Maternal preconception circulating blood biomarker mixtures, child behavioural symptom scores and the potential mediating role of neonatal brain microstructure: the S-PRESTO cohort
Bibliographic record
Abstract
Human brain development starts in the embryonic period. Maternal preconception nutrition and nutrient availability to the embryo may influence brain development at this critical period following conception and early cellular differentiation, thereby affecting offspring neurodevelopmental and behavioural disorder risk. However, studying this is challenging due to difficulties in characterizing preconception nutritional status and few studies have objective neurodevelopmental imaging measures in children. We investigated the associations of maternal preconception circulating blood nutrient-related biomarker mixtures (~15 weeks before conception) with child behavioural symptoms (Child Behaviour Checklist (CBCL), aged 3 years) within the Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO) study. The CBCL preschool form evaluates child behaviours based on syndrome scales and Diagnostic and Statistical Manual of Mental Disorders (DSM) oriented scales. These scales consist of internalizing problems, externalizing problems, anxiety problems, pervasive developmental problems, oppositional defiant, etc. We applied data-driven clustering and a method for modelling mixtures (Bayesian kernel machine regression, BKMR) to account for complex, non-linear dependencies between 67 biomarkers. We used effect decomposition analyses to explore the potential mediating role of neonatal (week 1) brain microstructure, specifically orientation dispersion indices (ODI) of 49 cortical and subcortical grey matter regions. We found that higher levels of a nutrient cluster including thiamine, thiamine monophosphate (TMP), pyridoxal phosphate, pyridoxic acid, and pyridoxal were associated with a higher CBCL score for internalizing problems (posterior inclusion probability (PIP) = 0.768). Specifically, thiamine independently influenced CBCL (Conditional PIP = 0.775). Higher maternal preconception thiamine level was also associated with a lower right subthalamic nucleus ODI (P-value = 0.01) while a lower right subthalamic nucleus ODI was associated with higher CBCL scores for multiple domains (P-value < 0.05). One potential mechanism is the suboptimal metabolism of free thiamine to active vitamin B1, but additional follow-up and replication studies in other cohorts are needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".