119 Change in Serum Total Lipid Profiles in Dogs Supplemented with Camelina, Flaxseed, or Canola oil for 16 Weeks
Bibliographic record
Abstract
Abstract There are few data describing the effects of different oils on fatty acid (FA) metabolism in dogs. Our objectives were to assess the effects of camelina (CAM), flaxseed (FLAX), and canola oil (OLA) on the serum total lipid FA profiles over a 16-week feeding study. Thirty healthy, adult dogs of various breeds [17 females, 13 males; 7.2±3.1 years and 27.4±14.0 kg (mean±SD)] underwent a 4-week dietary wash-in period on a commercial, low-fat kibble top dressed with 8.2g sunflower oil/100g food intake. Dogs were blocked by breed, age, and size, and assigned to receive CAM, FLAX, or OLA for 16-weeks at the same dose as wash-in. Serum total FA were measured using gas chromatography from dogs at baseline and week 16. Data were analyzed with ANOVA using PROC GLIMMIX of SAS. In all treatments, the final percent area of pooled and individual omega-3 (n-3) and omega-6 (n-6) FAs were inversely related, with n-3 being greater at week 16 and n-6 less at week 16 compared with baseline (p< 0.05). Conversely, dihomo-γ-linolenic acid showed accumulation at week 16 and docosahexaenoic acid was less at week 16 than baseline for all treatments (p< 0.05). Stearidonic acid was greater in FLAX than OLA but both were similar to CAM, and α-linolenic differed among all treatments (FLAX >CAM >OLA) (p< 0.05). Eicosadienoic acid was less in FLAX than CAM and OLA (p< 0.05). Omega-9 FA were greater at week 16 for all treatments and individual FAs (p< 0.05). Eicosaenoic and mead acid were greater in CAM than FLAX and OLA, and oleic acid was greater in FLAX than OLA, but similar to CAM (p< 0.05). Differences among dogs fed camelina, flax, and canola oils were due to the inherent FA concentrations of the oil and suggest all oils are suitable for increasing serum n-3 concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".