Children’s docosahexaenoic acid (DHA), but not maternal DHA in pregnancy, is associated with psychometric tests scores at 5‐6 years of age (124.2)
Bibliographic record
Abstract
Docosahexaenoic acid (DHA) is an important component of neural lipids, affecting neural function throughout life. However, the dietary, genetic and other variables that impact DHA transfer to the brain, and the potential for early deficiency to have lasting effects remains unclear. We addressed the effect of DHA in gestation and children’s intake and blood lipid DHA on cognition at 5.75 years of age. Pregnant women, n=271 were enrolled at 16 weeks gestation and randomized to 400 mg DHA/day or placebo until infant delivery; 95 children returned for follow up, and were assessed with 153 additional children. Diet was assessed using 3 day records and frequency questionnaires, and venous blood was collected. Cognitive tests included the Peabody Picture Vocabulary Test (PPVT) and Kaufman Assessment Battery for Children (KABC). The median (IQR) DHA intake was 49.4 (23.8‐102) mg/day. The red blood cell mean ± SD DHA was 5.14 ± 1.50 percent total fatty acids. No evidence of persisting effects of DHA in pregnancy on child development were found. Children’s own DHA status, but not DHA intake, was associated with better PPVT and KABC Sequential, Learning, Simultaneous, and Mental Performance scores (P <0.05). Child blood lipid 22:5n‐6 and the 22:5n‐6/DHA ratio were associated with poorer PPVT and KABC performance. DHA intake explained only 15% of variability in RBC DHA, raising complex questions on the relationship between diet, DHA transfer to membrane lipids, and neural function.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".