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Prediction of first-trimester preeclampsia: Relevance of the oxidative stress marker MDA in a combination model with PP-13, PAPP-A and beta-HCG

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Akademik Birimler

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Asıltas, Burak
Sürmen-Gür, Esma

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Asıltaş, B.
Sürmen-Gür, E.
Uncu, G.

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Elsevier B.V.

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Objective: Early diagnosis of preeclampsia (PE) is very important and various parameters, individually or in combined models, are reported useful for prediction of PE. The objective of this study is to investigate the predictive value of pregnancy-associated plasma protein-A (PAPP-A), placental protein-13 (PP-13), human Chorionic Gonadotropin (B-HCG), and oxidative stress marker malondialdehyde (MDA), individually and in combination. Materials and methods: Maternal sera of 38 cases with PE and 122 controls were collected for first trimester screening and tested for PAPP-A and B-HCG by chemiluminescence, for PP-13 by using ELISA, and for MDA by high-performance liquid chromatography. Combined models of parameters were constituted as “MDA + PP-13” “PP-13 + PAPP-A + B-HCG” and “MDA + PP-13 + PAPP-A + B-HCG”. The diagnostic performances of serum markers of preeclampsia were examined by nonparametric receiver-operator characteristics (ROC) analysis. Results: PP-13 levels were significantly lower (p < 0.001) and MDA levels were significantly higher (p < 0.001) in PE. The area under the ROC curve (AUC) for MDA and PP-13 were greater than those for PAPP-A and B-HCG (p < 0.001). The AUCs of the combined models were significantly larger than those of individual parameters. The combined model “MDA + PP-13 + PAPP-A + B-HCG” exhibited the best predictive outcome with an AUC of 0.91 [95% CI 0.86–0.95], 97% [95% CI 86.2–99.9] sensitivity and 75% [95% CI 66.5–82.6] specificity, and was significantly different from that of “PAPP-A + PP–13 + B-HCG” model, but similar to that of “MDA + PP-13” model. Conclusion: Combined models consisting of various parameters of different origin, may provide better predictive outcomes, and oxidative markers should be considered in combination with other placental biomarkers in prediction of PE.

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