Newborn Differential DNA Methylation and Subcortical Brain Volumes as Early Signs of Severe Neurodevelopmental Delay in a South African Birth Cohort Study
Bibliographic record
Abstract
Abstract Objective The first two years of life are a critical period of rapid brain development. Since early neurodevelopment is influenced by prenatal risk factors and genetics, neonatal biomarkers can potentially provide the opportunity to detect early signs of neurodevelopmental delay. We analyzed associations between DNA methylation (DNAm) levels from cord blood, neonatal magnetic resonance imaging (MRI) neuroimaging data, and neurodevelopment at two years of age. Methods Neurodevelopment was assessed in 161 children from the South African Drakenstein Child Health Study at two years of age using the Bayley Scales of Infant and Toddler Development III. We performed an epigenome-wide association study of neurodevelopmental delay using DNAm levels from cord blood. A mediation analysis was conducted in 51 children to analyze if associations between differential DNAm and neurodevelopmental delay were mediated by altered neonatal brain volumes. Results We found epigenome-wide significant associations between differential DNAm at the SPTBN4 locus (cg26971411, p-value=3.10×10 −08 ), an intergenic region on chromosome 11 (cg00490349, p-value=2.41×10 −08 ) and a differentially methylated region on chromosome 1 (FDR p-value for the region=9.06×10 −05 ) and severe neurodevelopmental delay. While these associations were not mediated by neonatal brain volume, neonatal caudate volumes were independently associated with neurodevelopmental delay, particularly in language (p=0.0443) and motor (p=0.0082) domains. Conclusion Differential DNAm levels from cord blood and increased neonatal caudate volumes were independently associated with severe neurodevelopmental delay at two years of age. These findings suggest that neurobiological signals for severe developmental delay may be detectable in very early life with implications for identification and intervention design.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".