogs, the isoflavone diet reduced body fat accumulation in the neutered male dogs and neutered female dogs by 85% and 27%, respectively. CLA and L‐carnitine completely negated the isoflavone effects on body fat in the neutered male dogs, and slightly reduced the isoflavone effects on the body fat in the neutered female dogs. The isoflavone and blend diets did not significantly affect lean body mass, total white blood cells, thyroid hormone profile, and other blood biochemical parameters in the study. In summary, the isoflavone diet was very effective in reducing body fat accumulation in neutered dogs with the effects more pronounced in the male dogs.
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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) | 0.000 | 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 | 0.000 |
| 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".