Plasma incorporation, apparent retroconversion and β-oxidation of 13C-docosahexaenoic acid in the elderly
Bibliographic record
Abstract
BACKGROUND: Higher fish or higher docosahexaenoic acid (DHA) intake normally correlates positively with higher plasma DHA level, but recent evidence suggests that the positive relationship between intake and plasma levels of DHA is less clear in the elderly. METHODS: We compared the metabolism of 13C-DHA in six healthy elderly (mean - 77 y old) and six young adults (mean - 27 y old). All participants were given a single oral dose of 50 mg of uniformly labelled 13C-DHA. Tracer incorporation into fatty acids of plasma triglycerides, free fatty acids, cholesteryl esters and phospholipids, as well as apparent retroconversion and β-oxidation of 13C-DHA were evaluated 4 h, 24 h, 7d and 28d later. RESULTS: Plasma incorporation and β-oxidation of 13C-DHA reached a maximum within 4 h in both groups, but 13C-DHA was transiently higher in all plasma lipids of the elderly 4 h to 28d later. At 4 h post-dose, 13C-DHA β-oxidation was 1.9 times higher in the elderly, but over 7d, cumulative β-oxidation of 13C-DHA was not different in the two groups (35% in the elderly and 38% in the young). Apparent retroconversion of 13C-DHA was well below 10% of 13C-DHA recovered in plasma at all time points, and was 2.1 times higher in the elderly 24 h and 7d after tracer intake. CONCLUSIONS: We conclude that 13C-DHA metabolism changes significantly during healthy aging. Since DHA is a potentially important molecule in neuro-protection, these changes may be relevant to the higher vulnerability of the elderly to cognitive decline.
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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) | 0.000 | 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) | 0.001 | 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".