Dietary fat and carbohydrate have different effects on body weight, energy expenditure, glucose homeostasis and behaviour in adult cats fed to energy requirement
Bibliographic record
Abstract
The effects of dietary carbohydrate and fat on feline health are not well understood. The effects of feeding diets moderately high in fat (HF; n 10; 30 % fat, 26 % carbohydrate as fed) or carbohydrate (HC; n 10; 11 % fat, 47 % carbohydrate), for 84 d, were investigated in healthy, adult cats (3·5 (sd 0·5) years). Data on indirect calorimetry, blood biomarkers, activity, play and cognition were collected at baseline, and at intervals throughout the study. Body composition was measured by dual-energy X-ray absorptiometry at baseline and on day 85. There were no significant main effects of diet on body weight and composition. When data were analysed over study day within diet, cats fed HF diets experienced a significant increase in body fat (P = 0·001) and body weight (P = 0·043) in contrast to cats consuming the HC diet that experienced no change in body fat or body weight (P = 0·762) throughout the study. Overall, energy expenditure was similar between diets (P = 0·356 (fasted), P = 0·086 (postprandial)) and respiratory quotient declined with exposure to the HF diet and increased with exposure to the HC diet (P < 0·001; fasted and postprandial). There was no difference in insulin sensitivity as an overall effect of diet (P = 0·266). Activity declined from baseline with exposure to both diets (HC: P = 0·002; HF: P = 0·01) but was not different between diets (P = 0·247). There was no effect of diet on play (P = 0·387) and cats consuming either the HF or HC diet did not successfully learn the cognitive test. Overall, cats adapt to dietary macronutrient content, and the implications of feeding HC and HF diets on risk for adiposity as driven by metabolic and behavioural mechanisms are discussed.
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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".