Placental Metabolomics of Fetal Growth Restriction.

Fetal growth restriction is an obstetrical pathological condition that causes high neonatal mortality and morbidity. The mechanisms of its onset are not completely understood. Metabolites were extracted from 493 placentas from non-complicated pregnancies in Hamilton Country, TN (USA), and analyzed by gas chromatography-mass spectrometry (GC-MS). Newborns were classified according to raw fetal weight (low birth weight (LBW; <2500 g) and non-low birth weight (Non-LBW; >2500 g)), and according to the calculated birth weight centile as it relates to gestational age (small for gestational age (SGA), large for gestational age (LGA), and adequate for gestational age (AGA)). Mothers of LBW infants had a lower pre-pregnancy weight (66.2 ± 17.9 kg vs. 73.4 ± 21.3 kg, p < 0.0001), a lower body mass index (BMI) (25.27 ± 6.58 vs. 27.73 ± 7.83, p < 0.001), and a shorter gestation age (246.4 ± 24.0 days vs. 267.2 ± 19.4 days p < 0.001) compared with non-LBW. Marital status, tobacco use, and fetus sex affected birth weight centile classification according to gestational age. Multivariate statistical comparisons of the extracted metabolomes revealed that asparagine, aspartic acid, deoxyribose, erythritol, glycerophosphocholine, tyrosine, isoleucine, serine, and lactic acid were higher in both SGA and LBW placentas, while taurine, ethanolamine, ß-hydroxybutyrate, and glycine were lower in both SGA and LBW. Several metabolic pathways are implicated in fetal growth restriction, including those related to the hypoxia response and amino-acid uptake and metabolism. Inflammatory pathways are also involved, suggesting that fetal growth restriction might share some mechanisms with preeclampsia.

A screening test proposal for congenital defects based on maternal serum metabolomics profile.

BACKGROUND: Historically, noninvasive techniques are only able to identify chromosomal anomalies that accounted for <50% of all congenital defects; the other congenital defects are diagnosed via ultrasound evaluations in the later stages of pregnancy. Metabolomic analysis may provide an important improvement, potentially addressing the need for novel noninvasive and multicomprehensive early prenatal screening tools. A growing body of evidence outlines notable metabolic alterations in different biofluids derived from pregnant women carrying fetuses with malformations, suggesting that such an approach may allow the discovery of biomarkers common to most fetal malformations. In addition, metabolomic investigations are inexpensive, fast, and risk-free and often generate high performance screening tests that may allow early detection of a given pathology. OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of an ensemble machine learning model based on maternal serum metabolomic signatures for detecting fetal malformations, including both chromosomal anomalies and structural defects. STUDY DESIGN: This was a multicenter observational retrospective study that included 2 different arms. In the first arm, a total of 654 Italian pregnant women (334 cases with fetuses with malformations and 320 controls with normal developing fetuses) were enrolled and used to train an ensemble machine learning classification model based on serum metabolomics profiles. In the second arm, serum samples obtained from 1935 participants of the New Zealand Screening for Pregnancy Endpoints study were blindly analyzed and used as a validation cohort. Untargeted metabolomics analysis was performed via gas chromatography-mass spectrometry. Of note, 9 individual machine learning classification models were built and optimized via cross-validation (partial least squares-discriminant analysis, linear discriminant analysis, naïve Bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine, and logistic regression). An ensemble of the models was developed according to a voting scheme statistically weighted by the cross-validation accuracy and classification confidence of the individual models. This ensemble machine learning system was used to screen the validation cohort. RESULTS: Significant metabolic differences were detected in women carrying fetuses with malformations, who exhibited lower amounts of palmitic, myristic, and stearic acids; N-α-acetyllysine; glucose; L-acetylcarnitine; fructose; para-cresol; and xylose and higher levels of serine, alanine, urea, progesterone, and valine (P<.05), compared with controls. When applied to the validation cohort, the screening test showed a 99.4%±0.6% accuracy (specificity of 99.9%±0.1% [1892 of 1894 controls correctly identified] with a sensitivity of 78%±6% [32 of 41 fetal malformations correctly identified]). CONCLUSION: This study provided clinical validation of a metabolomics-based prenatal screening test to detect the presence of congenital defects. Further investigations are needed to enable the identification of the type of malformation and to confirm these findings on even larger study populations.

Noninvasive screening for congenital heart defects using a serum metabolomics approach.

OBJECTIVE: Heart anomalies represent nearly one-third of all congenital anomalies. They are currently diagnosed using ultrasound. However, there is a strong need for a more accurate and less operator-dependent screening method. Here we report a metabolomics characterization of maternal serum in order to describe a metabolomic fingerprint representative of heart congenital anomalies. METHODS: Metabolomic profiles were obtained from serum of 350 mothers (280 controls and 70 cases). Nine classification models were built and optimized. An ensemble model was built based on the results from the individual models. RESULTS: The ensemble machine learning model correctly classified all cases and controls. Malonic, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were determined as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal heart anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the revelation of the associated metabolites and their respective biochemical pathways allows a better understanding of the overall pathophysiology of affected pregnancies.

Development and Validation of a Serum Metabolomic Signature for Endometrial Cancer Screening in Postmenopausal Women.

Importance: Endometrial carcinoma (EC) is the most commonly diagnosed gynecologic cancer. Its early detection is advisable because 20% of women have advanced disease at the time of diagnosis. Objective: To clinically validate a metabolomics-based classification algorithm as a screening test for EC. Design, Setting, and Participants: This diagnostic study enrolled 2 cohorts. A multicenter prospective cohort, with 50 cases (postmenopausal women with EC; International Federation of Gynecology and Obstetrics stage I-III and grade G1-G3) and 70 controls (no EC but matched on age, years from menopause, tobacco use, and comorbidities), was used to train multiple classification models. The accuracy of each trained model was then used as a statistical weight to produce an ensemble machine learning algorithm for testing, which was validated with a subsequent prospective cohort of 1430 postmenopausal women. The study was conducted at the San Giovanni di Dio e Ruggi d'Aragona University Hospital of Salerno (Italy) and Lega Italiana per la Lotta contro i Tumori clinic in Avellino (Italy). Data collection was conducted from January 2018 to February 2019, and analysis was conducted from January to March 2019. Main Outcomes and Measures: The presence or absence of EC based on evaluation of the blood metabolome. Metabolites were extracted from dried blood samples from all participants and analyzed by gas chromatography-mass spectrometry. A confusion matrix was used to summarize test results. Performance indices included sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios, and accuracy. Confirmation or exclusion of EC in women with a positive test result was by means of hysteroscopy. Participants with negative results were followed up 1 year after enrollment to investigate the appearance of EC signs. Results: The study population consisted of 1550 postmenopausal women. The mean (SD) age was 68.2 (11.7) years for participants with no EC in the training cohort, 69.4 (13.8) years for women with EC in the training cohort, and 59.7 (7.7) years for women in the validation cohort. Application of the ensemble machine learning to the validation cohort resulted in 16 true-positives, 2 false-positives, and 0 false-negatives, and it correctly classified more than 99% of samples. Disease prevalence was 1.12% (16 of 1430). Conclusions and Relevance: In this study, dried blood metabolomic profile was used to assess the presence or absence of EC in postmenopausal women not receiving hormonal therapy with greater than 99% accuracy.

Tactics: an open-source platform for planning, simulating and validating stereotactic surgery.

Frame-based stereotaxy is widely used for planning and implanting deep-brain electrodes. In 2013, as part of a clinical study on deep-brain stimulation for treatment-resistant depression, our group identified a need for software to simulate and plan stereotactic procedures. Shortcomings in extant commercial systems encouraged us to develop Tactics. Tactics is purpose-designed for frame-based stereotactic placement of electrodes. The workflow is far simpler than commercial systems. By simulating specific electrode placement, immediate in-context view of each electrode contact, and the cortical entry site are available within seconds. Post implantation, electrode placement is verified by linearly registering post-operative images. Tactics has been particularly helpful for invasive electroencephalography electrodes where as many as 20 electrodes are planned and placed within minutes. Currently, no commercial system has a workflow supporting the efficient placement of this many electrodes. Tactics includes a novel implementation of automated frame localization and a user-extensible mechanism for importing electrode specifications for visualization of individual electrode contacts. The system was systematically validated, through comparison against gold-standard techniques and quantitative analysis of targeting accuracy using a purpose-built imaging phantom mountable by a stereotactic frame. Internal to our research group, Tactics has been used to plan over 300 depth-electrode targets and trajectories in over 50 surgical cases, and to plan dozens of stereotactic biopsies. Source code and pre-built binaries for Tactics are public and open-source, enabling use and contribution by the extended community.

Evaluation of a novel screening method for fetal aneuploidy using cell-free DNA in maternal plasma.

Objective: To evaluate the test performance of a novel sequencing technology using molecular inversion probes applied to cell-free DNA screening for fetal aneuploidy. Methods: Two cohorts were included in the evaluation; a risk-based cohort of women receiving diagnostic testing in the first and second trimesters was combined with stored samples from pregnancies with fetuses known to be aneuploid or euploid. All samples were blinded to testing personnel before being analyzed, and validation occurred after the study closed and results were merged. Results: Using the new sequencing technology, 1414 samples were analyzed. The findings showed sensitivities and specificities for the common trisomies and the sex chromosome aneuploidies at >99% (Trisomy 21 sensitivity 99.2 CI 95.6­99.2; specificity 99.9 CI 99.6­99.9). Positive predictive values among the trisomies varied from 85.2% (Trisomy 18) to 99.0% (Trisomy 21), reflecting their prevalence rates in the study. Comparisons with a meta-analysis of recent cell-free DNA screening publications demonstrated equivalent test performance. Conclusion: This new technology demonstrates equivalent test performance compared with alternative sequencing approaches, and demonstrates that each chromosome can be successfully interrogated using a single probe.

Serum metallome in pregnant women and the relationship with congenital malformations of the central nervous system: a case-control study.

BACKGROUND: Congenital malformations of the central nervous system (CNS) consist of a wide range of birth defects of multifactorial origin. METHODS: Concentrations of 44 metals were determined by Inductively Coupled Plasma Mass Spectrometry in serum of 111 mothers in the second trimester of pregnancy who carried a malformed fetus and compared them with serum concentrations of the same metals in 90 mothers with a normally developed fetus at the same week of pregnancy. Data are reported as means ± standard deviations. RESULTS: We found a direct relationship between congenital defects of the CNS and maternal serum concentration of aluminum: it was statistically higher in women carrying a fetus with this class of malformation, compared both to mothers carrying a fetus with another class of malformation (6.45 ± 15.15 µg/L Vs 1.44 ± 4.21 µg/L, p < 0.0006) and to Controls (i.e. mothers carrying a normally-developed fetus) (6.45 ± 15.15 µg/L Vs 0.11 ± 0.51 µg/L, p < 0.0006). Moreover, Aluminum abundances were below the limit of detection in the majority of control samples. CONCLUSION: CAluminum may play a role in the onset of central nervous system malformations, although the exact Aluminum species and related specific type of malformation needs further elucidation.

Metabolomic change due to combined treatment with myo-inositol, D-chiro-inositol and glucomannan in polycystic ovarian syndrome patients: a pilot study.

BACKGROUND: Polycystic ovarian syndrome (PCOS) is a highly variable syndrome and one of the most common female endocrine disorders. Although the association inositols-glucomannan may represent a good therapeutic strategy in the treatment of PCOS women with insulin resistance, the effect of inositols on the metabolomic profile of these women has not been described yet. RESULTS: Fifteen PCOS-patients and 15 controls were enrolled. Patients were treated with myo-inositol (1.75 g/day), D-chiro-inositol (0.25 g/day) and glucomannan (4 g/day) for 3 months. Blood concentrations of glucose, insulin, triglycerides and cholesterol, and ovary volumes and antral follicles count, as well as metabolomic profiles, were evaluated for control subjects and for cases before and after treatment. PCOS-patients had higher BMI compared with Controls, BMI decreased significantly after 3 months of treatment although it remained significantly higher compared to controls. 3-methyl-1-hydroxybutyl-thiamine-diphosphate, valine, phenylalanine, ketoisocapric, linoleic, lactic, glyceric, citric and palmitic acid, glucose, glutamine, creatinine, arginine, choline and tocopherol emerged as the most relevant metabolites for distinguishing cases from controls. CONCLUSION: Our pilot study has identified a complex network of serum molecules that appear to be correlated with PCOS, and with a combined treatment with inositols and glucomannan. TRIAL REGISTRATION: ClinicalTial.gov, NCT03608813 . Registered 1st August 2018 - Retrospectively registered, .

Placental tissue metabolome analysis by GC-MS: Oven-drying is a viable sample preparation method.

Analysis of the human placenta metabolome has great potential to advance the understanding of complicated pregnancies and deleterious fetal outcomes in remote populations, but samples preparation can present unique challenges. Herein, we introduce oven-drying as a simple and widely available method of sample preparation that will facilitate investigations of the placental metabolome from remote and under-studied populations. Placentae from complicated and uncomplicated pregnancies were prepared in three ways (oven-dried at 60 °C, fresh, lyophilized) for metabolome analysis via gas chromatography-mass spectrometry (GC-MS). Multiple computer models (e.g. PLS-DA, ANN) were employed to classify and determine if there was a difference in placentae metabolome and a group of metabolites with high variable importance in projection scores across the three preparations and by complicated vs. control groups. The analyses used herein were shown to be thorough and sensitive. Indeed, significant differences were detected in metabolomes of complicated vs. uncomplicated pregnancies; however, there were no statistical differences in the metabolome of placentae prepared by oven-drying vs. lyophilization vs. fresh placentae. Oven-drying is a viable sample preparation method for placentae intended for use in metabolite analysis via GC-MS. These results open many possibilities for researching metabolome patterns associated with fetal outcomes in remote and resource-poor communities worldwide.

A metabolomics-based approach for non-invasive screening of fetal central nervous system anomalies.

BACKGROUND: Central nervous system anomalies represent a wide range of congenital birth defects, with an incidence of approximately 1% of all births. They are currently diagnosed using ultrasound evaluation. However, there is strong need for a more accurate and less operator-dependent screening method. OBJECTIVES: To perform a characterization of maternal serum in order to build a metabolomic fingerprint resulting from congenital anomalies of the central nervous system. METHODS: This is a case-control pilot study. Metabolomic profiles were obtained from serum of 168 mothers (98 controls and 70 cases), using gas chromatography coupled to mass spectrometry. Nine machine learning and classification models were built and optimized. An ensemble model was built based on results from the individual models. All samples were randomly divided into two groups. One was used as training set, the other one for diagnostic performance assessment. RESULTS: Ensemble machine learning model correctly classified all cases and controls. Propanoic, lactic, gluconic, benzoic, oxalic, 2-hydroxy-3-methylbutyric, acetic, lauric, myristic and stearic acid and myo-inositol and mannose were selected as the most relevant metabolites in class separation. CONCLUSION: The metabolomic signature of second trimester maternal serum from pregnancies affected by a fetal central nervous system anomaly is quantifiably different from that of a normal pregnancy. Maternal serum metabolomics is therefore a promising tool for the accurate and sensitive screening of such congenital defects. Moreover, the details of the most relevant metabolites and their respective biochemical pathways allow better understanding of the overall pathophysiology of affected pregnancies.

Elective appendectomy at the time of cesarean delivery: a randomized controlled trial.

OBJECTIVE: The purpose of this study was to compare postoperative morbidity in patients who underwent cesarean delivery with and without elective appendectomy. STUDY DESIGN: Subjects who underwent cesarean delivery were assigned randomly by computer-generated randomization to either standard cesarean delivery or cesarean delivery with appendectomy. Primary variables that were measured were operative times and markers of morbidity. Secondary outcome was appendiceal pathologic condition. RESULTS: Ninety-three subjects whose condition required cesarean delivery from July 2002 to May 2006 were enrolled (control subjects, 48; active subjects, 45). Operative time in the study group was increased by 8.8 minutes (P < or = .028). Postoperative morbidity findings were similar. Pathologic evaluation revealed 9 abnormalities that included acute appendicitis in 2 patients. CONCLUSION: Elective appendectomy at the time of cesarean delivery does not increase inpatient morbidity. Consideration can be given safely to elective appendectomy at the time of cesarean delivery in selected cases, such as women with palpable fecaliths and/or an abnormal appearing appendix, a history of pelvic pain, endometriosis, or anticipated intraabdominal adhesions.