Distinct DNA Methylation Signatures in Maternal Blood Reveal Unique Immune Cell Shifts in Preeclampsia and the Pregnancy-Postpartum Transition
Bibliographic record
Abstract
1.0 Abstract Preeclampsia (PE) is a hypertensive disorder of pregnancy characterized by immune dysregulation and significant risks to maternal and fetal health. While current management relies on high-risk patient monitoring and early diagnosis, these methods are costly and burdensome, especially for low-risk pregnancies. There is a pressing need for non-invasive tools to predict and monitor PE. DNA methylation (DNAm) is a type of DNA modification that influences gene expression, and has been associated with immune cell dynamics and PE pathogenesis. This study explores whether DNAm-based immune cell composition profiling can provide insights into PE-related immune dysregulation. We conducted a search in the Gene Expression Omnibus (GEO) for DNA methylation datasets using Illumina 27K, 450K, and EPIC arrays from maternal blood in both healthy and PE pregnancies. We found two studies that met our criteria, involving a total of 24 healthy pregnancies and 14 with PE. To estimate the composition of immune cells (including CD8T, CD4T, Monocytes, Natural Killer, Neutrophils, Eosinophils, and B cells) based on DNA methylation data, we employed the R package EpiDISH. We used a linear model to compare statistical differences in the proportions of immune cells between PE cases and the control group. Longitudinal trends were also examined to capture immune cell shifts from pregnancy to postpartum. We found that monocyte proportions were significantly reduced in preeclamptic pregnancies compared to normotensive pregnancies (p=0.013). No significant differences were observed in other immune cell types, including T cells, B cells, neutrophils, eosinophils, and natural killer cells. Longitudinal analyses revealed substantial immune cell shifts in the postpartum period, including increased monocytes, B cells, CD4+ T cells, and CD8+ T cells, emphasizing the importance of gestational age in immune dynamics. These findings support DNAm profiling as a valuable tool for understanding immune cell dynamics in PE. Reduced monocyte proportions in PE highlight the role of immune dysregulation in its pathogenesis. Longitudinal sampling provides additional insights into the evolution of immune changes throughout pregnancy and postpartum, offering potential for developing predictive and monitoring tools for PE. Future studies with larger, more diverse cohorts are essential to refine the utility of DNAm in pregnancy complications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".