Value of second-trimester uterine artery pulsatility index in pregnancies with systemic lupus erythematosus and/or antiphospholipid syndrome

Prediction by uterine artery Doppler screening of small-for-gestational-age neonates at 19-24 weeks' gestation

OBJECTIVE

Small-for-gestational-age (SGA) neonates are at increased risk of perinatal mortality and morbidity. We aimed to investigate the performance of uterine artery pulsatility index (UtA-PI) at 19-24 weeks' gestation to predict the delivery of a SGA neonate in a Chinese population.

METHODS

This was a retrospective cohort study using data obtained between January 2010 and June 2018. Doppler ultrasonography was performed at 19-24 weeks' gestation. SGA was defined as birth weight below the 10th centile according to the INTERGROWTH-21st fetal growth standards. The performance of UtA-PI to predict the delivery of a SGA neonate was assessed using receiver-operating-characteristics (ROC)-curve analysis.

RESULTS

We included 6964 singleton pregnancies, of which 748 (11%) delivered a SGA neonate, including 115 (15%) women with preterm delivery. Increased UtA-PI was associated with an elevated risk of SGA, both in neonates delivered at or after 37 weeks' gestation (term SGA) and those delivered before 37 weeks (preterm SGA). The areas under the ROC curve (AUCs) for UtA-PI were 64.4% (95% CI, 61.5-67.3%) and 75.8% (95% CI, 69.3-82.3%) for term and preterm SGA, respectively. The performance of combined screening by maternal demographic/clinical characteristics and estimated fetal weight in the detection of term and preterm SGA was improved significantly by the addition of UtA-PI, although the increase in AUC was modest (2.4% for term SGA and 4.9% for preterm SGA).

CONCLUSIONS

This is the first Chinese study to evaluate the role of UtA-PI at 19-24 weeks' gestation in the prediction of the delivery of a neonate with SGA. The addition of UtA-PI to traditional risk factors improved the screening performance for SGA, and this improvement was greater in predicting preterm SGA compared with term SGA. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Potential genetic biomarkers predict adverse pregnancy outcome during early and mid-pregnancy in women with systemic lupus erythematosus

BACKGROUND

Effectively predicting the risk of adverse pregnancy outcome (APO) in women with systemic lupus erythematosus (SLE) during early and mid-pregnancy is a challenge. This study was aimed to identify potential markers for early prediction of APO risk in women with SLE.

METHODS

The GSE108497 gene expression dataset containing 120 samples (36 patients, 84 controls) was downloaded from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was performed, and differentially expressed genes (DEGs) were screened to define candidate APO marker genes. Next, three individual machine learning methods, random forest, support vector machine-recursive feature elimination, and least absolute shrinkage and selection operator, were combined to identify feature genes from the APO candidate set. The predictive performance of feature genes for APO risk was assessed using area under the receiver operating characteristic curve (AUC) and calibration curves. The potential functions of these feature genes were finally analyzed by conventional gene set enrichment analysis and CIBERSORT algorithm analysis.

RESULTS

We identified 321 significantly up-regulated genes and 307 down-regulated genes between patients and controls, along with 181 potential functionally associated genes in the WGCNA analysis. By integrating these results, we revealed 70 APO candidate genes. Three feature genes, SEZ6, NRAD1, and LPAR4, were identified by machine learning methods. Of these, SEZ6 (AUC = 0.753) showed the highest in-sample predictive performance for APO risk in pregnant women with SLE, followed by NRAD1 (AUC = 0.694) and LPAR4 (AUC = 0.654). After performing leave-one-out cross validation, corresponding AUCs for SEZ6, NRAD1, and LPAR4 were 0.731, 0.668, and 0.626, respectively. Moreover, CIBERSORT analysis showed a positive correlation between regulatory T cell levels and SEZ6 expression (P < 0.01), along with a negative correlation between M2 macrophages levels and LPAR4 expression (P < 0.01).

CONCLUSIONS

Our preliminary findings suggested that SEZ6, NRAD1, and LPAR4 might represent the useful genetic biomarkers for predicting APO risk during early and mid-pregnancy in women with SLE, and enhanced our understanding of the origins of pregnancy complications in pregnant women with SLE. However, further validation was required.