Maternal immune response during pregnancy and neurodevelopmental outcomes: A longitudinal approach
Bibliographic record
Abstract
Background and objectives: The neurodevelopment of the offspring is suggested to be influenced by the maternal immune system's responses throughout pregnancy, which in turn is also vulnerable to maternal psychosocial stress conditions. Therefore, our main goal was to investigate whether maternal peripheral immunological biomarkers (IB) during two stages of gestation are associated with distinct neurodevelopmental trajectories in the first two years of life. As a second goal, we also explored the association between maternal distal (childhood) and proximal (gestation) stressful experiences and the immunological markers assessed during pregnancy. Methods: Maternal childhood trauma, depressive and anxiety symptoms, and peripheral IB (IFNγ, IL-10, IL1β, IL6, IL8, TNFα, EGF, IL13, IL17, IL1Ra and IL4) were measured at baseline (8-16 weeks of pregnancy) and at 30 weeks of pregnancy in 160 women. The participants had the blood samples collected from two randomized clinical trials conducted by the same team and methods in the same community. A Principal Component Analysis (PCA) was implemented to create meaningful composite variables that describe the cytokines joint variation. Finally, linear mixed-effects modeling was used to investigate the influence of inflammatory biomarkers, maternal childhood trauma, anxiety, and depressive symptoms on Bayley's III scores trajectories. Results: The IB profile during the 3rd trimester of pregnancy predicted the offspring's neurodevelopmental trajectories in the first two years of life. The components derived from PCA were important predictors and captured different immune responses, reflecting both pro- and anti-inflammatory states. Maternal stressful experiences did not correlate with the immunological markers. Although not a reliable predictor alone, maternal psychosocial stress at the 1st trimester of pregnancy interacted with the mother's immune response while predicting the neurodevelopmental scores during the first two years of life. Conclusions: Our results underscore the importance of the maternal immune response during pregnancy in shaping the neurodevelopmental trajectory of the offspring. Additionally, we observed that the maternal distress at the early stages of pregnancy has an incremental effect on the neurodevelopmental outcome but depends upon the immune response.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".