Abstract / Summary
Background: Preeclampsia is a pregnancy-specific hypertensive disorder characterized by systemic endothelial dysfunction, metabolic disturbances, and inflammatory activation. However, comprehensive biochemical and inflammatory biomarker profiles associated with disease severity, particularly severe preeclampsia, remain insufficiently defined. This study aimed to investigate serum biochemical and inflammatory markers in preeclampsia and to evaluate their clinical predictive value for severe disease.
Methods: In this retrospective case-control study, pregnant women were classified into severe preeclampsia (n=30), mild preeclampsia (n=30), and normal pregnancy control groups (n=30). Serum biochemical parameters, including alanine aminotransferase, aspartate aminotransferase, lactate dehydrogenase (LDH), uric acid, creatinine, albumin, total bilirubin, triglycerides, and total cholesterol, were measured. Inflammatory markers, including C-reactive protein (CRP), interleukin-6 (IL-6), interleukin-8, tumor necrosis factor-a, procalcitonin, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio, were analyzed. Group comparisons, Spearman correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed.
Results: Both biochemical and inflammatory markers showed significant stepwise alterations across the three groups, with the most pronounced abnormalities observed in severe preeclampsia (P < 0.05). LDH, uric acid, CRP, IL-6, and albumin were significantly associated with disease severity. Multivariable logistic regression identified elevated LDH, uric acid, CRP IL-6, and reduced albumin as independent predictors of severe preeclampsia. ROC analysis demonstrated that the combined biomarker model achieved good discriminative performance, with an area under the curve of 0.89 (95% CI: 0.81-0.96).
Conclusions: Severe preeclampsia is characterized by distinct serum biochemical and inflammatory biomarker profiles. An integrated biomarker-based model may serve as a practical tool for early identification and risk stratification of severe preeclampsia.