Changes of uterocervical angle and cervical length in early and mid-pregnancy and their value in predicting spontaneous preterm birth

Objective: To explore the feasibility of transvaginal ultrasound measurement of uterocervical angle (UCA) and cervical length (CL) in early and mid-pregnancy and evaluate their combined prediction of spontaneous preterm birth (sPTB) in singleton pregnancies. Patients and Methods: This retrospective study comprised 274 pregnant women who underwent transvaginal ultrasound measurement of CL in mid-pregnancy (15-23+6 weeks); in 75 among them, CL also had been measured in early-pregnancy (<14 weeks). These 274 pregnant women were further divided into a preterm group (n = 149, <37 weeks gestation) and a control group (n = 125, >37 weeks gestation) according to delivery before or after 37 weeks, respectively. In the preterm group, 35 patients delivered before 34 weeks and the remaining 114 delivered between 34 and 37 weeks. Results: The optimal threshold of CL to predict preterm birth risk in women with <37 weeks gestation was 3.38 cm, and the optimal threshold of the UCA to predict preterm birth risk in the same group of women was 96°. The optimal threshold of CL to predict preterm birth risk in women with <34 weeks gestation was 2.54 cm, while that of the UCA in the same group of patients was 106°. The area under the curve for predicting preterm birth by combining the UCA and CL measurements was greater than that by using the UCA or CL alone (p < 0.01). The sensitivity and specificity for predicting preterm birth at <34 weeks gestation was 71.7% and 86.4%, respectively; and the sensitivity and specificity for predicting preterm birth at <37 weeks gestation was 87.6% and 80.6%, respectively. The difference between the two groups in CL and UCA were not significant in early pregnancy (p > 0.01), but only in mid-pregnancy (p < 0.01). There was a negative correlation between UCA and gestational week at delivery (r = -0.361, p < 0.001) and a positive correlation between CL and gestational week at delivery (r = 0.346, p < 0.001) in mid-pregnancy. The proportion of deliveries at <34 weeks was highest when the UCA was >105°, and the proportion of deliveries between 35 and 37 weeks was highest when the UCA was between 95° and 105°. The proportion of deliveries at <34 weeks was highest when the CL was <2.5 cm. Conclusion: The combination of UCA and CL has a better ability to predict preterm birth than either measurement alone. A more obtuse UCA or a shorter CL is associated with an earlier spontaneous preterm birth. The UCA increases from early to mid-pregnancy, while the CL decreases from early to mid-pregnancy.

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.