EC Gynaecology

Objective: Preeclampsia is a hypertensive disorder of pregnancy that affects an estimated 2 - 10% of pregnant women worldwide. In this study, the expression profiles of four placental proteins: vascular endothelial growth factor (VEGF₁₆₅), VEGF₁₆₅b, matrix metalloproteinase-9 (MMP-9) and tumor necrosis factor-a (TNF-a) were compared between placentas obtained from either normal pregnancy or preeclampsia. By applying binary logistic regression analysis we determined if the protein levels could be predictive of preeclampsia.

Methods: Placentas from normotensive women and from women with preeclampsia, diagnosed by the American College of Obstetricians and Gynecologists’ (ACOG) criteria, were collected after term delivery. Chorionic villi were isolated. Proteins were analyzed independently by Enzyme-linked immunosorbent assay (ELISA) using kits from R&D Systems. Independent t-test, Spearman’s correlation, linear regression and binary logistic regression analysis, using preeclampsia as the dependent variable, were performed. A receiver operating characteristic (ROC) analysis was additionally performed to evaluate the performance of the four proteins in detecting preeclampsia. 

Results: Placental protein expressions of 113 normotensive pregnancies were significantly different from 36 pregnancies with preeclampsia. Linear regression analysis revealed that the variables VEGF₁₆₅, VEGF₁₆₅b, MMP-9, TNF-a, maternal age, gestational age (GA), and placental weight were not collinear. Binary logistic regression analysis further revealed a Chi² statistics of 70.8 with a p value <0.001, when all variables were simultaneously included in the model. Hosemer and Lemeshow test showed that the model provided a good fit of the data (>0.05). Cox and Snell R² and Nagelkerke R² revealed that the model explained between 38% and 57% of the variance in the dependent variables. Binary logistic regression analysis showed that the model's overall percentage of correct predictions improved from 75.8% at baseline to 84.6%. The “Variables in the Equation” table of binary regression analysis revealed that all four studied proteins were significant predictors of preeclampsia. The Area Under the Curve (AUC) scores of the ROC analyses revealed that both VEGF₁₆₅b and MMP-9 proteins can moderately distinguish preeclampsia from normal.

Conclusion: The findings of 9% improvement in the percentage of correct prediction when VEGF₁₆₅, VEGF₁₆₅b, MMP-9 and TNF-a placental proteins were included in the binary logistic regression model indicates, that the model's ability to classify preeclampsia accurately has been enhanced by including these proteins. The AUC scores for VEGF₁₆₅b and MMP-9 demonstrate modest discriminatory ability to distinguish between positive and negative cases; suggesting that the protein-based tests are potentially useful tools, and further research or refinement might be needed. 

 Keywords: Placental Protein Expressions; Normal Human Pregnancy; Preeclampsia; VEGF₁₆₅ Protein; VEGF₁₆₅b Protein; TNF-a Protein; MMP-9 Proteins; Linear Regression Analysis; Binary Logistic Regression Analysis; ROC Curve Analysis; AUC

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