Stress and depression risk in pregnancy associates with altered immune function
Bibliographic record
Abstract
OBJECTIVES: Psychological factors such as well-being, stress, and depression can influence immune function, with dysregulated inflammation during pregnancy contributing to adverse outcomes. While the role of inflammatory markers has been studied in pregnancy complications like preterm birth and preeclampsia, few studies explore how psychological states impact cellular and serum immune responses in pregnant women. In this study, we investigated associations between psychological factors and inflammatory markers from peripheral blood mononuclear cells (PBMCs) and serum in early and late pregnancy. METHODS: This secondary analysis of 70 pregnant women from the MicrobeMom2 RCT investigated associations between psychological factors and inflammatory markers from peripheral blood mononuclear cells (PBMCs) and serum in early and late pregnancy. Wellbeing, stress, and depression risk were assessed using the WHO-5 Well-being Index, Perceived Stress Questionnaire, and Edinburgh Postnatal Depression Scale. Associations between immune markers and psychological factors were analysed using independent t-tests, ANOVA, and linear regression. RESULTS: Higher well-being correlated with lower leptin levels in late pregnancy serum. Higher stress scores were associated with decreased PBMC-secreted TNF-α in early pregnancy. Increased depression risk was associated with lower serum TNF-α and ICAM1 in early pregnancy and reduced IL17A in late pregnancy. CONCLUSIONS: Well-being, stress, and depression risk are associated with an altered immune response during early and late pregnancy, which may contribute to the relationship between suboptimal psychological states and adverse pregnancy outcomes.
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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) | 0.000 | 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".