Plasma SerpinA5 in conjunction with uterine artery pulsatility index and clinical risk factor for the early prediction of preeclampsia

Uterine arteries pulsatility index by Doppler ultrasound in the prediction of preeclampsia: an updated systematic review and meta-analysis

BACKGROUND

Preeclampsia is a common pregnancy complication with serious potential risks for maternal and neonatal health. Early prediction of preeclampsia is crucial for timely prevention, surveillance, and treatment to improve maternal and neonatal outcomes. This systematic review aimed to summarize the available evidence on the prediction of preeclampsia based on Doppler ultrasound of uterine arteries at different gestational ages.

METHODS

A systematic literature search and meta-analysis were conducted to evaluate the sensitivity and specificity of the pulsatility index of Doppler ultrasound of uterine arteries for predicting preeclampsia. The timing of ultrasound scans within and beyond 20 weeks of gestational age was compared to assess its effect on the sensitivity and specificity of the pulsatility index.

RESULTS

This meta-analysis included 27 studies and 81,673 subjects (3309 preeclampsia patients and 78,364 controls). The pulsatility index had moderate sensitivity (0.586) and high specificity for predicting preeclampsia (0.879) (summary point: sensitivity 0.59; 1-specificity 0.12). Subgroup analysis revealed that ultrasound scans performed within 20 weeks of gestational age did not significantly affect the sensitivity and specificity for predicting preeclampsia. The summary receiver operator characteristic curve showed the pulsatility index's optimal range of sensitivity and specificity.

CONCLUSIONS

The uterine arteries pulsatility index measured by Doppler ultrasound is useful and effective for predicting preeclampsia and should be implemented in the clinical practice. The timing of ultrasound scans at different gestational age ranges does not significantly affect the sensitivity and specificity.

Improvement in Clinical Features of L-NAME-Induced Preeclampsia-like Rats through Reduced SERPINA5 Expression

Pre-eclampsia (PE) is a severe pregnancy disorder that poses a significant health risk to both mother and fetus, with no preventive or therapeutic measures. Our previous research suggested an association between elevated SERPINA5 levels and PE features. This study investigated whether SERPINA5 could be a potential therapeutic target for PE. We established PE-like features in pregnant rats using L-NAME (75 mg/kg/d) treatment. Adenoviruses carrying overexpressed or suppressed SERPINA5 genes were intravenously injected into these PE rats on the fifth and seventh days of pregnancy. We evaluated the rats' systolic blood pressure, urine protein concentration, and placental and fetal metrics and histology. Placental gene expression following SERPINA5 overexpression was evaluated using mRNA sequencing. The L-NAME-induced PE rat model observed a significant increase in placental and peripheral SERPINA5 levels. The overexpression of SERPINA5 exacerbated L-NAME-induced hypertension and proteinuria in pregnant rats. A histology examination revealed a smaller placental junctional zone in L-NAME + overexpressing rats. Placental gene expression analysis in the L-NAME + overexpressing group indicated increased coagulation activation. L-NAME-induced hypertension and proteinuria were mitigated when SERPINA5 expression was suppressed. Additionally, placental development was improved in the SERPINA5-suppressed group. Our findings suggested that SERPINA5 may worsen L-NAME-induced PE-like features by promoting the activation of the coagulation cascade. Therefore, reducing SERPINA5 expression could potentially serve as a therapeutic strategy for PE.

Fibrinogen beta chain may be a potential predict biomarker for pre-eclampsia: A preliminary study

OBJECTIVE

There are no approaches for the early detection of pre-eclampsia (PE). Using parallel reaction monitoring proteomics, we investigated 79 maternal serum protein changes before PE onset and its predictive capability.

METHODS

We conducted a nested case-control study with 60 PE patients and 60 normotensive pregnant women matched for age and gestational week. These differentially expressed proteins were quantified using the data-dependent acquisition (DDA) combined parallel response monitoring (PRM) approach, and a PE prediction model was developed using the least absolute shrinkage and selection operator (LASSO) regression. We further examined the link between these biomarkers and PE using bioinformatics. ELISA assay was used to investigate the expression and clinical significance of the critical variables.

RESULTS

Among the 79 analyzed proteins, we identified 11 serum proteins with significantly abnormal expression. Fibrinogen beta chain (FGB) was likely connected with the progression of PE due to the positive correlation between their levels and those of hypertension and proteinuria. In addition, an early prediction model for PE with an AUC of 92% was developed using LASSO regression.

CONCLUSION

Our research employs predictive algorithms and screens for relevant variables, which could result in a potential approach to enhancing PE prediction.

Development of a prediction model on preeclampsia using machine learning-based method: a retrospective cohort study in China

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).

Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.

Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).

Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.