Biomarkers of Inflammation and Their Association With the Severity and Onset of Preeclampsia: A Systematic Review
Bibliographic record
Abstract
Preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality, with systemic inflammation playing a central role in its pathogenesis. Despite extensive research on inflammatory biomarkers, inconsistencies persist regarding their associations with disease severity and onset. This systematic review synthesizes current evidence on the relationship between inflammatory biomarkers and PE, focusing on their diagnostic and prognostic potential. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines, a comprehensive search was conducted across five databases (PubMed, Scopus, Web of Science, Embase, and CINAHL) to identify observational studies investigating inflammatory biomarkers in PE. Eligible studies included case-control, cross-sectional, and cohort designs with normotensive controls. Data extraction covered study characteristics, biomarker profiles, and clinical outcomes. Methodological quality was assessed using the Newcastle-Ottawa Scale. In total, 13 studies were included, predominantly from diverse geographical regions. Pro-inflammatory cytokines and acute-phase proteins (C-reactive protein) were consistently elevated in PE, with distinct profiles for early-onset (placental-driven inflammation) and late-onset (systemic inflammation) subtypes. Biomarkers such as neopterin and soluble urokinase-type plasminogen activator receptor showed promise in stratifying disease severity. Maternal-fetal inflammatory cascades were evident, with correlations between maternal biomarkers and adverse neonatal outcomes. However, heterogeneity in study designs, biomarker measurement timing, and inconsistent adjustments for confounders limited comparability. Quality assessment revealed seven low-risk and six moderate-risk studies, with no high-risk bias. Inflammatory biomarkers demonstrate significant associations with PE severity and onset, supporting their role in disease monitoring and risk stratification. However, methodological inconsistencies highlight the need for standardized protocols and larger, longitudinal studies to validate their clinical utility. Future research should integrate multi-omics approaches to refine biomarker panels and elucidate causal pathways, ultimately guiding targeted interventions.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".