Docosahexaenoic acid and arachidonic acid levels are correlated in human milk: Implications for new European infant formula regulations
Bibliographic record
Abstract
From February 2022, all infant formula sold in the European Union must contain docosahexaenoic acid (DHA) at ~0.33%-1.14% of total fat with no minimum requirement for arachidonic acid (ARA). This work examines the association between DHA and ARA levels in human milk, the gold standard for infant feeding. Human milk (n = 470) was collected over 12-weeks postpartum from lactating mothers (n = 100) of infants born weighing <1250 g (NCT02137473). Fatty acids were analyzed by gas chromatography. ARA and DHA concentrations were associated in human milk (β = 0.47 [95% confidence interval 0.38-0.56] mol%), including transitional and mature milk, but not colostrum. This remained significant upon adjustment for percentages of other saturated, monounsaturated, n-3, or n-6 fatty acids, day of sample collection, or maternal characteristics (body mass index, ethnicity, education, and income). Infant formulas containing relatively high concentrations of DHA without ARA, as permitted by the new regulations, would not reflect the balance of these fatty acids in human milk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.058 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".