Cord serum cytokines at birth and children's trajectories of mood dysregulation symptoms from 3 to 8 years: The EDEN birth cohort
Bibliographic record
Abstract
imbalance immune activity plays a role in the development of neurodevelopmental and psychiatric disorders in children. Mood dysregulation (MD) is a debilitating transnosographic syndrome whose underlying pathophysiological mechanisms could be revealed by studying its biomarkers using the Research Domain Criteria (RDoC) model. Our aim was to study the association between the network of cord serum cytokines, and mood dysregulation trajectories in offsprings between 3 and 8 years of age. We used the data of a study nested in the French birth cohort EDEN that took place from 2003 to 2014 and followed mother-child dyads from the second trimester of pregnancy until the children were 8 years of age. The 2002 mother-child dyads were recruited from the general population through their pregnancy follow-up in two French university hospitals. 871 of them were included in the nested cohort and cord serum cytokine levels were measured at birth. Children's mood dysregulation symptoms were assessed with the Strengths and Difficulties Questionnaire Dysregulation Profile at the ages 3, 5 and 8 years in order to model their mood dysregulation trajectories. Out of the 871 participating dyads, 53% of the children were male. 2.1% of the children presented a high mood dysregulation trajectory whereas the others were considered as physiological variations. We found a significant negative association between TNF-α cord serum levels and a high mood dysregulation trajectory when considering confounding factors such as maternal depression during pregnancy (adjusted Odds Ratio (aOR) = 0.35, 95% Confidence Interval (CI) [0.18-0.67]). Immune imbalance at birth could play a role in the onset of mood dysregulation symptoms. Our findings throw new light on putative immune mechanisms implicated in the development of mood dysregulation and should lead to future animal and epidemiological studies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".