Gestational cytokine concentrations and neurocognitive development at 7 years
Bibliographic record
Abstract
Gestational inflammation may contribute to brain abnormalities associated with childhood neuropsychiatric disorders. Limited knowledge exists regarding the associations of maternal cytokine levels during pregnancy with offspring neurocognitive development. We assayed the concentrations of five cytokines (interleukin (IL)-6, IL-1β, IL-8, tumor necrosis factor alpha (TNF-α), and IL-10) up to four times in the 2nd and 3rd trimesters of pregnancy using stored prenatal sera from 1366 participants in the New England Family Study (enrollment 1959-1966). Intelligence (IQ), academic achievement, and neuropsychological functioning of singleton offspring were assessed at age 7 years using standardized tests. We used linear mixed models with random effects to estimate the cumulative exposure to each cytokine during 2nd and 3rd trimesters, and then related cumulative cytokine exposure to a wide range of offspring neurocognitive outcomes. We found that children of women with higher levels of the pro-inflammatory cytokine, TNF-α, in the 2nd and 3rd trimesters had lower IQ (B = -2.51, 99% CI: -4.84,-0.18), higher problem scores in visual-motor maturity (B = 0.12, 99% CI: 0.001,0.24), and lower Draw-a-Person test scores (B = -1.28, 99% CI: -2.49,-0.07). Higher gestational levels of IL-8, another pro-inflammatory molecule, were associated with better Draw-a-Person test scores and tactile finger recognition scores. Other cytokines were not associated with our outcome of interest. The opposing directions of associations observed between TNF-α and IL-8 with childhood outcomes suggest pleiotropic effects of gestational inflammation across the domains of neurocognitive functioning. Although the path to psychopathological disturbances in children is no doubt multifactorial, our findings point to a potential role for immune processes in the neurocognitive development of children.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".