DHA Supplemented in Peptamen Diet Offers No Advantage in Pathways to Amyloidosis: Is It Time to Evaluate Composite Lipid Diet?
Bibliographic record
Abstract
Numerous reports have documented the beneficial effects of dietary docosahexaenoic acid (DHA) on beta-amyloid production and Alzheimer's disease (AD). However, none of these studies have examined and compared DHA, in combination with other dietary nutrients, for its effects on plaque pathogenesis. Potential interactions of DHA with other dietary nutrients and fatty acids are conventionally ignored. Here we investigated DHA with two dietary regimes; peptamen (pep+DHA) and low fat diet (low fat+DHA). Peptamen base liquid diet is a standard sole-source nutrition for patients with gastrointestinal dysfunction. Here we demonstrate that a robust AD transgenic mouse model shows an increased tendency to produce beta-amyloid peptides and amyloid plaques when fed a pep+DHA diet. The increase in beta-amyloid peptides was due to an elevated trend in the levels of beta-secretase amyloid precursor protein (APP) cleaving enzyme (BACE), the proteolytic C-terminal fragment beta of APP and reduced levels of insulin degrading enzyme that endoproteolyse beta-amyloid. On the contrary, TgCRND8 mice on low fat+DHA diet (based on an approximately 18% reduction of fat intake) ameliorate the production of abeta peptides and consequently amyloid plaques. Our work not only demonstrates that DHA when taken with peptamen may have a tendency to confer a detrimental affect on the amyloid plaque build up but also reinforces the importance of studying composite lipids or nutrients rather than single lipids or nutrients for their effects on pathways important to plaque development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".