Safety of dietary nitrate supplementation by calcium nitrate for finishing pigs as measured by methemoglobin and serum and tissue nitrate levels
Bibliographic record
Abstract
Abstract Nitrate supplementation has been studied as a beneficial constituent of the human diet, particularly for its effects on vascular health through vasodilation. Recent studies have focused on the benefits of nitrate supplementation in animals, especially in swine. Up to 1,200 mg/kg dietary nitrate supplementation from Ca nitrate was beneficial in farrowing and lactating sows and their offspring, and up to 6,000 mg/kg supplemental nitrate showed no adverse health effects in sows or piglets. Controlled study data evaluating the safety of nitrate supplementation to growing swine of any weight class is scant. Therefore, an experiment was conducted to test the hypothesis that increased inclusion rates of dietary nitrate through the addition of Ca nitrate in diets would not influence concentrations of nitrate or nitrite in serum and tissue, nor blood hemoglobin and methemoglobin. Forty-eight individually housed pigs (initial weight 119.1 ± 5.3 kg) were randomly allotted to four dietary treatments containing 0, 500, 1,000, or 2,000 mg/kg dietary nitrate and fed experimental diets for 28 d. Growth performance was not influenced (P > 0.10) by dietary treatment. The most sensitive safety endpoint, methemoglobin, did not change (P > 0.10) with dietary nitrate exposure up to 2,000 mg/kg. Serum and tissue nitrate and nitrite levels, myoglobin, and hemoglobin were not adversely affected (P > 0.10). Total myoglobin in the loin linearly increased (P < 0.05) with greater dietary nitrate in the diet, which is correlated with the red color of meat. This work established the safety of up to 2,000 mg/kg dietary nitrate from Ca nitrate as an ingredient in food for finishing pigs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".