PSVIII-B-10 Feeding Senior Labrador Retrievers Hydrolyzed Whey Protein Isolate to Prevent Sarcopenia
Bibliographic record
Abstract
Abstract Conditions which affect humans are often closely followed by our companion animals, especially in aging populations. Canines often present with muscle wasting diseases such as sarcopenia and cachexia as a result of aging or chronic disease. In this 26wk study, our goal was to evaluate the effects of feeding 1.5x AAFCO CP from whey protein isolate and a pea isolate compared to feeding senior dogs a standard AAFCO CP diet (45g CP/1000 kcal DM) on body composition in exercised senior Labrador Retrievers. Thirty-six (36) Labrador Retrievers (18m/18f; 8-12yrs) were sorted into three equal groups and fed basal AAFCO diet plus hydrolyzed whey isolate, pea isolate, or added fat and sugar (control). All dogs ran 1.6km run twice weekly throughout the trial. Body composition was determined by dual-energy x-ray absorptiometry (DXA) at Weeks 0, 12, 20, and 26. All dogs were weighed weekly and feed intake measured daily. Body weights and feed intake were similar between groups. Whey group gained 1.69% fat and 0.6kg fat mass compared to control group gaining 7.97% fat and 3.05kg fat mass (p=0.045; p=0.005). From Wk0 to Wk26, whey group lost only 0.38kg lean/fat ratio compared to control group’s -1.75kg loss (p=0.021). From Wk12 to Wk26 and Wk18 to Wk26, whey group maintained a higher lean:fat ratio compared to pea and control group (p=0.101; p=0.049). Based on these results, senior Labrador Retrievers fed 1.5x AAFCO CP from basal plus hydrolyzed whey protein isolate during an exercise regimen maintained higher lean:fat ratio compared to seniors fed basal plus pea protein and seniors fed only basal. Senior dogs fed hydrolyzed whey protein isolate also had increased fat loss vs seniors consuming only the basal diet.
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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 | 0.000 | 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) | 0.002 | 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".