Evidence for the essentiality of arachidonic and docosahexaenoic acid in the postnatal maternal and infant diet for the development of the infant’s immune system early in life
Bibliographic record
Abstract
Long-chain polyunsaturated fatty acids (LCPUFA), especially the balance between arachidonic (AA) and docosahexaenoic (DHA) acids are known to have important immunomodulatory roles during the postnatal period when the immune system is rapidly developing. AA and DHA are required in infant formula in many countries but are optional in North America. The rationale for adding these LCPUFA to full-term formula is based on their presence in breast milk and randomized controlled studies that suggest improved cognitive function in preterm infants, but results are more variable in full-term infants. Recently, the European Food Safety Authority has proposed, based on a lack of functional evidence, that AA is not required in infant formula for full-term infants during the first year of life but DHA should remain mandatory. The purpose of this review is to review the evidence from epidemiological and intervention studies regarding the essentiality of AA and DHA in the postnatal infant and maternal diet (breast-feeding) for the immune system development early in life. Although studies support the essentiality of DHA for the immune system development, more research is needed to rule out the essentiality of AA. Nevertheless, intervention studies have demonstrated improvement in many markers of immune function in infants fed formula supplemented with AA and DHA compared with unsupplemented formula, which appears to consistently result in beneficial health outcomes including reduction in the risk of developing allergic and atopic disease early in life.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.003 | 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".