Aging, cognitive decline, apolipoprotein E and docosahexaenoic acid metabolism
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In Canada, ∼17 millions of adults between 30–64 years old could benefit from a prevention strategy to lower the risk of Alzheimer’s disease (AD). My group is working on a population that is particularly at risk of AD, the carriers of an epsilon 4 allele of apolipoprotein E ( E4 ), a genetic risk. Around 20% of the population in industrial countries have this genetic risk but not all carriers will develop AD, suggesting that environmental factors modulate the clinical manifestation and risk of AD in the carriers. My group has discovered that the metabolism of docosahexaenoic acid (DHA) is disrupted during aging and in E4 carriers, a finding replicated in homozygous mice knocked-in for human E4 allele ( hAPOE4 ). We recently showed that a diet containing DHA prevented behavioral deficits in hAPOE4 mice. Another group reported in E4 carriers that the ratio of arachidonic acid (ARA): DHA is disrupted in the plasma and constitute a preclinical marker of mild cognitive impairment/AD in E4 carriers. Using our kinetics approaches with uniformly labelled carbon 13 fatty acids, we showed that the kinetics of 13 C-DHA is modified by age and E4 carriage. The kinetics of 13 C-arachidonic acid was however not modified by age conversely to that of 13 C-eicosapentaenoic acid (EPA). We also reported that the synthesis of 13 C-DHA from 13 C-EPA started 2 h after the tracer intake in older adults conversely to 7 d in young men. Whether old men needs in DHA is higher or whether their ability to use it is lower remains to be established. These differences in the DHA and EPA metabolism seems, however related to physiological modifications occurring during aging and in E4 carriers and obscure the relationship between plasma DHA and EPA levels, dietary fatty fish intake and cognitive status.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it