Pre-natal and post-natal growth trajectories and childhood cognitive ability and mental health
Bibliographic record
Abstract
BACKGROUND: Most studies of the associations between pre-natal or post-natal growth and cognitive ability have been based on children with pathologically slow growth measured between two time points only, rather than children with normal growth trajectories estimated from multiple measures of growth. METHODS: We investigated the associations of pre-natal and post-natal trajectories in both weight and length/height through the first 5 years of life with cognitive ability and mental health at 6.5 years of age among healthy children. Our study is based on 11 899 children who were born healthy at ≥37 completed weeks with birth weight ≥2500 g and had up to 13 measures of weight and length/height from birth to age 5 years and cognitive ability and behaviour measured at 6.5 years. Using a linear spline random-effects model with 2 knots at 3 and 12 months, we estimated growth trajectories for each child from birth to age 5 years in weight and length/height in four periods: gestational age-specific birth weight and length (pre-natal 'growth'), early infancy (0-3 months), late infancy (3-12 months) and early childhood (1-5 years). We used generalized estimating equations to estimate mean differences in IQ and mental health according to pre-natal and post-natal growth trajectory. IQ was measured using the Wechsler Abbreviated Scales of Intelligence, and mental health was assessed using the Strengths and Difficulties Questionnaire. RESULTS: A 1 standard deviation (SD) in birth weight was positively associated with cognitive ability (0.82 IQ points, 95% CI: 0.54-1.10) after adjusting for confounders. For post-natal weight gain trajectories, a 1 SD faster weight gain was associated with an increase of 0.77 (95% CI: 0.42-1.11) IQ points for early infancy, 0.30 (95% CI: 0.02-0.58) points for late infancy, and 0.40 (95% CI: 0.04-0.76) for early childhood after adjusting for confounders and for earlier growth. For length/height trajectories, the magnitudes of increase in cognitive ability were similar to each other (~0.6 points) across the four periods. Pre-natal and infancy growth, but not early childhood growth, were associated with reduced externalising behaviours. CONCLUSIONS: Although the effect sizes are small and residual confounding cannot be excluded, our results suggest that among healthy children, faster growth from the pre-natal period through age 5 years is positively associated with cognitive ability, whereas faster growth in the pre-natal period and infancy is positively associated with mental health at early school age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".