The balance of n-6 and n-3 fatty acids in canine, feline, and equine nutrition: exploring sources and the significance of alpha-linolenic acid
Bibliographic record
Abstract
Both n-6 and n-3 fatty acids (FA) have numerous significant physiological roles for mammals. The interplay between these families of FA is of interest in companion animal nutrition due to the influence of the n-6:n-3 FA ratio on the modulation of the inflammatory response in disease management and treatment. As both human and animal diets have shifted to greater consumption of vegetable oils rich in n-6 FA, the supplementation of n-3 FA to canine, feline, and equine diets has been advocated for. Although fish oils are commonly added to supply the long-chain n-3 FA eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA), a heavy reliance on this ingredient by the human, pet food, and equine supplement industries is not environmentally sustainable. Instead, sustainable sourcing of plant-based oils rich in n-3 α-linolenic acid (ALA), such as flaxseed and camelina oils, emerges as a viable option to support an optimal n-6:n-3 FA ratio. Moreover, ALA may offer health benefits that extend beyond its role as a precursor for endogenous EPA and DHA production. The following review underlines the metabolism and recommendations of n-6 and n-3 FA for dogs, cats, and horses and the ratio between them in promoting optimal health and inflammation management. Additionally, insights into both marine and plant-based n-3 FA sources will be discussed, along with the commercial practicality of using plant oils rich in ALA for the provision of n-3 FA to companion animals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".