BACKGROUND: Small-for-gestational-age (SGA), commonly caused by poor placentation, is a major contributor to global perinatal mortality and morbidity. Maternal serum levels of placental protein and angiogenic factors are changed in SGA. Using data from a population-based pregnancy cohort, we estimated the relationships between levels of second-trimester pregnancy-associated plasma protein-A (PAPP-A), placental growth factor (PlGF), and serum soluble fms-like tyrosine kinase-1 (sFlt-1) with SGA.
METHODS: Three thousand pregnant women were enrolled. Trained health workers prospectively collected data at home visits. Maternal blood samples were collected, serum aliquots were prepared and stored at -80℃. Included in the analysis were 1,718 women who delivered a singleton live birth baby and provided a blood sample at 24-28 weeks of gestation. We used Mann-Whitney U test to examine differences of the median biomarker concentrations between SGA (< 10th centile birthweight for gestational age) and appropriate-for-gestational-age (AGA). We created biomarker concentration quartiles and estimated the risk ratios (RRs) and 95% confidence intervals (CIs) for SGA by quartiles separately for each biomarker. A modified Poisson regression was used to determine the association of the placental biomarkers with SGA, adjusting for potential confounders.
RESULTS: The median PlGF level was lower in SGA pregnancies (934 pg/mL, IQR 613-1411 pg/mL) than in the AGA (1050 pg/mL, IQR 679-1642 pg/mL; p < 0.001). The median sFlt-1/PlGF ratio was higher in SGA pregnancies (2.00, IQR 1.18-3.24) compared to AGA pregnancies (1.77, IQR 1.06-2.90; p = 0.006). In multivariate regression analysis, women in the lowest quartile of PAPP-A showed 25% higher risk of SGA (95% CI 1.09-1.44; p = 0.002). For PlGF, SGA risk was higher in women in the lowest (aRR 1.40, 95% CI 1.21-1.62; p < 0.001) and 2nd quartiles (aRR 1.30, 95% CI 1.12-1.51; p = 0.001). Women in the highest and 3rd quartiles of sFlt-1 were at reduced risk of SGA delivery (aRR 0.80, 95% CI 0.70-0.92; p = 0.002, and aRR 0.86, 95% CI 0.75-0.98; p = 0.028, respectively). Women in the highest quartile of sFlt-1/PlGF ratio showed 18% higher risk of SGA delivery (95% CI 1.02-1.36; p = 0.025).
CONCLUSIONS: This study provides evidence that PAPP-A, PlGF, and sFlt-1/PlGF ratio measurements may be useful second-trimester biomarkers for SGA.

OBJECTIVE: This study aimed to determine the association of first-trimester maternal serum biomarkers with preterm birth (PTB), fetal growth restriction (FGR) and hypertensive disorders of pregnancy (HDP) in twin pregnancies.
METHODS: This is a retrospective cohort study of twin pregnancies followed at Maternidade Dr. Alfredo da Costa, Lisbon, Portugal, between January 2010 and December 2022. We included women who completed first-trimester screening in our unit and had ongoing pregnancies with two live fetuses, and delivered after 24 weeks. Maternal characteristics, pregnancy-associated plasma protein-A (PAPP-A) and β-human chorionic gonadotropin (β-hCG) levels were analyzed for different outcomes: small for gestational age (SGA), gestational hypertension (GH), early and late-onset pre-eclampsia (PE), as well as the composite outcome of PTB associated with FGR and/or HDP. Univariable, multivariable logistic regression analyses and receiver-operating characteristic curve were used.
RESULTS: 466 twin pregnancies met the inclusion criteria. Overall, 185 (39.7%) pregnancies were affected by SGA < 5th percentile and/or HDP. PAPP-A demonstrated a linear association with gestational age at birth and mean birth weight. PAPP-A proved to be an independent risk factor for SGA and PTB (< 34 and < 36 weeks) related to FGR and/or HDP. None of the women with PAPP-A MoM > 90th percentile developed early-onset PE or PTB < 34 weeks.
CONCLUSIONS: A high serum PAPP-A (> 90th percentile) ruled out early-onset PE and PTB < 34 weeks. Unless other major risk factors for hypertensive disorders are present, these women should not be considered candidates for aspirin prophylaxis. Nevertheless, close monitoring of all TwP for adverse obstetric outcomes is still recommended.

UNASSIGNED: Fetal Medicine Foundation (FMF) studies suggest that preterm preeclampsia can be predicted in the first trimester by combining biophysical, biochemical, and ultrasound markers and prevented using aspirin. We aimed to evaluate the FMF preterm preeclampsia screening test in nulliparous women.
UNASSIGNED: We conducted a prospective multicenter cohort study of nulliparous women recruited at 11 to 14 weeks. Maternal characteristics, mean arterial blood pressure, PAPP-A (pregnancy-associated plasma protein A), PlGF (placental growth factor) in maternal blood, and uterine artery pulsatility index were collected at recruitment. The risk of preterm preeclampsia was calculated by a third party blinded to pregnancy outcomes. Receiver operating characteristic curves were used to estimate the detection rate (sensitivity) and the false-positive rate (1-specificity) for preterm (<37 weeks) and for early-onset (<34 weeks) preeclampsia according to the FMF screening test and according to the American College of Obstetricians and Gynecologists criteria.
UNASSIGNED: We recruited 7554 participants including 7325 (97%) who remained eligible after 20 weeks of which 65 (0.9%) developed preterm preeclampsia, and 22 (0.3%) developed early-onset preeclampsia. Using the FMF algorithm (cutoff of ≥1 in 110 for preterm preeclampsia), the detection rate was 63.1% for preterm preeclampsia and 77.3% for early-onset preeclampsia at a false-positive rate of 15.8%. Using the American College of Obstetricians and Gynecologists criteria, the equivalent detection rates would have been 61.5% and 59.1%, respectively, for a false-positive rate of 34.3%.
UNASSIGNED: The first-trimester FMF preeclampsia screening test predicts two-thirds of preterm preeclampsia and three-quarters of early-onset preeclampsia in nulliparous women, with a false-positive rate of ≈16%.
UNASSIGNED: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02189148.

OBJECTIVE: To compare the predictive performance of three different mathematical models for first-trimester screening of pre-eclampsia (PE), which combine maternal risk factors with mean arterial pressure (MAP), uterine artery pulsatility index (UtA-PI) and serum placental growth factor (PlGF), and two risk-scoring systems.
METHODS: This was a prospective cohort study performed in eight fetal medicine units in five different regions of Spain between September 2017 and December 2019. All pregnant women with singleton pregnancy and a non-malformed live fetus attending their routine ultrasound examination at 11 + 0 to 13 + 6 weeks\' gestation were invited to participate in the study. Maternal characteristics and medical history were recorded and measurements of MAP, UtA-PI, serum PlGF and pregnancy-associated plasma protein-A (PAPP-A) were converted into multiples of the median (MoM). Risks for term PE, preterm PE (< 37 weeks\' gestation) and early PE (< 34 weeks\' gestation) were calculated according to the FMF competing-risks model, the Crovetto et al. logistic regression model and the Serra et al. Gaussian model. PE classification was also performed based on the recommendations of the National Institute for Health and Care Excellence (NICE) and the American College of Obstetricians and Gynecologists (ACOG). We estimated detection rates (DR) with their 95% CIs at a fixed 10% screen-positive rate (SPR), as well as the area under the receiver-operating-characteristics curve (AUC) for preterm PE, early PE and all PE for the three mathematical models. For the scoring systems, we calculated DR and SPR. Risk calibration was also assessed.
RESULTS: The study population comprised 10 110 singleton pregnancies, including 32 (0.3%) that developed early PE, 72 (0.7%) that developed preterm PE and 230 (2.3%) with any PE. At a fixed 10% SPR, the FMF, Crovetto et al. and Serra et al. models detected 82.7% (95% CI, 69.6-95.8%), 73.8% (95% CI, 58.7-88.9%) and 79.8% (95% CI, 66.1-93.5%) of early PE; 72.7% (95% CI, 62.9-82.6%), 69.2% (95% CI, 58.8-79.6%) and 74.1% (95% CI, 64.2-83.9%) of preterm PE; and 55.1% (95% CI, 48.8-61.4%), 47.1% (95% CI, 40.6-53.5%) and 53.9% (95% CI, 47.4-60.4%) of all PE, respectively. The best correlation between predicted and observed cases was achieved by the FMF model, with an AUC of 0.911 (95% CI, 0.879-0.943), a slope of 0.983 (95% CI, 0.846-1.120) and an intercept of 0.154 (95% CI, -0.091 to 0.397). The NICE criteria identified 46.7% (95% CI, 35.3-58.0%) of preterm PE at 11% SPR and ACOG criteria identified 65.9% (95% CI, 55.4-76.4%) of preterm PE at 33.8% SPR.
CONCLUSIONS: The best performance of screening for preterm PE is achieved by mathematical models that combine maternal factors with MAP, UtA-PI and PlGF, as compared to risk-scoring systems such as those of NICE and ACOG. While all three algorithms show similar results in terms of overall prediction, the FMF model showed the best performance at an individual level. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

BACKGROUND: The exact mechanism by which aspirin prevents preeclampsia remains unclear. Its effects on serum placental biomarkers throughout pregnancy are also unknown.
OBJECTIVE: To investigate the effects of aspirin on serum pregnancy-associated plasma protein A and placental growth factor trajectories using repeated measures from women at increased risk of preterm preeclampsia.
METHODS: This was a longitudinal secondary analysis of the Combined Multimarker Screening and Randomized Patient Treatment with Aspirin for Evidence-based Preeclampsia Prevention trial using repeated measures of pregnancy-associated plasma protein A and placental growth factor. In the trial, 1620 women at increased risk of preterm preeclampsia were identified using the Fetal Medicine Foundation algorithm at 11 to 13+6 weeks of gestation, of whom 798 were randomly assigned to receive aspirin 150 mg and 822 to receive placebo daily from before 14 weeks to 36 weeks of gestation. Serum biomarkers were measured at baseline and follow-up visits at 19 to 24, 32 to 34, and 36 weeks of gestation. Generalized additive mixed models with treatment by gestational age interaction terms were used to investigate the effect of aspirin on biomarker trajectories over time.
RESULTS: Overall, there were 5507 pregnancy-associated plasma protein A and 5523 placental growth factor measurements. Raw pregnancy-associated plasma protein A values increased over time, and raw placental growth factor increased until 32 weeks of gestation followed by a decline. The multiple of the median mean values of the same biomarkers were consistently below 1.0 multiple of the median, reflecting the high-risk profile of the study population. Trajectories of mean pregnancy-associated plasma protein A and placental growth factor multiple of the median values did not differ significantly between the aspirin and placebo groups (aspirin treatment by gestational age interaction P values: .259 and .335, respectively).
CONCLUSIONS: In women at increased risk of preterm preeclampsia, aspirin 150 mg daily had no significant effects on pregnancy-associated plasma protein A or placental growth factor trajectories when compared to placebo.

OBJECTIVE: This study aimed to investigate the association between first-trimester Pregnancy-associated plasma protein A (PAPP-A) levels and subsequent gestational diabetes mellitus (GDM) development.
METHODS: The study was conducted on 5854 pregnant women who attended routine prenatal care. Maternal biomarkers, including PAPP-A and free beta hCG, were measured for all women in a referral laboratory and converted to MoM values. Pregnant women were divided into two groups, based on the serum concentration of PAPP-A, (PAPP-A > 0.4 (normal) and PAPP-A < 0.4 (low)). Data on the screening test for GDM and pregnancy outcomes were collected and analyzed with appropriate tests.
RESULTS: Of the 5854 pregnant women, 889 (15.19%) developed GDM. The maternal PAPP-A MoM concentrations were significantly lower in GDM cases compared to controls. Indeed, gestational age at delivery and birth weight were significantly lower (p < 0.001) in PAPP-A MoM < 0.4, and the rate of intrauterine growth restriction (IUGR) was significantly higher (p < 0.001). ROC analysis revealed that the sensitivity and specificity of MoM concentration for predicting GDM were 53.3% and 51.9%, respectively.
CONCLUSIONS: Lower maternal PAPP-A in early pregnancy can lead to glucose intolerance and increase the risk of subsequent GDM development. In addition, decreased serum concentration of PAPP-A is significantly correlated to lower birth weight and IUGR.

OBJECTIVE: This study aims to evaluate if low levels of serum maternal pregnancy associated plasma protein-A (PAPP-A) during the first trimester are related to increased umbilical artery pulsatility index (UA PI) later in pregnancy, in cases of estimated fetal weight between the 3rd and 10th percentiles, in order to establish PAPP-A as a predictor of this particular cases of fetal growth restriction (FGR).
METHODS: An observational, retrospective cohort study, conducted at a tertiary University Hospital located in Oporto, Portugal. Pregnant women who did the first trimester combined screening, between May 2013 and June 2020 and gave birth in the same hospital, with an estimated fetal weight (EFW) between the 3rd and 10th percentiles were included. The primary outcome is the difference in increased UA PI prevalence between two groups: PAPP-A<0.45 MoM and PAPP-A≥0.45 MoM. As secondary outcomes were evaluated differences in neonatal weight, gestational age at delivery, cesarean delivery, neonatal intensive care unit hospitalization, 5-min Apgar score below 7 and live birth rate between the same two groups.
RESULTS: We included 664 pregnancies: 110 cases of PAPP-A<0.45 MoM and 554 cases with PAPP-A≥0.45 MoM. Increased UA PI prevalence, which was the primary outcome of this study, was significantly different between the two groups (p=0.005), as the PAPP-A<0.45 MoM group presents a higher prevalence (12.7 %) when compared to the PAPP-A≥0.45 MoM group (5.4 %). The secondary outcome cesarean delivery rate was significantly different between the groups (p=0.014), as the PAPP-A<0.45 MoM group presents a higher prevalence (42.7 %) than the PAPP-A≥0.45 MoM group (30.1 %). No other secondary outcomes showed differences between the two groups.
CONCLUSIONS: There is an association of low serum maternal PAPP-A (<0.45 MoM) during the first trimester and increased UA PI (>95th percentile) later in pregnancy, in cases of EFW between the 3rd and 10th percentiles. However, this association is not strong enough alone for low PAPP-A to be a reliable predictor of increased UA PI in this population.

OBJECTIVE: To determine the inter-relationships between five first-trimester biomarkers (pregnancy associated plasma protein A [PAPP-A], alpha-fetoprotein [AFP], beta human chorionic gonadotrophin [beta-hCG], placenta growth factor [PlGF] and soluble fms-like tyrosine kinase receptor-1 [sFlt-1]) and a range of adverse pregnancy outcomes (APOs).
METHODS: Prospective cohort study of nulliparous singleton pregnancy.
METHODS: Cambridge, UK.
METHODS: 4056 pregnancy outcome prediction study participants.
METHODS: The biomarker concentrations were measured in maternal serum at ~12 weeks of gestation. Univariable analysis of APOs was performed using logistic regression. Multivariable analysis used best subsets logistic regression with cross-validation.
METHODS: Pre-eclampsia (PE), small for gestational age (SGA), including severe SGA (birthweight <3rd), fetal growth restriction (FGR), preterm birth (PTB, both induced and spontaneous [iPTB and sPTB, respectively]), pre-viable loss and stillbirth, plus combinations of outcomes.
RESULTS: Lower values of PAPP-A, PlGF and sFlt-1 and higher values of AFP were associated with FGR (OR for 1 SD higher value 0.59 [95% CI 0.48-0.74], OR 0.56 [95% CI 0.44-0.70], OR 0.68 [95% CI 0.54-0.87] and OR 1.53 [95% CI 1.25-1.88]), severe SGA (OR 0.59 [95% CI 0.49-0.72], OR 0.71 [95% CI 0.57-0.87], OR 0.74 [95% CI 0.60-0.91] and OR 1.41 [95% CI 1.17-1.71]), sPTB (OR  0.61 [95% CI 0.50-0.73], OR 0.79 [95% CI 0.66-0.96], OR 0.57 [95% CI 0.47-0.70] and OR 1.41 [95% CI 1.18-1.67]) and iPTB (OR  0.72 [95% CI 0.57-0.91], OR 0.62 [95% CI 0.49-0.78], OR 0.71 [95% CI 0.56-0.90] and OR 1.44 [95% CI 1.16-1.78]), respectively. When combinations of biomarkers were assessed, PAPP-A and AFP were independently associated with severe SGA; PAPP-A alone with PE + PTB; PlGF alone with severe PE; PlGF, beta-hCG, AFP and PAPP-A with the combination of PE and SGA; AFP and sFlt-1 with sPTB; and AFP and PlGF with iPTB.
CONCLUSIONS: Combinations of first-trimester placental biomarkers are associated with APOs. However, the patterns vary for different types of APO, indicating heterogeneity in the underlying pathophysiological pathways.

Effective first-trimester screening for pre-eclampsia (PE) can be achieved using a competing-risks model that combines risk factors from the maternal history with multiples of the median (MoM) values of biomarkers. A new model using artificial intelligence through machine-learning methods has been shown to achieve similar screening performance without the need for conversion of raw data of biomarkers into MoM. This study aimed to investigate whether this model can be used across populations without specific adaptations.
Previously, a machine-learning model derived with the use of a fully connected neural network for first-trimester prediction of early (< 34 weeks), preterm (< 37 weeks) and all PE was developed and tested in a cohort of pregnant women in the UK. The model was based on maternal risk factors and mean arterial blood pressure (MAP), uterine artery pulsatility index (UtA-PI), placental growth factor (PlGF) and pregnancy-associated plasma protein-A (PAPP-A). In this study, the model was applied to a dataset of 10 110 singleton pregnancies examined in Spain who participated in the first-trimester PE validation (PREVAL) study, in which first-trimester screening for PE was carried out using the Fetal Medicine Foundation (FMF) competing-risks model. The performance of screening was assessed by examining the area under the receiver-operating-characteristics curve (AUC) and detection rate (DR) at a 10% screen-positive rate (SPR). These indices were compared with those derived from the application of the FMF competing-risks model. The performance of screening was poor if no adjustment was made for the analyzer used to measure PlGF, which was different in the UK and Spain. Therefore, adjustment for the analyzer used was performed using simple linear regression.
The DRs at 10% SPR for early, preterm and all PE with the machine-learning model were 84.4% (95% CI, 67.2-94.7%), 77.8% (95% CI, 66.4-86.7%) and 55.7% (95% CI, 49.0-62.2%), respectively, with the corresponding AUCs of 0.920 (95% CI, 0.864-0.975), 0.913 (95% CI, 0.882-0.944) and 0.846 (95% CI, 0.820-0.872). This performance was achieved with the use of three of the biomarkers (MAP, UtA-PI and PlGF); inclusion of PAPP-A did not provide significant improvement in DR. The machine-learning model had similar performance to that achieved by the FMF competing-risks model (DR at 10% SPR, 82.7% (95% CI, 69.6-95.8%) for early PE, 72.7% (95% CI, 62.9-82.6%) for preterm PE and 55.1% (95% CI, 48.8-61.4%) for all PE) without requiring specific adaptations to the population.
A machine-learning model for first-trimester prediction of PE based on a neural network provides effective screening for PE that can be applied in different populations. However, before doing so, it is essential to make adjustments for the analyzer used for biochemical testing. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

UNASSIGNED: To investigate the relationship and predictive value of first-trimester pregnancy-associated plasma protein A (PAPP-A), maternal factors, and biochemical parameters with gestational diabetes mellitus (GDM) in southern China mothers.
UNASSIGNED: This study recruited 4872 pregnant women. PAPP-A, the free beta subunit of human chorionic gonadotropin (free β-HCG), fasting plasma glucose (FPG), total cholesterol (TC), triglycerides (TG), and high- and low-density lipoproteins (HDL, LDL) were measured at 11-13+ weeks of gestation. GDM was diagnosed based on a 75 g oral glucose tolerance test at 24-28 weeks of gestation. We performed stepwise logistic regression analysis to determine the odds ratio (OR) and the 95% confidence interval (CI) of GDM. We used Receiver Operating Characteristic (ROC) curves with the area under the curve (AUC) to evaluate the predictive value of PAPP-A, maternal factors, and biochemical markers. The significance of the differences between the AUC values was assessed using the DeLong test.
UNASSIGNED: GDM was diagnosed in 750 (15.39%) women. Independent factors for GDM were age, pre-gestational BMI, GWG before a diagnosis of GDM, previous history of GDM, family history of diabetes, FPG, TG, LDL, PAPP-A, and TC. The AUC of PAPP-A was 0.56 (95% CI 0.53-0.58). The AUC of a model based on combined maternal factors, biochemical markers, and PAPP-A was 0.70 (95% CI 0.68-0.72). Differences in AUC values between PAPP-A alone and the model based on combined maternal factors, biochemical markers, and PAPP-A were statistically significant (Z= 9.983, P<0.001).
UNASSIGNED: A Low serum PAPP-A level in the first trimester is an independent risk factor for developing GDM later in pregnancy. However, it is not a good independent predictor although the predictive value of a low serum PAPP-A level increases when combined with maternal factors and biochemical markers.