Predictive performance of first trimester serum galectin‑13/PP‑13 in preeclampsia screening: A systematic review and meta‑analysis

Angiogenic factors for early prediction of preeclampsia

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Galectin-13 and Laeverin Levels Interfere with Human Fetoplacental Growth

Galectin-13 (Gal-13) is predominantly produced by the syncytiotrophoblast, while laeverin is expressed on the outgrowing extravillous trophoblast, and both are thought to be biomarkers of preeclampsia. The aim of this study was to assess the correlation between concentrations of Gal-13 and laeverin measured in maternal serum and amniotic fluid at 16-22 weeks of gestation and the sonographic assessment of the fetoplacental measurements. Fetal biometric data and placental volume and perfusion indices were measured in 62 singleton pregnancies. Serum and amniotic levels of Gal-13 and laeverin levels were measured using a sandwich ELISA. Both amniotic fluid and serum Gal-13 levels expressed a negative correlation to the plasma laeverin level in mid-pregnancy. Serum laeverin level correlated positively with the gestational length at delivery (β = 0.39, p < 0.05), while the amniotic laeverin level correlated well with the abdominal circumference of the fetus (β = 0.44, p < 0.05). Furthermore, laeverin level in the amnion correlated positively with the estimated fetal weight (β = 0.48, p < 0.05) and with the placental volume (β = 0.32, p < 0.05). Logistic regression analyses revealed that a higher circulating Gal-13 level represents a slightly significant risk factor (OR: 1.01) for hypertension-related diseases during pregnancy. It is a novelty that laeverin can be detected in the amniotic fluid, and amnion laeverin concentration represents a potential biomarker of fetoplacental growth.

First Trimester Placental Biomarkers for Pregnancy Outcomes

The placenta plays a key role in several adverse obstetrical outcomes, such as preeclampsia, intrauterine growth restriction and gestational diabetes mellitus. The early identification of at-risk pregnancies could significantly improve the management, therapy and prognosis of these pregnancies, especially if these at-risk pregnancies are identified in the first trimester. The aim of this review was to summarize the possible biomarkers that can be used to diagnose early placental dysfunction and, consequently, at-risk pregnancies. We divided the biomarkers into proteins and non-proteins. Among the protein biomarkers, some are already used in clinical practice, such as the sFLT1/PLGF ratio or PAPP-A; others are not yet validated, such as HTRA1, Gal-3 and CD93. In the literature, many studies analyzed the role of several protein biomarkers, but their results are contrasting. On the other hand, some non-protein biomarkers, such as miR-125b, miR-518b and miR-628-3p, seem to be linked to an increased risk of complicated pregnancy. Thus, a first trimester heterogeneous biomarkers panel containing protein and non-protein biomarkers may be more appropriate to identify and discriminate several complications that can affect pregnancies.

Current understanding of the pathogenesis of placenta accreta spectrum disorder with focus on mitochondrial function

AIM

The refinement of assisted reproductive technology, including the development of cryopreservation techniques (vitrification) and ovarian stimulation protocols, makes frozen embryo transfer (FET) an alternative to fresh ET and has contributed to the success of assisted reproductive technology. Compared with fresh ET cycles, FET cycles were associated with better in vitro fertilization outcomes; however, the occurrence of pregnancy-induced hypertension, preeclampsia, and placenta accreta spectrum (PAS) was higher in FET cycles. PAS has been increasing steadily in incidence as a life-threatening condition along with cesarean rates worldwide. In this review, we summarize the current understanding of the pathogenesis of PAS and discuss future research directions.

METHODS

A literature search was performed in the PubMed and Google Scholar databases.

RESULTS

Risk factors associated with PAS incidence include a primary defect of the decidua basalis or scar dehiscence, aberrant vascular remodeling, and abnormally invasive trophoblasts, or a combination thereof. Freezing, thawing, and hormone replacement manipulations have been shown to affect multiple cellular pathways, including cell proliferation, invasion, epithelial-to-mesenchymal transition (EMT), and mitochondrial function. Molecules involved in abnormal migration and EMT of extravillous trophoblast cells are beginning to be identified in PAS placentas. Many of these molecules were also found to be involved in mitochondrial biogenesis and dynamics.

CONCLUSION

The etiology of PAS may be a multifactorial genesis with intrinsic predisposition (e.g., placental abnormalities) and certain environmental factors (e.g., defective decidua) as triggers for its development. A distinctive feature of this review is its focus on the potential factors linking mitochondrial function to PAS development.

Biomarkers for Early Prediction and Management of Preeclampsia: A Comprehensive Review

Preeclampsia is a common complication of pregnancy. It is a multi-organ disorder that remains one of the main causes of maternal morbidity and mortality. Additionally, preeclampsia leads to many complications that can occur in the fetus or newborn. Preeclampsia occurs in about 1 in 20 pregnant women. This review focuses on the prediction of preeclampsia in women, using various biomarkers, in particular, a factor combining the use of soluble FMS-like tyrosinokinase-1 (sFlt-1) and placental growth factor (PlGF). A low value of the sFlt-1/PlGF ratio rules out the occurrence of preeclampsia within 4 weeks of the test result, and its high value predicts the occurrence of preeclampsia within even 1 week. The review also highlights other factors, such as pregnancy-associated plasma protein A, placental protein 13, disintegrin and metalloprotease 12, ß-human chorionic gonadotropin, inhibin-A, soluble endoglin, nitric oxide, and growth differentiation factor 15. Biomarker testing offers reliable and cost-effective screening methods for early detection, prognosis, and monitoring of preeclampsia. Early diagnosis in groups of women at high risk for preeclampsia allows for quick intervention, preventing the undesirable effects of preeclampsia. However, further research is needed to validate and optimize the use of biomarkers for more accurate prediction and diagnosis. This article aims to review the role of biomarkers, including the sFlt1/PlGF ratio, in the prognosis and management of preeclampsia.

Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study

(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and their associations in a group of singleton pregnancies; (2) Methods: This observational prospective study included 210 singleton pregnancies that underwent first trimester screenings at our institution. We computed the predictive performance of four machine learning-based methods, namely decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), by incorporating clinical and paraclinical data; (3) Results: The RF algorithm showed superior performance for the prediction of PE (accuracy: 96.3%), IUGR (accuracy: 95.9%), and its subtypes (early onset IUGR, accuracy: 96.2%, and late-onset IUGR, accuracy: 95.2%), as well as their association (accuracy: 95.1%). Both SVM and NB similarly predicted IUGR (accuracy: 95.3%), while SVM outperformed NB (accuracy: 95.8 vs. 94.7%) in predicting PE; (4) Conclusions: The integration of machine learning-based algorithms in the first-trimester screening of PE and IUGR could improve the overall detection rate of these disorders, but this hypothesis should be confirmed in larger cohorts of pregnant patients from various geographical areas.

Intronic variants of LGALS13 gene encoding placental protein (PP13) are linked with increased risk of infection-associated spontaneous preterm birth

PROBLEM

Spontaneous preterm birth (sPTB) is a global health issue. Studies suggest infection and infection-based inflammatory responses are major risk factors for sPTB. Considering the important role of anti-inflammatory proteins in pregnancy, the study aimed to find the association between anti-inflammatory LGALS13 gene variants IVS2-22 A/G (rs2233706) and IVS3+72 T/A (rs2233708) and the risk of sPTB during Chlamydia trachomatis, Mycoplasma hominis and Ureaplasma urealyticum infection in Indian population.

METHOD OF STUDY

Placental samples of 160 sPTB and 160 term women were collected. Pathogens were detected by PCR. The genotyping of LGALS13 gene variants IVS2-22 A/G (rs2233706) and IVS3+72 T/A (rs2233708) was done by qualitative real-time PCR using allelic discrimination method (VIC- and FAM-labeled).

RESULTS

The frequency of AG or GG genotype of LGALS13 IVS2-22A/G polymorphism (rs2233706) was 75.5% in infected sPTB cases and 14.4% in uninfected sPTB cases and 7.3% in term birth controls (p < .0001), while the frequency of TA or AA genotype of LGALS13 IVS3+72T/A polymorphism (rs2233708) was 83.6% in infected sPTB cases and 18% in uninfected sPTB cases and 12.7% in term birth controls (p < .0001). The genotypic frequencies for both the variants of LGALS13 were statistically significant (p < .0001) in the infected sPTB versus uninfected sPTB and term birth controls.

CONCLUSIONS

Study reveals strong association between the presence of immunological gene variants LGALS13 IVS2-22 A/G (rs2233706) and LGALS13 IVS3+72 T/A (rs2233708) and risk of sPTB during C. trachomatis, M. hominis and U. urealyticum infection.

From Biomarkers to the Molecular Mechanism of Preeclampsia-A Comprehensive Literature Review

Preeclampsia (PE) is a prevalent obstetric illness affecting pregnant women worldwide. This comprehensive literature review aims to examine the role of biomarkers and understand the molecular mechanisms underlying PE. The review encompasses studies on biomarkers for predicting, diagnosing, and monitoring PE, focusing on their molecular mechanisms in maternal blood or urine samples. Past research has advanced our understanding of PE pathogenesis, but the etiology remains unclear. Biomarkers such as PlGF, sFlt-1, PP-13, and PAPP-A have shown promise in risk classification and preventive measures, although challenges exist, including low detection rates and discrepancies in predicting different PE subtypes. Future perspectives highlight the importance of larger prospective studies to explore predictive biomarkers and their molecular mechanisms, improving screening efficacy and distinguishing between early-onset and late-onset PE. Biomarker assessments offer reliable and cost-effective screening methods for early detection, prognosis, and monitoring of PE. Early identification of high-risk women enables timely intervention, preventing adverse outcomes. Further research is needed to validate and optimize biomarker models for accurate prediction and diagnosis, ultimately improving maternal and fetal health outcomes.

Predictive Performance of Machine Learning-Based Methods for the Prediction of Preeclampsia-A Prospective Study

(1) Background: Preeclampsia (PE) prediction in the first trimester of pregnancy is a challenge for clinicians. The aim of this study was to evaluate and compare the predictive performances of machine learning-based models for the prediction of preeclampsia and its subtypes. (2) Methods: This prospective case-control study evaluated pregnancies that occurred in women who attended a tertiary maternity hospital in Romania between November 2019 and September 2022. The patients' clinical and paraclinical characteristics were evaluated in the first trimester and were included in four machine learning-based models: decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), and their predictive performance was assessed. (3) Results: Early-onset PE was best predicted by DT (accuracy: 94.1%) and SVM (accuracy: 91.2%) models, while NB (accuracy: 98.6%) and RF (accuracy: 92.8%) models had the highest performance when used to predict all types of PE. The predictive performance of these models was modest for moderate and severe types of PE, with accuracies ranging from 70.6% and 82.4%. (4) Conclusions: The machine learning-based models could be useful tools for EO-PE prediction and could differentiate patients who will develop PE as early as the first trimester of pregnancy.