First trimester low maternal serum pregnancy associated plasma protein-A (PAPP-A) as a screening method for adverse pregnancy outcomes

OBJECTIVES

To evaluate different cut-off values of first trimester pregnancy associated plasma protein-A (PAPP-A) in screening for adverse pregnancy outcomes in a retrospective cohort study.

METHODS

During the study period of 1.1.2014-31.12.2018, total of 23,482 women with singleton pregnancies participated in first trimester combined screening for chromosomal abnormalities. Maternal serum PAPP-A multiple of medians (MoM) levels were measured, and study population was divided into three study groups of PAPP-A ≤0.40 (n=1,030), ≤0.35 (n=630) and ≤0.30 (n=363) MoM.

RESULTS

Small for gestational age (SGA), preterm birth (PTB) and composite outcome (SGA, hypertensive disorder of pregnancy (HDP) and/or PTB) were more frequent in all three PAPP-A MoM study groups and pre-eclampsia in ≤0.40 and ≤0.35 study groups than in their control groups (p < 0.05). The odds ratio (OR) for SGA varied from 3.7 to 5.4 and sensitivity and specificity from 6.9 to 13.8% and from 95.9 to 98.6%, between study groups. Using PAPP-A ≤0.30 MoM as a screening cut-off instead of PAPP-A ≤0.40 MoM, resulted in approximately 50% reduction in screening detection of SGA and PTB.

CONCLUSIONS

PAPP-A ≤0.40 MoM should be considered as a primary screening cut-off for adverse pregnancy outcomes as approximately 23% will develop either SGA, HDP or PTB. It seems to be the best cut-off to screen for SGA.

Blood and saliva SARS-CoV-2 antibody levels in self-collected dried spot samples

We examined the usefulness of dried spot blood and saliva samples in SARS-CoV-2 antibody analyses. We analyzed 1231 self-collected dried spot blood and saliva samples from healthcare workers. Participants filled in a questionnaire on their COVID-19 exposures, infections, and vaccinations. Anti-SARS-CoV-2 IgG, IgA, and IgM levels were determined from both samples using the GSP/DELFIA method. The level of exposure was the strongest determinant of all blood antibody classes and saliva IgG, increasing as follows: (1) no exposure (healthy, non-vaccinated), (2) exposed, (3) former COVID-19 infection, (4) one vaccination, (5) two vaccinations, and (6) vaccination and former infection. While the blood IgG assay had a 99.5% sensitivity and 75.3% specificity to distinguish participants with two vaccinations from all other types of exposure, the corresponding percentages for saliva IgG were 85.3% and 65.7%. Both blood and saliva IgG-seropositivity proportions followed similar trends to the exposures reported in the questionnaires. Self-collected dry blood and saliva spot samples combined with the GSP/DELFIA technique comprise a valuable tool to investigate an individual's immune response to SARS-CoV-2 exposure or vaccination. Saliva IgG has high potential to monitor vaccination response wane, since the sample is non-invasive and easy to collect.

Adaptive risk prediction system with incremental and transfer learning

Currently, popular methods for prenatal risk assessment of fetal aneuploidies are based on multivariate probabilistic modelling, that are built on decades of scientific research and large-scale multi-center clinical studies. These static models that are deployed to screening labs are rarely updated or adapted to local population characteristics. In this article, we propose an adaptive risk prediction system or ARPS, which considers these changing characteristics and automatically deploys updated risk models. 8 years of real-life Down syndrome screening data was used to firstly develop a distribution shift detection method that captures significant changes in the patient population and secondly a probabilistic risk modelling system that adapts to new data when these changes are detected. Various candidate systems that utilize transfer -and incremental learning that implement different levels of plasticity were tested. Distribution shift detection using a windowed approach provides a computationally less expensive alternative to fitting models at every data block step while not sacrificing performance. This was possible when utilizing transfer learning. Deploying an ARPS to a lab requires careful consideration of the parameters regarding the distribution shift detection and model updating, as they are affected by lab throughput and the incidence of the screened rare disorder. When this is done, ARPS could be also utilized for other population screening problems. We demonstrate with a large real-life dataset that our best performing novel Incremental-Learning-Population-to-Population-Transfer-Learning design can achieve on par prediction performance without human intervention, when compared to a deployed risk screening algorithm that has been manually updated over several years.

Synthetic minority oversampling of vital statistics data with generative adversarial networks

Objective

Minority oversampling is a standard approach used for adjusting the ratio between the classes on imbalanced data. However, established methods often provide modest improvements in classification performance when applied to data with extremely imbalanced class distribution and to mixed-type data. This is usual for vital statistics data, in which the outcome incidence dictates the amount of positive observations. In this article, we developed a novel neural network-based oversampling method called actGAN (activation-specific generative adversarial network) that can derive useful synthetic observations in terms of increasing prediction performance in this context.

Materials and Methods

From vital statistics data, the outcome of early stillbirth was chosen to be predicted based on demographics, pregnancy history, and infections. The data contained 363 560 live births and 139 early stillbirths, resulting in class imbalance of 99.96% and 0.04%. The hyperparameters of actGAN and a baseline method SMOTE-NC (Synthetic Minority Over-sampling Technique-Nominal Continuous) were tuned with Bayesian optimization, and both were compared against a cost-sensitive learning-only approach.

Results

While SMOTE-NC provided mixed results, actGAN was able to improve true positive rate at a clinically significant false positive rate and area under the curve from the receiver-operating characteristic curve consistently.

Discussion

Including an activation-specific output layer to a generator network of actGAN enables the addition of information about the underlying data structure, which overperforms the nominal mechanism of SMOTE-NC.

Conclusions

actGAN provides an improvement to the prediction performance for our learning task. Our developed method could be applied to other mixed-type data prediction tasks that are known to be afflicted by class imbalance and limited data availability.

Evaluation of Circulating Cardiovascular Biomarker Levels for Early Detection of Congenital Heart Disease in Newborns in Sweden

IMPORTANCE

Congenital heart disease (CHD) is the most common congenital malformation in humans worldwide. Circulating cardiovascular biomarkers could potentially improve the early detection of CHD, even in asymptomatic newborns.

OBJECTIVES

To assess the performance of a dried blood spot (DBS) test to measure the cardiovascular biomarker amino terminal fragment of the prohormone brain-type natriuretic peptide (NT-proBNP) levels in newborns and to compare DBS with standard EDTA analysis in control newborns during the first week of life.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted in a single regional pediatric service in southern Sweden. Healthy, term neonates born between July 1, 2018, and May 31, 2019, were prospectively enrolled and compared against retrospectively identified newborns with CHD born between September 1, 2003, and September 30, 2019. Neonates who required inpatient treatment beyond the standard postnatal care were excluded.

EXPOSURE

New DBS test for NT-proBNP quantification in newborns that used 3 μL of blood vs the current screening standard.

MAIN OUTCOMES AND MEASURES

Performance of the new test and when combined with pulse oximetry screening was measured by receiver operating characteristic curve analysis. Performance of the new test and EDTA screening was compared using Pearson linear correlation analysis.

RESULTS

The DBS samples of 115 neonates (81 control newborns and 34 newborns with CHD, of whom 63 were boys [55%] and the mean [SD] gestational age was 39.6 [1.4] weeks) were analyzed. The new NT-proBNP test alone identified 71% (n = 24 of 34) of all CHD cases and 68% (n = 13 of 19) of critical CHD cases as soon as 2 days after birth. Detection of any CHD type improved to 82% (n = 28 of 34 newborns) and detection of critical CHD improved to 89% (n = 17 of 19 newborns) when combined pulse oximetry screening and NT-proBNP test results were used. Performance of the NT-proBNP test was excellent when control newborns were matched to newborns with CHD born between July 1, 2018, and May 31, 2019 (area under the curve, 0.96; SE, 0.027; 95% CI, 0.908-1.0; asymptotic P < .05).

CONCLUSIONS AND RELEVANCE

This study found that NT-proBNP assay using minimal DBS samples appears to be timely and accurate in detecting CHD in newborns and to discriminate well between healthy newborns and newborns with various types of CHD. This finding warrants further studies in larger cohorts and highlights the potential of NT-proBNP to improve neonatal CHD screening.

Predicting risk of stillbirth and preterm pregnancies with machine learning

Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers.

Glycosylated fibronectin as a first trimester marker for gestational diabetes

Purpose

To evaluate the performance of first trimester maternal serum glycosylated (Sambucus nigra lectin-reactive) fibronectin in prediction of gestational diabetes mellitus (GDM).

Methods

In this case–control study, first trimester maternal serum glycosylated fibronectin and fibronectin were measured in 19 women who consequently developed GDM and in 59 control women with normal pregnancy outcomes. Adiponectin was used as a reference protein to evaluate relation of glycoprotein to SNA-lectin-reactive assay format. Samples were taken during gestational weeks 9⁺⁶–11⁺⁶. Data concerning GDM was obtained from the National Institute for Health and Welfare, which records the pregnancy outcomes of all women in Finland.

Results

There was no difference in maternal serum glycosylated fibronectin concentrations between women with consequent GDM [447.5 μg/mL, interquartile range (IQR) 254.4–540.9 μg/mL] and control women (437.6 μg/mL, IQR 357.1–569.1 μg/mL). Maternal serum fibronectin levels were significantly lower in GDM group (224.2 μg/mL, IQR 156.8–270.6 μg/mL), compared to the control group (264.8 μg/mL, IQR 224.6–330.6 μg/mL, p < 0.01). There was no difference in assay formats for adiponectin.

Conclusion

There was no association between first trimester maternal serum glycosylated (SNA-reactive) fibronectin and GDM.

PlGF isoform 3 in maternal serum and placental tissue

OBJECTIVES

Four isoforms originating from alternative splicing of PGF gene have been reported for placental growth factor (PlGF). Main PlGF isoforms 1 and 2 have been associated with screening and diagnosis of pre-eclampsia (PE). Despite of the vast amount of research around PlGF in PE, protein levels of isoforms PlGF-3 and -4 have not been reported in human serum samples.

STUDY DESIGN

In this study a PlGF-3 specific DELFIA research immunoassay based on a custom recombinant Fab binder was developed and characterized. Serum levels of a third PlGF isoform during pregnancy were determined and screening performance of PlGF-3 for PE and small for gestational age (SGA) was investigated.

MAIN OUTCOME MEASURES

Levels of serum and placental tissue PlGF 3 and predictive power of PlGF-3 for Pre-eclampsia and SGA.

RESULTS

PlGF-3 was below the detection limit of 1.6 pg/mL in most of the serum samples collected during pregnancy. Detected protein levels of PlGF-3 were not associated to be predictive for PE or SGA. However, measurable, and relatively higher amounts of PlGF-3 was extracted from placental tissue samples.

CONCLUSION

Data obtained indicates that very low amounts of PlGF-3 is present in blood but significantly higher amounts of protein is present in placental tissue where it is prominently associated with cellular membranes.

A Novel Early Pregnancy Risk Prediction Model for Gestational Diabetes Mellitus

INTRODUCTION

Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort.

METHODS

Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11-13+6 weeks' gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks' gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves.

RESULTS

Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89-0.94) overall, and 0.93 (95% CI 0.89-0.96) for early GDM, in a combined multivariate prediction model.

CONCLUSIONS

A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

First trimester combined screening biochemistry in detection of congenital heart defects

Objective: To evaluate the performance of first trimester biochemical markers, pregnancy-associated plasma protein-A (PAPP-A), free beta human chorionic gonadotropin (fβ-hCG), and nuchal translucency (NT) in detection of severe congenital heart defects (CHDs). Methods: During the study period from 1 January 2008 to 31 December 2011, biochemical markers and NT were measured in 31,144 women as part of voluntary first trimester screening program for Down's syndrome in Northern Finland. Data for 71 severe CHD cases and 762 controls were obtained from the hospital records and from the National Medical Birth Register, which records the birth of all liveborn and stillborn infants, and from the National Register of Congenital Malformations that receives information about all the CHD cases diagnosed in Finland. Results: Both PAPP-A and fβ-hCG multiple of median (MoM) values were decreased in all severe CHDs: 0.71 and 0.69 in ventricular septal defects (VSDs), 0.58 and 0.88 in tetralogy of Fallot cases (TOFs), 0.82 and 0.89 in hypoplastic left heart syndromes (HLHSs), and 0.88 and 0.96 in multiple defects, respectively. NT was increased in all study groups except of VSD group. ROC AUC was 0.72 for VSD when combining prior risk with PAPP-A and fβ-hCG. Adding NT did not improve the detection rate. With normal NT but decreased (<0.5 MoM) PAPP-A and fβ-hCG odds ratios for VSD and HLHS were 19.5 and 25.6, respectively. Conclusions: Maternal serum biochemistry improves the detection of CHDs compared to NT measurement only. In cases with normal NT measurement but low concentrations of both PAPP-A and fβ-hCG, an alert for possible CHD, especially VSD, could be given with thorough examination of fetal heart in later ultrasound scans.

Prediction of fetal growth restriction using estimated fetal weight vs a combined screening model in the third trimester

OBJECTIVES

To compare the performance of third-trimester screening, based on estimated fetal weight centile (EFWc) vs a combined model including maternal baseline characteristics, fetoplacental ultrasound and maternal biochemical markers, for the prediction of small-for-gestational-age (SGA) neonates and late-onset fetal growth restriction (FGR).

METHODS

This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester (32 + 0 to 36 + 6 weeks' gestation) evaluation. Maternal baseline characteristics, mean arterial pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently delivered a SGA neonate (n = 175), defined as birth weight < 10^th centile according to customized standards, and in a control group (n = 875). Among SGA cases, those with birth weight < 3^rd centile and/or abnormal uterine artery pulsatility index (UtA-PI) and/or abnormal cerebroplacental ratio (CPR) were classified as FGR. Logistic regression predictive models were developed for SGA and FGR, and their performance was compared with that obtained using EFWc alone.

RESULTS

In SGA cases, EFWc, CPR Z-score and maternal serum concentrations of unconjugated estriol and PlGF were significantly lower, while mean UtA-PI Z-score and lipocalin-2 and inhibin A concentrations were significantly higher, compared with controls. Using EFWc alone, 52% (area under receiver-operating characteristics curve (AUC), 0.82 (95% CI, 0.77-0.85)) of SGA and 64% (AUC, 0.86 (95% CI, 0.81-0.91)) of FGR cases were predicted at a 10% false-positive rate. A combined screening model including a-priori risk (maternal characteristics), EFWc, UtA-PI, PlGF and estriol (with lipocalin-2 for SGA) achieved a detection rate of 61% (AUC, 0.86 (95% CI, 0.83-0.89)) for SGA cases and 77% (AUC, 0.92 (95% CI, 0.88-0.95)) for FGR. The combined model for the prediction of SGA and FGR performed significantly better than did using EFWc alone (P < 0.001 and P = 0.002, respectively).

CONCLUSIONS

A multivariable integrative model of maternal characteristics, fetoplacental ultrasound and maternal biochemical markers modestly improved the detection of SGA and FGR cases at 32-36 weeks' gestation when compared with screening based on EFWc alone. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

Performance of first trimester biochemical markers and mean arterial pressure in prediction of early-onset pre-eclampsia

OBJECTIVE

To develop a predictive risk model for early-onset pre-eclampsia (EO-PE) using maternal characteristics, combined screening markers, previously reported biomarkers for PE and mean arterial pressure (MAP).

METHODS

This retrospective study was conducted at Oulu University hospital between 2006 and 2010. Maternal serum from first trimester combined screening was further analyzed for alpha fetoprotein (AFP), placental growth factor (PlGF), soluble tumor necrosis factor receptor-1 (sTNFR1), retinol binding protein-4 (RBP4), a disintegrin and metalloprotease-12 (ADAM12), soluble P-selectin (sP-selectin), follistatin like-3 (FSTL3), adiponectin, angiopoietin-2 (Ang-2) and sex hormone binding globulin (SHBG). First, the training sample set with 29 cases of EO-PE and 652 controls was developed to study whether these biomarkers separately or in combination with prior risk (maternal characteristics, first trimester pregnancy associated plasma protein-A (PAPP-A) and free beta human chorionic gonadotrophin (fβ-hCG)) could be used to predict the development of EO-PE. Second, the developed risk models were validated with a test sample set of 42 EO-PE and 141 control subjects. For the test set MAP data was also available.

RESULTS

Single marker statistically significant (ANOVA p<0.05) changes between control and EO-PE pregnancies were observed with AFP, RBP4 and sTNFR1 with both training and test sample sets. Based on the test sample set performances, the best detection rate, 47% for a 10% false positive rate, was achieved with PlGF and sTNFR1 added with prior risk and MAP.

CONCLUSION

Based on our results, the best first trimester biomarkers to predict the subsequent EO-PE were AFP, PlGF, RBP4 and sTNFR1. The risk models that performed best for the prediction of EO-PE included prior risk, MAP, sTNFR1 and AFP or PlGF or RBP4.

Performance of third-trimester combined screening model for prediction of adverse perinatal outcome

OBJECTIVE

To explore the potential value of third-trimester combined screening for the prediction of adverse perinatal outcome (APO) in the general population and among small-for-gestational-age (SGA) fetuses.

METHODS

This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester evaluation (32 + 0 to 36 + 6 weeks' gestation). Maternal baseline characteristics, mean arterial blood pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently had an APO (n = 148) and in a control group without perinatal complications (n = 902). APO was defined as the occurrence of stillbirth, umbilical artery cord blood pH < 7.15, 5-min Apgar score < 7 or emergency operative delivery for fetal distress. Logistic regression models were developed for the prediction of APO in the general population and among SGA cases (defined as customized birth weight < 10^th centile).

RESULTS

The prevalence of APO was 9.3% in the general population and 27.4% among SGA cases. In the general population, a combined screening model including a-priori risk (maternal characteristics), estimated fetal weight (EFW) centile, umbilical artery pulsatility index (UA-PI), estriol and PlGF achieved a detection rate for APO of 26% (area under receiver-operating characteristics curve (AUC), 0.59 (95% CI, 0.54-0.65)), at a 10% false-positive rate (FPR). Among SGA cases, a model including a-priori risk, EFW centile, UA-PI, cerebroplacental ratio, estriol and PlGF predicted 62% of APO (AUC, 0.86 (95% CI, 0.80-0.92)) at a FPR of 10%.

CONCLUSIONS

The use of fetal ultrasound and maternal biochemical markers at 32-36 weeks provides a poor prediction of APO in the general population. Although it remains limited, the performance of the screening model is improved when applied to fetuses with suboptimal fetal growth. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.

First trimester prediction of gestational diabetes mellitus: A clinical model based on maternal demographic parameters

AIM

Develop a first trimester risk prediction model for GDM based on maternal clinical characteristics in a large metropolitan multi-ethnic population and compare its performance to that of other recently published GDM prediction models and clinical risk scoring systems.

METHODS

A retrospective case control study of 248 women who developed GDM and 732 controls who did not. Maternal clinical parameters were prospectively obtained at 11-13⁺⁶ weeks' gestation. A predictive multivariate regression model for GDM was developed, evaluated using areas under the receiver-operating characteristic (AUC) curve. The performance of this model was then compared with other published GDM prediction models applied to our cohort and our existing clinical risk scoring system.

RESULTS

Previous GDM, family history of diabetes, age, south/east Asian ethnicity, parity and body mass index (BMI) were significant predictors for GDM. The AUC of our multivariate regression model was 0.88 (95% Confidence Interval 0.85-0.92). This performed better than other predictive models applied to our cohort (AUCs 0.77-0.82).

CONCLUSION

A multivariate model based on weighted maternal clinical risk factors accurately predicts GDM in early pregnancy and performs better than other proposed multivariate and clinical risk scoring models in a multiethnic cohort.

A technical and clinical evaluation of a new assay for inhibin A and its use in second trimester Down syndrome screening

BACKGROUND

The objective of the study was to compare a new AutoDELFIA® Inhibin A kit (B064-102) with the Access Inhibin A kit (A36097) using clinical specimens and to evaluate the AutoDELFIA® Inhibin A assay performance in screening for Down syndrome in the second trimester of pregnancy.

METHODS

Using clinical samples, we performed a method comparison between new and existing inhibin A kits and assessed AutoDELFIA® Inhibin A kit precision performance. Normal median values for the second trimester of pregnancy were also determined. Finally, we evaluated the screening performance of the AutoDELFIA® Inhibin A kit together with other second trimester biomarkers for the detection of Down syndrome.

RESULTS

The two methods showed a high degree of correlation (r=0.99, Pearson and Spearman correlation), and the average relative level difference between the methods at a concentration range of 41.7-1925 pg/mL was 19.6% [95% confidence interval (CI) from 17.6% to 21.5%]. The acceptable precision of the AutoDELFIA® Inhibin A kit was demonstrated: the within-lot CV% varied from 1.9% to 3.9%. The screening performance results show that AutoDELFIA® Inhibin A when added to a combination of other second trimester serum markers [human alpha foetoprotein (hAFP), free beta subunit of human chorionic gonadotropin (free hCGβ) and unconjugated estriol (uE3) or hAFP and free hCGβ] improves the detection rate of screening in both combinations.

CONCLUSIONS

The performance of the AutoDELFIA® Inhibin A assay is highly acceptable for routine laboratory use for screening Down syndrome in the second trimester of pregnancy.

First-Trimester Maternal Serum Amino Acids and Acylcarnitines Are Significant Predictors of Gestational Diabetes

BACKGROUND

Current screening methods for gestational diabetes mellitus (GDM) are insufficient in detecting the risk of GDM in the first trimester of the pregnancy. Recent metabolomic studies have detected altered amino acid and acylcarnitine concentrations in type 2 diabetes (T2D). Because of the similarities between T2D and GDM, the determination of these metabolites may be useful in early screening for GDM.

AIM

To evaluate the association between GDM and first-trimester maternal serum concentrations of ten amino acids and 31 acylcarnitines.

METHODS

This retrospective case-control study included data from pregnant women screened at Oulu University Hospital between 1.1.2008 and 31.12.2011. A total of 31,146 women participated voluntarily in a first-trimester combined screening (for chromosomal abnormalities). The study population included 69 women who developed GDM during pregnancy and 295 women without diabetes before or after pregnancy. The serum concentrations of ten amino acids and 31 acylcarnitines were analyzed from frozen serum samples taken in the first-trimester screening. Multiple of median (MoM) values were compared between the two groups.

RESULTS

In the GDM group, serum levels of arginine were significantly higher (1.13 MoM vs. 0.97 MoM), and those of glycine (0.93 MoM vs. 1.03 MoM) and 3-hydroxy-isovalerylcarnitine (0.86 MoM vs. 1.03 MoM) significantly lower compared to the control group (all p < 0.01). In each case, arginine, glycine, and 3-hydroxy-isovaleryl-carnitine would have detected 46%, 32%, and 39% of GDM cases, with a false-positive rate of 20%. Combining these three metabolites with the first-trimester serum marker pregnancy-associated plasma protein A (PAPP-A) and prior risk (age, BMI, and smoking) achieved a detection rate of 72%.

CONCLUSION

There are significant differences in the serum levels of arginine, glycine, and 3-hydroxy-isovalerylcarnitine between controls and women who subsequently develop GDM. These differences were already existent in the first trimester of the pregnancy. The use of metabolites in combination with prior risk and first-trimester PAPP-A represents a reliable method to identify women at risk of GDM.

Improved sensorimotor function by rolipram following focal cerebral ischemia in rats

PURPOSE

Rolipram, a specific phosphodiesterase 4 inhibitor (PDE4), is suggested to facilitate functional recovery following brain injury by activation of cAMP/CREB pathway. We examined the effect of rolipram on sensorimotor recovery in rats following transient occlusion of the middle cerebral artery (MCAO).

METHODS

Rats were subjected to transient MCAO for 2 h. Rolipram was administered at a dose of 0.1 or 1 mg/kg (i.p., twice a day, for 13 days) starting administration on postoperative day 2. Sensorimotor outcome was assessed using limb-placing, beam-walking and cylinder tests at baseline and 7, 14, and 21 days after MCAO.

RESULTS

Rolipram decreased locomotor activity and rearing, produced atypical head twitches, and possible hyperalgesia immediately after treatments, which were all considered as acute side effects. The analysis of hindlimb function utilizing beam-walking tests showed that overall performance was impaired in MCAO vehicle rats (p < 0.01) and MCAO rats treated with rolipram, at a dose of 0.1 mg/kg (p < 0.01), compared to sham-operated rats. Interestingly, MCAO rats treated with rolipram at a dose of 1.0 mg/kg had significantly fewer slips when traversing an elevated beam than those treated with a dose of 0.1 mg/kg (p < 0.05) indicating improved sensorimotor function. More importantly, hindlimb function at the higher rolipram dose was not different from sham-operated rats after cessation of drug treatment at day 21. There was a significant group effect (p < 0.001) in the cylinder test, however, this was due to the decreased use of the impaired forelimb in MCAO rats compared to sham-operated rats at day 7, 14 and 21. In addition, MCAO rats treated with rolipram seemed to use their impaired forelimbs less compared to MCAO controls. Limb-placing performance was severely impaired but not different among MCAO rats.

CONCLUSIONS

The present data suggest that rolipram provides some improvement in sensorimotor recovery in MCAO rats possibly by augmenting cAMP/CREB signalling, but this is masked by its side effects.

Chronic antidepressant treatment selectively increases expression of plasticity-related proteins in the hippocampus and medial prefrontal cortex of the rat

Antidepressants protect against hippocampal volume loss in humans and reverse stress-induced atrophic changes in animals thus supporting the hypothesis that the pathophysiology of stress-related disorders such as depression involves reductions in neuronal connectivity and this effect is reversible by antidepressant treatment. However, it is unclear which brain areas demonstrate such alterations in plasticity in response to antidepressant treatment. The aim of the present study was to examine the effect of antidepressant treatment on the expression of three plasticity-associated marker proteins, the polysialylated form of nerve cell adhesion molecule (PSA-NCAM), phosphorylated cyclic-AMP response element binding protein (pCREB) and growth-associated protein 43 (GAP-43), in the rat brain. To this end, rats were treated either acutely (60 min) or chronically (21 days) with imipramine (30 and 15 mg/kg, respectively) and the expression of PSA-NCAM, pCREB, and GAP-43 was assessed using immunohistochemistry. Initial mapping revealed that chronic imipramine treatment increased expression of these plasticity-associated proteins in the hippocampus, medial prefrontal cortex and piriform cortex but not in the other brain regions examined. Since PSA-NCAM and pCREB are expressed in recently-generated neurons in the dentate gyrus, it is likely that chronic imipramine treatment increased their expression in the hippocampus at least partially by increasing neurogenesis. In contrast, since chronic imipramine treatment is not associated with neurogenesis in the medial prefrontal cortex, increased expression of PSA-NCAM and pCREB in the prelimbic cortex implicates changes in synaptic connectivity in this brain region. Acute treatment with imipramine increased the number of pCREB positive nuclei in the hippocampus and the prefrontal cortex but did not alter expression of GAP-43 or PSA-NCAM in any of the brain regions examined. Taken together, the results of the present study suggest that antidepressant treatment increases synaptic plasticity and connectivity in brain regions associated with mood disorders.

Brain-derived neurotrophic factor and antidepressant drugs have different but coordinated effects on neuronal turnover, proliferation, and survival in the adult dentate gyrus

Antidepressants increase proliferation of neuronal progenitor cells and expression of brain-derived neurotrophic factor (BDNF) in the hippocampus. We investigated the role of BDNF signaling in antidepressant-induced neurogenesis by using transgenic mice with either reduced BDNF levels (BDNF+/-) or impaired trkB activation (trkB.T1-overexpressing mice). In both transgenic strains, chronic (21 d) imipramine treatment increased the number of bromodeoxyuridine (BrdU)-positive cells to degree similar to that seen in wild-type mice 24 h after BrdU administration, although the basal proliferation rate was increased in both transgenic strains. Three weeks after BrdU administration and the last antidepressant injection, the amount of newborn (BrdU- or TUC-4-positive) cells was significantly reduced in both BDNF+/- and trkB.T1-overexpressing mice, which suggests that normal BDNF signaling is required for the long-term survival of newborn hippocampal neurons. Moreover, the antidepressant-induced increase in the surviving BrdU-positive neurons seen in wild-type mice 3 weeks after treatment was essentially lost in mice with reduced BDNF signaling. Furthermore, we observed that chronic treatment with imipramine or fluoxetine produced a temporally similar increase in both BrdU-positive and terminal deoxynucleotidyl transferase-mediated biotinylated UTP nick end-labeled neurons in the dentate gyrus, indicating that these drugs simultaneously increase both neurogenesis and neuronal elimination. These data suggest that antidepressants increase turnover of hippocampal neurons rather than neurogenesis per se and that BDNF signaling is required for the long-term survival of newborn neurons in mouse hippocampus.

Activation of the TrkB neurotrophin receptor is induced by antidepressant drugs and is required for antidepressant-induced behavioral effects

Recent studies have indicated that exogenously administered neurotrophins produce antidepressant-like behavioral effects. We have here investigated the role of endogenous brain-derived neurotrophic factor (BDNF) and its receptor trkB in the mechanism of action of antidepressant drugs. We found that trkB.T1-overexpressing transgenic mice, which show reduced trkB activation in brain, as well as heterozygous BDNF null (BDNF(+/)-) mice, were resistant to the effects of antidepressants in the forced swim test, indicating that normal trkB signaling is required for the behavioral effects typically produced by antidepressants. In contrast, neurotrophin-3(+/)- mice showed a normal behavioral response to antidepressants. Furthermore, acute as well as chronic antidepressant treatment induced autophosphorylation and activation of trkB in cerebral cortex, particularly in the prefrontal and anterior cingulate cortex and hippocampus. Tyrosines in the trkB autophosphorylation site were phosphorylated in response to antidepressants, but phosphorylation of the shc binding site was not observed. Nevertheless, phosphorylation of cAMP response element-binding protein was increased by antidepressants in the prefrontal cortex concomitantly with trkB phosphorylation and this response was reduced in trkB.T1-overexpressing mice. Our data suggest that antidepressants acutely increase trkB signaling in a BDNF-dependent manner in cerebral cortex and that this signaling is required for the behavioral effects typical of antidepressant drugs. Neurotrophin signaling increased by antidepressants may induce formation and stabilization of synaptic connectivity, which gradually leads to the clinical antidepressive effects and mood recovery.