Association between perinatal methylation of the neuronal differentiation regulator<i>HES1</i>and later childhood neurocognitive function and behaviour
Bibliographic record
Abstract
BACKGROUND: Early life environments induce long-term changes in neurocognitive development and behaviour. In animal models, early environmental cues affect neuropsychological phenotypes via epigenetic processes but, as yet, there is little direct evidence for such mechanisms in humans. METHOD: We examined the relation between DNA methylation at birth and child neuropsychological outcomes in two culturally diverse populations using a genome-wide methylation analysis and validation by pyrosequencing. RESULTS: Within the UK Southampton Women's Survey (SWS) we first identified 41 differentially methylated regions of interest (DMROI) at birth associated with child's full-scale IQ at age 4 years. Associations between HES1 DMROI methylation and later cognitive function were confirmed by pyrosequencing in 175 SWS children. Consistent with these findings, higher HES1 methylation was associated with higher executive memory function in a second independent group of 200 SWS 7-year-olds. Finally, we examined a pathway for this relationship within a Singaporean cohort (n = 108). Here, HES1 DMROI methylation predicted differences in early infant behaviour, known to be associated with academic success. In vitro, methylation of HES1 inhibited ETS transcription factor binding, suggesting a functional role of this site. CONCLUSIONS: Thus, our findings suggest that perinatal epigenetic processes mark later neurocognitive function and behaviour, providing support for a role of epigenetic processes in mediating the long-term consequences of early life environment on 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 | 0.000 |
| 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".