Differential impact of eicosapentaenoic acid and docosahexaenoic acid in an animal model of Alzheimer’s disease
Bibliographic record
Abstract
Dietary supplementation with n-3 polyunsaturated fatty acids improves cognitive performance in several animal models of Alzheimer's disease (AD), an effect often associated with reduced amyloid-beta and/or tau pathologies. However, it remains unclear to what extent eicosapentaenoic (EPA) provides additional benefits compared to docosahexaenoic acid (DHA). Here, male and female 3xTg-AD mice were fed for 3 months (13–16 months of age) the following diets: (1) control (no DHA/EPA), (2) DHA (1.1g/kg) and low EPA (0.4g/kg), or (3) DHA (0.9g/kg) with high EPA (9.2g/kg). The DHA and DHA + EPA diets respectively increased DHA by 19% and 8% in the frontal cortex of 3xTg-AD mice, compared to controls. Levels of EPA, which were below the detection limit after the control diet, reached 0.14% and 0.29% of total brain fatty acids after the DHA and DHA + EPA diet, respectively. DHA and DHA + EPA diets lowered brain arachidonic acid levels and the n-6:n-3 docosapentaenoic acid ratio. Brain uptake of free 14 C-DHA measured through intracarotid brain perfusion, but not of 14 C-EPA, was lower in 3xTg-AD than in NonTg mice. DHA and DHA + EPA diets in 3xTg-AD mice reduced cortical soluble phosphorylated tau (pS202) (−34% high-DHA, −34% DHA + EPA, P < 0.05) while increasing p21-activated kinase (+58% and +83%, P < 0.001; respectively). High EPA intake lowered insoluble phosphorylated tau (−31% vs. DHA, P < 0.05). No diet effect on amyloid-beta levels was observed. In conclusion, dietary intake of DHA and EPA leads to differential changes in brain PUFA while altering cerebral biomarkers consistent with beneficial effects against AD-like neuropathology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".