Placental cytochrome P450 methylomes in infants exposed to prenatal opioids: exploring the effects of neonatal opioid withdrawal syndrome on health horizons

Background: Neonatal opioid withdrawal syndrome (NOWS), arises due to increased opioid use during pregnancy. Cytochrome P450 (CYP) enzymes play a pivotal role in metabolizing a wide range of substances in the human body, including opioids, other drugs, toxins, and endogenous compounds. The association between CYP gene methylation and opioid effects is unexplored and it could offer promising insights. Objective: To investigate the impact of prenatal opioid exposure on disrupted CYPs in infants and their anticipated long-term clinical implications. Study Design: DNA methylation levels of CYP genes were analyzed in a cohort of 96 placental tissues using Illumina Infinium MethylationEPIC (850 k) BeadChips. This involved three groups of placental tissues: 32 from mothers with infants exposed to opioids prenatally requiring pharmacologic treatment for NOWS, 32 from mothers with prenatally opioid-exposed infants not needing NOWS treatment, and 32 from unexposed control mothers. Results: The study identified 20 significantly differentially methylated CpG sites associated with 17 distinct CYP genes, with 14 CpGs showing reduced methylation across 14 genes (CYP19A1, CYP1A2, CYP4V2, CYP1B1, CYP24A1, CYP26B1, CYP26C1, CYP2C18, CYP2C9, CYP2U1, CYP39A1, CYP2R1, CYP4Z1, CYP2D7P1 and), while 8 exhibited hypermethylation (CYP51A1, CYP26B1, CYP2R1, CYP2U1, CYP4X1, CYP1A2, CYP2W1, and CYP4V2). Genes such as CYP1A2, CYP26B1, CYP2R1, CYP2U1, and CYP4V2 exhibited both increased and decreased methylation. These genes are crucial for metabolizing eicosanoids, fatty acids, drugs, and diverse substances. Conclusion: The study identified profound methylation changes in multiple CYP genes in the placental tissues relevant to NOWS. This suggests that disruption of DNA methylation patterns in CYP transcripts might play a role in NOWS and may serve as valuable biomarkers, suggesting a future pathway for personalized treatment. Further research is needed to confirm these findings and explore their potential for diagnosis and treatment.

The longitudinal biochemical profiling of TBI in a drop weight model of TBI

Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains to be elucidated. Metabolomics has the potential to identify metabolic pathways and unique biochemical profiles associated with TBI. Herein, we employed a longitudinal metabolomics approach to study TBI in a weight drop mouse model to reveal metabolic changes associated with TBI pathogenesis, severity, and secondary injury. Using proton nuclear magnetic resonance (1H NMR) spectroscopy, we biochemically profiled post-mortem brain from mice that suffered mild TBI (N = 25; 13 male and 12 female), severe TBI (N = 24; 11 male and 13 female) and sham controls (N = 16; 11 male and 5 female) at baseline, day 1 and day 7 following the injury. 1H NMR-based metabolomics, in combination with bioinformatic analyses, highlights a few significant metabolites associated with TBI severity and perturbed metabolism related to the injury. We report that the concentrations of taurine, creatinine, adenine, dimethylamine, histidine, N-Acetyl aspartate, and glucose 1-phosphate are all associated with TBI severity. Longitudinal metabolic observation of brain tissue revealed that mild TBI and severe TBI lead distinct metabolic profile changes. A multi-class model was able to classify the severity of injury as well as time after TBI with estimated 86% accuracy. Further, we identified a high degree of correlation between respective hemisphere metabolic profiles (r > 0.84, p < 0.05, Pearson correlation). This study highlights the metabolic changes associated with underlying TBI severity and secondary injury. While comprehensive, future studies should investigate whether: (a) the biochemical pathways highlighted here are recapitulated in the brain of TBI sufferers and (b) if the panel of biomarkers are also as effective in less invasively harvested biomatrices, for objective and rapid identification of TBI severity and prognosis.

Placental microRNA methylome signatures may serve as biomarkers and therapeutic targets for prenatally opioid-exposed infants with neonatal opioid withdrawal syndrome

Introduction: The neonate exposed to opioids in utero faces a constellation of withdrawal symptoms postpartum commonly called neonatal opioid withdrawal syndrome (NOWS). The incidence of NOWS has increased in recent years due to the opioid epidemic. MicroRNAs (miRNAs) are small non-coding RNA molecules that play a crucial role in gene regulation. Epigenetic variations in microRNAs (miRNAs) and their impact on addiction-related processes is a rapidly evolving area of research. Methods: The Illumina Infinium Methylation EPIC BeadChip was used to analyze DNA methylation levels of miRNA-encoding genes in 96 human placental tissues to identify miRNA gene methylation profiles as-sociated with NOWS: 32 from mothers whose prenatally opioid-exposed infants required pharmacologic management for NOWS, 32 from mothers whose prenatally opioid-exposed infants did not require treat-ment for NOWS, and 32 unexposed controls. Results: The study identified 46 significantly differentially methylated (FDR p-value ≤ 0.05) CpGs associated with 47 unique miRNAs, with a receiver operating characteristic (ROC) area under the curve (AUC) ≥0.75 including 28 hypomethylated and 18 hypermethylated CpGs as potentially associated with NOWS. These dysregulated microRNA methylation patterns may be a contributing factor to NOWS pathogenesis. Conclusion: This is the first study to analyze miRNA methylation profiles in NOWS infants and illustrates the unique role miRNAs might have in diagnosing and treating the disease. Furthermore, these data may provide a step toward feasible precision medicine for NOWS babies as well.

Fetal effects of mild maternal COVID-19 infection: metabolomic profiling of cord blood

INTRODUCTION

The impact of maternal coronavirus disease 2019 (COVID-19) infection on fetal health remains to be precisely characterized.

OBJECTIVES

Using metabolomic profiling of newborn umbilical cord blood, we aimed to investigate the potential fetal biological consequences of maternal COVID-19 infection.

METHODS

Cord blood plasma samples from 23 mild COVID-19 cases (mother infected/newborn negative) and 23 gestational age-matched controls were analyzed using nuclear magnetic spectroscopy and liquid chromatography coupled with mass spectrometry. Metabolite set enrichment analysis (MSEA) was used to evaluate altered biochemical pathways due to COVID-19 intrauterine exposure. Logistic regression models were developed using metabolites to predict intrauterine exposure.

RESULTS

Significant concentration differences between groups (p-value < 0.05) were observed in 19 metabolites. Elevated levels of glucocorticoids, pyruvate, lactate, purine metabolites, phenylalanine, and branched-chain amino acids of valine and isoleucine were discovered in cases while ceramide subclasses were decreased. The top metabolite model including cortisol and ceramide (d18:1/23:0) achieved an Area under the Receiver Operating Characteristics curve (95% CI) = 0.841 (0.725-0.957) for detecting fetal exposure to maternal COVID-19 infection. MSEA highlighted steroidogenesis, pyruvate metabolism, gluconeogenesis, and the Warburg effect as the major perturbed metabolic pathways (p-value < 0.05). These changes indicate fetal increased oxidative metabolism, hyperinsulinemia, and inflammatory response.

CONCLUSION

We present fetal biochemical changes related to intrauterine inflammation and altered energy metabolism in cases of mild maternal COVID-19 infection despite the absence of viral infection. Elucidation of the long-term consequences of these findings is imperative considering the large number of exposures in the population.

Alzheimer's Precision Neurology: Epigenetics of Cytochrome P450 Genes in Circulating Cell-Free DNA for Disease Prediction and Mechanism

Precision neurology combines high-throughput technologies and statistical modeling to identify novel disease pathways and predictive biomarkers in Alzheimer's disease (AD). Brain cytochrome P450 (CYP) genes are major regulators of cholesterol, sex hormone, and xenobiotic metabolism, and they could play important roles in neurodegenerative disorders. Increasing evidence suggests that epigenetic factors contribute to AD development. We evaluated cytosine ('CpG')-based DNA methylation changes in AD using circulating cell-free DNA (cfDNA), to which neuronal cells are known to contribute. We investigated CYP-based mechanisms for AD pathogenesis and epigenetic biomarkers for disease detection. We performed a case-control study using 25 patients with AD and 23 cognitively healthy controls using the cfDNA of CYP genes. We performed a logistic regression analysis using the MetaboAnalyst software computer program and a molecular pathway analysis based on epigenetically altered CYP genes using the Cytoscape program. We identified 130 significantly (false discovery rate correction q-value < 0.05) differentially methylated CpG sites within the CYP genes. The top two differentially methylated genes identified were CYP51A1 and CYP2S1. The significant molecular pathways that were perturbed in AD cfDNA were (i) androgen and estrogen biosynthesis and metabolism, (ii) C21 steroid hormone biosynthesis and metabolism, and (iii) arachidonic acid metabolism. Existing evidence suggests a potential role of each of these biochemical pathways in AD pathogenesis. Next, we randomly divided the study group into discovery and validation sub-sets, each consisting of patients with AD and control patients. Regression models for AD prediction based on CYP CpG methylation markers were developed in the discovery or training group and tested in the independent validation group. The CYP biomarkers achieved a high predictive accuracy. After a 10-fold cross-validation, the combination of cg17852385/cg23101118 + cg14355428/cg22536554 achieved an AUC (95% CI) of 0.928 (0.787~1.00), with 100% sensitivity and 92.3% specificity for AD detection in the discovery group. The performance remained high in the independent validation or test group, achieving an AUC (95% CI) of 0.942 (0.905~0.979) with a 90% sensitivity and specificity. Our findings suggest that the epigenetic modification of CYP genes may play an important role in AD pathogenesis and that circulating CYP-based cfDNA biomarkers have the potential to accurately and non-invasively detect AD.

Artificial Intelligence and Circulating Cell-Free DNA Methylation Profiling: Mechanism and Detection of Alzheimer's Disease

Background: Despite extensive efforts, significant gaps remain in our understanding of Alzheimer’s disease (AD) pathophysiology. Novel approaches using circulating cell-free DNA (cfDNA) have the potential to revolutionize our understanding of neurodegenerative disorders. Methods: We performed DNA methylation profiling of cfDNA from AD patients and compared them to cognitively normal controls. Six Artificial Intelligence (AI) platforms were utilized for the diagnosis of AD while enrichment analysis was used to elucidate the pathogenesis of AD. Results: A total of 3684 CpGs were significantly (adj. p-value < 0.05) differentially methylated in AD versus controls. All six AI algorithms achieved high predictive accuracy (AUC = 0.949−0.998) in an independent test group. As an example, Deep Learning (DL) achieved an AUC (95% CI) = 0.99 (0.95−1.0), with 94.5% sensitivity and specificity. Conclusion: We describe numerous epigenetically altered genes which were previously reported to be differentially expressed in the brain of AD sufferers. Genes identified by AI to be the best predictors of AD were either known to be expressed in the brain or have been previously linked to AD. We highlight enrichment in the Calcium signaling pathway, Glutamatergic synapse, Hedgehog signaling pathway, Axon guidance and Olfactory transduction in AD sufferers. To the best of our knowledge, this is the first reported genome-wide DNA methylation study using cfDNA to detect AD.

Artificial intelligence and placental DNA methylation: newborn prediction and molecular mechanisms of autism in preterm children

BACKGROUND

Autism Spectrum Disorder (ASD) represents a heterogeneous group of disorders with a complex genetic and epigenomic etiology. DNA methylation is the most extensively studied epigenomic mechanism and correlates with altered gene expression. Artificial intelligence (AI) is a powerful tool for group segregation and for handling the large volume of data generated in omics experiments.

METHODS

We performed genome-wide methylation analysis for differential methylation of cytosine nucleotide (CpG) was performed in 20 postpartum placental tissue samples from preterm births. Ten newborns went on to develop autism (Autistic Disorder subtype) and there were 10 unaffected controls. AI including Deep Learning (AI-DL) platforms were used to identify and rank cytosine methylation markers for ASD detection. Ingenuity Pathway Analysis (IPA) to identify genes and molecular pathways that were dysregulated in autism.

RESULTS

We identified 4870 CpG loci comprising 2868 genes that were significantly differentially methylated in ASD compared to controls. Of these 431 CpGs met the stringent EWAS threshold (p-value <5 × 10^-8) along with ≥10% methylation difference between CpGs in cases and controls. DL accurately predicted autism with an AUC (95% CI) of 1.00 (1-1) and sensitivity and specificity of 100% using a combination of 5 CpGs [cg13858611 (NRN1), cg09228833 (ZNF217), cg06179765 (GPNMB), cg08814105 (NKX2-5), cg27092191 (ZNF267)] CpG markers. IPA identified five prenatally dysregulated molecular pathways linked to ASD.

CONCLUSIONS

The present study provides substantial evidence that epigenetic differences in placental tissue are associated with autism development and raises the prospect of early and accurate detection of the disorder.

New-onset postpartum preeclampsia: epigenetic mechanism and prediction

OBJECTIVE

Placental cytosine (CpG) methylation was measured to predict new-onset postpartum preeclampsia (NOPP) and interrogate its molecular pathogenesis.

METHODS

NOPP was defined as patients with a new diagnosis of postpartum preeclampsia developing ≥48 h to ≤6 weeks after delivery with no prior hypertensive disorders. Placental tissue was obtained from 12 NOPP cases and 12 normotensive controls. Genome-wide individual cytosine (CpG) methylation level was measured with the Infinium MethylationEPIC BeadChip array. Significant differential methylation (NOPP vs. controls) for individual CpG loci was defined as false discovery rate (FDR) p value <.05. Gene functional enrichment using Qiagen's ingenuity pathway analysis (IPA) was performed to help elucidate the molecular pathogenesis of NOPP. A logistic regression model for NOPP prediction based on the methylation level in a combination of CpG loci was generated. The area under the receiver operating characteristic curves (AUC [95% CI]) sensitivity, and specificity for NOPP prediction based on the CpG methylation level was calculated for each locus.

RESULTS

There were 537 (in 540 separate genes) significantly (FDR p<.05 with a ≥ 2.0-fold methylation difference) differentially methylated CpG loci between the groups. A total of 143 individual CpG markers had excellent individual predictive accuracy for NOPP prediction (AUC ≥0.80), of which 14 markers had outstanding accuracy (AUC ≥0.90). A logistic regression model based on five CpG markers yielded an AUC (95% CI)=0.99 (0.95-0.99) with sensitivity 95% and specificity 93% for NOPP prediction. IPA revealed dysregulation of critical pathways (e.g., angiogenesis, chronic inflammation, and epithelial-mesenchymal transition) known to be linked to classic preeclampsia, in addition to other previously undescribed genes/pathways.

CONCLUSIONS

There was significant placental epigenetic dysregulation in NOPP. NOPP shared both common and unique molecular pathways with classic preeclampsia. Finally, we have identified novel potential biomarkers for the early post-partum prediction of NOPP.

Placental DNA methylation changes and the early prediction of autism in full-term newborns

Autism spectrum disorder (ASD) is associated with abnormal brain development during fetal life. Overall, increasing evidence indicates an important role of epigenetic dysfunction in ASD. The placenta is critical to and produces neurotransmitters that regulate fetal brain development. We hypothesized that placental DNA methylation changes are a feature of the fetal development of the autistic brain and importantly could help to elucidate the early pathogenesis and prediction of these disorders. Genome-wide methylation using placental tissue from the full-term autistic disorder subtype was performed using the Illumina 450K array. The study consisted of 14 cases and 10 control subjects. Significantly epigenetically altered CpG loci (FDR p-value <0.05) in autism were identified. Ingenuity Pathway Analysis (IPA) was further used to identify molecular pathways that were over-represented (epigenetically dysregulated) in autism. Six Artificial Intelligence (AI) algorithms including Deep Learning (DL) to determine the predictive accuracy of CpG markers for autism detection. We identified 9655 CpGs differentially methylated in autism. Among them, 2802 CpGs were inter- or non-genic and 6853 intragenic. The latter involved 4129 genes. AI analysis of differentially methylated loci appeared highly accurate for autism detection. DL yielded an AUC (95% CI) of 1.00 (1.00-1.00) for autism detection using intra- or intergenic markers by themselves or combined. The biological functional enrichment showed, four significant functions that were affected in autism: quantity of synapse, microtubule dynamics, neuritogenesis, and abnormal morphology of neurons. In this preliminary study, significant placental DNA methylation changes. AI had high accuracy for the prediction of subsequent autism development in newborns. Finally, biologically functional relevant gene pathways were identified that may play a significant role in early fetal neurodevelopmental influences on later cognition and social behavior.

Placental DNA methylation profiles in opioid-exposed pregnancies and associations with the neonatal opioid withdrawal syndrome

Opioid abuse during pregnancy can result in Neonatal Opioid Withdrawal Syndrome (NOWS). We investigated genome-wide methylation analyses of 96 placental tissue samples, including 32 prenatally opioid-exposed infants with NOWS who needed therapy (+Opioids/+NOWS), 32 prenatally opioid-exposed infants with NOWS who did not require treatment (+Opioids/-NOWS), and 32 prenatally unexposed controls (-Opioids/-NOWS, control). Statistics, bioinformatics, Artificial Intelligence (AI), including Deep Learning (DL), and Ingenuity Pathway Analyses (IPA) were performed. We identified 17 dysregulated pathways thought to be important in the pathophysiology of NOWS and reported accurate AI prediction of NOWS diagnoses. The DL had an AUC (95% CI) =0.98 (0.95-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS from the +Opioids/-NOWS group and AUCs (95% CI) =1.00 (1.0-1.0) with a sensitivity and specificity of 100% for distinguishing NOWS versus control and + Opioids/-NOWS group versus controls. This study provides strong evidence of methylation dysregulation of placental tissue in NOWS development.

Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers

INTRODUCTION

Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues.

OBJECTIVES

As little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers.

METHODS

Using a combination of ¹H NMR and DI/LC-MS/MS we quantitatively profiled the metabolome of the posterolateral cerebellum from post-mortem human brain harvested from people who suffered with ASD (n = 11) and compared them with age-matched controls (n = 10).

RESULTS

We accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively.

CONCLUSION

For the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.

Metabolomic identification of placental alterations in fetal growth restriction

Introduction: Fetal growth restriction (FGR), viz., birth weight <10th percentile is a common pregnancy complication which increases the risk of adverse fetal and newborn outcomes. The placenta is the key organ for fetal growth as it controls oxygen and nutrient availability. This study aims to elucidate the mechanisms of and identify putative placental biomarkers for FGR using high-resolution metabolomics.Methods: Placenta samples from 19 FGR cases and 30 controls were analyzed using proton magnetic resonance (¹H NMR) spectroscopy and direct flow injection mass spectrometry with reverse-phase liquid-chromatography mass spectrometry (DI-LC-MS/MS). Significant concentration differences (p-value <.05) in 179 of the 220 metabolites were measured.Results: Of the 179 metabolites, 176 (98.3%) had reduced placental levels in FGR cases. The best performing metabolite model: 3-hydroxybutyrate, glycine and PCaaC42:0 achieved an AUC (95% CI) = 0.912 (0.814-1.000) with a sensitivity of 86.7% and specificity of 84.2% for FGR detection. Metabolite set enrichment analysis (MSEA) revealed significant (p < .05) perturbation of multiple placental metabolite pathways including urea metabolism, ammonia recycling, porphyrin metabolism, bile acid biosynthesis, galactose metabolism and perturbed protein biosynthesis.Conclusion: The placental metabolic pathway analysis revealed abnormalities that are consistent with fetal hepatic dysfunction in FGR. Near global reduction of metabolite concentrations was found in the placenta from FGR cases and metabolites demonstrated excellent diagnostic accuracy for FGR detection.

Precision cardiovascular medicine: artificial intelligence and epigenetics for the pathogenesis and prediction of coarctation in neonates

Background: Advances in omics and computational Artificial Intelligence (AI) have been said to be key to meeting the objectives of precision cardiovascular medicine. The focus of precision medicine includes a better assessment of disease risk and understanding of disease mechanisms. Our objective was to determine whether significant epigenetic changes occur in isolated, non-syndromic CoA. Further, we evaluated the AI analysis of DNA methylation for the prediction of CoA.Methods: Genome-wide DNA methylation analysis of newborn blood DNA was performed in 24 isolated, non-syndromic CoA cases and 16 controls using the Illumina HumanMethylation450 BeadChip arrays. Cytosine nucleotide (CpG) methylation changes in CoA in each of 450,000 CpG loci were determined. Ingenuity pathway analysis (IPA) was performed to identify molecular and disease pathways that were epigenetically dysregulated. Using methylation data, six artificial intelligence (AI) platforms including deep learning (DL) was used for CoA detection.Results: We identified significant (FDR p-value ≤ .05) methylation changes in 65 different CpG sites located in 75 genes in CoA subjects. DL achieved an AUC (95% CI) = 0.97 (0.80-1) with 95% sensitivity and 98% specificity. Gene ontology (GO) analysis yielded epigenetic alterations in important cardiovascular developmental genes and biological processes: abnormal morphology of cardiovascular system, left ventricular dysfunction, heart conduction disorder, thrombus formation, and coronary artery disease.Conclusion: In an exploratory study we report the use of AI and epigenomics to achieve important objectives of precision cardiovascular medicine. Accurate prediction of CoA was achieved using a newborn blood spot. Further, we provided evidence of a significant epigenetic etiology in isolated CoA development.

Metabolomic identification of novel diagnostic biomarkers in ectopic pregnancy

INTRODUCTION

Ectopic pregnancy (EP) is a potentially life-threatening condition and early diagnosis still remains a challenge, causing a delay in management leading to tubal rupture.

OBJECTIVES

To identify putative plasma biomarkers for the detection of tubal EP and elucidate altered biochemical pathways in EP compared to intrauterine pregnancies.

METHODS

This case-control study included prospective recruitment of 39 tubal EP cases and 89 early intrauterine pregnancy controls. Plasma samples were biochemically profiled using proton nuclear magnetic resonance spectroscopy (¹H NMR). To avoid over-fitting, datasets were randomly divided into a discovery group (26 cases vs 60 controls) and a test group (13 cases and 29 controls). Logistic regression models were developed in the discovery group and validated in the independent test group. Area under the receiver operating characteristics curve (AUC), 95% confidence interval (CI), sensitivity, and specificity values were calculated.

RESULTS

In total 13 of 43 (30.3%) metabolite concentrations were significantly altered in EP plasma (p < 0.05). Metabolomic profiling yielded significant separation between EP and controls (p < 0.05). Independent validation of a two-metabolite model consisting of lactate and acetate, achieved an AUC (95% CI) = 0.935 (0.843-1.000) with a sensitivity of 92.3% and specificity of 96.6%. The second metabolite model (D-glucose, pyruvate, acetoacetate) performed well with an AUC (95% CI) = 0.822 (0.657-0.988) and a sensitivity of 84.6% and specificity of 86.2%.

CONCLUSION

We report novel metabolomic biomarkers with a high accuracy for the detection of EP. Accurate biomarkers could potentially result in improved early diagnosis of tubal EP cases.

Artificial intelligence and amniotic fluid multiomics: prediction of perinatal outcome in asymptomatic women with short cervix

OBJECTIVE

To evaluate the application of artificial intelligence (AI), i.e. deep learning and other machine-learning techniques, to amniotic fluid (AF) metabolomics and proteomics, alone and in combination with sonographic, clinical and demographic factors, in the prediction of perinatal outcome in asymptomatic pregnant women with short cervical length (CL).

METHODS

AF samples, which had been obtained in the second trimester from asymptomatic women with short CL (< 15 mm) identified on transvaginal ultrasound, were analyzed. CL, funneling and the presence of AF 'sludge' were assessed in all cases close to the time of amniocentesis. A combination of liquid chromatography coupled with mass spectrometry and proton nuclear magnetic resonance spectroscopy-based metabolomics, as well as targeted proteomics analysis, including chemokines, cytokines and growth factors, was performed on the AF samples. To determine the robustness of the markers, we used six different machine-learning techniques, including deep learning, to predict preterm delivery < 34 weeks, latency period prior to delivery < 28 days after amniocentesis and requirement for admission to a neonatal intensive care unit (NICU). Omics biomarkers were evaluated alone and in combination with standard sonographic, clinical and demographic factors to predict outcome. Predictive accuracy was assessed using the area under the receiver-operating characteristics curve (AUC) with 95% CI, sensitivity and specificity.

RESULTS

Of the 32 patients included in the study, complete omics, demographic and clinical data and outcome information were available for 26. Of these, 11 (42.3%) patients delivered ≥ 34 weeks, while 15 (57.7%) delivered < 34 weeks. There was no statistically significant difference in CL between these two groups (mean ± SD, 11.2 ± 4.4 mm vs 8.9 ± 5.3 mm, P = 0.31). Using combined omics, demographic and clinical data, deep learning displayed good to excellent performance, with an AUC (95% CI) of 0.890 (0.810-0.970) for delivery < 34 weeks' gestation, 0.890 (0.790-0.990) for delivery < 28 days post-amniocentesis and 0.792 (0.689-0.894) for NICU admission. These values were higher overall than for the other five machine-learning methods, although each individual machine-learning technique yielded statistically significant prediction of the different perinatal outcomes.

CONCLUSIONS

This is the first study to report use of AI with AF proteomics and metabolomics and ultrasound assessment in pregnancy. Machine learning, particularly deep learning, achieved good to excellent prediction of perinatal outcome in asymptomatic pregnant women with short CL in the second trimester. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Placental epigenetics for evaluation of fetal congenital heart defects: Ventricular Septal Defect (VSD)

Ventricular Septal Defect (VSD), the most common congenital heart defect, is characterized by a hole in the septum between the right and left ventricles. The pathogenesis of VSD is unknown in most clinical cases. There is a paucity of data relevant to epigenetic changes in VSD. The placenta is a fetal tissue crucial in cardiac development and a potentially useful surrogate for evaluating the development of heart tissue. To understand epigenetic mechanisms that may play a role in the development of VSD, genome-wide DNA methylation assay on placentas of 8 term subjects with isolated VSD and no known or suspected genetic syndromes and 10 unaffected controls was performed using the Illumina HumanMethylation450 BeadChip assay. We identified a total of 80 highly accurate potential CpGs in 80 genes for detection of VSD; area under the receiver operating characteristic curve (AUC ROC) 1.0 with significant 95% CI (FDR) p-values < 0.05 for each individual locus. The biological processes and functions for many of these differentially methylated genes are previously known to be associated with heart development or disease, including cardiac ventricle development (HEY2, ISL1), heart looping (SRF), cardiac muscle cell differentiation (ACTC1, HEY2), cardiac septum development (ISL1), heart morphogenesis (SRF, HEY2, ISL1, HEYL), Notch signaling pathway (HEY2, HEYL), cardiac chamber development (ISL1), and cardiac muscle tissue development (ACTC1, ISL1). In addition, we identified 8 microRNAs that have the potential to be biomarkers for the detection of VSD including: miR-191, miR-548F1, miR-148A, miR-423, miR-92B, miR-611, miR-2110, and miR-548H4. To our knowledge this is the first report in which placental analysis has been used for determining the pathogenesis of and predicting VSD.

Metabolomic Profiling of Cerebral Palsy Brain Tissue Reveals Novel Central Biomarkers and Biochemical Pathways Associated with the Disease: A Pilot Study

Cerebral palsy (CP) is one of the most common causes of motor disability in childhood, with complex and heterogeneous etiopathophysiology and clinical presentation. Understanding the metabolic processes associated with the disease may aid in the discovery of preventive measures and therapy. Tissue samples (caudate nucleus) were obtained from post-mortem CP cases (n = 9) and age- and gender-matched control subjects (n = 11). We employed a targeted metabolomics approach using both ¹H NMR and direct injection liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 55 metabolites using ¹H NMR and 186 using DI/LC-MS/MS. Among the 222 detected metabolites, 27 showed significant concentration changes between CP cases and controls. Glycerophospholipids and urea were the most commonly selected metabolites used to develop predictive models capable of discriminating between CP and controls. Metabolomics enrichment analysis identified folate, propanoate, and androgen/estrogen metabolism as the top three significantly perturbed pathways. We report for the first time the metabolomic profiling of post-mortem brain tissue from patients who died from cerebral palsy. These findings could help to further investigate the complex etiopathophysiology of CP while identifying predictive, central biomarkers of CP.

Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

INTRODUCTION

Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy.

OBJECTIVES

The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer.

METHODS

A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls.

RESULTS

Using partial least squares-discriminant analysis, we observed significant separation between all groups (p < 0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI) = 0.940 and 0.929 for HG and LG, respectively.

CONCLUSION

These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

Metabolomic Profiling of Bile Acids in an Experimental Model of Prodromal Parkinson's Disease

For people with Parkinson’s disease (PD), considered the most common neurodegenerative disease behind Alzheimer’s disease, accurate diagnosis is dependent on many factors; however, misdiagnosis is extremely common in the prodromal phases of the disease, when treatment is thought to be most effective. Currently, there are no robust biomarkers that aid in the early diagnosis of PD. Following previously reported work by our group, we accurately measured the concentrations of 18 bile acids in the serum of a prodromal mouse model of PD. We identified three bile acids at significantly different concentrations (p < 0.05) when mice representing a prodromal PD model were compared with controls. These include ω-murichoclic acid (MCAo), tauroursodeoxycholic acid (TUDCA) and ursodeoxycholic acid (UDCA). All were down-regulated in prodromal PD mice with TUDCA and UDCA at significantly lower levels (17-fold and 14-fold decrease, respectively). Using the concentration of three bile acids combined with logistic regression, we can discriminate between prodromal PD mice from control mice with high accuracy (AUC (95% CI) = 0.906 (0.777–1.000)) following cross validation. Our study highlights the need to investigate bile acids as potential biomarkers that predict PD and possibly reflect the progression of manifest PD.

Biochemical Profiling of the Brain and Blood Metabolome in a Mouse Model of Prodromal Parkinson's Disease Reveals Distinct Metabolic Profiles

Parkinson's disease is the second most common neurodegenerative disease. In the vast majority of cases the origin is not genetic and the cause is not well understood, although progressive accumulation of α-synuclein aggregates appears central to the pathogenesis. Currently, treatments that slow disease progression are lacking, and there are no robust biomarkers that can facilitate the development of such treatments or act as aids in early diagnosis. Therefore, we have defined metabolomic changes in the brain and serum in an animal model of prodromal Parkinson's disease. We biochemically profiled the brain tissue and serum in a mouse model with progressive synucleinopathy propagation in the brain triggered by unilateral injection of preformed α-synuclein fibrils in the olfactory bulb. In total, we accurately identified and quantified 71 metabolites in the brain and 182 in serum using ¹H NMR and targeted mass spectrometry, respectively. Using multivariate analysis, we accurately identified which metabolites explain the most variation between cases and controls. Using pathway enrichment analysis, we highlight significantly perturbed biochemical pathways in the brain and correlate these with the progression of the disease. Furthermore, we identified the top six discriminatory metabolites and were able to develop a model capable of identifying animals with the pathology from healthy controls with high accuracy (AUC (95% CI) = 0.861 (0.755-0.968)). Our study highlights the utility of metabolomics in identifying elements of Parkinson's disease pathogenesis and for the development of early diagnostic biomarkers of the disease.

First-trimester metabolomic prediction of stillbirth

Background: Stillbirth remains a major problem in both developing and developed countries. Omics evaluation of stillbirth has been highlighted as a top research priority. Objective: To identify new putative first-trimester biomarkers in maternal serum for stillbirth prediction using metabolomics-based approach. Methods: Targeted, nuclear magnetic resonance (NMR) and mass spectrometry (MS), and untargeted liquid chromatography-MS (LC-MS) metabolomic analyses were performed on first-trimester maternal serum obtained from 60 cases that subsequently had a stillbirth and 120 matched controls. Metabolites by themselves or in combination with clinical factors were used to develop logistic regression models for stillbirth prediction. Prediction of stillbirths overall, early (<28 weeks and <32 weeks), those related to growth restriction/placental disorder, and unexplained stillbirths were evaluated. Results: Targeted metabolites including glycine, acetic acid, L-carnitine, creatine, lysoPCaC18:1, PCaeC34:3, and PCaeC44:4 predicted stillbirth overall with an area under the curve [AUC, 95% confidence interval (CI)] = 0.707 (0.628-0.785). When combined with clinical predictors the AUC value increased to 0.740 (0.667-0.812). First-trimester targeted metabolites also significantly predicted early, unexplained, and placental-related stillbirths. Untargeted LC-MS features combined with other clinical predictors achieved an AUC (95%CI) = 0.860 (0.793-0.927) for the prediction of stillbirths overall. We found novel preliminary evidence that, verruculotoxin, a toxin produced by common household molds, might be linked to stillbirth. Conclusions: We have identified novel biomarkers for stillbirth using metabolomics and demonstrated the feasibility of first-trimester prediction.

Diagnostic Biomarkers of Alzheimer's Disease as Identified in Saliva using 1H NMR-Based Metabolomics

Using 1H NMR metabolomics, we biochemically profiled saliva samples collected from healthy-controls (n = 12), mild cognitive impairment (MCI) sufferers (n = 8), and Alzheimer's disease (AD) patients (n = 9). We accurately identified significant concentration changes in 22 metabolites in the saliva of MCI and AD patients compared to controls. This pilot study demonstrates the potential for using metabolomics and saliva for the early diagnosis of AD. Given the ease and convenience of collecting saliva, the development of accurate and sensitive salivary biomarkers would be ideal for screening those at greatest risk of developing AD.

Metabolomic prediction of endometrial cancer

INTRODUCTION

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

OBJECTIVE

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

METHODS

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I-II) and 10 late-stage (FIGO stages III-IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

RESULTS

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706-0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689-0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group.

CONCLUSIONS

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

Targeted Metabolic Profiling of Post-Mortem Brain from Infants Who Died from Sudden Infant Death Syndrome

Currently little is known about the underlying pathophysiology associated with SIDS, and no objective biomarkers exist for the accurate identification of those at greatest risk of dying from SIDS. Using targeted metabolomics, we aim to profile the medulla oblongata of infants who have died from SIDS (n = 16) and directly compare their biochemical profile with age matched controls. Combining data acquired using ¹H NMR and targeted DI-LC-MS/MS, we have identified fatty acid oxidation as a pivotal biochemical pathway perturbed in the brains of those infants who have from SIDS (p = 0.0016). Further we have identified a potential central biomarker with an AUC (95% CI) = 0.933 (0.845-1.000) having high sensitivity (0.933) and specificity (0.875) values for discriminating between control and SIDS brains. This is the first reported study to use targeted metabolomics for the study of PM brain from infants who have died from SIDS. We have identified pathways associated with the disease and central biomarkers for early screening/diagnosis.

Metabolomic profiling of brain from infants who died from Sudden Infant Death Syndrome reveals novel predictive biomarkers

OBJECTIVE

Sudden Infant Death Syndrome (SIDS) is defined as the sudden death of an infant <1 year of age that cannot be explained following a thorough investigation. Currently, no reliable clinical biomarkers are available for the prediction of infants who will die of SIDS.

STUDY DESIGN

This study aimed to profile the medulla oblongata from postmortem human brain from SIDS victims (n=16) and compare their profiles with that of age-matched controls (n=7).

RESULTS

Using LC-Orbitrap-MS, we detected 12 710 features in electrospray ionization positive (ESI+) mode and 8243 in ESI- mode from polar extracts of brain. Five features acquired in ESI+ mode produced a predictive model for SIDS with an area under the receiver operating characteristic curve (AUC) of 1 (confidence interval (CI): 0.995-1) and a predictive power of 97.4%. Three biomarkers acquired in ESI- mode produced a predictive model with an AUC of 0.866 (CI: 0.767-0.942) and a predictive power of 77.6%. We confidently identified 5 of these features (l-(+)-ergothioneine, nicotinic acid, succinic acid, adenosine monophosphate and azelaic acid) and putatively identify another 4 out of the 15 in total.

CONCLUSIONS

This study underscores the potential value of metabolomics for studying SIDS. Further characterization of the metabolome of postmortem SIDS brains could lead to the identification of potential antemortem biomarkers for novel prevention strategies for SIDS.

Metabolomic determination of pathogenesis of late-onset preeclampsia

OBJECTIVE

Our primary objective was to apply metabolomic pathway analysis of first trimester maternal serum to provide an insight into the pathogenesis of late-onset preeclampsia (late-PE) and thereby identify plausible therapeutic targets for PE.

METHODS

NMR-based metabolomics analysis was performed on 29 cases of late-PE and 55 unaffected controls. In order to achieve sufficient statistical power to perform the pathway analysis, these cases were combined with a group of previously analyzed specimens, 30 late-PE cases and 60 unaffected controls. Specimens from both groups of cases and controls were collected in the same clinical centers during the same time period. In addition, NMR analyses were performed in the same lab and using the same techniques.

RESULTS

We identified abnormalities in branch chain amino acids (valine, leucine and isoleucine) and propanoate, glycolysis, gluconeogenesis and ketone body metabolic pathways. The results suggest insulin resistance and metabolic syndrome, mitochondrial dysfunction and disturbance of energy metabolism, oxidative stress and lipid dysfunction in the pathogenesis of late PE and suggest a potential role for agents that reduce insulin resistance in PE.

CONCLUSIONS

Branched chain amino acids are known markers of insulin resistance and strongly predict future diabetes development. The analysis provides independent evidence linking insulin resistance and late-PE and suggests a potentially important therapeutic role for pharmacologic agents that reduce insulin resistance for late-PE.

The Zika virus and pregnancy: evidence, management, and prevention

OBJECTIVE

To comprehensively review the available evidence and existing consensus reports and guidelines regarding the pregnancy and reproductive implications of the mosquito-transmitted Zika virus (ZIKV) infection. A primary focus was to provide pertinent information to aid clinicians in the management of pregnancies at risk for, exposed to, or with confirmed ZIKV infection.

METHOD

An extensive literature review was performed using Pubmed. Practice guidelines and consensus reports were accessed from international, national, and professional organizations' websites. The clinical articles for ZIKV infection testing varied from case reports to small epidemiologic studies.

RESULTS

A ZIKV epidemic has been declared in several countries in the Americas. Fifty-two travel-associated ZIKV infection cases have been reported throughout the USA (as of February 10, 2016). The consequences of congenital fetal/newborn ZIKV infection could potentially have devastating consequences including miscarriage, fetal death, and major anomalies such as microcephaly, brain and brain-stem defects, and long-term neurologic sequelae. While not definitive, current evidence suggests the existence of nonvector-borne transmission through sexual activity with an infected male partner. For women at risk for sexual transmission, condom use is advised, especially during pregnancy.

CONCLUSION

While ZIKV infection appears to be a mild disease in the general population the potential consequences to the fetus and newborn could be profound. Management guidelines are currently evolving and will be significantly impacted as new evidence develops. It is therefore imperative that obstetric health-care providers keep abreast of this rapidly evolving information landscape that has so far characterized this outbreak.

Novel Metabolite Biomarkers of Huntington's Disease As Detected by High-Resolution Mass Spectrometry

Huntington's disease (HD) is a fatal autosomal-dominant neurodegenerative disorder that affects approximately 3-10 people per 100 000 in the Western world. The median age of onset is 40 years, with death typically following 15-20 years later. In this study, we biochemically profiled post-mortem frontal lobe and striatum from HD sufferers (n = 14) and compared their profiles with controls (n = 14). LC-LTQ-Orbitrap-MS detected a total of 5579 and 5880 features for frontal lobe and striatum, respectively. An ROC curve combining two spectral features from frontal lobe had an AUC value of 0.916 (0.794 to 1.000) and following statistical cross-validation had an 83% predictive accuracy for HD. Similarly, two striatum biomarkers gave an ROC AUC of 0.935 (0.806 to 1.000) and after statistical cross-validation predicted HD with 91.8% accuracy. A range of metabolite disturbances were evident including but-2-enoic acid and uric acid, which were altered in both frontal lobe and striatum. A total of seven biochemical pathways (three in frontal lobe and four in striatum) were significantly altered as a result of HD. This study highlights the utility of high-resolution metabolomics for the study of HD. Further characterization of the brain metabolome could lead to the identification of new biomarkers and novel treatment strategies for HD.

Epigenetic markers for newborn congenital heart defect (CHD)

OBJECTIVE

Our objective was to determine whether there were significant differences in genome-wide DNA methylation in newborns with major congenital heart defect (CHD) compared to controls. We also evaluated methylation of cytosines in CpG motifs for the detection of these CHDs.

METHODS

Genome-wide DNA methylation analysis was performed on DNA from 60 newborns with various CHDs, including hypoplastic left heart syndrome, ventricular septal deficit, atrial septal defect, pulmonary stenosis, coarctation of the aorta and Tetralogy of Fallot, and 32 controls.

RESULTS

Highly significant differences in cytosine methylation were seen in a large number of genes throughout the genome for all CHD categories. Gene ontology analysis of CHD overall indicated over-represented biological processes involving cell development and differentiation, and anatomical structure morphogenesis. Methylation of individual cytosines in CpG motifs had high diagnostic accuracy for the detection of CHD. For example, for coarctation one predictive model based on levels of particular cytosine nucleotides achieved a sensitivity of 100% and specificity of 93.8% (AUC = 0.974, p < 0.00001).

CONCLUSION

Profound differences in cytosine methylation were observed in hundreds of genes in newborns with different types of CHD. There appears to be the potential for development of accurate genetic biomarkers for CHD detection in newborns.

Validation of metabolomic models for prediction of early-onset preeclampsia

OBJECTIVE

We sought to perform validation studies of previously published and newly derived first-trimester metabolomic algorithms for prediction of early preeclampsia (PE).

STUDY DESIGN

Nuclear magnetic resonance-based metabolomic analysis was performed on first-trimester serum in 50 women who subsequently developed early PE and in 108 first-trimester controls. Random stratification and allocation was used to divide cases into a discovery group (30 early PE and 65 controls) for generation of the biomarker model(s) and a validation group (20 early PE and 43 controls) to ensure an unbiased assessment of the predictive algorithms. Cross-validation testing on the different algorithms was performed to confirm their robustness before use. Metabolites, demographic features, clinical characteristics, and uterine Doppler pulsatility index data were evaluated. Area under the receiver operator characteristic curve (AUC), 95% confidence interval (CI), sensitivity, and specificity of the biomarker models were derived.

RESULTS

Validation testing found that the metabolite-only model had an AUC of 0.835 (95% CI, 0.769-0.941) with a 75% sensitivity and 74.4% specificity and for the metabolites plus uterine Doppler pulsatility index model it was 0.916 (95% CI, 0.836-0.996), 90%, and 88.4%, respectively. Predictive metabolites included arginine and 2-hydroxybutyrate, which are known to be involved in vascular dilation, and insulin resistance and impaired glucose regulation, respectively.

CONCLUSION

We found confirmatory evidence that first-trimester metabolomic biomarkers can predict future development of early PE.

Fetal anomalies and long-term effects associated with substance abuse in pregnancy: a literature review

OBJECTIVES

Substance abuse in pregnancy remains a major public health problem. Fetal teratogenicity results from the effect of these substances during fetal development, particularly when used in combination. This review will focus on and attempt to clarify the existing literature regarding the association of substance abuse on the development of congenital anomalies and the long-term implications in exposed offspring.

METHODS

Systematic review of available English literature using the PubMed database of all peer-reviewed articles on the subject.

RESULTS

A total of 128 articles were included in this review. Alcohol was the most common substance associated with fetal anomalies, particularly facial dysmorphisms and alterations in the central nervous system development. Adverse maternal environments associated with risky behaviors and lack of adequate prenatal care precludes the timely detection of fetal anomalies, confounding most studies linking causality. In addition, although methodological differences and limited availability of well-designed trials exist, substance abuse in pregnancy has been associated with adverse long-term outcomes in infant growth, behavior, cognition, language and achievement.

CONCLUSION

The literature summarized in this review suggests that drug exposure during pregnancy may increase the risk of congenital anomalies and long-term adverse effects in exposed children and adolescents. These conclusions must be tempered by the many confounders associated with drug use. A multidisciplinary approach is paramount for appropriate counseling regarding the known immediate and long-term risks of substance abuse in pregnancy.

MR imaging of the fetal brain at 1.5T and 3.0T field strengths: comparing specific absorption rate (SAR) and image quality

OBJECTIVES

Our two objectives were to evaluate the feasibility of fetal brain magnetic resonance imaging (MRI) using a fast spin echo sequence at 3.0T field strength with low radio frequency (rf) energy deposition (as measured by specific absorption rate: SAR) and to compare image quality, tissue contrast and conspicuity between 1.5T and 3.0T MRI.

METHODS

T2 weighted images of the fetal brain at 1.5T were compared to similar data obtained in the same fetus using a modified sequence at 3.0T. Quantitative whole-body SAR and normalized image signal to noise ratio (SNR), a nominal scoring scheme based evaluation of diagnostic image quality, and tissue contrast and conspicuity for specific anatomical structures in the brain were compared between 1.5T and 3.0T.

RESULTS

Twelve pregnant women underwent both 1.5T and 3.0T MRI examinations. The image SNR was significantly higher (P=0.03) and whole-body SAR was significantly lower (P<0.0001) for images obtained at 3.0T compared to 1.5T. All cases at both field strengths were scored as having diagnostic image quality. Images from 3.0T MRI (compared to 1.5T) were equal (57%; 21/37) or superior (35%; 13/37) for tissue contrast and equal (61%; 20/33) or superior (33%, 11/33) for conspicuity.

CONCLUSIONS

It is possible to obtain fetal brain images with higher resolution and better SNR at 3.0T with simultaneous reduction in SAR compared to 1.5T. Images of the fetal brain obtained at 3.0T demonstrated superior tissue contrast and conspicuity compared to 1.5T.

Metabolomic prediction of fetal congenital heart defect in the first trimester

OBJECTIVE

The objective of the study was to identify metabolomic markers in maternal first-trimester serum for the detection of fetal congenital heart defects (CHDs).

STUDY DESIGN

Mass spectrometry (direct injection/liquid chromatography and tandem mass spectrometry) and nuclear magnetic resonance spectrometry-based metabolomic analyses were performed between 11 weeks' and 13 weeks 6 days' gestation on maternal serum. A total of 27 CHD cases and 59 controls were compared. There were no known or suspected chromosomal or syndromic abnormalities indicated.

RESULTS

A total of 174 metabolites were identified and quantified using the 2 analytical methods. There were 14 overlapping metabolites between platforms. We identified 123 metabolites that demonstrated significant differences on a univariate analysis in maternal first-trimester serum in CHD vs normal cases. There was a significant disturbance in acylcarnitine, sphingomyelin, and other metabolite levels in CHD pregnancies. Predictive algorithms were developed for CHD detection. High sensitivity (0.929; 95% confidence interval [CI], 0.92-1.00) and specificity (0.932; 95% CI, 0.78-1.00) for CHD detection were achieved (area under the curve, 0.992; 95% CI, 0.973-1.0).

CONCLUSION

In the first such report, we demonstrated the feasibility of the use of metabolomic developing biomarkers for the first-trimester prediction of CHD. Abnormal lipid metabolism appeared to be a significant feature of CHD pregnancies.

Second- and third-trimester biochemical and ultrasound markers predictive of ischemic placental disease

Ischemic placental disease is a recently coined term that describes the vascular insufficiency now believed to be an important etiologic factor in preeclampsia, intrauterine fetal growth restriction, and placental abruption. Given the increased risk for poor maternal and fetal outcomes, early prediction and prevention of this disorder is of significant clinical interest for many. In this article, we review the second- and third-trimester serum and ultrasound markers predictive of ischemic placental disease. Limited first-trimester data is also presented. While current studies report a statistical association between marker levels and various adverse perinatal outcomes, the observed diagnostic accuracy is below the threshold required for clinical utility. An exception to this generalization is uterine artery Doppler for the prediction of early-onset preeclampsia. Metabolomics is a relatively new analytic platform that holds promise as a first-trimester marker for the prediction of both early- and late-onset preeclampsia.

MR venography of the fetal brain using susceptibility weighted imaging

PURPOSE

To evaluate the feasibility of performing fetal brain magnetic resonance venography using susceptibility weighted imaging (SWI).

MATERIALS AND METHODS

After obtaining informed consent, pregnant women in the second and third trimester were imaged using a modified SWI sequence. Fetal SWI acquisition was repeated when fetal or maternal motion was encountered. The median and maximum number of times an SWI sequence was repeated was four and six respectively. All SWI image data were systematically evaluated by a pediatric neuroradiologist for image quality using an ordinal scoring scheme: 1. diagnostic; 2. diagnostic with artifacts; and 3. nondiagnostic. The best score in an individual fetus was used for further statistical analysis. Visibility of venous vasculature was also scored using a dichotomous variable. A subset of SWI data was re-evaluated by the first and independently by a second pediatric neuroradiologist. Kappa coefficients were computed to assess intra-rater and inter-rater reliability.

RESULTS

SWI image data from a total of 22 fetuses were analyzed. Median gestational age and interquartile range of the fetuses imaged were 32 (29.9-34.9) weeks. In 68.2% of the cases (n = 15), there was no artifact; 22.7% (n = 5) had minor artifacts and 9.1% (n = 2) of the data was of nondiagnostic quality. Cerebral venous vasculature was visible in 86.4% (n = 19) of the cases. Substantial agreement (Kappa = 0.73; 95% confidence interval 0.44-1.00)) was observed for intra-rater reliability and moderate agreement (Kappa = 0.48; 95% confidence interval 0.19-0.77) was observed for inter-rater reliability.

CONCLUSION

It is feasible to perform fetal brain venography in humans using SWI.

Techniques, terminology, and indications for MRI in pregnancy

Fetal MRI is now a well-established imaging modality for the diagnostic evaluation of fetuses with congenital anomalies. In this article, the authors provide a brief overview of the physical principles involved in fetal MRI imaging, the sequences that are used in clinical practice today, current indications, and limitations. A review of current evidence supports the following indications for fetal MRI: suspected central nervous system anomalies, neck and oropharyngeal masses, diaphragmatic hernia, abdominal masses or bowel pathology not fully characterized by ultrasonography, and suspected fetal infection. Other indications should be decided on a case-by-case basis with close collaboration between the departments of maternal-fetal medicine and radiology. More research is needed to determine the role of fetal MRI in functional neuroimaging at higher magnetic field strengths (3T).

Metabolomic analysis for first-trimester trisomy 18 detection

OBJECTIVE

The purpose of this study was to determine whether nuclear magnetic resonance-based metabolomic markers in first-trimester maternal serum can detect fetuses with trisomy 18.

STUDY DESIGN

This was a study of pregnancies between 11 weeks and 13 weeks 6 days' gestation. We analyzed 30 cases of trisomy 18 and a total of 114 euploid cases. Nuclear magnetic resonance-based metabolomic analysis was performed. A further analysis was performed that compared 30 cases with trisomy 18 and 30 trisomy 21 (T21) cases.

RESULTS

Metabolomic markers were sensitive for trisomy 18 detection. A combination of 2-hydroxybutyrate, glycerol and maternal age had a 73.3% sensitivity and 96.6% specificity for trisomy 18 detection, with an area under the receiver operating curve: 0.92 (P < .001). Other metabolite markers, which include trimethylamine, were sensitive for distinguishing trisomy 18 from T21 cases.

CONCLUSION

This is the first report of prenatal trisomy 18 detection that has been based on metabolomic analysis. Preliminary results suggest that such markers are sensitive not only for the detection of fetal trisomy 18 but also for distinguishing this aneuploidy from T21.

Metabolomic analysis for first-trimester Down syndrome prediction

OBJECTIVE

The objective of the study was to perform first-trimester maternal serum metabolomic analysis and compare the results in aneuploid vs Down syndrome (DS) pregnancies.

STUDY DESIGN

This was a case-control study of pregnancies between 11+0 and 13+6 weeks. There were 30 DS cases and 60 controls in which first-trimester maternal serum was analyzed. Nuclear magnetic resonance-based metabolomic analysis was performed for DS prediction.

RESULTS

Concentrations of 11 metabolites were significantly different in the serum of DS pregnancies. The combination of 3-hydroxyisovalerate, 3-hydroxybuterate, and maternal age had a 51.9% sensitivity at 1.9% false-positive rate for DS detection. One multimarker algorithm had 70% sensitivity at 1.7% false-positive rate. Novel markers such as 3-hydroxybutyrate, involved in brain growth and myelination, and 2-hydroxybutyrate, involved in the defense against oxidative stress, were found to be abnormal.

CONCLUSION

The study reports novel metabolomic markers for the first-trimester prediction of fetal DS. Metabolomics provided insights into the cellular dysfunction in DS.

First-trimester metabolomic detection of late-onset preeclampsia

OBJECTIVE

We sought to identify first-trimester maternal serum biomarkers for the prediction of late-onset preeclampsia (PE) using metabolomic analysis.

STUDY DESIGN

In a case-control study, nuclear magnetic resonance-based metabolomic analysis was performed on first-trimester maternal serum between 11(+0)-13(+6) weeks of gestation. There were 30 cases of late-onset PE, i.e., requiring delivery ≥37 weeks, and 59 unaffected controls. The concentrations of 40 metabolites were compared between the 2 groups. We also compared 30 early-onset cases to the late-onset group.

RESULTS

A total of 14 metabolites were significantly elevated and 3 significantly reduced in first-trimester serum of late-onset PE patients. A complex model consisting of multiple metabolites and maternal demographic characteristics had a 76.6% sensitivity at 100% specificity for PE detection. A simplified model using fewer predictors yielded 60% sensitivity at 96.6% specificity. Strong separation of late- vs early-onset PE groups was achieved.

CONCLUSION

Significant differences in the first-trimester metabolites were noted in women who went on to developed late-onset PE and between early- and late-onset PE.

Fetal male gender and the benefits of treatment of mild gestational diabetes mellitus

OBJECTIVE

We evaluated whether improvements in pregnancy outcomes after treatment of mild gestational diabetes mellitus differed in magnitude on the basis of fetal gender.

STUDY DESIGN

This is a secondary analysis of a masked randomized controlled trial of treatment for mild gestational diabetes mellitus. The results included preeclampsia or gestational hypertension, birthweight, neonatal fat mass, and composite adverse outcomes for both neonate (preterm birth, small for gestational age, or neonatal intensive care unit admission) and mother (labor induction, cesarean delivery, preeclampsia, or gestational hypertension). After stratification according to fetal gender, the interaction of gender with treatment status was estimated for these outcomes.

RESULTS

Of the 469 pregnancies with male fetuses, 244 pregnancies were assigned randomly to treatment, and 225 pregnancies were assigned randomly to routine care. Of the 463 pregnancies with female fetuses, 233 pregnancies were assigned randomly to treatment, and 230 pregnancies were assigned randomly to routine care. The interaction of gender with treatment status was significant for fat mass (P = .04) and birthweight percentile (P = .02). Among women who were assigned to the treatment group, male offspring were significantly more likely to have both a lower birthweight percentile (50.7 ± 29.2 vs 62.5 ± 30.2 percentile; P < .0001) and less neonatal fat mass (487 ± 229.6 g vs 416.6 ± 172.8 g; P = .0005,) whereas these differences were not significant among female offspring. There was no interaction between fetal gender and treatment group with regard to other outcomes.

CONCLUSION

The magnitude of the reduction of a newborn's birthweight percentile and neonatal fat mass that were related to the treatment of mild gestational diabetes mellitus appears greater for male neonates.