Untargeted Metabolomic Biomarker Discovery for the Detection of Ectopic Pregnancy.

Ectopic pregnancy (EP) is the leading cause of maternal morbidity and mortality in the first trimester. Using an untargeted metabolomic approach, we sought to identify putative plasma biomarkers using tandem liquid chromatography-mass spectrometry for the detection of tubal EP. This case-control study included the prospective recruitment of 50 tubal EP cases and 50 early intrauterine pregnancy controls. To avoid over-fitting, logistic regression models were developed in a randomly selected discovery group (30 cases vs. 30 controls) and validated in the test group (20 cases vs. 20 controls). In total, 585 mass spectral features were detected, of which 221 molecular features were significantly altered in EP plasma (p < 0.05). Molecular networking and metabolite identification was employed using the Global Natural Products Social Molecular Networking (GNPS) database, which identified 97 metabolites at a high confidence level. Top significant metabolites include subclasses of sphingolipids, carnitines, glycerophosphocholines, and tryptophan metabolism. The top regression model, consisting of D-erythro-sphingosine and oleoyl-carnitine, was validated in a test group and achieved an area under receiving operating curve (AUC) (95% CI) = 0.962 (0.910-1) with a sensitivity of 100% and specificity of 95.9%. Metabolite alterations indicate alterations related to inflammation and abnormal placentation in EP. The validation of these metabolite biomarkers in the future could potentially result in improved early diagnosis.

Metabolomic prediction of severe maternal and newborn complications in preeclampsia.

INTRODUCTION: Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management. OBJECTIVES: Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications. METHODS: Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE. RESULTS: Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes. CONCLUSION: Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Metabolomics: An Emerging "Omics" Platform for Systems Biology and Its Implications for Huntington Disease Research.

Huntington's disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.

Neurocutaneous Disorders in Pregnancy.

Importance: Neurocutaneous disorders have significant implications for care of the pregnant patient. As neurocutaneous disorders are uncommon, obstetricians may be unfamiliar with these disorders and with recommendations for appropriate care of this population. Objective: This review aims to summarize existing literature on the interaction between neurocutaneous disorders and pregnancy and to provide a guide for physicians caring for an affected patient. Evidence Acquisition: A PubMed, MEDLINE, and Google Scholar search was carried out with a broad range of combinations of the medical subject headings (MeSH) terms "pregnancy," "Sturge -Weber," "Neurofibromatosis Type 1," "neurofibromatosis type 2," "von Hippel Lindau," "Tuberous Sclerosis," "neurocutaneous disorder," "treatment," "congenital malformations," "neurodevelopmental defects," "miscarriage," "breastfeeding," "autoimmune," "pathophysiology," and "management." References of included articles were searched to identify any articles that may have been missed after the above method was used. Results: Neurocutaneous disorders are associated with increased pregnancy-associated maternal and fetal/neonatal morbidity, largely surrounding hypertensive disorders, epilepsy, and medication exposure. Some features of neurocutaneous disorders may be worsened or accelerated by pregnancy. Neurocutaneous disorders can often be diagnosed prenatally. Therefore, directed assessment should be offered to affected individuals with a personal or family history of a neurocutaneous disorder. Conclusion and Relevance: Patients affected by neurocutaneous disorders who are pregnant or planning for future pregnancy should be carefully followed by a multidisciplinary team, which could include maternal-fetal medicine, neurology, and anesthesia, as well as other relevant subspecialists. Additional research is required regarding optimal counseling and management of these patients.

Precision oncology: Artificial intelligence, circulating cell-free DNA, and the minimally invasive detection of pancreatic cancer-A pilot study.

BACKGROUND: Pancreatic cancer (PC) is among the most lethal cancers. The lack of effective tools for early detection results in late tumor detection and, consequently, high mortality rate. Precision oncology aims to develop targeted individual treatments based on advanced computational approaches of omics data. Biomarkers, such as global alteration of cytosine (CpG) methylation, can be pivotal for these objectives. In this study, we performed DNA methylation profiling of pancreatic cancer patients using circulating cell-free DNA (cfDNA) and artificial intelligence (AI) including Deep Learning (DL) for minimally invasive detection to elucidate the epigenetic pathogenesis of PC. METHODS: The Illumina Infinium HD Assay was used for genome-wide DNA methylation profiling of cfDNA in treatment-naïve patients. Six AI algorithms were used to determine PC detection accuracy based on cytosine (CpG) methylation markers. Additional strategies for minimizing overfitting were employed. The molecular pathogenesis was interrogated using enrichment analysis. RESULTS: In total, we identified 4556 significantly differentially methylated CpGs (q-value < 0.05; Bonferroni correction) in PC versus controls. Highly accurate PC detection was achieved with all 6 AI platforms (Area under the receiver operator characteristics curve [0.90-1.00]). For example, DL achieved AUC (95% CI): 1.00 (0.95-1.00), with a sensitivity and specificity of 100%. A separate modeling approach based on logistic regression-based yielded an AUC (95% CI) 1.0 (1.0-1.0) with a sensitivity and specificity of 100% for PC detection. The top four biological pathways that were epigenetically altered in PC and are known to be linked with cancer are discussed. CONCLUSION: Using a minimally invasive approach, AI, and epigenetic analysis of circulating cfDNA, high predictive accuracy for PC was achieved. From a clinical perspective, our findings suggest that that early detection leading to improved overall survival may be achievable in the future.

Maternal SARS-COV-2 infection and prematurity: the Southern Michigan COVID-19 collaborative.

OBJECTIVE: COVID-19 has been reported to increase the risk of prematurity, however, due to the frequent absence of unaffected controls as well as inadequate accounting for confounders in many studies, the question requires further investigation. We sought to determine the impact of COVID-19 disease on preterm birth (PTB) overall, as well as related subcategories such as early prematurity, spontaneous, medically indicated preterm birth, and preterm labor (PTL). We assessed the impact of confounders such as COVID-19 risk factors, a-priori risk factors for PTB, symptomatology, and disease severity on rates of prematurity. METHODS: This was a retrospective cohort study of pregnant women from March 2020 till October 1st, 2020. The study included patients from 14 obstetric centers in Michigan, USA. Cases were defined as women diagnosed with COVID-19 at any point during their pregnancy. Cases were matched with uninfected women who delivered in the same unit, within 30 d of the delivery of the index case. Outcomes of interest were frequencies of prematurity overall and subcategories of preterm birth (early, spontaneous/medically indicated, preterm labor, and premature preterm rupture of membranes) in cases compared to controls. The impact of modifiers of these outcomes was documented with extensive control for potential confounders. A p value <.05 was used to infer significance. RESULTS: The rate of prematurity was 8.9% in controls, 9.4% in asymptomatic cases, 26.5% in symptomatic COVID-19 cases, and 58.8% among cases admitted to the ICU. Gestational age at delivery was noted to decrease with disease severity. Cases were at an increased risk of prematurity overall [adjusted relative risk (aRR) = 1.62 (1.2-2.18)] and of early prematurity (<34 weeks) [aRR = 1.8 (1.02-3.16)] when compared to controls. Medically indicated prematurity related to preeclampsia [aRR = 2.46 (1.47-4.12)] or other indications [aRR = 2.32 (1.12-4.79)], were the primary drivers of overall prematurity risk. Symptomatic cases were at an increased risk of preterm labor [aRR = 1.74 (1.04-2.8)] and spontaneous preterm birth due to premature preterm rupture of membranes [aRR = 2.2(1.05-4.55)] when compared to controls and asymptomatic cases combined. The gestational age at delivery followed a dose-response relation with disease severity, as more severe cases tended to deliver earlier (Wilcoxon p < .05). CONCLUSIONS: COVID-19 is an independent risk factor for preterm birth. The increased preterm birth rate in COVID-19 was primarily driven by medically indicated delivery, with preeclampsia as the principal risk factor. Symptomatic status and disease severity were significant drivers of preterm birth.

Evaluation During Postpartum Hemorrhage.

Postpartum hemorrhage is an obstetric emergency that is the leading and the most preventable cause of maternal death that occurs on the day of birth. The treatment of postpartum hemorrhage in a timely fashion is crucial to prevent morbidity and mortality. The accurate assessment of blood loss during delivery and the postpartum period remains a major challenge. Hence, it is imperative to have a standardized evaluation strategy for accurate assessment of blood loss, adequate classification of hemorrhage, and timely initiated interventions. The multidisciplinary evaluation strategy should be in place regardless of the delivery route.

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.

Does Maternal SARS-CoV-2 Infection or SARS-CoV-2 Vaccination Trigger an Inflammatory Response in the Fetus? A Prospective Cohort Study.

OBJECTIVES: SARS-CoV-2 infection triggers a significant maternal inflammatory response. There is a dearth of information regarding whether maternal SARS-CoV-2 infection at admission for delivery or SARS-CoV-2 vaccination triggers an inflammatory response in the fetus. This study aims at evaluating fetal inflammatory response to maternal SARS-CoV-2 infection or SARS-CoV-2 vaccination compared to control group. Design, Participants, Setting, and Methods: A prospective cohort study was performed with a total of 61 pregnant women who presented for delivery at a single medical center (William Beaumont Hospital, Royal Oak, MI). All mothers were tested for SARS-CoV-2 infection using polymerase chain reaction (PCR) on admission to labor and delivery unit. Three groups were evaluated: 22 pregnant with a positive SARS-CoV-2 test (case group), 23 pregnant women with a negative SARS-CoV-2 test (control group), and 16 pregnant women who had recent SAR-CoV-2 vaccination and a negative SARS-CoV-2 test (vaccine group). At delivery, cord blood was collected to determine the levels of IL-6, C-reactive protein (CRP), and SARS-CoV-2 nucleocapsid IgG and IgM antibodies. In all cases, the newborn had a negative PCR test or showed no clinical findings consistent with SARS-CoV-2 infection. RESULTS: Mean (SD) IL-6 level was not significantly different for the three groups: case group 9.00 ± 3.340 pg/mL, control group 5.19 ± 0.759 pg/mL, and vaccine group 7.11 ± 2.468 pg/mL (p value 0.855). Pairwise comparison also revealed no statistical difference for IL-6 concentrations with p values for case versus control, case versus vaccine, and control versus vaccine = 0.57, 0.91, and 0.74, respectively. Similarly, there was no statistically significant difference in the frequency of elevated IL-6 (>11 pg/mL) between groups (p value 0.89). CRP levels across the three groups were not statistically significant different (p value 0.634). Pairwise comparison of CRP levels among the different groups was also not statistically different. SARS-CoV-2 nucleocapsid IgG was positive in 12 out of 22 cord blood samples in the case group, 2 out of 23 of the control group (indicating old resolved maternal infection), and 0 out of 16 of the vaccine group. SARS-CoV-2 nucleocapsid IgM was negative in all cord blood samples of the case group, control group, and vaccine group. LIMITATIONS: A total number of 61 mothers enrolled in the study which represents a relatively small number of patients. Most patients with positive SARS-CoV-2 PCR were mainly asymptomatic. In addition, our vaccine group received the mRNA-based vaccines (mRNA1273 and BNT162b2). We did not study fetal response to other SARS-CoV-2 vaccines. CONCLUSION: In our prospective cohort, neither IL-6 nor CRP indicated increased inflammation in the cord blood of newborns of SARS-CoV-2-infected or vaccinated mothers.

Urine metabolomic biomarkers for prediction of isolated fetal congenital heart defect.

OBJECTIVE: To identify maternal second and third trimester urine metabolomic biomarkers for the detection of fetal congenital heart defects (CHDs). STUDY DESIGN: This was a prospective study. Metabolomic analysis of randomly collected maternal urine was performed, comparing pregnancies with isolated, non-syndromic CHDs versus unaffected controls. Mass spectrometry (liquid chromatography and direct injection and tandem mass spectrometry, LC-MS-MS) as well as nuclear magnetic resonance spectrometry, 1H NMR, were used to perform the analyses between 14 0/7 and 37 0/7 weeks gestation. A total of 36 CHD cases and 41 controls were compared. Predictive algorithms using urine markers alone or combined with, clinical and ultrasound (US) (four-chamber view) predictors were developed and compared. RESULTS: A total of 222 metabolites were identified, of which 16 were overlapping between the two platforms. Twenty-three metabolite concentrations were found in significantly altered in CHD gestations on univariate analysis. The concentration of methionine was most significantly altered. A predictive algorithm combining metabolites (histamine, choline, glucose, formate, methionine, and carnitine) plus US four-chamber view achieved an AUC = 0.894; 95% CI, 0814-0.973 with a sensitivity of 83.8% and specificity of 87.8%. Enrichment pathway analysis identified several lipid related pathways that are dysregulated in CHD, including phospholipid biosynthesis, phosphatidylcholine biosynthesis, phosphatidylethanolamine biosynthesis, and fatty acid metabolism. This could be consistent with the increased risk of CHD in diabetic pregnancies. CONCLUSIONS: We report a novel, noninvasive approach, based on the analysis of maternal urine for isolated CHD detection. Further, the dysregulation of lipid- and folate metabolism appears to support prior data on the mechanism of CHD.

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.

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.

Artificial Intelligence and the detection of pediatric concussion using epigenomic analysis.

Concussion, also referred to as mild traumatic brain injury (mTBI) is the most common type of traumatic brain injury. Currently concussion is an area ofintensescientific interest to better understand the biological mechanisms and for biomarker development. We evaluated whole genome-wide blood DNA cytosine ('CpG') methylation in 17 pediatric concussion isolated cases and 18 unaffected controls using Illumina Infinium MethylationEPIC assay. Pathway analysis was performed using Ingenuity Pathway Analysis to help elucidate the epigenetic and molecular mechanisms of the disorder. Area under the receiver operating characteristics (AUC) curves and FDR p-values were calculated for mTBI detection based on CpG methylation levels. Multiple Artificial Intelligence (AI) platforms including Deep Learning (DL), the newest form of AI, were used to predict concussion based on i) CpG methylation markers alone, and ii) combined epigenetic, clinical and demographic predictors. We found 449 CpG sites (473 genes), those were statistically significantly methylated in mTBI compared to controls. There were four CpGs with excellent individual accuracy (AUC ≥ 0.90-1.00) while 119 displayed good accuracy (AUC ≥ 0.80-0.89) for the prediction of mTBI. The CpG methylation changes ≥10% were observed in many CpG loci after concussion suggesting biological significance. Pathway analysis identified several biologically important neurological pathways that were perturbed including those associated with: impaired brain function, cognition, memory, neurotransmission, intellectual disability and behavioral change and associated disorders. The combination of epigenomic and clinical predictors were highly accurate for the detection of concusion using AI techniques. Using DL/AI, a combination of epigenomic and clinical markers had sensitivity and specificity ≧95% for prediction of mTBI. In this novel study, we identified significant methylation changes in multiple genes in response to mTBI. Gene pathways that were epigenetically dysregulated included several known to be involved in neurological function, thus giving biological plausibility to our findings.

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 the analysis of multi-platform metabolomics data for the detection of intrauterine growth restriction.

OBJECTIVE: To interrogate the pathogenesis of intrauterine growth restriction (IUGR) and apply Artificial Intelligence (AI) techniques to multi-platform i.e. nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) based metabolomic analysis for the prediction of IUGR. MATERIALS AND METHODS: MS and NMR based metabolomic analysis were performed on cord blood serum from 40 IUGR (birth weight < 10th percentile) cases and 40 controls. Three variable selection algorithms namely: Correlation-based feature selection (CFS), Partial least squares regression (PLS) and Learning Vector Quantization (LVQ) were tested for their diagnostic performance. For each selected set of metabolites and the panel consists of metabolites common in three selection algorithms so-called overlapping set (OL), support vector machine (SVM) models were developed for which parameter selection was performed busing 10-fold cross validations. Area under the receiver operating characteristics curve (AUC), sensitivity and specificity values were calculated for IUGR diagnosis. Metabolite set enrichment analysis (MSEA) was performed to identify which metabolic pathways were perturbed as a direct result of IUGR in cord blood serum. RESULTS: All selected metabolites and their overlapping set achieved statistically significant accuracies in the range of 0.78-0.82 for their optimized SVM models. The model utilizing all metabolites in the dataset had an AUC = 0.91 with a sensitivity of 0.83 and specificity equal to 0.80. CFS and OL (Creatinine, C2, C4, lysoPC.a.C16.1, lysoPC.a.C20.3, lysoPC.a.C28.1, PC.aa.C24.0) showed the highest performance with sensitivity (0.87) and specificity (0.87), respectively. MSEA revealed significantly altered metabolic pathways in IUGR cases. Dysregulated pathways include: beta oxidation of very long fatty acids, oxidation of branched chain fatty acids, phospholipid biosynthesis, lysine degradation, urea cycle and fatty acid metabolism. CONCLUSION: A systematically selected panel of metabolites was shown to accurately detect IUGR in newborn cord blood serum. Significant disturbance of hepatic function and energy generating pathways were found in IUGR cases.

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.

Diminished ovarian reserve in patients with psoriasis.

OBJECTIVE: Psoriasis is a multi-systemic chronic inflammatory skin disease. Previous data suggests that women with some chronic inflammatory diseases have diminished ovarian reserve. This study explores ovarian reserve in patients with psoriasis. MATERIALS AND METHODS: We prospectively analyzed 14 female patients with psoriasis and 35 healthy age and body mass index matched controls. An interview explored demographic characteristics, obstetrical history and menstrual characteristics. Psoriatic area severity index (PASI) in patients was assessed. Estrogen, follicle-stimulating hormone (FSH), luteinizing hormone (LH), thyroid stimulating hormone and with gynecologic ultrasonography, ovarian volume and antral follicular count (AFC) were measured in both study and control groups. These values were analyzed with changes of the PASI in the patient group. RESULTS: Patients with psoriasis had significantly higher levels of FSH and FSH/LH ratio than healthy controls (p = 0.039, p = 0.005 respectively). AFC of psoriasis patients were significantly lower than healthy controls (p = 0.002).There were no significant difference among other hormone levels and ovarian volumes (p > 0.05). The hormone levels, ovarian volume and AFC were not correlated with PASI of the patients. CONCLUSION: The results of the study suggest that patients with psoriasis may have diminished ovarian reserve.

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.

Integrated Proteomic and Metabolomic prediction of Term Preeclampsia.

Term preeclampsia (tPE), ≥37 weeks, is the most common form of PE and the most difficult to predict. Little is known about its pathogenesis. This study aims to elucidate the pathogenesis and assess early prediction of tPE using serial integrated metabolomic and proteomic systems biology approaches. Serial first- (11-14 weeks) and third-trimester (30-34 weeks) serum samples were analyzed using targeted metabolomic (1H NMR and DI-LC-MS/MS) and proteomic (MALDI-TOF/TOF-MS) platforms. We analyzed 35 tPE cases and 63 controls. Serial first- (sphingomyelin C18:1 and urea) and third-trimester (hexose and citrate) metabolite screening predicted tPE with an area under the receiver operating characteristic curve (AUC) (95% CI) = 0.817 (0.732-0.902) and a sensitivity of 81.6% and specificity of 71.0%. Serial first [TATA box binding protein-associated factor (TBP)] and third-trimester [Testis-expressed sequence 15 protein (TEX15)] protein biomarkers highly accurately predicted tPE with an AUC (95% CI) of 0.987 (0.961-1.000), sensitivity 100% and specificity 98.4%. Integrated pathway over-representation analysis combining metabolomic and proteomic data revealed significant alterations in signal transduction, G protein coupled receptors, serotonin and glycosaminoglycan metabolisms among others. This is the first report of serial integrated and combined metabolomic and proteomic analysis of tPE. High predictive accuracy and potentially important pathogenic information were achieved.