Is docosahexaenoic acid synthesis from α-linolenic acid sufficient to supply the adult brain?
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is important for brain function, and can be obtained directly from the diet or synthesized in the body from α-linolenic acid (ALA). Debate exists as to whether DHA synthesized from ALA can provide sufficient DHA for the adult brain, as measures of DHA synthesis from ingested ALA are typically <1% of the oral ALA dose. However, the primary fate of orally administered ALA is β-oxidation and long-term storage in adipose tissue, suggesting that DHA synthesis measures involving oral ALA tracer ingestion may underestimate total DHA synthesis. There is also evidence that DHA synthesized from ALA can meet brain DHA requirements, as animals fed ALA-only diets have brain DHA concentrations similar to DHA-fed animals, and the brain DHA requirement is estimated to be only 2.4-3.8 mg/day in humans. This review summarizes evidence that DHA synthesis from ALA can provide sufficient DHA for the adult brain by examining work in humans and animals involving estimates of DHA synthesis and brain DHA requirements. Also, an update on methods to measure DHA synthesis in humans is presented highlighting a novel approach involving steady-state infusion of stable isotope-labeled ALA that bypasses several limitations of oral tracer ingestion. It is shown that this method produces estimates of DHA synthesis that are at least 3-fold higher than brain uptake rates in rats.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".