The prediction of early preeclampsia: Results from a longitudinal proteomics study

Proteome-Based Maternal Plasma and Serum Biomarkers for Preeclampsia: A Systematic Review and Meta-Analysis

Proteomics has emerged as a transformative tool in biomedical research, enabling comprehensive characterization of protein profiles in complex biological systems. In preeclampsia (PE) research, quantitative proteomic analyses of plasma and serum have revealed critical insights into disease mechanisms and potential biomarkers. Through a systematic review of 17 studies (2009-2024), we identified 561 differentially expressed plasma/serum proteins (p < 0.05) in PE patients versus healthy controls, with 122 proteins consistently replicated across ≥2 independent studies. Stratified analysis by clinical subtype (early-vs. late-onset PE) demonstrated both concordant and divergent protein expression patterns, reflecting heterogeneity in PE pathophysiology, methodological variations (e.g., sample processing, proteomic platforms), and differences between discovery-phase and targeted validation studies. The trimester-specific biomarker panels proposed here offer a framework for future large-scale, multicenter validation. By integrating advanced proteomic technologies with standardized preanalytical and analytical protocols, these findings advance opportunities for early prediction (first-trimester biomarker signatures); mechanistic insight (complement system involvement); and personalized management (subtype-specific therapeutic targets). This work underscores the potential of proteomics to reshape PE research, from molecular discovery to clinical translation, ultimately improving outcomes for this leading cause of maternal and perinatal morbidity.

Maternal Plasma Proteins Associated with Birth Weight: A Longitudinal, Large Scale Proteomic Study

Small infants for gestational age (SGA) and large infants for gestational age (LGA) have increased risk of complications during delivery and later in life. Prediction of the fetal weight is currently limited to biometric parameters obtained by ultrasound scans that can be imprecise. Biomarkers of fetal growth would be crucial for tailoring clinical management and optimizing outcomes for the mother and child. Seventy pregnant women participated in the current study, including 58, 7, and 5 giving birth to adequate for gestational age (AGA), SGA, and LGA infants, respectively. Maternal venous blood was drawn at gestational weeks 12-19, 21-27, and 28-34 and quantified for nearly 5000 proteins on the SomaLogic platform. We used machine learning algorithms with leave-one-out cross-validation to construct multiprotein models for prediction of birth weight groups. Random forest models using only 20 predefined proteins (selected by moderated t tests) were able to predict LGA with good discrimination (AUC > 0.8) at all three visits, while prediction of SGA was less successful. Protein differential abundance analysis revealed 148 proteins with higher abundance in LGA compared to AGA pregnancies, while only four proteins were differentially abundant between the SGA and AGA. The principal findings indicate that the maternal plasma proteome may hold potential biomarkers of LGA.

Current Evidence of Maternal Infection With Chlamydia trachomatis and Preeclampsia Risk

Chlamydia trachomatis is the most common bacterial sexually transmitted infection (STI) in the United States. Ascending C. trachomatis can cause pelvic inflammatory disease (PID), potentially leading to subsequent infertility, ectopic pregnancy, and adverse pregnancy outcomes. There is growing evidence implicating infections (e.g., COVID-19, cytomegalovirus) in preeclampsia etiology, a maternal hypertensive disorder and leading cause of maternal morbidity and mortality. However, few studies have investigated the impact of STIs on preeclampsia risk. In this review, we provide an overview of the potential association between C. trachomatis and preeclampsia and identify future research needs through a critical evaluation of epidemiologic, in vitro, and in vivo studies. Unfortunately, current methodological limitations such as lower-quality study designs, selection bias, confounding bias, and variations in chlamydia diagnostic methods inhibit our understanding of the impact of C. trachomatis on preeclampsia. In addition, bench-side approaches such as animal models and in vitro studies have not elucidated the mechanisms linking C. trachomatis to preeclampsia. Understanding the biological pathways that could be disrupted by chlamydia is important as it may ultimately guide the development and use of novel therapeutics to augment standard antibiotic therapy to reduce pathology.

Prediction of late-onset preeclampsia using plasma proteomics: a longitudinal multi-cohort study

Preeclampsia is a pregnancy disorder with substantial perinatal and maternal morbidity and mortality. Pregnant women at risk of preeclampsia would benefit from early detection for follow-up, timely interventions and delivery. Several attempts have been made to identify protein biomarkers of preeclampsia, but findings vary with demographics, clinical characteristics, and time of sampling. In the current study, we combined three independent longitudinal pregnancy cohorts (Detroit, Stanford and Oslo) resulting in 124 late-onset preeclampsia (LOPE) cases and 178 gestational age matched controls, and analyzed > 1000 proteins in maternal plasma sampled between 12 and 34 weeks of gestation. Differential abundance analysis of combined protein data revealed increased deviation in protein abundance trajectories throughout gestation in women destined to develop LOPE compared to controls. There were no differentially abundant proteins at time interval T1 (12-19 weeks), yet 31 differentially abundant proteins were found at time interval T2 (19-27 weeks), and 48 proteins at time interval T3 (27- 34 weeks). Multi-protein random forest models assessed via cross-validation predicted LOPE with an area under the ROC curve of 0.72 (0.65-0.78), 0.76 (0.71-0.81) and 0.80 (0.75-0.85) at time interval T1, T2 and T3, respectively. The results at T3 were confirmed using a leave-one-cohort-out analysis suggesting cross-cohort consistency, and at T1 and T2 when the largest two cohorts were used as training sets.

The mediating role of steroid hormones in the relationship between bisphenol A and its alternatives bisphenol S and F exposure and preeclampsia

Steroid hormone imbalance is believed to increase the odds of developing PE. Bisphenol A (BPA) and its substitutes (e.g., bisphenol S (BPS) and bisphenol F (BPF)) have estrogen-like effects, and its exposure may be related to the development of preeclampsia (PE). To explore the effects of bisphenol exposure on maternal serum steroid hormones and the potential mediating role of steroid hormones in the association between bisphenol exposure and developing PE, concentrations of bisphenols and steroid hormones in serum samples of 383 pregnant women were examined before delivery (including 160 PE cases and 223 control cases). Multivariable logistic and linear models were used to explore the associations of maternal serum bisphenols concentrations with both maternal steroid hormones and PE risk. Mediation modeling was employed to evaluate the mediating role of steroid hormones in the association between bisphenols and PE. Results showed that maternal serum BPS concentrations were positively associated with testosterone (T) concentrations. The mediation analyses suggested that approximately 10.17 % of the associations between BPS concentrations and the development of PE might be mediated by maternal T. In conclusion, maternal exposure to BPS during pregnancy is linked to higher maternal T concentrations, which might increase the odds of developing PE. T might mediate the association between BPS exposure and the development of PE.

Protein biomarker signatures of preeclampsia - a longitudinal 5000-multiplex proteomics study

We aimed to explore novel biomarker candidates and biomarker signatures of late-onset preeclampsia (LOPE) by profiling samples collected in a longitudinal discovery cohort with a high-throughput proteomics platform. Using the Somalogic 5000-plex platform, we analyzed proteins in plasma samples collected at three visits (gestational weeks (GW) 12-19, 20-26 and 28-34 in 35 women with LOPE (birth ≥ 34 GW) and 70 healthy pregnant women). To identify biomarker signatures, we combined Elastic Net with Stability Selection for stable variable selection and validated their predictive performance in a validation cohort. The biomarker signature with the highest predictive performance (AUC 0.88 (95% CI 0.85-0.97)) was identified in the last trimester of pregnancy (GW 28-34) and included the Fatty acid amid hydrolase 2 (FAAH2), HtrA serine peptidase 1 (HTRA1) and Interleukin-17 receptor C (IL17RC) together with sFLT1 and maternal age, BMI and nulliparity. Our biomarker signature showed increased or similar predictive performance to the sFLT1/PGF-ratio within our data set, and we were able to validate the biomarker signature in a validation cohort (AUC ≥ 0.90). Further validation of these candidates should be performed using another protein quantification platform in an independent cohort where the negative and positive predictive values can be validly calculated.

First-Trimester Preeclampsia-Induced Disturbance in Maternal Blood Serum Proteome: A Pilot Study

Preeclampsia (PE) is a complex and multifaceted obstetric syndrome characterized by several distinct molecular subtypes. It complicates up to 5% of pregnancies and significantly contributes to maternal and newborn morbidity, thereby diminishing the long-term quality of life for affected women. Due to the widespread dissatisfaction with the effectiveness of existing approaches for assessing PE risk, there is a pressing need for ongoing research to identify newer, more accurate predictors. This study aimed to investigate early changes in the maternal serum proteome and associated signaling pathways. The levels of 125 maternal serum proteins at 11-13 weeks of gestation were quantified using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM MS) with the BAK-125 kit. Ten serum proteins emerged as potential early markers for PE: Apolipoprotein M (APOM), Complement C1q subcomponent subunit B (C1QB), Lysozyme (LYZ), Prothrombin (F2), Albumin (ALB), Zinc-alpha-2-glycoprotein (AZGP1), Tenascin-X (TNXB), Alpha-1-antitrypsin (SERPINA1), Attractin (ATRN), and Apolipoprotein A-IV (APOA4). Notably, nine of these proteins have previously been associated with PE in prior research, underscoring the consistency and reliability of our findings. These proteins play key roles in critical molecular processes, including complement and coagulation cascades, platelet activation, and insulin-like growth factor pathways. To improve the early prediction of PE, a highly effective Support Vector Machine (SVM) model was developed, analyzing 19 maternal serum proteins from the first trimester. This model achieved an area under the curve (AUC) of 0.91, with 87% sensitivity and 95% specificity, and a hazard ratio (HR) of 13.5 (4.6-40.8) with p < 0.001. These findings demonstrate that serum protein-based SVM models possess significantly higher predictive power compared to the routine first-trimester screening test, highlighting their superior utility in the early detection and risk stratification of PE.

Proteomic Profiles of Maternal Plasma Extracellular Vesicles for Prediction of Preeclampsia

PROBLEM

Preeclampsia is a heterogeneous syndrome of diverse etiologies and molecular pathways leading to distinct clinical subtypes. Herein, we aimed to characterize the extracellular vesicle (EV)-associated and soluble fractions of the maternal plasma proteome in patients with preeclampsia and to assess their value for disease prediction.

METHOD OF STUDY

This case-control study included 24 women with term preeclampsia, 23 women with preterm preeclampsia, and 94 healthy pregnant controls. Blood samples were collected from cases on average 7 weeks before the diagnosis of preeclampsia and were matched to control samples. Soluble and EV fractions were separated from maternal plasma; EVs were confirmed by cryo-EM, NanoSight, and flow cytometry; and 82 proteins were analyzed with bead-based, multiplexed immunoassays. Quantile regression analysis and random forest models were implemented to evaluate protein concentration differences and their predictive accuracy. Preeclampsia subgroups defined by molecular profiles were identified by hierarchical cluster analysis. Significance was set at p < 0.05 or false discovery rate-adjusted q < 0.1.

RESULTS

In preterm preeclampsia, PlGF, PTX3, and VEGFR-1 displayed differential abundance in both soluble and EV fractions, whereas angiogenin, CD40L, endoglin, galectin-1, IL-27, CCL19, and TIMP1 were changed only in the soluble fraction (q < 0.1). The direction of changes in the EV fraction was consistent with that in the soluble fraction for nine proteins. In term preeclampsia, CCL3 had increased abundance in both fractions (q < 0.1). The combined EV and soluble fraction proteomic profiles predicted preterm and term preeclampsia with an AUC of 78% (95% CI, 66%-90%) and 68% (95% CI, 56%-80%), respectively. Three clusters of preeclampsia featuring distinct clinical characteristics and placental pathology were identified based on combined protein data.

CONCLUSIONS

Our findings reveal distinct alterations of the maternal EV-associated and soluble plasma proteome in preterm and term preeclampsia and identify molecular subgroups of patients with distinct clinical and placental histopathologic features.

Large-Scale Proteomics in Early Pregnancy and Hypertensive Disorders of Pregnancy

Importance

There is no consensus regarding the best method for prediction of hypertensive disorders of pregnancy (HDP), including gestational hypertension and preeclampsia.

Objective

To determine predictive ability in early pregnancy of large-scale proteomics for prediction of HDP.

Design, Setting, and Participants

This was a nested case-control study, conducted in 2022 to 2023, using clinical data and plasma samples collected between 2010 and 2013 during the first trimester, with follow-up until pregnancy outcome. This multicenter observational study took place at 8 academic medical centers in the US. Nulliparous individuals during first-trimester clinical visits were included. Participants with HDP were selected as cases; controls were selected from those who delivered at or after 37 weeks without any HDP, preterm birth, or small-for-gestational-age infant. Age, self-reported race and ethnicity, body mass index, diabetes, health insurance, and fetal sex were available covariates.

Exposures

Proteomics using an aptamer-based assay that included 6481 unique human proteins was performed on stored plasma. Covariates were used in predictive models.

Main Outcomes and Measures

Prediction models were developed using the elastic net, and analyses were performed on a randomly partitioned training dataset comprising 80% of study participants, with the remaining 20% used as an independent testing dataset. Primary measure of predictive performance was area under the receiver operating characteristic curve (AUC).

Results

This study included 753 HDP cases and 1097 controls with a mean (SD) age of 26.9 (5.5) years. Maternal race and ethnicity were 51 Asian (2.8%), 275 non-Hispanic Black (14.9%), 275 Hispanic (14.9%), 1161 non-Hispanic White (62.8% ), and 88 recorded as other (4.8%), which included those who did not identify according to these designations. The elastic net model, allowing for forced inclusion of prespecified covariates, was used to adjust protein-based models for clinical and demographic variables. Under this approach, no proteins were selected to augment the clinical and demographic covariates. The predictive performance of the resulting model was modest, with a training set AUC of 0.64 (95% CI, 0.61-0.67) and a test set AUC of 0.62 (95% CI, 0.56-0.68). Further adjustment for study site yielded only minimal changes in AUCs.

Conclusions and Relevance

In this case-control study with detailed clinical data and stored plasma samples available in the first trimester, an aptamer-based proteomics panel did not meaningfully add to predictive utility over and above clinical and demographic factors that are routinely available.

Five-Feature Models to Predict Preeclampsia Onset Time From Electronic Health Record Data: Development and Validation Study

BACKGROUND

 Preeclampsia is a potentially fatal complication during pregnancy, characterized by high blood pressure and the presence of excessive proteins in the urine. Due to its complexity, the prediction of preeclampsia onset is often difficult and inaccurate.

OBJECTIVE

 This study aimed to create quantitative models to predict the onset gestational age of preeclampsia using electronic health records.

METHODS

 We retrospectively collected 1178 preeclamptic pregnancy records from the University of Michigan Health System as the discovery cohort, and 881 records from the University of Florida Health System as the validation cohort. We constructed 2 Cox-proportional hazards models: 1 baseline model using maternal and pregnancy characteristics, and the other full model with additional laboratory findings, vitals, and medications. We built the models using 80% of the discovery data, tested the remaining 20% of the discovery data, and validated with the University of Florida data. We further stratified the patients into high- and low-risk groups for preeclampsia onset risk assessment.

RESULTS

 The baseline model reached Concordance indices of 0.64 and 0.61 in the 20% testing data and the validation data, respectively, while the full model increased these Concordance indices to 0.69 and 0.61, respectively. For preeclampsia diagnosed at 34 weeks, the baseline and full models had area under the curve (AUC) values of 0.65 and 0.70, and AUC values of 0.69 and 0.70 for preeclampsia diagnosed at 37 weeks, respectively. Both models contain 5 selective features, among which the number of fetuses in the pregnancy, hypertension, and parity are shared between the 2 models with similar hazard ratios and significant P values. In the full model, maximum diastolic blood pressure in early pregnancy was the predominant feature.

CONCLUSIONS

 Electronic health records data provide useful information to predict the gestational age of preeclampsia onset. Stratification of the cohorts using 5-predictor Cox-proportional hazards models provides clinicians with convenient tools to assess the onset time of preeclampsia in patients.

Different Proteomic Profiles Regarding Antihypertensive Therapy in Preeclampsia Pregnant

Preeclampsia (PE) is a hypertensive pregnancy syndrome associated with target organ damage and increased cardiovascular risks, necessitating antihypertensive therapy. However, approximately 40% of patients are nonresponsive to treatment, which results in worse clinical outcomes. This study aimed to compare circulating proteomic profiles and identify differentially expressed proteins among 10 responsive (R-PE), 10 nonresponsive (NR-PE) patients, and 10 healthy pregnant controls (HP). We also explored correlations between these proteins and clinical data. Plasma protein relative quantification was performed using mass spectrometry, followed by bioinformatics analyses with the UniProt database, PatternLab for Proteomics 4.0, and MetaboAnalyst software (version 6.0). Considering a fold change of 1.5, four proteins were differentially expressed between NR-PE and R-PE: one upregulated (fibronectin) and three downregulated (pregnancy-specific beta-1-glycoprotein 1, complement C4B, and complement C4A). Between NR-PE and HP, six proteins were differentially expressed: two upregulated (clusterin and plasmin heavy chain A) and four downregulated (apolipoprotein L1, heparin cofactor II, complement C4B, and haptoglobin-related protein). Three proteins were differentially expressed between R-PE and HP: one downregulated (transthyretin) and two upregulated (apolipoprotein C1 and hemoglobin subunit beta). These findings suggest a complex interplay of these proteins involved in inflammatory, immune, and metabolic processes with antihypertensive therapy responsiveness and PE pathophysiology.

Machine learning: a new era for cardiovascular pregnancy physiology and cardio-obstetrics research

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.

Large-scale proteomics in the first trimester of pregnancy predict psychopathology and temperament in preschool children: an exploratory study

BACKGROUND

Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes that contribute to vulnerability for the emergence of psychopathology. Most studies, however, have focused on a handful of proinflammatory cytokines and have not explored a range of prenatal biological pathways that may be involved in increasing postnatal risk for emotional and behavioral difficulties.

METHODS

Using extreme gradient boosted machine learning models, we explored large-scale proteomics, considering over 1,000 proteins from first trimester blood samples, to predict behavior in early childhood. Mothers reported on their 3- to 5-year-old children's (N = 89, 51% female) temperament (Child Behavior Questionnaire) and psychopathology (Child Behavior Checklist).

RESULTS

We found that machine learning models of prenatal proteomics predict 5%-10% of the variance in children's sadness, perceptual sensitivity, attention problems, and emotional reactivity. Enrichment analyses identified immune function, nervous system development, and cell signaling pathways as being particularly important in predicting children's outcomes.

CONCLUSIONS

Our findings, though exploratory, suggest processes in early pregnancy that are related to functioning in early childhood. Predictive features included far more proteins than have been considered in prior work. Specifically, proteins implicated in inflammation, in the development of the central nervous system, and in key cell-signaling pathways were enriched in relation to child temperament and psychopathology measures.

Upregulation of Siglec-6 induces mitochondrial dysfunction by promoting GPR20 expression in early-onset preeclampsia

BACKGROUND

Preeclampsia, especially early-onset preeclampsia (EO-PE), is a pregnancy complication that has serious consequences for the health of both the mother and the fetus. Although abnormal placentation due to mitochondrial dysfunction is speculated to contribute to the development of EO-PE, the underlying mechanisms have yet to be fully elucidated.

METHODS

The expression and localization of Siglec-6 in the placenta from normal pregnancies, preterm birth and EO-PE patients were examined by RT-qPCR, Western blot and IHC. Transwell assays were performed to evaluate the effect of Siglec-6 on trophoblast cell migration and invasion. Seahorse experiments were conducted to assess the impact of disrupting Siglec-6 expression on mitochondrial function. Co-IP assay was used to examine the interaction of Siglec-6 with SHP1/SHP2. RNA-seq was employed to investigate the mechanism by which Siglec-6 inhibits mitochondrial function in trophoblast cells.

RESULTS

The expression of Siglec-6 in extravillous trophoblasts is increased in placental tissues from EO-PE patients. Siglec-6 inhibits trophoblast cell migration and invasion and impairs mitochondrial function. Mechanismly, Siglec-6 inhibits the activation of NF-κB by recruiting SHP1/SHP2, leading to increased expression of GPR20. Notably, the importance of GPR20 function downstream of Siglec-6 in trophoblasts is supported by the observation that GPR20 downregulation rescues defects caused by Siglec-6 overexpression. Finally, overexpression of Siglec-6 in the placenta induces a preeclampsia-like phenotype in a pregnant mouse model.

CONCLUSIONS

This study indicates that the regulatory pathway Siglec-6/GPR20 has a crucial role in regulating trophoblast mitochondrial function, and we suggest that Siglec-6 and GPR20 could serve as potential markers and targets for the clinical diagnosis and therapy of EO-PE.

The vaginal immunoproteome for the prediction of spontaneous preterm birth: A retrospective longitudinal study

Background

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. Most cases of preterm birth occur spontaneously and result from preterm labor with intact (spontaneous preterm labor [sPTL]) or ruptured (preterm prelabor rupture of membranes [PPROM]) membranes. The prediction of spontaneous preterm birth (sPTB) remains underpowered due to its syndromic nature and the dearth of independent analyses of the vaginal host immune response. Thus, we conducted the largest longitudinal investigation targeting vaginal immune mediators, referred to herein as the immunoproteome, in a population at high risk for sPTB.

Methods

Vaginal swabs were collected across gestation from pregnant women who ultimately underwent term birth, sPTL, or PPROM. Cytokines, chemokines, growth factors, and antimicrobial peptides in the samples were quantified via specific and sensitive immunoassays. Predictive models were constructed from immune mediator concentrations.

Results

Throughout uncomplicated gestation, the vaginal immunoproteome harbors a cytokine network with a homeostatic profile. Yet, the vaginal immunoproteome is skewed toward a pro-inflammatory state in pregnant women who ultimately experience sPTL and PPROM. Such an inflammatory profile includes increased monocyte chemoattractants, cytokines indicative of macrophage and T-cell activation, and reduced antimicrobial proteins/peptides. The vaginal immunoproteome has improved predictive value over maternal characteristics alone for identifying women at risk for early (<34 weeks) sPTB.

Conclusions

The vaginal immunoproteome undergoes homeostatic changes throughout gestation and deviations from this shift are associated with sPTB. Furthermore, the vaginal immunoproteome can be leveraged as a potential biomarker for early sPTB, a subset of sPTB associated with extremely adverse neonatal outcomes.

Funding

This research was conducted by the Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services (NICHD/NIH/DHHS) under contract HHSN275201300006C. ALT, KRT, and NGL were supported by the Wayne State University Perinatal Initiative in Maternal, Perinatal and Child Health.

Exosomal small RNA profiling in first-trimester maternal blood explores early molecular pathways of preterm preeclampsia

Introduction

Preeclampsia (PE) is a severe obstetrical syndrome characterized by new-onset hypertension and proteinuria and it is often associated with fetal intrauterine growth restriction (IUGR). PE leads to long-term health complications, so early diagnosis would be crucial for timely prevention. There are multiple etiologies and subtypes of PE, and this heterogeneity has hindered accurate identification in the presymptomatic phase. Recent investigations have pointed to the potential role of small regulatory RNAs in PE, and these species, which travel in extracellular vesicles (EVs) in the circulation, have raised the possibility of non-invasive diagnostics. The aim of this study was to investigate the behavior of exosomal regulatory small RNAs in the most severe subtype of PE with IUGR.

Methods

We isolated exosomal EVs from first-trimester peripheral blood plasma samples of women who later developed preterm PE with IUGR (n=6) and gestational age-matched healthy controls (n=14). The small RNA content of EVs and their differential expression were determined by next-generation sequencing and further validated by quantitative real-time PCR. We also applied the rigorous exceRpt bioinformatics pipeline for small RNA identification, followed by target verification and Gene Ontology analysis.

Results

Overall, >2700 small RNAs were identified in all samples and, of interest, the majority belonged to the RNA interference (RNAi) pathways. Among the RNAi species, 16 differentially expressed microRNAs were up-regulated in PE, whereas up-regulated and down-regulated members were equally found among the six identified Piwi-associated RNAs. Gene ontology analysis of the predicted small RNA targets showed enrichment of genes in pathways related to immune processes involved in decidualization, placentation and embryonic development, indicating that dysregulation of the induced small RNAs is connected to the impairment of immune pathways in preeclampsia development. Finally, the subsequent validation experiments revealed that the hsa_piR_016658 piRNA is a promising biomarker candidate for preterm PE associated with IUGR.

Discussion

Our rigorously designed study in a homogeneous group of patients unraveled small RNAs in circulating maternal exosomes that act on physiological pathways dysregulated in preterm PE with IUGR. Therefore, our small RNA hits are not only suitable biomarker candidates, but the revealed biological pathways may further inform us about the complex pathology of this severe PE subtype.

Classification of preeclampsia according to molecular clusters with the goal of achieving personalized prevention

The prevention of pre-eclampsia is difficult due to the syndromic nature and multiple underlying mechanisms of this severe complication of pregnancy. The current clinical distinction between early- and late-onset disease, although clinically useful, does not reflect the true nature and complexity of the pathologic processes leading to pre-eclampsia. The current gaps in knowledge on the heterogeneous molecular pathways of this syndrome and the lack of adequate, specific diagnostic methods are major obstacles to early screening and tailored preventive strategies. The development of novel diagnostic tools for detecting the activation of the identified disease pathways would enable early, accurate screening and personalized preventive therapies. We implemented a holistic approach that includes the utilization of different proteomic profiling methods of maternal plasma samples collected from various ethnic populations and the application of systems biology analysis to plasma proteomic, maternal demographic, clinical characteristic, and placental histopathologic data. This approach enabled the identification of four molecular subclasses of pre-eclampsia in which distinct and shared disease mechanisms are activated. The current review summarizes the results and conclusions from these studies and the research and clinical implications of our findings.

Molecular epidemiology of pregnancy using omics data: advances, success stories, and challenges

Multi-omics approaches have been successfully applied to investigate pregnancy and health outcomes at a molecular and genetic level in several studies. As omics technologies advance, research areas are open to study further. Here we discuss overall trends and examples of successfully using omics technologies and techniques (e.g., genomics, proteomics, metabolomics, and metagenomics) to investigate the molecular epidemiology of pregnancy. In addition, we outline omics applications and study characteristics of pregnancy for understanding fundamental biology, causal health, and physiological relationships, risk and prediction modeling, diagnostics, and correlations.

Epigenetic phenotype of plasma cell-free DNA in the prediction of early-onset preeclampsia

BACKGROUND

In the current study, we sought to characterise the methylation haplotypes and nucleosome positioning patterns of placental DNA and plasma cell-free DNA of pregnant women with early-onset preeclampsia using whole genome bisulphite sequencing (WGBS) and methylation capture bisulphite sequencing (MCBS) and further develop and examine the diagnostic performance of a generalised linear model (GLM) by incorporating the epigenetic features for early-onset preeclampsia.

METHODS

This case-control study recruited pregnant women aged at least 18 years who delivered their babies at our Hospital. In addition, non-pregnant women with no previous history of diseases were included. Placental samples of the villous parenchyma were taken at the time of delivery and venous blood was drawn from pregnant women during non-invasive prenatal testing at 12-15 weeks of pregnancy and nonpregnant women during the physical check-up. WGBS and MCBS were carried out of extracted genomic DNA. Then, we established the GLM by incorporating preeclampsia-specific methylation haplotypes and nucleosome positioning patterns and examined the diagnostic performance of the model by receiver operating characteristic (ROC) curve analysis.

RESULTS

The study included 135 pregnant women and 50 non-pregnant women. Our high-depth MCBS revealed notably different DNA methylation and nucleosome positioning patterns between women with and without preeclampsia. Preeclampsia-specific hypermethylated sites were found predominantly in the promoter regions and particularly enriched in CTCF on the X chromosome. Totally, 2379 preeclampsia-specific methylation haplotypes were found across the entire genome. ROC analysis showed that the area under the ROC curve (AUC) was 0.938 (95%CI 0.877, 1.000). At a GLM cut-off of 0.341, the AUC was the maximum, with a sensitivity of 95.6% and a specificity of 89.7%.

CONCLUSION

Pregnant women with early-onset preeclampsia exhibit DNA methylation and nucleosome positioning patterns in placental and plasma DNA.

Metabolic dynamics and prediction of sFGR and adverse fetal outcomes: a prospective longitudinal cohort study

BACKGROUND

Selective fetal growth restriction (sFGR) is an extreme complication that significantly increases the risk of perinatal mortality and long-term adverse neurological outcomes in offspring, affecting approximately 15% of monochorionic diamniotic (MCDA) twin pregnancies. The lack of longitudinal cohort studies hinders the early prediction and intervention of sFGR.

METHODS

We constructed a prospective longitudinal cohort study of sFGR, and quantified 25 key metabolites in 337 samples from maternal plasma in the first, second, and third trimester and from cord plasma. In particular, our study examined fetal growth and brain injury data from ultrasonography and used the Ages and Stages Questionnaire-third edition subscale (ASQ-3) to evaluate the long-term neurocognitive behavioral development of infants aged 2-3 years. Furthermore, we correlated metabolite levels with ultrasound data, including physical development and brain injury indicators, and ASQ-3 data using Spearman's-based correlation tests. In addition, special combinations of differential metabolites were used to construct predictive models for the occurrence of sFGR and fetal brain injury.

RESULTS

Our findings revealed various dynamic patterns for these metabolites during pregnancy and a maximum of differential metabolites between sFGR and MCDA in the second trimester (n = 8). The combination of L-phenylalanine, L-leucine, and L-isoleucine in the second trimester, which were closely related to fetal growth indicators, was highly predictive of sFGR occurrence (area under the curve [AUC]: 0.878). The combination of L-serine, L-histidine, and L-arginine in the first trimester and creatinine in the second trimester was correlated with long-term neurocognitive behavioral development and showed the capacity to identify fetal brain injury with high accuracy (AUC: 0.94).

CONCLUSIONS

The performance of maternal plasma metabolites from the first and second trimester is superior to those from the third trimester and cord plasma in discerning sFGR and fetal brain injury. These metabolites may serve as useful biomarkers for early prediction and promising targets for early intervention in clinical settings.

Use of Traditional and Proteomic Methods in the Assessment of a Preclinical Model of Preeclampsia

Recent studies have demonstrated downregulation of breast cancer resistance protein (BCRP/ABCG2) in placenta obtained from women with preeclampsia (PE). BCRP is highly expressed in placenta and plays an important role in preventing xenobiotics from entering the fetal compartment. Although PE is often therapeutically managed with drugs that are substrates of BCRP, there are limited studies on the impact of PE on fetal drug exposure. Due to ethical concerns, use of preclinical models is an important approach. Thus, by using proteomic and traditional methods, we characterized transporter changes in an immunologic rat model of PE to determine its utility and predictive value for future drug disposition studies. PE was induced by daily administration of low-dose endotoxin (0.01-0.04 mg/kg) to rats on gestational days (GD) 13-16, urine was collected, and rats were sacrificed on GD17 or GD18. PE rats shared similar phenotype to PE patients, including proteinuria, and increased levels of tumor necrosis factor α and interleukin 6. Transcript and protein levels of Bcrp were significantly downregulated in placenta of PE rats on GD18. multidrug resistance 1a, multidrug resistance 1b, and organic anion transporting polypeptide 2B1 mRNA were also decreased in PE. Proteomics revealed activation of various hallmarks of PE including immune activation, oxidative stress, endoplasmic reticulum stress and apoptosis. Overall, our results demonstrated that the immunologic PE rat model exhibits numerous similarities to human PE along with dysregulation of placental transporters. Therefore, this model may be useful in examining the impact of PE on the maternal and fetal disposition of BCRP substrates. SIGNIFICANCE STATEMENT: Fully characterizing preclinical models of disease is necessary to determine their validity to human conditions. Combining traditional and proteomic methods of model characterization, we identified numerous phenotypic similarities between our model of preeclampsia and human disease. The alignment with human pathophysiological changes allows for more confident use of this preclinical model.

Preterm preeclampsia screening using biomarkers: combining phenotypic classifiers into robust prediction models

BACKGROUND

Preeclampsia screening is a critical component of antenatal care worldwide. Currently, the most developed screening test for preeclampsia at 11 to 13 weeks' gestation integrates maternal demographic characteristics and medical history with 3 biomarkers-serum placental growth factor, mean arterial pressure, and uterine artery pulsatility index-to identify approximately 75% of women who develop preterm preeclampsia with delivery before 37 weeks of gestation. It is generally accepted that further improvements to preeclampsia screening require the use of additional biomarkers. We recently reported that the levels of specific metabolites and metabolite ratios are associated with preterm preeclampsia. Notably, for several of these markers, preterm preeclampsia prediction varied according to maternal body mass index class. These findings motivated us to study whether patient classification allowed for combining metabolites with the current biomarkers more effectively to improve prediction of preterm preeclampsia.

OBJECTIVE

This study aimed to investigate whether metabolite biomarkers can improve biomarker-based preterm preeclampsia prediction in 3 screening resource scenarios according to the availability of: (1) placental growth factor, (2) placental growth factor+mean arterial pressure, and (3) placental growth factor+mean arterial pressure+uterine artery pulsatility index.

STUDY DESIGN

This was an observational case-control study, drawn from a large prospective screening study at 11 to 13 weeks' gestation on the prediction of pregnancy complications, conducted at King's College Hospital, London, United Kingdom. Maternal blood samples were also collected for subsequent research studies. We used liquid chromatography-mass spectrometry to quantify levels of 50 metabolites previously associated with pregnancy complications in plasma samples from singleton pregnancies. Biomarker data, normalized using multiples of medians, on 1635 control and 106 preterm preeclampsia pregnancies were available for model development. Modeling was performed using a methodology that generated a prediction model for preterm preeclampsia in 4 consecutive steps: (1) z-normalization of predictors, (2) combinatorial modeling of so-called (weak) classifiers in the unstratified patient set and in discrete patient strata based on body mass index and/or race, (3) selection of classifiers, and (4) aggregation of the selected classifiers (ie, bagging) into the final prediction model. The prediction performance of models was evaluated using the area under the receiver operating characteristic curve, and detection rate at 10% false-positive rate.

RESULTS

First, the predictor development methodology itself was evaluated. The patient set was split into a training set (2/3) and a test set (1/3) for predictor model development and internal validation. A prediction model was developed for each of the 3 different predictor panels, that is, placental growth factor+metabolites, placental growth factor+mean arterial pressure+metabolites, and placental growth factor+mean arterial pressure+uterine artery pulsatility index+metabolites. For all 3 models, the area under the receiver operating characteristic curve in the test set did not differ significantly from that of the training set. Next, a prediction model was developed using the complete data set for the 3 predictor panels. Among the 50 metabolites available for modeling, 26 were selected across the 3 prediction models; 21 contributed to at least 2 out of the 3 prediction models developed. Each time, area under the receiver operating characteristic curve and detection rate were significantly higher with the new prediction model than with the reference model. Markedly, the estimated detection rate with the placental growth factor+mean arterial pressure+metabolites prediction model in all patients was 0.58 (95% confidence interval, 0.49-0.70), a 15% increase (P<.001) over the detection rate of 0.43 (95% confidence interval, 0.33-0.55) estimated for the reference placental growth factor+mean arterial pressure. The same prediction model significantly improved detection in Black (14%) and White (19%) patients, and in the normal-weight group (18.5≤body mass index<25) and the obese group (body mass index≥30), with respectively 19% and 20% more cases detected, but not in the overweight group, when compared with the reference model. Similar improvement patterns in detection rates were found in the other 2 scenarios, but with smaller improvement amplitudes.

CONCLUSION

Metabolite biomarkers can be combined with the established biomarkers of placental growth factor, mean arterial pressure, and uterine artery pulsatility index to improve the biomarker component of early-pregnancy preterm preeclampsia prediction tests. Classification of the pregnant women according to the maternal characteristics of body mass index and/or race proved instrumental in achieving improved prediction. This suggests that maternal phenotyping can have a role in improving the prediction of obstetrical syndromes such as preeclampsia.

Can single-cell and spatial omics unravel the pathophysiology of pre-eclampsia?

Pre-eclampsia is a leading cause of maternal and fetal morbidity and mortality. Characterised by the onset of hypertension and proteinuria in the second half of pregnancy, it can lead to maternal end-organ injury such as cerebral ischemia and oedema, pulmonary oedema and renal failure, and potentially fatal outcomes for both mother and fetus. The causes of the different maternal end-organ phenotypes of pre-eclampsia and why some women develop pre-eclampsia condition early in pregnancy have yet to be elucidated. Omics methods include proteomics, genomics, metabolomics, transcriptomics. These omics techniques, previously mostly used on bulk tissue and individually, are increasingly available at a single cellular level and can be combined with each other. Multi-omics techniques on a single-cell or spatial level provide us with a powerful tool to understand the pathophysiology of pre-eclampsia. This review will explore the status of omics methods and how they can and could contribute to understanding the pathophysiology of pre-eclampsia.

Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine

BACKGROUND

Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures.

OBJECTIVE

This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome.

STUDY DESIGN

Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins.

RESULTS

A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42-0.79; P=3.5×10^-6) than the serum signature (R=0.45; confidence interval, 0.18-0.66; P=2.2×10^-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34-0.75; P=4.8×10^-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27-0.72; P=2.6×10^-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins.

CONCLUSION

Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Maternal Serum Activin A, Inhibin A and Follistatin-Related Proteins across Preeclampsia: Insights into Their Role in Pathogenesis and Prediction

BACKGROUND

Within the endocrine-paracrine signalling network at the maternal-foetal interface, the activin-inhibin-follistatin system modulates extravillous trophoblast invasion, suggesting a potential role in preeclampsia pathogenesis. This study aimed to compile the evidence published in the last decade regarding the variation in maternal serum activins, inhibin- and follistatin-related proteins in preeclamptic pregnancies compared to healthy pregnancies, and to discuss their role in predicting and understanding the pathophysiology of preeclampsia.

MATERIAL AND METHODS

A scoping review was conducted in MEDLINE, EMBASE and LILACS databases to identify studies published within the last ten years (2012-2022).

RESULTS

Thirty studies were included. None of the studies addressed maternal serum changes of isoforms different from activin A, inhibin A, follistatin, and follistatin-like 3. Sixteen studies evaluated the potential of these isoforms in predicting preeclampsia through the area under the curve from a receiver operating characteristic curve.

CONCLUSIONS

In preeclampsia, inhibin A is upregulated in all trimesters, whereas activin A increases exclusively in the late second and third trimesters. Serum follistatin levels are reduced in women with preeclampsia during the late second and third trimesters. However, changes in follistatin-like 3 remain inconclusive. Inhibin A and activin A can potentially serve as biomarkers of early-onset preeclampsia based on the outcomes of the receiver operating characteristic curve analysis. Further investigations are encouraged to explore the feasibility of quantifying maternal serum levels of activin A and inhibin A as a clinical tool in early preeclampsia prediction.

Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications

Introduction

Machine learning (ML) corresponds to a wide variety of methods that use mathematics, statistics and computational science to learn from multiple variables simultaneously. By means of pattern recognition, ML methods are able to find hidden correlations and accomplish accurate predictions regarding different conditions. ML has been successfully used to solve varied problems in different areas of science, such as psychology, economics, biology and chemistry. Therefore, we wondered how far it has penetrated into the field of obstetrics and gynecology.

Aim

To describe the state of art regarding the use of ML in the context of pregnancy diseases and complications.

Methodology

Publications were searched in PubMed, Web of Science and Google Scholar. Seven subjects of interest were considered: gestational diabetes mellitus, preeclampsia, perinatal death, spontaneous abortion, preterm birth, cesarean section, and fetal malformations.

Current state

ML has been widely applied in all the included subjects. Its uses are varied, the most common being the prediction of perinatal disorders. Other ML applications include (but are not restricted to) biomarker discovery, risk estimation, correlation assessment, pharmacological treatment prediction, drug screening, data acquisition and data extraction. Most of the reviewed articles were published in the last five years. The most employed ML methods in the field are non-linear. Except for logistic regression, linear methods are rarely used.

Future challenges

To improve data recording, storage and update in medical and research settings from different realities. To develop more accurate and understandable ML models using data from cutting-edge instruments. To carry out validation and impact analysis studies of currently existing high-accuracy ML models.

Conclusion

The use of ML in pregnancy diseases and complications is quite recent, and has increased over the last few years. The applications are varied and point not only to the diagnosis, but also to the management, treatment, and pathophysiological understanding of perinatal alterations. Facing the challenges that come with working with different types of data, the handling of increasingly large amounts of information, the development of emerging technologies, and the need of translational studies, it is expected that the use of ML continue growing in the field of obstetrics and gynecology.

Pregnancy-specific responses to COVID-19 revealed by high-throughput proteomics of human plasma

BACKGROUND

Pregnant women are at greater risk of adverse outcomes, including mortality, as well as obstetrical complications resulting from COVID-19. However, pregnancy-specific changes that underlie such worsened outcomes remain unclear.

METHODS

Plasma samples were collected from pregnant women and non-pregnant individuals (male and female) with (n = 72 pregnant, 52 non-pregnant) and without (n = 29 pregnant, 41 non-pregnant) COVID-19. COVID-19 patients were grouped as asymptomatic, mild, moderate, severe, or critically ill according to NIH classifications. Proteomic profiling of 7,288 analytes corresponding to 6,596 unique protein targets was performed using the SOMAmer platform.

RESULTS

Herein, we profile the plasma proteome of pregnant and non-pregnant COVID-19 patients and controls and show alterations that display a dose-response relationship with disease severity; yet, such proteomic perturbations are dampened during pregnancy. In both pregnant and non-pregnant state, the proteome response induced by COVID-19 shows enrichment of mediators implicated in cytokine storm, endothelial dysfunction, and angiogenesis. Shared and pregnancy-specific proteomic changes are identified: pregnant women display a tailored response that may protect the conceptus from heightened inflammation, while non-pregnant individuals display a stronger response to repel infection. Furthermore, the plasma proteome can accurately identify COVID-19 patients, even when asymptomatic or with mild symptoms.

CONCLUSION

This study represents the most comprehensive characterization of the plasma proteome of pregnant and non-pregnant COVID-19 patients. Our findings emphasize the distinct immune modulation between the non-pregnant and pregnant states, providing insight into the pathogenesis of COVID-19 as well as a potential explanation for the more severe outcomes observed in pregnant women.

Activin A and pathologies of pregnancy: a review

Activin A is a two-subunit protein belonging to the transforming growth factor β superfamily. First discovered almost three decades ago, it has since been implicated in diverse physiological roles, ranging from wound repair to reproduction. After 30 years of research, altered activin A levels are now understood to be associated with the development of various diseases, making activin A a potential therapeutic target. In pregnancy, the placenta and fetal membranes are major producers of activin A, with significantly enhanced serum concentrations now recognised as a contributor to numerous gestational disorders. Evidence now suggests that circulating levels of activin A may be clinically relevant in the early detection of pregnancy complications, including miscarriage and preeclampsia. This review aims to summarise our current understanding of activin A as a potential diagnostic marker in common pregnancy pathologies.

The Use of Systemic Inflammatory Markers From Routine Blood Tests in Predicting Preeclampsia and the Impact of Age on Marker Levels

Our study aimed to investigate the relationship between preeclampsia (PE) and blood levels of neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SII) in the first trimester of pregnancy. In addition to examining the potential correlation between these inflammatory markers and PE, we aimed to compare the levels based on age to determine whether there are potential age-related differences in marker levels. Over a six-month period, we reviewed the complete blood count (CBC) analysis results of 126 subjects, where 63 patients had a documented history of PE and 63 were healthy pregnant females. We found that age had no statistically significant effect on NLR, MLR, or SII levels, but there was a statistically significant difference in PLR levels between the 18-25 and 26-35 age groups. The study also revealed that the MLR and PLR in the 18-25 age group of preeclampsia patients were statistically significantly lower than those of healthy patients, whereas the PLR and SII in the 26-35 age group of preeclampsia patients were statistically significantly higher than those of healthy patients. The results suggest that systemic inflammatory response (SIR) markers may be able to predict the development of preeclampsia. The study also emphasized the importance of taking age into account, specifically the 18-25 and 26-35 age groups, when assessing the risk of preeclampsia. Further research is needed however to corroborate existing findings and determine the importance of the examined inflammatory markers in the diagnosis of PE.

The amniotic fluid proteome changes with term labor and informs biomarker discovery in maternal plasma

The intra-uterine components of labor, namely, myometrial contractility, cervical ripening, and decidua/membrane activation, have been extensively characterized and involve a local pro-inflammatory milieu of cellular and soluble immune mediators. Targeted profiling has demonstrated that such processes extend to the intra-amniotic space, yet unbiased analyses of the proteome of human amniotic fluid during labor are lacking. Herein, we utilized an aptamer-based platform to characterize 1,310 amniotic fluid proteins and found that the proteome undergoes substantial changes with term labor (251 proteins with differential abundance, q < 0.1, and fold change > 1.25). Proteins with increased abundance in labor are enriched for immune and inflammatory processes, consistent with prior reports of labor-associated changes in the intra-uterine space. By integrating the amniotic fluid proteome with previously generated placental-derived single-cell RNA-seq data, we demonstrated the labor-driven upregulation of signatures corresponding to stromal-3 and decidual cells. We also determined that changes in amniotic fluid protein abundance are reflected in the maternal plasma proteome. Collectively, these findings provide novel insights into the amniotic fluid proteome in term labor and support its potential use as a source of biomarkers to distinguish between true and false labor by using maternal blood samples.

PLACENTAL BIOMARKERS: PP13, VEGF IN DIAGNOSTICS OF EARLY AND LATE PREECLAMPSIA

OBJECTIVE

The aim: To investigate role of CD23, VEGF and PP13 in diagnostics of early and late preeclampsia, and their benefit for prediction of preeclampsia.

PATIENTS AND METHODS

Materials and methods: Investigation included 40 placentas from deliveries in women with preeclampsia (main group) and 40 placentas from physiological delivery in somatically healthy women, who had no complications during pregnancy (control group). Placentas in the main group were devided into two sub-groups (20 in each) - with early and late preeclampsia. Each group underwent both hystomorphometrical and immunohystochemical investigation with biomarkers CD23, VEGF and PP13.

RESULTS

Results: Positive immunohystochemical reaction to PP13 was determined in all samples of syncitiotrophoblast of villi of chorion. Investigations showed that expression of PP13 in sub-groups with early and late preeclampsia was a lot lower comparing to control group (normal pregnancies). Positive immunohystochemical reaction to VEGF was determined in all samples of endothelia of the capillaries of the villi of chorion. Our investigation showed that expression of VEGF in sub-groups with early and late PE was a lot lower comparing to a control group. Immunohystochemical reaction to CD23 was comperatively lower in all samples in endothelia of the capillariesof the villi of chorion and cyncithiotrophoblast.

CONCLUSION

Conclusions: Determined specialties of the expression of angiogenic factors ( PlGF, VEGF, endoglin) and production of PP13, by altered expression of VEGF, PlGF in first trimester of pregnancy, which is associated with lowest production of PP13, accompanied by placental dysfunction and preeclampsia.

Molecular subclasses of preeclampsia characterized by a longitudinal maternal proteomics study: distinct biomarkers, disease pathways and options for prevention

OBJECTIVES

The heterogeneous nature of preeclampsia is a major obstacle to early screening and prevention, and a molecular taxonomy of disease is needed. We have previously identified four subclasses of preeclampsia based on first-trimester plasma proteomic profiles. Herein, we expanded this approach by using a more comprehensive panel of proteins profiled in longitudinal samples.

METHODS

Proteomic data collected longitudinally from plasma samples of women who developed preeclampsia (n=109) and of controls (n=90) were available from our previous report on 1,125 proteins. Consensus clustering was performed to identify subgroups of patients with preeclampsia based on data from five gestational-age intervals by using select interval-specific features. Demographic, clinical, and proteomic differences among clusters were determined. Differentially abundant proteins were used to identify cluster-specific perturbed KEGG pathways.

RESULTS

Four molecular clusters with different clinical phenotypes were discovered by longitudinal proteomic profiling. Cluster 1 involves metabolic and prothrombotic changes with high rates of early-onset preeclampsia and small-for-gestational-age neonates; Cluster 2 includes maternal anti-fetal rejection mechanisms and recurrent preeclampsia cases; Cluster 3 is associated with extracellular matrix regulation and comprises cases of mostly mild, late-onset preeclampsia; and Cluster 4 is characterized by angiogenic imbalance and a high prevalence of early-onset disease.

CONCLUSIONS

This study is an independent validation and further refining of molecular subclasses of preeclampsia identified by a different proteomic platform and study population. The results lay the groundwork for novel diagnostic and personalized tools of prevention.

Metabolomic analysis-identified 2-hydroxybutyric acid might be a key metabolite of severe preeclampsia

This study set out to determine the key metabolite changes underlying the pathophysiology of severe preeclampsia (PE) using metabolic analysis. We collected sera from 10 patients with severe PE and from 10 healthy pregnant women of the same trimester and analyzed them using liquid chromatography mass spectrometry. A total of 3,138 differential metabolites were screened, resulting in the identification of 124 differential metabolites. Kyoto encyclopedia of genes and genomes pathway analysis revealed that they were mainly enriched in the following metabolic pathways: central carbon metabolism in cancer; protein digestion and absorption; aminoacyl-transfer RNA biosynthesis; mineral absorption; alanine, aspartate, and glutamate metabolism; and prostate cancer. After analysis of 124 differential metabolites, 2-hydroxybutyric acid was found to be the most critical differential metabolite, and its use allowed the differentiation of women with severe PE from healthy pregnant women. In summary, our analysis revealed that 2-hydroxybutyric acid is a potential key metabolite for distinguishing severe PE from healthy controls and is also a marker for the early diagnosis of severe PE, thus allowing early intervention.

Early prediction and longitudinal modeling of preeclampsia from multiomics

Preeclampsia is a complex disease of pregnancy whose physiopathology remains unclear. We developed machine-learning models for early prediction of preeclampsia (first 16 weeks of pregnancy) and over gestation by analyzing six omics datasets from a longitudinal cohort of pregnant women. For early pregnancy, a prediction model using nine urine metabolites had the highest accuracy and was validated on an independent cohort (area under the receiver-operating characteristic curve [AUC] = 0.88, 95% confidence interval [CI] [0.76, 0.99] cross-validated; AUC = 0.83, 95% CI [0.62,1] validated). Univariate analysis demonstrated statistical significance of identified metabolites. An integrated multiomics model further improved accuracy (AUC = 0.94). Several biological pathways were identified including tryptophan, caffeine, and arachidonic acid metabolisms. Integration with immune cytometry data suggested novel associations between immune and proteomic dynamics. While further validation in a larger population is necessary, these encouraging results can serve as a basis for a simple, early diagnostic test for preeclampsia.

Plasma Olink Proteomics Identifies CCL20 as a Novel Predictive and Diagnostic Inflammatory Marker for Preeclampsia

Inflammation is generally thought to be involved in the occurrence and development of preeclampsia (PE), but its specific effect on PE remains unclear. In the present study, the expression levels of 92 inflammation-related proteins were measured in the late pregnancy maternal plasma from patients with PE (n = 15) and normal pregnant controls (n = 15) using the Olink inflammation panel based on the highly sensitive and specific proximity extension assay technology. A total of 28 inflammation-related markers differed between the PE and control groups. Among them, fibroblast growth factor 21 (FGF-21) and cysteine-cysteine motif chemokine ligand 20 (CCL20) had the largest fold changes. We further validated the levels of CCL20 in the late (43 with PE and 44 controls) and early (37 with PE and 37 controls) pregnancy maternal plasma using enzyme-linked immunosorbent assay (ELISA). To the best of our knowledge, for the first time, CCL20 was found to be upregulated in the late and early pregnancy plasma of patients with PE and had an area under the curve (AUC) of 0.753 and 0.668, respectively. In conclusion, patients with PE had increased levels of most inflammatory markers, and CCL20 might be a novel potential predictive and diagnostic biomarker for PE.

Maternal plasma proteome profiling of biomarkers and pathogenic mechanisms of early-onset and late-onset preeclampsia

Preeclampsia is still the leading cause of morbidity and mortality in pregnancy without a cure. There are two phenotypes of preeclampsia, early-onset (EOPE) and late-onset (LOPE) with poorly defined pathogenic differences. This study aimed to facilitate better understanding of the mechanisms of pathophysiology of EOPE and LOPE, and identify specific biomarkers or therapeutic targets. In this study, we conducted an untargeted, label-free quantitative proteomic analyses of plasma samples from pregnant women with EOPE (n = 17) and LOPE (n = 11), and age, BMI-matched normotensive controls (n = 18). Targeted proteomics approach was also employed to validate a subset of proteins (n = 17). In total, there were 26 and 20 differentially abundant proteins between EOPE or LOPE, and normotensive controls, respectively. A series of angiogenic and inflammatory proteins, including insulin-like growth factor-binding protein 4 (IGFBP4; EOPE: FDR = 0.0030 and LOPE: FDR = 0.00396) and inter-alpha-trypsin inhibitor heavy chain H2-4 (ITIH2-4), were significantly altered in abundance in both phenotypes. Through validation we confirmed that ITIH2 was perturbed only in LOPE (p = 0.005) whereas ITIH3 and ITIH4 were perturbed in both phenotypes (p < 0.05). Overall, lipid metabolism/transport proteins associated with atherosclerosis were highly abundant in LOPE, however, ECM proteins had a more pronounced role in EOPE. The complement cascade and binding and uptake of ligands by scavenger receptors, pathways, were associated with both EOPE and LOPE.

Human Plasma Proteome During Normal Pregnancy

The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.

Preeclampsia: a report and recommendations of the workshop of the Society for Maternal-Fetal Medicine and the Preeclampsia Foundation

Preeclampsia is a substantial cause of perinatal and maternal morbidity and mortality. The prevalence of this condition has increased over the past several decades. Additional opportunities are needed to foster interdisciplinary collaborations and improve patient care in the setting of preeclampsia. In recognition of the Preeclampsia Foundation's 20th anniversary and its work to advance preeclampsia research and clinical agendas, a 2-day virtual workshop on preeclampsia was cosponsored by the Society for Maternal-Fetal Medicine and the Preeclampsia Foundation and held January 25-26, 2021 in conjunction with the 41st annual pregnancy meeting. Leaders with expertise in preeclampsia research, obstetrical care, primary care medicine, cardiology, endocrinology, global health, and patient advocacy gathered to discuss preeclampsia prediction, prevention, management, and long-term impacts. The goals of the workshop were to review the following issues and create consensus concerning research and clinical recommendations: This report, developed collaboratively between the SMFM and the Preeclampsia Foundation, presents the key findings and consensus-based recommendations from the workshop participants.

ALASCA: An R package for longitudinal and cross-sectional analysis of multivariate data by ASCA-based methods

The increasing availability of multivariate data within biomedical research calls for appropriate statistical methods that can describe and model complex relationships between variables. The extended ANOVA simultaneous component analysis (ASCA⁺) framework combines general linear models and principal component analysis (PCA) to decompose and visualize the separate effects of experimental factors. It has recently been demonstrated how linear mixed models can be included in the framework to analyze data from longitudinal experimental designs with repeated measurements (RM-ASCA⁺). The ALASCA package for R makes the ASCA⁺ framework accessible for general use and includes multiple methods for validation and visualization. The package is especially useful for longitudinal data and the ability to easily adjust for covariates is an important strength. This paper demonstrates how the ALASCA package can be applied to gain insights into multivariate data from interventional as well as observational designs. Publicly available data sets from four studies are used to demonstrate the methods available (proteomics, metabolomics, and transcriptomics).

Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology

BACKGROUND

The major challenge for obstetrics is the prediction and prevention of the great obstetrical syndromes. We propose that defining obstetrical diseases by the combination of clinical presentation and disease mechanisms as inferred by placental pathology will aid in the discovery of biomarkers and add specificity to those already known.

OBJECTIVE

To describe the longitudinal profile of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and the PlGF/sFlt-1 ratio throughout gestation, and to determine whether the association between abnormal biomarker profiles and obstetrical syndromes is strengthened by information derived from placental examination, eg, the presence or absence of placental lesions of maternal vascular malperfusion.

STUDY DESIGN

This retrospective case cohort study was based on a parent cohort of 4006 pregnant women enrolled prospectively. The case cohort of 1499 pregnant women included 1000 randomly selected patients from the parent cohort and all additional patients with obstetrical syndromes from the parent cohort. Pregnant women were classified into six groups: 1) term delivery without pregnancy complications (n=540; control); 2) preterm labor and delivery (n=203); 3) preterm premature rupture of the membranes (n=112); 4) preeclampsia (n=230); 5) small-for-gestational-age neonate (n=334); and 6) other pregnancy complications (n=182). Maternal plasma concentrations of PlGF and sFlt-1 were determined by enzyme-linked immunosorbent assays in 7560 longitudinal samples. Placental pathologists, masked to clinical outcomes, diagnosed the presence or absence of placental lesions of maternal vascular malperfusion. Comparisons between mean biomarker concentrations in cases and controls were performed by utilizing longitudinal generalized additive models. Comparisons were made between controls and each obstetrical syndrome with and without subclassifying cases according to the presence or absence of placental lesions of maternal vascular malperfusion.

RESULTS

1) When obstetrical syndromes are classified based on the presence or absence of placental lesions of maternal vascular malperfusion, significant differences in the mean plasma concentrations of PlGF, sFlt-1, and the PlGF/sFlt-1 ratio between cases and controls emerge earlier in gestation; 2) the strength of association between an abnormal PlGF/sFlt-1 ratio and the occurrence of obstetrical syndromes increases when placental lesions of maternal vascular malperfusion are present (adjusted odds ratio [aOR], 13.6 vs 6.7 for preeclampsia; aOR, 8.1 vs 4.4 for small-for-gestational-age neonates; aOR, 5.5 vs 2.1 for preterm premature rupture of the membranes; and aOR, 3.3 vs 2.1 for preterm labor (all P<0.05); and 3) the PlGF/sFlt-1 ratio at 28 to 32 weeks of gestation is abnormal in patients who subsequently delivered due to preterm labor with intact membranes and in those with preterm premature rupture of the membranes if both groups have placental lesions of maternal vascular malperfusion. Such association is not significant in patients with these obstetrical syndromes who do not have placental lesions.

CONCLUSION

Classification of obstetrical syndromes according to the presence or absence of placental lesions of maternal vascular malperfusion allows biomarkers to be informative earlier in gestation and enhances the strength of association between biomarkers and clinical outcomes. We propose that a new taxonomy of obstetrical disorders informed by placental pathology will facilitate the discovery and implementation of biomarkers as well as the prediction and prevention of such disorders.

Pregnancy-specific responses to COVID-19 are revealed by high-throughput proteomics of human plasma

Pregnant women are at greater risk of adverse outcomes, including mortality, as well as obstetrical complications resulting from COVID-19. However, pregnancy-specific changes that underlie such worsened outcomes remain unclear. Herein, we profiled the plasma proteome of pregnant and non-pregnant COVID-19 patients and controls and showed alterations that display a dose-response relationship with disease severity; yet, such proteomic perturbations are dampened during pregnancy. In both pregnant and non-pregnant state, the proteome response induced by COVID-19 showed enrichment of mediators implicated in cytokine storm, endothelial dysfunction, and angiogenesis. Shared and pregnancy-specific proteomic changes were identified: pregnant women display a tailored response that may protect the conceptus from heightened inflammation, while non-pregnant individuals display a stronger response to repel infection. Furthermore, the plasma proteome can accurately identify COVID-19 patients, even when asymptomatic or with mild symptoms. This study represents the most comprehensive characterization of the plasma proteome of pregnant and non-pregnant COVID-19 patients.

Preeclampsia subtypes: Clinical aspects regarding pathogenesis, signs, and management with special attention to diuretic administration

During normal pregnancy, blood volume increases by nearly two liters. Distinctively, the absence coupled with the extreme extent regarding the volume expansion, are likely accompanied with pathological conditions. Undoubtedly, preeclampsia, defined as the appearance of hypertension and organ deficiency, such as proteinuria during the second half of pregnancy, is not a homogenous disease. Clinically speaking, two main types of preeclampsia can be distinguished, in which a marked difference between them is vascular condition, and consequently, the blood volume. The "classic" preeclampsia, as a two-phase disease, described in the first, latent phase, in which, placenta development is diminished. Agents from this malperfused placenta generate a maternal disease, the second phase, in which endothelial damage leads to hypertension and organ damage due to vasoconstriction and thrombotic microangiopathy. In this hypovolemia-associated condition, decreasing platelet count, signs of hemolysis, renal and liver involvement are characteristic findings; proteinuria is marked and increasing. In the terminal phase, visible edema develops due to increasing capillary transparency, augmenting end-organ damages. "Classic" preeclampsia is a severe and quickly progressing condition with placental insufficiency and consequent fetal growth restriction and oligohydramnios. The outcome of this condition often leads to fetal hypoxia, eclampsia or placental abruption. The management is limited to a diligent prolongation of pregnancy to accomplish improved neonatal pulmonary function, careful diminishing high blood pressure, and delivery induction in due time. The other subtype, associated with relaxed vasculature and high cardiac output, is a maternal disease, in which obesity is an important risk factor since predisposes to enhanced water retention, hypertension, and a weakened endothelial dysfunction. Initially, enhanced water retention leads to lowered extremity edema, which oftentimes progresses to a generalized form and hypertension. In several cases, proteinuria appears most likely due to tissue edema. This condition already fully meets preeclampsia criteria. Laboratory alterations, including proteinuria, are modest and platelet count remains within the normal range. Fetal weight is also normal or frequently over average due to enhanced placental blood supply. It is very likely, further water retention leads to venous congestion, a parenchyma stasis, responsible for ascites, eclampsia, or placental abruption. During the management of this hypervolemia-associated preeclampsia, the administration of diuretic furosemide treatment seemingly offers promise.

First-Trimester Serum Cytokine Profile in Pregnancies Conceived After Assisted Reproductive Technology (ART) With Subsequent Pregnancy-Induced Hypertension

Pregnancy-induced hypertension (PIH) is one of the most common pregnancy complications that seriously affects the mother and fetus. The incidence of PIH is higher in pregnancies conceived after assisted reproductive technology (ART) than in spontaneous pregnancies; thus, exploring potential serum biomarkers before PIH onset is of great significance for effective early prediction and prevention of PIH in the ART population. Cytokines are involved in the inflammatory response and immune regulation, which play an essential role in the pathogenesis of PIH. A description of the cytokine profile in the first trimester of pregnancy could help identify new diagnostic tools and develop targeted therapies for PIH in the ART population. The concentrations of classical predictive markers for PIH and another 48 cytokines were measured in the first-trimester pregnancy serum samples from 33 PIH patients and 33 matched normotensive controls (NC), both of whom conceived after ART treatment. The measured values were compared and analyzed between NC and PIH, followed by comprehensive bioinformatic analysis and logistic regression analysis. There was no significant difference in classical predictive markers, including Activin A, PlGF, sFLT1 (VEGFR), and sFLT1/PlGF, between the PIH and NC groups (P > 0.05), while 29 cytokines were significantly lower in the PIH group than in the NC group (P < 0.05). Logistic regression analysis revealed that 17 cytokines (IL-2Rα, M-CSF, IL-6, IL-2, β-NGF, IL-7, IL-12 (p70), SCF, IL-10, IL-9, MIG, GM-CSF, LIF, IL-1α, MCP-3, IL-4, and HGF) in the first-trimester pregnancy serum were significantly negatively correlated with the subsequent onset of PIH. With the top 3 cytokines (IL-7, MIG, and SCF) of receiver operating characteristic (ROC) analysis, we constructed an efficient multifactor combined detection and prediction model for PIH in ART pregnancy. Classical early predictors for hypertensive disorder complicating pregnancy cannot distinguish PIH from their normal peers in ART pregnancy. In comparison, the description of the cytokine profile in the first trimester of pregnancy enables us to distinguish high-risk ART pregnancy for PIH, permitting enough time for PIH prevention therapy. The cytokine profile we described also provides immunological insight into the further mechanistic exploration of PIH.

Longitudinal Proteomic Analysis of Plasma across Healthy Pregnancies Reveals Indicators of Gestational Age

Longitudinal changes in the blood proteome during gestation relate to fetal development and maternal homeostasis. Charting the maternal blood proteome in normal pregnancies is critical for establishing a baseline reference when assessing complications and disease. Using mass spectrometry-based shotgun proteomics, we surveyed the maternal plasma proteome across uncomplicated pregnancies. Results indicate a significant rise in proteins that govern placentation and are vital to the development and health of the fetus. Importantly, we uncovered proteome signatures that strongly correlated with gestational age. Fold increases and correlations between the plasma concentrations of ADAM12 (ρ = 0.973), PSG1 (ρ = 0.936), and/or CSH1/2 (ρ = 0.928) with gestational age were validated with ELISA. Proteomic and validation analyses demonstrate that the maternal plasma concentration of ADAM12, either independently or in combination with either PSG1 or CSH1/2, correlates with gestational age within ±8 days throughout pregnancy. These findings suggest that the gestational age in healthy pregnancies may be determined by referencing the concentration of select plasma proteins.

Relationship of Placental and Serum Lipoprotein-Associated Phospholipase A2 Levels with Hypertensive Disorders of Pregnancy

Background

A series of studies has shown that lipoprotein-associated phospholipase A2 (Lp-PLA2) is closely associated with abnormal lipid metabolism and vascular endothelial cell injury, but its role in hypertensive disorders of pregnancy (HDP) remains unclear. This study aims to determine the relationship between placental and serum LP-PLA2 levels and HDP, and to provide a feasible method for predicting HDP.

Methods

The placental and serum Lp-PLA2 levels of 63 patients with HDP (20, 25, and 18 cases with gestational hypertension, mild preeclampsia, and severe preeclampsia, respectively) and 20 women with normal pregnancies (control group) were measured via a combination of tissue microarray and immunohistochemistry, real-time quantitative RT-PCR and enzyme-linked immunosorbent assay (ELISA).

Results

1) The gene and protein expression levels of placental LP-PLA2: the HDP group had significantly higher levels than those of the control group (P < 0.05). The mild preeclampsia group had significantly higher levels than those of the control group (P < 0.05); the severe preeclampsia group had significantly higher levels than those of the mild preeclampsia group (P < 0.05). 2) Serum levels of Lp-PLA2: the HDP group had significantly higher levels than those of the control group (P < 0.05). The Lp-PLA2 levels increased gradually with the progression of the HDP; there were significant differences in the four groups using pair-wise comparisons (P < 0.05). 3) Serum levels of LP-PLA2 were positively correlated with placental LP-PLA2 levels in the HDP group (r = 0.435, P < 0.05).

Conclusion

Elevated Lp-PLA2 levels may be associated with the occurrence of HDP, and changes of Lp-PLA2 levels in the maternal blood may be regarded as a monitoring indicator for this disease.

Improving preeclampsia risk prediction by modeling pregnancy trajectories from routinely collected electronic medical record data

Preeclampsia is a heterogeneous and complex disease associated with rising morbidity and mortality in pregnant women and newborns in the US. Early recognition of patients at risk is a pressing clinical need to reduce the risk of adverse outcomes. We assessed whether information routinely collected in electronic medical records (EMR) could enhance the prediction of preeclampsia risk beyond what is achieved in standard of care assessments. We developed a digital phenotyping algorithm to curate 108,557 pregnancies from EMRs across the Mount Sinai Health System, accurately reconstructing pregnancy journeys and normalizing these journeys across different hospital EMR systems. We then applied machine learning approaches to a training dataset (N = 60,879) to construct predictive models of preeclampsia across three major pregnancy time periods (ante-, intra-, and postpartum). The resulting models predicted preeclampsia with high accuracy across the different pregnancy periods, with areas under the receiver operating characteristic curves (AUC) of 0.92, 0.82, and 0.89 at 37 gestational weeks, intrapartum and postpartum, respectively. We observed comparable performance in two independent patient cohorts. While our machine learning approach identified known risk factors of preeclampsia (such as blood pressure, weight, and maternal age), it also identified other potential risk factors, such as complete blood count related characteristics for the antepartum period. Our model not only has utility for earlier identification of patients at risk for preeclampsia, but given the prediction accuracy exceeds what is currently achieved in clinical practice, our model provides a path for promoting personalized precision therapeutic strategies for patients at risk.

Biophysical Markers of Suspected Preeclampsia, Fetal Growth Restriction and the Two Combined—How Accurate They Are?

Objectives—To conduct a secondary analysis of prediction accuracy of biophysical markers for suspected Preeclampsia (PE), Fetal Growth Restriction (FGR) and the two combined near delivery in a Slovenian cohort. Methods—This was a secondary analysis of a database of a total 125 Slovenian pregnant women attending a high-risk pregnancy clinic due to suspected PE (n = 31), FGR (n = 16) and PE + FGR (n = 42) from 28–39 weeks gestation and their corresponding term (n = 21) and preterm (PTD, n = 15) controls. Data for Mean Arterial blood Pressure (MAP) and Uterine artery pulsatility index (UtA PI) estimated by Doppler sonography were extracted from the database of patients who were tested at admission to the high-risk clinic with the suspected complications. The reactive hyperemia index (RHI), and the Augmentation Index (AIX%) were extracted from the patient database using measured values obtained with the assistance of the Endo PAT, a device set to measure the signal of the peripheral arterial tone (PAT) from the blood vessels endothelium. Linear regression coefficients, Box and Whisker plots, Area under the Curve (AUC) of receiver Operation Characteristic (ROC) curves, and multiple regression were used to assess the marker accuracy using detection rate (DR) and false-positive rate (FPR) and previously reported cut-offs for estimating the positive and negative predictive value (NPV and PPV). The SPSS non-parametric statistics (Kruskal Wallis and Mann–Whitney) and Spearman’s regression coefficient were used to assess marker accuracy; p < 0.05 was considered significant. Results—MAP values reached diagnostic accuracy (AUC = 1.00, DR = 100%) for early PE cases delivered < 34, whereas UtA Doppler PI values yielded such results for early FGR < 34 weeks and the two combined reached such accuracy for PE + FGR. To reach diagnostic accuracy for all cases of the complications, the Endo PAT markers with values for MAP and UtA Doppler PI were required for cases near delivery. Multiple regression analyses showed added value for advanced maternal age and gestational week in risk assessment for all cases of PE, FGR, and PE + FGR. Spearman’s regression coefficient yielded r > 0.6 for UtA Doppler PI over GA for PE and FGR, whereas for RHI over BMI, the regression coefficient was r > 0.5 (p < 0.001 for each). Very high correlations were also found between UtA Doppler PI and sFlt-1/PlGF or PlGF (r = −0.495, p < 0.001), especially in cases of FGR. Conclusion—The classical biophysical markers MAP and UtA Doppler PI provided diagnostic accuracy for PE and FGR < 34 wks gestation. A multiple biophysical marker analysis was required to reach diagnostic accuracy for all cases of these complications. The UtA Doppler PI and maternal serum sFlt-1/PlGF or PlGF were equally accurate for early cases to enable the choice of the markers for the clinical use according to the more accessible method.