Assessing non-protein nitrogen sources in commercial dry dog foods
Bibliographic record
Abstract
Abstract Protein is a macronutrient required by dogs for growth and maintenance metabolism. However, a portion of the crude protein listed on pet foods may actually arise from non-digestible organic nitrogen or potentially toxic inorganic non-protein nitrogen sources. Neither non-protein source is retained or used by the animal. However, these compounds may result in adverse effects such as methemoglobin formation and increased oxidative stress or potentially beneficial effects such as improved vascular distensibility and decreased inflammation. To analyze nitrogen retention and screen for non-protein nitrogen, four commercial, dry kibble dog foods and one laboratory-made diet were evaluated and then fed to beagles during two separate feeding trials. During the first trial, dogs were randomly assigned each diet (n = 4 dogs/diet) and fed chromium oxide-coated diets for 48 h, followed by total urine and marked fecal collection, as well as plasma collection for total nitrogen, nitrate, ammonia, and urea determination. The amount of nitrogen retained (93%–96%) did not differ among commercial diets. Protein total tract apparent digestibility (TTAD) ranged from 69% to 84%, with the high protein diets significantly higher than the laboratory-made and mid-ranged diets (1-way ANOVA: P < 0.05). The high protein diet also contained the highest concentration of nitrate with subsequent elevations in plasma nitrotyrosine levels (indicator of oxidative stress). During the second trial, eight dogs (n = 8) were fed the same diets for 6 d, after which echocardiography was completed with blood, urine, and feces collected. For health end-points, methemoblobin, plasma nitrotyrosine, and C-reactive protein (CRP; indicator of inflammation) levels were measured. Methemoglobin levels were significantly lower in the high protein diet (P > 0.05), possible due to the stimulation of methemoglobin reductase while nitrotyrosine was unchanged and CRP was undetectable. Furthermore, there was a positive relationship between crude protein, crude fat (simple linear regression: P = 0.02, r2 > 0.6), price (P = 0.08, r2 > 0.6), and caloric density (P = 0.11, r2 > 0.6). There were no significant cardiovascular differences among any of the diets (P > 0.05). Ultimately, this study shows that in commercial diets, price does reflect protein content but that feeding dogs high protein diets for a long period of time may provide an excess in calories without a change in cardiovascular function or detectable increases in inflammation.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".