Newborn Screening for High-Risk Congenital Heart Disease by Dried Blood Spot Biomarker Analysis.

Importance: Congenital heart disease (CHD) is the most common human organ malformation, affecting approximately 1 of 125 newborns globally. Objectives: Assessing the performance of 2 diagnostic tests using minimal amounts of dried blood spots (DBS) to identify high-risk CHD compared with controls in a Swedish cohort of neonates. Design, Setting, and Participants: This diagnostic study took place in Sweden between 2019 and 2023 and enrolled full-term babies born between 2005 and 2023. All cases were identified through centralized pediatric cardiothoracic surgical services in Lund and Gothenburg, Sweden. Controls were followed up for 1 year to ensure no late presentations of high-risk CHD occurred. Cases were verified through surgical records and echocardiography. Exposure: High-risk CHD, defined as cases requiring cardiac surgical management during infancy due to evolving signs of heart failure or types in which the postnatal circulation depends on patency of the arterial duct. Using 3-µL DBS samples, automated quantitative tests for NT-proBNP and interleukin 1 receptor-like 1 (IL-1 RL1; formerly known as soluble ST2) were compared against established CHD screening methods. Main Outcomes and Measures: Performance of DBS tests to detect high-risk CHD using receiver operating characteristic curves; Bland-Altman and Pearson correlation analyses to compare IL-1 RL1 DBS with plasma blood levels. Results: A total of 313 newborns were included (mean [SD] gestational age, 39.4 [1.3] weeks; 181 [57.8%] male). Mean (SD) birthweight was 3495 (483) grams. Analyzed DBS samples included 217 CHD cases and 96 controls. Among the CHD cases, 188 participants (89.3%) were high-risk types, of which 73 (38.8%) were suspected prenatally. Of the 188 high-risk cases, 94 (50.0%) passed pulse oximetry screening and 36 (19.1%) were initially discharged after birth without diagnoses. Combining NT-proBNP and IL-1 RL1 tests performed well in comparison with existing screening methods and enabled additional identification of asymptomatic babies with receiver operating characteristic area under the curve 0.95 (95% CI, 0.93-0.98). Conclusions and relevance: In this diagnostic study, NT-proBNP and IL-1 RL1 DBS assays identified high-risk CHD in a timely manner, including in asymptomatic newborns, and improved overall screening performance in this cohort from Sweden. Prospective evaluation of this novel approach is warranted.

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.

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.

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.

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.

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.

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.

Fetal nuchal translucency in severe congenital heart defects: experiences in Northern Finland.

OBJECTIVE: To evaluate the performance of first-trimester measurement of fetal nuchal translucency (NT) in the detection of severe congenital heart defects (CHDs). METHODS: During the study period of 1 January 2008 - 31 December 2011, NT was measured in 31,144 women as a part of voluntary first-trimester screening program for Down's syndrome in Northern Finland. NT was measured by personnel trained on the job by the experienced staff. No certification or annual audits are required in Finland. However, the recommendation is that the examiner should perform 200 scans on average per year. Severe CHD was classified as a defect requiring surgery in the first year of life or a defect that led to the termination of the pregnancy. All severe CHDs diagnosed during the study period in Northern Finland could not be included in this study since all women did not participate in the first-trimester screening and some cases were missing important data. RESULTS: Fourteen (17.7%) out of 79 severe CHDs were found with NT cutoff of 3.5 mm. Amongst the 79 severe CHD cases, there were 17 chromosomal abnormalities. With NT cutoffs of 2.0 and 1.5 mm the detection rates would have increased to 25.3% (n = 20) and 46.8% (n = 37). Using a randomly selected control group of 762 women with normal pregnancy outcomes, false positive rates (FPRs) were calculated. For NT cutoffs of 1.5, 2.0 and 3.5 mm, the FPRs were, 18.5, 3.3 and 0.4%, respectively. CONCLUSIONS: A greater than 3.5 mm NT measurement in the first-trimester ultrasound is an indication to suspect a fetal heart defect but its sensitivity to detect severe CHD is poor. In our study, only 17.7% of severe CHDs would have been detected with an NT cutoff of 3.5 mm.

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 (fß-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 fß-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 fß-hCG. Adding NT did not improve the detection rate. With normal NT but decreased (<0.5 MoM) PAPP-A and fß-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 fß-hCG, an alert for possible CHD, especially VSD, could be given with thorough examination of fetal heart in later ultrasound scans.

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 hCGß, 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.

Evaluation of machine learning algorithms for improved risk assessment for Down's syndrome.

Prenatal screening generates a great amount of data that is used for predicting risk of various disorders. Prenatal risk assessment is based on multiple clinical variables and overall performance is defined by how well the risk algorithm is optimized for the population in question. This article evaluates machine learning algorithms to improve performance of first trimester screening of Down syndrome. Machine learning algorithms pose an adaptive alternative to develop better risk assessment models using the existing clinical variables. Two real-world data sets were used to experiment with multiple classification algorithms. Implemented models were tested with a third, real-world, data set and performance was compared to a predicate method, a commercial risk assessment software. Best performing deep neural network model gave an area under the curve of 0.96 and detection rate of 78% with 1% false positive rate with the test data. Support vector machine model gave area under the curve of 0.95 and detection rate of 61% with 1% false positive rate with the same test data. When compared with the predicate method, the best support vector machine model was slightly inferior, but an optimized deep neural network model was able to give higher detection rates with same false positive rate or similar detection rate but with markedly lower false positive rate. This finding could further improve the first trimester screening for Down syndrome, by using existing clinical variables and a large training data derived from a specific population.

A first trimester prediction model for gestational diabetes utilizing aneuploidy and pre-eclampsia screening markers.

OBJECTIVE: We examined whether first trimester aneuploidy and pre-eclampsia screening markers predict gestational diabetes mellitus (GDM) in a large multi-ethnic cohort and the influence of local population characteristics on markers. METHODS: Clinical and first trimester markers (mean arterial pressure (MAP), uterine artery pulsatility index (UtA PI), pregnancy associated plasma protein A (PAPP-A), free-ß human chorionic gonadotropin (free-hCGß)) were measured in a case-control study of 980 women (248 with GDM, 732 controls) at 11 to 13 + 6 weeks' gestation. Clinical parameters, MAP-, UtA PI-, PAPP-A-, and free-hCGß-multiples-of-the-median (MoM) were compared between GDM and controls; stratified by ethnicity, parity, and GDM diagnosis <24 versus ≥24 weeks' gestation. GDM model screening performance was evaluated using AUROC. RESULTS: PAPP-A- and UtA PI-MoM were significantly lower in GDM versus controls (median ((IQR) PAPP-A-MoM 0.81 (0.58-1.20) versus 1.00 (0.70-1.46); UtA PI-MoM 1.01 (0.82-1.21) versus 1.05 (0.84-1.29); p < .05). Previous GDM, family history of diabetes, south/east Asian ethnicity, parity, BMI, MAP, UtA PI, and PAPP-A were significant predictors in multivariate analysis (p < .05). The AUC for a model based on clinical parameters was 0.88 (95%CI 0.85-0.92), increasing to 0.90 (95%CI 0.87-0.92) with first trimester markers combined. The combined model best predicted GDM <24 weeks' gestation (AUC 0.96 (95%CI 0.94-0.98)). CONCLUSIONS: Addition of aneuploidy and pre-eclampsia markers is cost-effective and enhances early GDM detection, accurately identifying early GDM, a high-risk cohort requiring early detection, and intervention. Ethnicity and parity modified marker association with GDM, suggesting differences in pathophysiology and vascular risk.

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 (fß-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.

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 hCGß) and unconjugated estriol (uE3) or hAFP and free hCGß] 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.

Design, synthesis and in vitro/in vivo evaluation of orally bioavailable prodrugs of a catechol-O-methyltransferase inhibitor.

Compound 1 is an investigational, nanomolar inhibitor of catechol-O-methyltransferase (COMT) that suffers from poor oral bioavailability, most probably due to its low lipophilicity throughout most of the gastrointestinal tract and, to a lesser extent, its rapid systemic clearance. Several lipophilic esters were designed as prodrugs and synthesized in an attempt to optimize presystemic drug absorption. A modest twofold increase in 6-h exposure of 1 was observed with two prodrugs, compared to that of 1, after oral treatment in rats.