The effects of a plant-based and a plant- and marine-based n-3 oil supplement on behavioral reactivity, heart rate variability, and plasma fatty acid profile in young healthy horses
Bibliographic record
Abstract
Behavioral reactivity in horses poses a welfare and safety risk to both the horse and the handler, however, beneficial effects have been observed when dietary fat is increased in replacement of sugar. Supplementation with the fatty acids (FA) eicosapentaenoic (EPA) and docosahexaenoic acid (DHA) appear to improve negative behaviors in rodents and humans, but the effect of α-linolenic acid (ALA), EPA, and DHA, specifically, on reactivity in horses is unknown. The objective of this study was to evaluate the effects of camelina oil (CAM; ALA-enriched) and a mix of camelina and algal oil (ALG; ALA-, EPA-, and DHA-enriched) both fed at a dose of 0.37 g oil/kg body weight on plasma FA, behavior, and heart rate variability (HRV) in young horses compared to a negative control (CON). Thirty-four client-owned horses aged 7 mo to 6 yr were enrolled. Horses were assigned to either CAM, ALG, or CON and underwent a novel object test (NOT) before and after a 6-wk supplementation period. Prior to each NOT, blood was collected for evaluation of plasma FA profile (n = 28). During the NOT, behavior was recorded using a predetermined ethogram and assessed in BORIS software by 2 raters (n = 29). Electrocardiogram (ECG) data was collected at baseline, during the NOT, and after the NOT (recovery). The ECG data was analyzed in Kubios software for determination of heart rate (HR) and several HRV parameters (n = 24). The treatment oils were treated as fixed effects, baseline measurements as covariates, and location as a random effect. Plasma DHA (P < 0.01) was greater and n-6:n-3 ratio (P < 0.01) was reduced in ALG than in CAM and CON, while ALA and EPA were similar among treatments (P > 0.05). When treatments were pooled, the maximum HR (P < 0.01) and the low frequency to high frequency ratio HRV parameter (P < 0.01) were greater during the NOT compared to baseline and recovery. Bucking (P = 0.03) and backing (P = 0.02) behaviors were reduced in the CAM group compared to the CON group, but neither group differed from ALG. All other behaviors, HR, and HRV parameters were similar among treatments (P > 0.05). Our results suggest that the NOT was successful in creating acute stress, however, feeding either CAM or ALG at this dose did not reduce reactivity in this cohort of horses. Further research is needed to understand the effects of specific FA, if any, on behavior and HRV in more specific populations of horses and specifically those deemed highly reactive.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".