Relationship between diet and plasma long-chain n-3 PUFAs in older people: impact of apolipoprotein E genotype
Bibliographic record
Abstract
The main risk factors for Alzheimer's disease, age and the ε4 allele of the APOE gene (APOE4), might modify the metabolism of n-3 PUFAs and in turn, their impact on cognition. The aim of this study was to investigate the association between dietary fat and plasma concentrations of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) in elderly persons, taking the APOE4 genotype into account. The sample was composed of 1,135 participants from the Three-City study aged 65 years and over, of whom 19% were APOE4 carriers. Mean plasma proportions of EPA [1.01%, standard deviation (SD) 0.60] and DHA (2.41%, SD 0.81) did not differ according to APOE4. In multivariate models, plasma EPA increased with frequency of fish consumption (P < 0.0001), alcohol intake (P = 0.0006), and female gender (P = 0.02), and decreased with intensive consumption of n-6 oils (P = 0.02). The positive association between fish consumption and plasma DHA was highly significant whatever the APOE genotype (P < 0.0001) but stronger in APOE4 noncarriers than in carriers (P = 0.06 for interaction). Plasma DHA increased significantly with age (P = 0.009) in APOE4 noncarriers only. These findings suggest that dietary habits, gender, and APOE4 genotype should be considered when designing interventions to increase n-3 PUFA blood levels in older people.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".