Regulation of inflammation during gestation and birth outcomes: Inflammatory cytokine balance predicts birth weight and length
Bibliographic record
Abstract
OBJECTIVES: The maternal environment during gestation influences offspring health at birth and throughout the life course. Recent research has demonstrated that endogenous immune processes such as dysregulated inflammation adversely impact birth outcomes, increasing the risk for preterm birth and restricted fetal growth. Prior analyses examining this association suggest a relationship between maternal C-reactive protein (CRP), a summary measure of inflammation, and offspring anthropometric outcomes. This study investigates pro- and anti-inflammatory cytokines, and their ratio, to gain deeper insight into the regulation of inflammation during pregnancy. METHODS: IL6, IL10, TNFɑ, and CRP were quantified in dried blood spots collected in the early third trimester (mean = 29.9 weeks) of 407 pregnancies in Metropolitan Cebu, Philippines. Relationships between these immune markers and offspring anthropometrics (birth weight, length, head circumference, and sum of skinfold thicknesses) were evaluated using multivariate regression analyses. Ratios of pro- to anti-inflammatory cytokines were generated. RESULTS: Higher maternal IL6 relative to IL10 was associated with reduced offspring weight and length at birth. Individual cytokines did not predict birth outcomes. CONCLUSIONS: Consistent with the idea that the relative balance of cytokines with pro- and anti-inflammatory effects is a key regulator of inflammation in pregnancy, the IL6:IL10 ratio, but neither cytokine on its own, predicted offspring birth outcomes. Our findings suggest that prior reports of association between CRP and fetal growth may reflect, in part, the balance between pro- and anti-inflammatory cytokines, and that the gestational environment is significantly shaped by cytokine imbalance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".