Associations of maternal zinc and magnesium with offspring learning abilities and cognitive development at 4 years in GUSTO
Bibliographic record
Abstract
Objectives: Minerals deficiencies during pregnancy have been shown to be associated with poorer cognitive outcomes in offspring. This study aimed to investigate associations of maternal plasma zinc and magnesium concentrations with cognitive development in 4-year old children from the Growing Up in Singapore Towards healthy Outcome cohort.Methods: Maternal plasma zinc and magnesium concentrations were measured at 26–28 weeks’ gestation. The Lollipop test of school readiness, tests of working memory, number knowledge, receptive vocabulary, and phonological awareness were performed in children at 4 years. Associations were examined in 715 mother-offspring pairs using linear regressions adjusted for key confounders.Results: Maternal plasma zinc and magnesium concentrations were 812 ± 144 µg/L and 19.9 ± 1.8 mg/L (mean±SD); 19% and 71% of mothers were zinc deficient and magnesium insufficient, respectively. After adjustment for multiple testing, higher maternal zinc concentrations (per SD increment) were associated with 0.35 higher scores in Lollipop subtest 2 of picture description and spatial identification (95% CI: 0.13, 0.58); higher maternal magnesium concentrations (per SD increment) were associated with 0.65 higher scores in Lollipop subtest 4 of letters and writing identification (95% CI: 0.23, 1.07).Discussion: No significant associations were observed for other tests, suggesting little long term influences of maternal zinc and magnesium on child's cognitive development.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".