Metabolic profiling uncovers a phenotypic signature of small for gestational age in early pregnancy

Ambient air pollution and adverse birth outcomes: A review of underlying mechanisms

Epidemiological data provide varying degrees of evidence for associations between prenatal exposure to ambient air pollutants and adverse birth outcomes (suboptimal measures of fetal growth, preterm birth and stillbirth). To assess further certainty of effects, this review examines the experimental literature base to identify mechanisms by which air pollution (particulate matter, nitrogen dioxide and ozone) could cause adverse effects on the developing fetus. It likely that this environmental insult impacts multiple biological pathways important for sustaining a healthy pregnancy, depending upon the composition of the pollutant mixture and the exposure window owing to changes in physiologic maturity of the placenta, its circulations and the fetus as pregnancy ensues. The current body of evidence indicates that the placenta is a target tissue, impacted by a variety of critical processes including nitrosative/oxidative stress, inflammation, endocrine disruption, epigenetic changes, as well as vascular dysregulation of the maternal-fetal unit. All of the above can disturb placental function and, as a consequence, could contribute to compromised fetal growth as well increasing the risk of stillbirth. Furthermore, given that there is often an increased inflammatory response associated with preterm labour, inflammation is a plausible mechanism mediating the effects of air pollution on premature delivery. In the light of increased urbanisation and an ever-changing climate, both of which increase ambient air pollution and negatively affect vulnerable populations such as pregnant individuals, it is hoped that the collective evidence may contribute to decisions taken to strengthen air quality policies, reductions in exposure to air pollution and subsequent improvements in the health of those not yet born.

Longitudinal lipidomic profiles during pregnancy and associations with neonatal anthropometry: findings from a multiracial cohort

BACKGROUND

Maternal lipidomic profiling offers promise for characterizing lipid metabolites during pregnancy, but longitudinal data are limited. This study aimed to examine associations of longitudinal lipidomic profiles during pregnancy with multiple neonatal anthropometry using data from a multiracial cohort.

METHODS

We measured untargeted plasma lipidome profiles among 321 pregnant women from the NICHD Fetal Growth Study-Singletons using plasma samples collected longitudinally during four study visits at gestational weeks (GW) 10-14, 15-26, 23-31, and 33-39, respectively. We evaluated individual lipidomic metabolites at each study visit in association with neonatal anthropometry. We also evaluated the associations longitudinally by constructing lipid networks using weighted correlation network analysis and common networks using consensus network analysis across four visits using linear mixed-effects models with the adjustment of false discover rate.

FINDINGS

Multiple triglycerides (TG) were positively associated with birth weight (BW), BW Z-score, length and head circumference, while some cholesteryl ester (CE), phosphatidylcholine (PC), sphingomyelines (SM), phosphatidylethanolamines (PE), and lysophosphatidylcholines (LPC 20:3) families were inversely associated with BW, length, abdominal and head circumference at different GWs. Longitudinal trajectories of TG, PC, and glucosylcermides (GlcCer) were associated with BW, and CE (18:2) with BW z-score, length, and sum of skinfolds (SS), while some PC and PE were significantly associated with abdominal and head circumference. Modules of TG at GW 10-14 and 15-26 mainly were associated with BW. At GW 33-39, two networks of LPC (20:3) and of PC, TG, and CE, showed inverse associations with abdominal circumference. Distinct trajectories within two consensus modules with changes in TG, CE, PC, and LPC showed significant differences in BW and length.

INTERPRETATION

The results demonstrated that longitudinal changes of TGs during early- and mid-pregnancy and changes of PC, LPC, and CE during late-pregnancy were significantly associated with neonatal anthropometry.

FUNDING

National Institute of Child Health and Human Development intramural funding.

High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population

Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.

Integrated metabolomics and machine learning approach to predict hypertensive disorders of pregnancy

BACKGROUND

Hypertensive disorders of pregnancy account for 3% to 10% of maternal-fetal morbidity and mortality worldwide. This condition has been considered one of the leading causes of maternal deaths in developing countries, such as India.

OBJECTIVE

This study aimed to discover hypertensive disorders of pregnancy-specific candidate urine metabolites as markers for hypertensive disorders of pregnancy by applying integrated metabolomics and machine learning approaches.

STUDY DESIGN

The targeted urinary metabolomics study was conducted in 70 healthy pregnant controls and 133 pregnant patients having hypertension as cases. Hypertensive disorders of pregnancy-specific metabolites for disease prediction were further extracted using univariate and multivariate statistical analyses. For machine learning analysis, 80% of the data were used for training (79 for hypertensive disorders of pregnancy and 42 for healthy pregnancy) and validation (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy), and 20% of the data were used for test sets (27 for hypertensive disorders of pregnancy and 14 for healthy pregnancy).

RESULTS

The statistical analysis using an unpaired t test revealed 44 differential metabolites. Pathway analysis showed mainly that purine and thiamine metabolism were altered in the group with hypertensive disorders of pregnancy compared with the healthy pregnancy group. The area under the receiver operating characteristic curves of the 5 most predominant metabolites were 0.98 (adenosine), 0.92 (adenosine monophosphate), 0.89 (deoxyadenosine), 0.81 (thiamine), and 0.81 (thiamine monophosphate). The best prediction accuracies were obtained using 2 machine learning models (95% for the gradient boost model and 98% for the decision tree) among the 5 used models. The machine learning models showed higher predictive performance for 3 metabolites (ie, thiamine monophosphate, adenosine monophosphate, and thiamine) among 5 metabolites. The combined accuracies of adenosine from all models were 98.6 in the training set and 95.6 in the test set. Moreover, the predictive performance of adenosine was higher than other metabolites. The relative feature importance of adenosine was also observed in the decision tree and the gradient boost model.

CONCLUSION

Among other metabolites, adenosine and thiamine metabolites were found to differentiate participants with hypertensive disorders of pregnancy from participants with healthy pregnancies; hence, these metabolites can serve as a promising noninvasive marker for the detection of hypertensive disorders of pregnancy.

Associations of maternal plasma and umbilical cord plasma metabolomics profiles with birth anthropometric measures

BACKGROUND

The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth might influence fetal growth and birth anthropometry. The objective was to examine how maternal plasma and umbilical cord plasma metabolites are associated with newborn anthropometric measures, a known predictor of future health outcomes.

METHODS

Pregnant women between 24 and 28 weeks of gestation were recruited as part of a prospective cohort study. Blood samples from 413 women at enrollment and 787 infant cord blood samples were analyzed using the Biocrates AbsoluteIDQ® p180 kit. Multivariable linear regression models were used to examine associations of cord and maternal metabolites with infant anthropometry at birth.

RESULTS

In cord blood samples from this rural cohort from New Hampshire of largely white residents, 13 metabolites showed negative associations, and 10 metabolites showed positive associations with birth weight Z-score. Acylcarnitine C5 showed negative association, and 4 lysophosphatidylcholines showed positive associations with birth length Z-score. Maternal blood metabolites did not significantly correlate with birth weight and length Z-scores.

CONCLUSIONS

Consistent findings were observed for several acylcarnitines that play a role in utilization of energy sources, and a lysophosphatidylcholine that is part of oxidative stress and inflammatory response pathways in cord plasma samples.

IMPACT

The metabolomics profiles of maternal plasma during pregnancy and cord plasma at birth may influence fetal growth and birth anthropometry. This study examines the independent effects of maternal gestational and infant cord blood metabolomes across different classes of metabolites on birth anthropometry. Acylcarnitine species were negatively associated and glycerophospholipids species were positively associated with weight and length Z-scores at birth in the cord plasma samples, but not in the maternal plasma samples. This study identifies lipid metabolites in infants that possibly may affect early growth.

Association between fetal abdominal growth trajectories, maternal metabolite signatures early in pregnancy, and childhood growth and adiposity: prospective observational multinational INTERBIO-21st fetal study

BACKGROUND

Obesity predominantly affects populations in high-income countries and those countries facing epidemiological transition. The risk of childhood obesity is increased among infants who had overweight or obesity at birth, but in low-resource settings one in five infants are born small for gestational age. We aimed to study the relationships between: (1) maternal metabolite signatures; (2) fetal abdominal growth; and (3) postnatal growth, adiposity, and neurodevelopment.

METHODS

In the prospective, multinational, observational INTERBIO-21st fetal study, conducted in maternity units in Pelotas (Brazil), Nairobi (Kenya), Karachi (Pakistan), Soweto (South Africa), Mae Sot (Thailand), and Oxford (UK), we enrolled women (≥18 years, with a BMI of less than 35 kg/m², natural conception, and a singleton pregnancy) who initiated antenatal care before 14 weeks' gestation. Ultrasound scans were performed every 5±1 weeks until delivery to measure fetal growth and feto-placental blood flow, and we used finite mixture models to derive growth trajectories of abdominal circumference. The infants' health, growth, and development were monitored from birth to age 2 years. Early pregnancy maternal blood and umbilical cord venous blood samples were collected for untargeted metabolomic analysis.

FINDINGS

From Feb 8, 2012, to Nov 30, 2019, we enrolled 3598 pregnant women and followed up their infants to 2 years of age. We identified four ultrasound-derived trajectories of fetal abdominal circumference growth that accelerated or decelerated within a crucial 20-25 week gestational age window: faltering growth, early accelerating growth, late accelerating growth, and median growth tracking. These distinct phenotypes had matching feto-placental blood flow patterns throughout pregnancy, and different growth, adiposity, vision, and neurodevelopment outcomes in early childhood. There were 709 maternal metabolites with positive effect for the faltering growth phenotype and 54 for the early accelerating growth phenotype; 31 maternal metabolites had a negative effect for the faltering growth phenotype and 76 for the early accelerating growth phenotype. Metabolites associated with the faltering growth phenotype had statistically significant odds ratios close to 1·5 (ie, suggesting upregulation of metabolic pathways of impaired fetal growth). The metabolites had a reciprocal relationship with the early accelerating growth phenotype, with statistically significant odds ratios close to 0.6 (ie, suggesting downregulation of fetal growth acceleration). The maternal metabolite signatures included 5-hydroxy-eicosatetraenoic acid, and 11 phosphatidylcholines linked to oxylipin or saturated fatty acid sidechains. The fungicide, chlorothalonil, was highly abundant in the early accelerating growth phenotype group.

INTERPRETATION

Early pregnancy lipid biology associated with fetal abdominal growth trajectories is an indicator of patterns of growth, adiposity, vision, and neurodevelopment up to the age of 2 years. Our findings could contribute to the earlier identification of infants at risk of obesity.

FUNDING

Bill & Melinda Gates Foundation.

Application of metabolomics in intrahepatic cholestasis of pregnancy: a systematic review

Background

Intrahepatic cholestasis of pregnancy (ICP) is a severe idiopathic disorder of bile metabolism; however, the etiology and pathogenesis of ICP remain unclear.

Aims

This study comprehensively reviewed metabolomics studies related to ICP, to help in identifying the pathophysiological changes of ICP and evaluating the potential application of metabolomics in its diagnosis.

Methods

Relevant articles were searched through 2 online databases (PubMed and Web of Science) from January 2000 to March 2022. The metabolites involved were systematically examined and compared. Pathway analysis was conducted through the online software MetaboAnalyst 5.0.

Results

A total of 14 papers reporting 212 metabolites were included in this study. There were several highly reported metabolites: bile acids, such as glycocholic acid, taurochenodeoxycholic acid, taurocholic acid, tauroursodeoxycholic acid, and glycochenodeoxycholic acid. Dysregulation of metabolic pathways involved bile acid metabolism and lipid metabolism. Metabolites related to lipid metabolism include phosphatidylcholine, phosphorylcholine, phosphatidylserine, sphingomyelin, and ceramide.

Conclusions

This study provides a systematic review of metabolomics of ICP and deepens our understanding of the etiology of ICP.

Current Knowledge in Skin Metabolomics: Updates from Literature Review

Metabolomic profiling is an emerging field consisting of the measurement of metabolites in a biological system. Since metabolites can vary in relation to different stimuli, specific metabolic patterns can be closely related to a pathological process. In the dermatological setting, skin metabolomics can provide useful biomarkers for the diagnosis, prognosis, and therapy of cutaneous disorders. The main goal of the present review is to present a comprehensive overview of the published studies in skin metabolomics. A search for journal articles focused on skin metabolomics was conducted on the MEDLINE, EMBASE, Cochrane, and Scopus electronic databases. Only research articles with electronically available English full text were taken into consideration. Studies specifically focused on cutaneous microbiomes were also excluded from the present search. A total of 97 papers matched all the research criteria and were therefore considered for the present work. Most of the publications were focused on inflammatory dermatoses and immune-mediated cutaneous disorders. Skin oncology also turned out to be a relevant field in metabolomic research. Only a few papers were focused on infectious diseases and rarer genetic disorders. All the major metabolomic alterations published so far in the dermatological setting are described extensively in this review.

The Utilization and Interpretation of Cardiac Biomarkers During Pregnancy: JACC: Advances Expert Panel

Cardiac biomarkers are widely used in the nonpregnant population when acute cardiovascular (CV) pathology is suspected; however, the behavior of these biomarkers in the context of pregnancy is less well understood. Pregnant individuals often have symptoms that mimic those of cardiac dysfunction, and complications of pregnancy may include CV disease. This paper will summarize our current knowledge on the use of cardiac biomarkers in pregnancy and provide suggestions on how to use these tools in clinical practice based on the available evidence. Natriuretic peptides and troponin should not be measured routinely in uncomplicated pregnancy, where values should remain low as in the nonpregnant population. In the context of pre-existing or suspected CV disease, these biomarkers retain their negative predictive value. Elevations of both natriuretic peptides and troponin may occur without clear clinical significance in the immediate postpartum period. Elevations of these markers should always prompt further investigation into possible CV pathology.

A Metabolomic Profiling of Intra-Uterine Growth Restriction in Placenta and Cord Blood Points to an Impairment of Lipid and Energetic Metabolism

(1) Background: Intrauterine growth restriction (IUGR) involves metabolic changes that may be responsible for an increased risk of metabolic and cardiovascular diseases in adulthood. Several metabolomic profiles have been reported in maternal blood and urine, amniotic fluid, cord blood and newborn urine, but the placenta has been poorly studied so far. (2) Methods: To decipher the origin of this metabolic reprogramming, we conducted a targeted metabolomics study replicated in two cohorts of placenta and one cohort of cord blood by measuring 188 metabolites by mass spectrometry. (3) Results: OPLS-DA multivariate analyses enabled clear discriminations between IUGR and controls, with good predictive capabilities and low overfitting in the two placental cohorts and in cord blood. A signature of 25 discriminating metabolites shared by both placental cohorts was identified. This signature points to sharp impairment of lipid and mitochondrial metabolism with an increased reliance on the creatine-phosphocreatine system by IUGR placentas. Increased placental insulin resistance and significant alteration of fatty acids oxidation, together with relatively higher phospholipase activity in IUGR placentas, were also highlighted. (4) Conclusions: Our results show a deep lipid and energetic remodeling in IUGR placentas that may have a lasting effect on the fetal metabolism.

Maternal Metals/Metalloid Blood Levels Are Associated With Lipidomic Profiles Among Pregnant Women in Puerto Rico

Background/Aim: The association between heavy metal exposure and adverse birth outcomes is well-established. However, there is a paucity of research identifying biomarker profiles that may improve the early detection of heavy metal-induced adverse birth outcomes. Because lipids are abundant in our body and associated with important signaling pathways, we assessed associations between maternal metals/metalloid blood levels with lipidomic profiles among 83 pregnant women in the Puerto Rico PROTECT birth cohort.

Methods: We measured 10 metals/metalloid blood levels during 24–28 weeks of pregnancy. Prenatal plasma lipidomic profiles were identified by liquid chromatography–mass spectrometry-based shotgun lipidomics. We derived sums for each lipid class and sums for each lipid sub-class (saturated, monounsaturated, polyunsaturated), which were then regressed on metals/metalloid. False discovery rate (FDR) adjusted p-values (q-values) were used to account for multiple comparisons.

Results: A total of 587 unique lipids from 19 lipid classes were profiled. When controlling for multiple comparisons, we observed that maternal exposure to manganese and zinc were negatively associated with plasmenyl-phosphatidylethanolamine (PLPE), particularly those containing polyunsaturated fatty acid (PUFA) chains. In contrast to manganese and zinc, arsenic and mercury were positively associated with PLPE and plasmenyl-phosphatidylcholine (PLPC).

Conclusion: Certain metals were significantly associated with lipids that are responsible for the biophysical properties of the cell membrane and antioxidant defense in lipid peroxidation. This study highlighted lipid-metal associations and we anticipate that this study will open up new avenues for developing diagnostic tools.

Per- and polyfluoroalkyl substance (PFAS) exposure, maternal metabolomic perturbation, and fetal growth in African American women: A meet-in-the-middle approach

BACKGROUND

Prenatal exposures to per- and polyfluoroalkyl substances (PFAS) have been linked to reduced fetal growth. However, the detailed molecular mechanisms remain largely unknown. This study aims to investigate biological pathways and intermediate biomarkers underlying the association between serum PFAS and fetal growth using high-resolution metabolomics in a cohort of pregnant African American women in the Atlanta area, Georgia.

METHODS

Serum perfluorohexane sulfonic acid (PFHxS), perfluorooctane sulfonic acid (PFOS), perfluorooctanoic acid (PFOA), and perfluorononanoic acid (PFNA) measurements and untargeted serum metabolomics profiling were conducted in 313 pregnant African American women at 8-14 weeks gestation. Multiple linear regression models were applied to assess the associations of PFAS with birth weight and small-for-gestational age (SGA) birth. A high-resolution metabolomics workflow including metabolome-wide association study, pathway enrichment analysis, and chemical annotation and confirmation with a meet-in-the-middle approach was performed to characterize the biological pathways and intermediate biomarkers of the PFAS-fetal growth relationship.

RESULTS

Each log2-unit increase in serum PFNA concentration was significantly associated with higher odds of SGA birth (OR = 1.32, 95% CI 1.07, 1.63); similar but borderline significant associations were found in PFOA (OR = 1.20, 95% CI 0.94, 1.49) with SGA. Among 25,516 metabolic features extracted from the serum samples, we successfully annotated and confirmed 10 overlapping metabolites associated with both PFAS and fetal growth endpoints, including glycine, taurine, uric acid, ferulic acid, 2-hexyl-3-phenyl-2-propenal, unsaturated fatty acid C18:1, androgenic hormone conjugate, parent bile acid, and bile acid-glycine conjugate. Also, we identified 21 overlapping metabolic pathways from pathway enrichment analyses. These overlapping metabolites and pathways were closely related to amino acid, lipid and fatty acid, bile acid, and androgenic hormone metabolism perturbations.

CONCLUSION

In this cohort of pregnant African American women, higher serum concentrations of PFOA and PFNA were associated with reduced fetal growth. Perturbations of biological pathways involved in amino acid, lipid and fatty acid, bile acid, and androgenic hormone metabolism were associated with PFAS exposures and reduced fetal growth, and uric acid was shown to be a potential intermediate biomarker. Our results provide opportunities for future studies to develop early detection and intervention for PFAS-induced fetal growth restriction.

Metabolomics Applied to Cord Serum in Preeclampsia Newborns: Implications for Neonatal Outcomes

Preeclampsia (PE) is one of the leading causes of maternal and perinatal morbidity and mortality. However, it is still uncertain how PE affects neonate metabolism. We conducted an untargeted metabolomics analysis of cord blood to explore the metabolic changes in PE neonates. Umbilical cord serum samples from neonates with preeclampsia (n = 29) and non-preeclampsia (non-PE) (n = 32) pregnancies were analyzed using the UHPLC-QE-MS metabolomic platform. Different metabolites were screened, and pathway analysis was conducted. A subgroup analysis was performed among PE neonates to compare the metabolome between appropriate-for-gestational-age infants (n = 21) and small-for-gestational-age (SGA) infants (n = 8). A total of 159 different metabolites were detected in PE and non-PE neonates. Creatinine, N4-acetylcytidine, sphingomyelin (D18:1/16:0), pseudouridine, uric acid, and indolelactic acid were the most significant differential metabolites in the cord serum of PE neonates. Differential metabolite levels were elevated in PE neonates and were involved in the following metabolic pathways: glycine, serine, and threonine metabolism; sphingolipid, glyoxylate, and dicarboxylate metabolism; and arginine biosynthesis. In PE neonates, SGA neonates showed increased levels of hexacosanoyl carnitine and decreased abundance of 3-hydroxybutyric acid and 3-sulfinoalanine. Taurine-related metabolism and ketone body-related pathways were mainly affected. Based on the UHPLC-QE-MS metabolomics analysis, we identified the metabolic profiles of PE and SGA neonates. The abundance of metabolites related to certain amino acid, sphingolipid, and energy metabolism increased in the umbilical cord serum of PE neonates.

Immunoreactive Trypsinogen and Free Carnitine Changes on Newborn Screening after Birth in Patients Who Develop Type 1 Diabetes

Are free carnitine concentrations on newborn screening (NBS) 48–72 h after birth lower in patients who develop type 1 diabetes than in controls? A retrospective case-control study of patients with type 1 diabetes was conducted. NBS results of patients from a Sydney hospital were compared against matched controls from the same hospital (1:5). Multiple imputation was performed for estimating missing data (gestational age) using gender and birthweight. Conditional logistic regression was used to control for confounding and to generate parameter estimates (α = 0.05). The Hommel approach was used for post-hoc analyses. Results are reported as medians and interquartile ranges. A total of 159 patients were eligible (80 females). Antibodies were detectable in 86. Median age at diagnosis was 8 years. Free carnitine concentrations were lower in patients than controls (25.50 µmol/L;18.98–33.61 vs. 27.26; 21.22–34.86 respectively) (p = 0.018). Immunoreactive trypsinogen was higher in this group (20.24 µg/L;16.15–29–52 vs. 18.71; 13.96–26.92) (p = 0.045), which did not persist in the post-hoc analysis. Carnitine levels are lower and immunoreactive trypsinogen might be higher, within 2–3 days of birth and years before development of type 1 diabetes as compared to controls, although the differences were well within reference ranges and provide insight into the pathogenesis into neonatal onset of type 1 diabetes development rather than use as a diagnostic tool. Given trypsinogen’s use for evaluation of new-onset type 1 diabetes, larger studies are warranted.

Maternal serum lipidomics identifies lysophosphatidic acid as a predictor of small for gestational age neonates

To discover lipidomic alterations during pregnancy in mothers who subsequently delivered small for gestational age (SGA) neonates and identify predictive lipid markers that can help recognize and manage these mothers, we carried out untargeted lipidomics on maternal serum samples collected between 24-28 weeks of gestation. We used a nested case-control study design and serum from mothers who delivered SGA and appropriate for gestational age babies. We applied untargeted lipidomics using mass spectrometry to characterize lipids and discover changes associated with SGA births during pregnancy. Multivariate pattern recognition software Collaborative Laboratory Integrated Reports (CLIR) was used for the post-analytical recognition of range differences in lipid ratios that could differentiate between SGA and control mothers and their integration for complete separation between the two groups. Here, we report changes in lipids from serum collected during pregnancy in mothers who delivered SGA neonates. In contrast to normal pregnancies where lysophosphatidic acid increased over the course of the pregnancy owing to increased activity of lysophospholipase D, we observed a decrease (32%; P = 0.05) of 20:4-lysophosphatidic acid in SGA mothers, which could potentially compromise fetal growth and development. Integration of lipid ratios in an interpretive tool (CLIR) could completely separate SGA mothers from controls demonstrating the power of untargeted lipidomic analyses for identifying novel predictive biomarkers. Additional studies are required for further assessment of the lipid biomarkers identified in this report.

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.

Maternal lipidomic signatures in relation to spontaneous preterm birth and large-for-gestational age neonates

Lipidome-wide metabolites may be useful biomarkers of pregnancy outcomes. We sought to characterize maternal lipidomic signatures associated with preterm birth and neonatal anthropometric parameters. Plasma samples were collected 24–28 weeks gestation, and lipidomic profiling was quantified using high-performance liquid chromatography tandem mass spectrometry. Lipid metabolites were analyzed individually and as whole lipid classes and subgroups based on degree of hydrocarbon chain saturation. Associations were estimated using linear and logistic regression. After false discovery adjustment (q < 0.15), four plasmenyl-phosphatidylethanolamines and three free fatty acids associated with increased risk for spontaneous preterm birth. Five phosphatidylinositols, two phosphatidylglycerols, and one phosphatidic acid were associated with large for gestational age neonates. The saturated plasmenyl-phosphatidylethanolamines held the association with increased risk for spontaneous preterm birth. Both the mono- and poly-unsaturated free fatty acids held the association for increased risk for spontaneous preterm birth. Mono- and poly-unsaturated phosphatidylinositols were associated with large for gestational age neonates. Whole lipid classes (plasmenyl-phophatidylcholines and plasmenyl-phosphatidylethanolamines) were associated with increased risk for large for gestational age at delivery. This study provides evidence that finer omics-scale analysis of the maternal lipidome may be more informative biomarkers of pregnancy outcomes compared to whole class level lipid analysis.

Chronic D-ribose and D-mannose overload induce depressive/anxiety-like behavior and spatial memory impairment in mice

The effects of different forms of monosaccharides on the brain remain unclear, though neuropsychiatric disorders undergo changes in glucose metabolism. This study assessed cell viability responses to five commonly consumed monosaccharides-D-ribose (RIB), D-glucose, D-mannose (MAN), D-xylose and L-arabinose-in cultured neuro-2a cells. Markedly decreased cell viability was observed in cells treated with RIB and MAN. We then showed that high-dose administration of RIB induced depressive- and anxiety-like behavior as well as spatial memory impairment in mice, while high-dose administration of MAN induced anxiety-like behavior and spatial memory impairment only. Moreover, significant pathological changes were observed in the hippocampus of high-dose RIB-treated mice by hematoxylin-eosin staining. Association analysis of the metabolome and transcriptome suggested that the anxiety-like behavior and spatial memory impairment induced by RIB and MAN may be attributed to the changes in four metabolites and 81 genes in the hippocampus, which is involved in amino acid metabolism and serotonin transport. In addition, combined with previous genome-wide association studies on depression, a correlation was found between the levels of Tnni3k and Tbx1 in the hippocampus and RIB induced depressive-like behavior. Finally, metabolite-gene network, qRT-PCR and western blot analysis showed that the insulin-POMC-MEK-TCF7L2 and MAPK-CREB-GRIN2A-CaMKII signaling pathways were respectively associated with RIB and MAN induced depressive/anxiety-like behavior and spatial memory impairment. Our findings clarified our understanding of the biological mechanisms underlying RIB and MAN induced depressive/anxiety-like behavior and spatial memory impairment in mice and highlighted the deleterious effects of high-dose RIB and MAN as long-term energy sources.

Glycerophospholipid and detoxification pathways associated with small for gestation age pathophysiology: discovery metabolomics analysis in the SCOPE cohort

INTRODUCTION

Small for gestational age (SGA) may be associated with neonatal morbidity and mortality. Our understanding of the molecular pathways implicated is poor.

OBJECTIVES

Our aim was to determine the metabolic pathways involved in the pathophysiology of SGA and examine their variation between maternal biofluid samples.

METHODS

Plasma (Cork) and urine (Cork, Auckland) samples were collected at 20 weeks' gestation from nulliparous low-risk pregnant women participating in the SCOPE study. Women who delivered an SGA infant (birthweight < 10th percentile) were matched to controls (uncomplicated pregnancies). Metabolomics (urine) and lipidomics (plasma) analyses were performed using ultra performance liquid chromatography-mass spectrometry. Features were ranked based on FDR adjusted p-values from empirical Bayes analysis, and significant features putatively identified.

RESULTS

Lipidomics plasma analysis revealed that 22 out of the 33 significantly altered lipids annotated were glycerophospholipids; all were detected in higher levels in SGA. Metabolomic analysis identified reduced expression of metabolites associated with detoxification (D-Glucuronic acid, Estriol-16-glucuronide), nutrient absorption and transport (Sulfolithocholic acid) pathways.

CONCLUSIONS

This study suggests higher levels of glycerophospholipids, and lower levels of specific urine metabolites are implicated in the pathophysiology of SGA. Further research is needed to confirm these findings in independent samples.

Developmental and Early Life Origins of Cardiometabolic Risk Factors: Novel Findings and Implications

The intent of this review is to critically consider the data that support the concept of programming and its implications. Birth weight and growth trajectories during childhood are associated with cardiometabolic disease in adult life. Both extremes, low and high birth weight coupled with postnatal growth increase the early presence of cardiometabolic risk factors and vascular imprinting, crucial elements of this framework. Data coming from epigenetics, proteomics, metabolomics, and microbiota added relevant information and contribute to better understanding of mechanisms as well as development of biomarkers helping to move forward to take actions. Research has reached a stage in which sufficiently robust data calls for new initiatives focused on early life. Prevention starting early in life is likely to have a very large impact on reducing disease incidence and its associated effects at the personal, economic, and social levels.

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

BACKGROUND

Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.

METHODS

We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women.

RESULTS

Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated.

CONCLUSIONS

Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.

Newborn Screening Samples for Diabetes Research: An Underused Resource

Inborn errors of metabolism and diabetes share common derangements in analytes of metabolic networks that are tested for in newborn screening, usually performed 48-72 h after birth. There is limited research examining the metabolic imprint of diabetes on newborn screening results. This paper aims to demonstrate the links between diabetes, biochemical genetics and newborn screening in investigating disease pathophysiology in diabetes, provide possible reasons for the lack of research in diabetes in newborn screening and offer recommendations on potential research areas. We performed a systematic search of the available literature from 1 April 1998 to 31 December 2018 involving newborn screening and diabetes using OVID, MEDLINE, Cochrane and the PROSPERO register, utilizing a modified extraction tool adapted from Cochrane. Eight studies were included after screening 1312 records. Five studies reanalyzed dried blood spots (DBS) on filter paper cards, and three studies utilized pre-existing results. The results of these studies and how they relate to cord blood studies, the use of cord blood versus newborn screening dried blood spots as a sample and considerations on newborn screening and diabetes research is further discussed. The timing of sampling of newborn screening allows insight into neonatal physiology in a catabolic state with minimal maternal and placental influence. This, combined with the wide coverage of newborn screening worldwide, may aid in our understanding of the origins of diabetes.

The Maternal-Fetal Interface in Small-for-Gestational-Age Pregnancies Is Associated With a Reduced Quantity of Human Decidual NK Cells With Weaker Functional Ability

Small for gestational age (SGA) refers to a birth weight that is less than the 10th percentile of the mean weight of infants at the same gestational age. This condition is associated with a variety of complications, and a high risk of cardiovascular and cerebrovascular diseases in adulthood. Decidual natural killer (dNK) cells at the maternal–fetal interface have received significant research attention in terms of normal pregnancy or miscarriage; however, data relating to SGA are limited. In this study, we aimed to investigate the characteristics and regulatory role of dNK cells at the maternal–fetal interface in SGA. Using immunofluorescence assays, we found that dNK cells maintained close contact with extra-villous trophoblasts, and the proportion of dNK cells in SGA decreased more than in appropriate for gestational age (AGA). Flow cytometry also showed that there was a significantly lower percentage of dNK cells in SGA (25.01 ± 2.43%) than in AGA (34.25 ± 2.30%) (p = 0.0103). The expression of the inhibitory receptor NKG2A on dNK cells and the secretion levels of both perforin and TGF-β1 from dNK cells were significantly higher in SGA than in AGA, while the cytotoxicity of dNK cells in SGA against K562 cells was attenuated. Compared to AGA, the functional ability of dNK cells in SGA showed significant functional impairment in promoting proliferation, migration, invasion, and tube formation in trophoblast cells or vascular endothelial cells. The abnormal function of dNK cells may affect fetal growth and development, and could therefore participate in the pathogenesis of SGA.

Maternal Amino Acid Profiles to Distinguish Constitutionally Small versus Growth-Restricted Fetuses Defined by Doppler Ultrasound: A Pilot Study

OBJECTIVE

Fetuses measuring below the 10th percentile for gestational age may be either constitutionally small for gestational age (SGA) or have pathologic fetal growth restriction (FGR). FGR is associated with adverse outcomes; however, identification of low-risk SGA cases is difficult. We performed a pilot study evaluating maternal markers of pathologic FGR, hypothesizing there are distinct amino acid signatures that might be used for diagnosis and development of new interventions.

STUDY DESIGN

This was a cohort study of healthy women with sonographic fetal estimated fetal weight <5th percentile divided into two groups based upon umbilical artery (UmA) Doppler studies or uterine artery (UtA) Doppler studies. We collected maternal blood samples prior to delivery and used ion pair reverse phase liquid chromatography-mass spectrometry or gas chromatography-mass spectrometry to assess 44 amino acids.

RESULTS

Among 14 women included, five had abnormal UmA, and three had abnormal UtA Doppler results. Those with abnormal UmA showed elevated ornithine. Those with abnormal UtA had lower dimethylglycine, isoleucine, methionine, phenylalanine, and 1-methylhistidine.

CONCLUSION

We found several amino acids that might identify pregnancies affected by pathologic FGR. These findings support the feasibility of future larger studies to identify maternal metabolic approaches to accurately stratify risk for small fetuses.

Maternal plasma metabolic markers of neonatal adiposity and associated maternal characteristics: The GUSTO study

Infant adiposity may be related to later metabolic health. Maternal metabolite profiling reflects both genetic and environmental influences and allows elucidation of metabolic pathways associated with infant adiposity. In this multi-ethnic Asian cohort, we aimed to (i) identify maternal plasma metabolites associated with infant adiposity and other birth outcomes and (ii) investigate the maternal characteristics associated with those metabolites. In 940 mother-offspring pairs, we performed gas chromatography-mass spectrometry and identified 134 metabolites in maternal fasting plasma at 26–28 weeks of gestation. At birth, neonatal triceps and subscapular skinfold thicknesses were measured by trained research personnel, while weight and length measures were abstracted from delivery records. Gestational age was estimated from first-trimester dating ultrasound. Associations were assessed by multivariable linear regression, with p-values corrected using the Benjamini-Hochberg approach. At a false discovery rate of 5%, we observed associations between 28 metabolites and neonatal sum of skinfold thicknesses (13 amino acid-related, 4 non-esterified fatty acids, 6 xenobiotics, and 5 unknown compounds). Few associations were observed with gestational duration, birth weight, or birth length. Maternal ethnicity, pre-pregnancy BMI, and diet quality during pregnancy had the strongest associations with the specific metabolome related to infant adiposity. Further studies are warranted to replicate our findings and to understand the underlying mechanisms.

Untargeted Metabolomics Screen of Mid-pregnancy Maternal Serum and Autism in Offspring

Discovering pathophysiologic networks in a blood-based approach may help to generate valuable tools for early treatment or preventive measures in autism. To date targeted or untargeted metabolomics approaches to identify metabolic features and pathways affecting fetal neurodevelopment have rarely been applied to pregnancy samples, that is, an early period potentially relevant for the development of autism spectrum disorders (ASD). We conducted a population-based study relying on autism diagnoses retrieved from California Department of Developmental Services record. After linking cases to and sampling controls from birth certificates, we retrieved stored maternal mid-pregnancy serum samples collected as part of the California Prenatal Screening Program from the California Biobank for children born 2004 to 2010 in the central valley of California. We retrieved serum for 52 mothers whose children developed autism and 62 population controls originally selected from all eligible children matched by birth year and child's sex. Also, we required that these mothers were relatively low or unexposed to air pollution and select pesticides during early pregnancy. We identified differences in metabolite levels in several metabolic pathways, including glycosphingolipid biosynthesis and metabolism, N-glycan and pyrimidine metabolism, bile acid pathways and, importantly, C21-steroid hormone biosynthesis and metabolism. Disturbances in these pathways have been shown to be relevant for neurodevelopment in rare genetic syndromes or implicated in previous studies of autism. This study provides new insight into maternal mid-pregnancy metabolic features possibly related to the development of autism and an incentive to explore whether these pathways and metabolites are useful for early diagnosis, treatment, or prevention. LAY SUMMARY: This study found that in mid-pregnancy the blood of mothers who give birth to a child that develops autism has some characteristic features that are different from those of blood samples taken from control mothers. These features are related to biologic mechanisms that can affect fetal brain development. In the future, these insights may help identify biomarkers for early autism diagnosis and treatment or preventive measures. Autism Res 2020, 13: 1258-1269. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

Effect of L-Ergothioneine on the metabolic plasma profile of the RUPP rat model of pre-eclampsia

INTRODUCTION

Pre-eclampsia is a major cause of maternal and fetal mortality and morbidity worldwide. Its pathophysiology remains unclear, but mitochondrial dysfunction and oxidative stress have been implicated. L-Ergothioneine is a naturally occurring, water-soluble betaine, that has demonstrated antioxidant properties. Using the reduced uterine perfusion pressure (RUPP) rat model of pre-eclampsia, this study aimed to define the plasma metabolic profile following treatment with L-Ergothioneine.

METHODS

The effect of L-Ergothioneine (ET) treatment was explored using in vivo treatment in rats: Sham control (SC, n = 5), RUPP control (RC, n = 5), Sham +ET (ST, n = 5), RUPP +ET (RT, n = 5). Differential expression of plasma metabolites were obtained using untargeted liquid chromatography coupled to mass spectrometry. Statistical analysis was performed on normalised data comparing RC to SC, RT to RC, and RT to ST. Metabolites significantly altered (FDR < 0.05) were identified through database search.

RESULTS

We report significantly lower levels of L-palmitoylcarnitine in RC compared to SC, a fatty acyl substrate involved in beta-oxidation in the mitochondria. We report that a metabolite that has been associated with oxidative stress (Glutamylcysteine) was detected at significantly higher levels in RT vs RC and RT vs ST. Five metabolites associated with inflammation were significantly lower in RT vs RC and three metabolites in RT vs ST, demonstrating the anti-inflammatory effects of ET in the RUPP rat model of pre-eclampsia.

CONCLUSIONS

L-Ergothioneine may help preserve mitochondrial function by increasing antioxidant levels, and reducing inflammatory responses associated with pre-eclampsia. This study shows the potential of L-Ergothioneine as a treatment for pre-eclampsia.

Metabolome Wide Association Study of serum DDT and DDE in Pregnancy and Early Postpartum

The advancement of high-resolution metabolomics (HRM) and metabolome-wide-association study (MWAS) enables the readout of environmental effects in human specimens. We used HRM to understand DDT-induced alterations of in utero environment and potential health effects. Endogenous metabolites were measured in 397 maternal perinatal serum samples collected during 1959-1967 in the Child Health and Development Studies (CHDS) and in 16 maternal postnatal serum samples of mice treated with or without DDT. MWAS was performed to assess associations between metabolites and p,p'-DDT, o,p'-DDT and p,p'-DDE levels, followed by pathway analysis. Distinct metabolic profiles were found with p,p'-DDT and p,p'-DDE. Amino acids such arginine had a strong association with p,p'-DDT and o,p'-DDT in both women and mice, whereas lipids and acyl-carnitine intermediates were found exclusively associated with p,p'-DDE in CHDS women indicating mitochondrial impairment. It suggests that the role of serine and fatty acid metabolism on the causal disease pathway should be examined.

Association between phospholipid metabolism in plasma and spontaneous preterm birth: a discovery lipidomic analysis in the cork pregnancy cohort

INTRODUCTION

Preterm birth (PTB) is defined as birth occurring before 37 weeks' gestation, affects 5-9% of all pregnancies in developed countries, and is the leading cause of perinatal mortality. Spontaneous preterm birth (sPTB) accounts for 31-50% of all PTB, but the underlying pathophysiology is poorly understood.

OBJECTIVE

This study aimed to decipher the lipidomics pathways involved in pathophysiology of sPTB.

METHODS

Blood samples were taken from SCreening fOr Pregnancy Endpoints (SCOPE), an international study that recruited 5628 nulliparous women, with a singleton low-risk pregnancy. Our analysis focused on plasma from SCOPE in Cork. Discovery profiling of the samples was undertaken using liquid chromatography-mass spectrometry Lipidomics, and features significantly altered between sPTB (n = 16) and Control (n = 32) groups were identified using empirical Bayes testing, adjusting for multiple comparisons.

RESULTS

Twenty-six lipids showed lower levels in plasma of sPTB compared to controls (adjusted p < 0.05), including 20 glycerophospholipids (12 phosphatidylcholines, 7 phosphatidylethanolamines, 1 phosphatidylinositol) and 6 sphingolipids (2 ceramides and 4 sphingomyelines). In addition, a diaglyceride, DG (34:4), was detected in higher levels in sPTB compared to controls.

CONCLUSIONS

We report reduced levels of plasma phospholipids in sPTB. Phospholipid integrity is linked to biological membrane stability and inflammation, while storage and breakdown of lipids have previously been implicated in pregnancy complications. The contribution of phospholipids to sPTB as a cause or effect is still unclear; however, our results of differential plasma phospholipid expression represent another step in advancing our understanding of the aetiology of sPTB. Further work is needed to validate these findings in independent pregnancy cohorts.

Newborn metabolomic profile mirrors that of mother in pregnancy

BACKGROUND

Pregnancy is characterized by multiple metabolic processes to allow proper foetal development and ensure adequate stores. Little is known about the interactions between maternal and foetal metabolism during the last phase of pregnancy. Metabolomic offers potential to discover changes in maternal metabolism in pregnancy and their relation to the newborn metabolic status.

OBJECTIVE

In this study we tested the hypothesis that metabolomic status in newborns at birth depends upon the metabolomic profile of their mothers in the last phase of pregnancy.

STUDY DESIGN

Urine samples were collected from 36 pregnant women three weeks before delivery and from 21 healthy term newborns within 48 h after birth. Urines were analysed using proton nuclear magnetic resonance (1H NMR) spectroscopy and NMR urine spectra were evaluated through Principal Components Analysis.

RESULTS

The first component of the PCA analysis showed two distinct metabolic groups: pregnant women and newborns. A significant correlation was found between urine metabolic profiles of newborns and those of their mothers.

CONCLUSION

Urine metabolomic profiles of newborns at birth mirrors that of their mothers in the last phase of pregnancy. The metabolomic approach appears to be crucial to understand the maternal effects on foetal programming and infant outcomes.

Examining the predictive accuracy of metabolomics for small-for-gestational-age babies: a systematic review

INTRODUCTION

To date, there is no robust enough test to predict small-for-gestational-age (SGA) infants, who are at increased lifelong risk of morbidity and mortality.

OBJECTIVE

To determine the accuracy of metabolomics in predicting SGA babies and elucidate which metabolites are predictive of this condition.

DATA SOURCES

Two independent researchers explored 11 electronic databases and grey literature in February 2018 and November 2018, covering publications from 1998 to 2018. Both researchers performed data extraction and quality assessment independently. A third researcher resolved discrepancies.

STUDY ELIGIBILITY CRITERIA

Cohort or nested case-control studies were included which investigated pregnant women and performed metabolomics analysis to evaluate SGA infants. The primary outcome was birth weight <10th centile-as a surrogate for fetal growth restriction-by population-based or customised charts.

STUDY APPRAISAL AND SYNTHESIS METHODS

Two independent researchers extracted data on study design, obstetric variables and sampling, metabolomics technique, chemical class of metabolites, and prediction accuracy measures. Authors were contacted to provide additional data when necessary.

RESULTS

A total of 9181 references were retrieved. Of these, 273 were duplicate, 8760 were removed by title or abstract, and 133 were excluded by full-text content. Thus, 15 studies were included. Only two studies used the fifth centile as a cut-off, and most reports sampled second-trimester pregnant women. Liquid chromatography coupled to mass spectrometry was the most common metabolomics approach. Untargeted studies in the second trimester provided the largest number of predictive metabolites, using maternal blood or hair. Fatty acids, phosphosphingolipids and amino acids were the most prevalent predictive chemical subclasses.

CONCLUSIONS AND IMPLICATIONS

Significant heterogeneity of participant characteristics and methods employed among studies precluded a meta-analysis. Compounds related to lipid metabolism should be validated up to the second trimester in different settings.

PROSPERO REGISTRATION NUMBER

CRD42018089985.

Blood-based biomarkers in the maternal circulation associated with fetal growth restriction

Fetal growth restriction (FGR) is associated with threefold to fourfold increased risk of stillbirth. Identifying FGR, through its commonly used surrogate-the small-for-gestational-age (SGA, estimated fetal weight and/or abdominal circumference <10th centile) fetus-and instituting fetal surveillance and timely delivery decrease stillbirth risk. Methods available to clinicians for antenatal identification of SGA fetuses have surprisingly poor sensitivity. About 80% of cases remain undetected. Measuring the symphysis-fundal height detects only 20% of SGA fetuses, and even universal third trimester ultrasound detects, at best, 57% of those born SGA. There is an urgent need to find better ways to identify this at-risk cohort. This review summarises efforts to identify molecular biomarkers (proteins, metabolites, or ribonucleic acids) that could be used to better predict FGR. Most studies examining potential biomarkers to date have utilised case-control study designs without proceeding to validation in independent cohorts. To develop a robust test for FGR, large prospective studies are required with a priori validation plans and cohorts. Given that current clinical care detects 20% of SGA fetuses, even a screening test with ≥60% sensitivity at 90% specificity could be clinically useful, if developed. This may be an achievable aspiration. If discovered, such a test may decrease stillbirth.

Big data and stratified medicine: what does it mean for children?

Stratified medicine in paediatrics is increasingly becoming a reality, as our understanding of disease pathogenesis improves and novel treatment targets emerge. We have already seen some success in paediatrics in targeted therapies such as cystic fibrosis for specific cystic fibrosis transmembrane conductance regulator variants. With the increased speed and decreased cost of processing and analysing data from rare disease registries, we are increasingly able to use a systems biology approach (including '-omics') to screen across populations for molecules and genes of interest. Improving our understanding of the molecular mechanisms underlying disease, and how to classify patients according to these will lead the way for targeted therapies for individual patients. This review article will summarise how 'big data' and the 'omics' are being used and developed, and taking examples from paediatric renal medicine and rheumatology, demonstrate progress being made towards stratified medicine for children.

Metabolomics applied to maternal and perinatal health: a review of new frontiers with a translation potential

The prediction or early diagnosis of maternal complications is challenging mostly because the main conditions, such as preeclampsia, preterm birth, fetal growth restriction, and gestational diabetes mellitus, are complex syndromes with multiple underlying mechanisms related to their occurrence. Limited advances in maternal and perinatal health in recent decades with respect to preventing these disorders have led to new approaches, and "omics" sciences have emerged as a potential field to be explored. Metabolomics is the study of a set of metabolites in a given sample and can represent the metabolic functioning of a cell, tissue or organism. Metabolomics has some advantages over genomics, transcriptomics, and proteomics, as metabolites are the final result of the interactions of genes, RNAs and proteins. Considering the recent "boom" in metabolomic studies and their importance in the research agenda, we here review the topic, explaining the rationale and theory of the metabolomic approach in different areas of maternal and perinatal health research for clinical practitioners. We also demonstrate the main exploratory studies of these maternal complications, commenting on their promising findings. The potential translational application of metabolomic studies, especially for the identification of predictive biomarkers, is supported by the current findings, although they require external validation in larger datasets and with alternative methodologies.

Metabolomics approach in lung tissue of septic rats and the interventional effects of Xuebijing injection using UHPLC-Q-Orbitrap-HRMS

Sepsis is the dysregulated host response to an infection which leads to life-threatening organ dysfunction. Metabolomic profiling in bio-fluid or tissue is vital for elucidating the pathogenesis of sepsis and evaluating therapeutic effects of medication. In this study, an untargeted metabolomics approach was applied to study the metabolic changes in lung tissue of septic rats induced by cecal ligation and puncture (CLP) and investigate the treatment effects of Xubijing injection (XBJ). Metabolomics analyses were performed on ultra-high performance liquid chromatography-Q Exactive hybrid quadrupole-orbitrap high-resolution accurate mass spectrometry (UHPLC-Q-Orbitrap-HRMS) together with multivariate statistical analysis. A total of 26 differential metabolites between CLP and sham-operated group were identified. The altered metabolic pathways included energy metabolism, amino metabolism, lipid metabolism, fatty acid metabolism and hormone metabolism. Among the 26-varied metabolites, 15 were significantly regulated after XBJ treatment. The metabolic pathway network of sepsis was drawn to interpret the pathological feature of lung damage caused by sepsis and the underlying regulating mechanism of XBJ on the molecular levels. Our findings display that LC-MS-based metabolomics is a useful tool for uncovering the underlying molecular mechanism of sepsis, and XBJ may exert therapeutic effect by regulating multiple metabolic pathways.

Metabolomics of Pregnancy Complications: Emerging Application of Maternal Hair

In recent years, the study of metabolomics has begun to receive increasing international attention, especially as it pertains to medical research. This is due in part to the potential for discovery of new biomarkers in the metabolome and to a new understanding of the "exposome", which refers to the endogenous and exogenous compounds that reflect external exposures. Consequently, metabolomics research into pregnancy-related issues has increased. Biomarkers discovered through metabolomics may shed some light on the etiology of certain pregnancy-related complications and their adverse effects on future maternal health and infant development and improve current clinical management. The discoveries and methods used in these studies will be compiled and summarized within the following paper. A further focus of this paper is the use of hair as a biological sample, which is gaining increasing attention across diverse fields due to its noninvasive sampling method and the metabolome stability. Its significance in exposome studies will be considered in this review, as well as the potential to associate exposures with adverse pregnancy outcomes. Currently, hair has been used in only two metabolomics studies relating to fetal growth restriction (FGR) and gestational diabetes mellitus (GDM).

Metabolomics for predicting fetal growth restriction: protocol for a systematic review and meta-analysis

INTRODUCTION

Fetal growth restriction (FGR) is a relevant research and clinical concern since it is related to higher risks of adverse outcomes at any period of life. Current predictive tools in pregnancy (clinical factors, ultrasound scan, placenta-related biomarkers) fail to identify the true growth-restricted fetus. However, technologies based on metabolomics have generated interesting findings and seem promising. In this systematic review, we will address diagnostic accuracy of metabolomics analyses in predicting FGR.

METHODS AND ANALYSIS

Our primary outcome is small for gestational age infant, as a surrogate for FGR, defined as birth weight below the 10th centile by customised or population-based curves for gestational age. A detailed systematic literature search will be carried in electronic databases and conference abstracts, using the keywords 'fetal growth retardation', 'metabolomics', 'pregnancy' and 'screening' (and their variations). We will include original peer-reviewed articles published from 1998 to 2018, involving pregnancies of fetuses without congenital malformations; sample collection must have been performed before clinical recognition of growth impairment. If additional information is required, authors will be contacted. Reviews, case reports, cross-sectional studies, non-human research and commentaries papers will be excluded. Sample characteristics and the diagnostic accuracy data will be retrieved and analysed. If data allows, we will perform a meta-analysis.

ETHICS AND DISSEMINATION

As this is a systematic review, no ethical approval is necessary. This protocol will be publicised in our institutional websites and results will be submitted for publication in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42018089985.

Metabolic profiling and targeted lipidomics reveals a disturbed lipid profile in mothers and fetuses with intrauterine growth restriction

Fetal growth may be impaired by poor placental function or maternal conditions, each of which can influence the transfer of nutrients and oxygen from the mother to the developing fetus. Large-scale studies of metabolites (metabolomics) are key to understand cellular metabolism and pathophysiology of human conditions. Herein, maternal and cord blood plasma samples were used for NMR-based metabolic fingerprinting and profiling, including analysis of the enrichment of circulating lipid classes and subclasses, as well as the number of sub-fraction particles and their size. Changes in phosphatidylcholines and glycoproteins were prominent in growth-restricted fetuses indicating significant alterations in their abundance and biophysical properties. Lipoprotein profiles showed significantly lower plasma concentrations of cholesterol-intermediate density lipoprotein (IDL), triglycerides-IDL and high-density lipoprotein (HDL) in mothers of growth-restricted fetuses compared to controls (p < 0.05). In contrast, growth-restricted fetuses had significantly higher plasma concentrations of cholesterol and triglycerides transporting lipoproteins [LDL, IDL, and VLDL, (p < 0.005; all)], as well as increased VLDL particle types (large, medium and small). Significant changes in plasma concentrations of formate, histidine, isoleucine and citrate in growth-restricted fetuses were also observed. Comprehensive metabolic profiling reveals that both, mother and fetuses of pregnancies complicated with fetal growth restriction have a substantial disruption in lipid metabolism.

Association between maternal exposure to phthalates and lower language ability in offspring derived from hair metabolome analysis

The fetus undergoes a crucial period of neurodevelopment in utero. The maternal hair metabolome provides an integrated record of the metabolic state of the mother prior to, and during pregnancy. We investigated whether variation in the maternal hair metabolome was associated with neurodevelopmental differences across infants. Maternal hair samples and infant neurocognitive assessments (using the Bayley III Scales of Infant Development at 24 months) were obtained for 373 infant-mother dyads between 26–28 weeks’ gestation from the Growing Up in Singapore Towards Healthy Outcomes cohort. The hair metabolome was analysed using gas chromatography-mass spectrometry. Intensity measurements were obtained for 276 compounds. After controlling for maternal education, ethnicity, and infant sex, associations between metabolites and expressive language skills were detected, but not for receptive language, cognitive or motor skills. The results confirm previous research associating higher levels of phthalates with lower language ability. In addition, scores were positively associated with a cluster of compounds, including adipic acid and medium-chain fatty acids. The data support associations between the maternal hair metabolome and neurodevelopmental processes of the fetus. The association between phthalates and lower language ability highlights a modifiable risk factor that warrants further investigation.

Cord Blood Metabolic Signatures of Birth Weight: A Population-Based Study

Birth weight is an important indicator of maternal and fetal health and a predictor of health in later life. However, the determinants of variance in birth weight are still poorly understood. We aimed to identify the biological pathways, which may be perturbed by environmental exposures, that are important in determining birth weight. We applied untargeted mass-spectrometry-based metabolomics to 481 cord blood samples collected at delivery in four birth cohorts from across Europe: ENVIRONAGE (Belgium), INMA (Spain), Piccolipiu (Italy), and Rhea (Greece). We performed a metabolome-wide association scan for birth weight on over 4000 metabolic features, controlling the false discovery rate at 5%. Annotation of compounds was conducted through reference to authentic standards. We identified 68 metabolites significantly associated with birth weight, including vitamin A, progesterone, docosahexaenoic acid, indolelactic acid, and multiple acylcarnitines and phosphatidylcholines. We observed enrichment (p < 0.05) of the tryptophan metabolism, prostaglandin formation, C21-steroid hormone signaling, carnitine shuttle, and glycerophospholipid metabolism pathways. Vitamin A was associated with both maternal smoking and birth weight, suggesting a mediation pathway. Our findings shed new light on the pathways central to fetal growth and will have implications for antenatal and perinatal care and potentially for health in later life.

First and second trimester urinary metabolic profiles and fetal growth restriction: an exploratory nested case-control study within the infant development and environment study

BACKGROUND

Routine prenatal care fails to identify a large proportion of women at risk of fetal growth restriction (FGR). Metabolomics, the comprehensive analysis of low molecular weight molecules (metabolites) in biological samples, can provide new and earlier biomarkers of prenatal health. Recent research has suggested possible predictive first trimester urine metabolites correlating to fetal growth restriction in the third trimester. Our objective in this current study was to examine urinary metabolic profiles in the first and second trimester of pregnancy in relation to third trimester FGR in a US population from a large, multi-center cohort study of healthy pregnant women.

METHODS

We conducted a nested case-control study within The Infant Development and the Environment Study (TIDES), a population-based multi-center pregnancy cohort study. We identified 53 cases of FGR based on the AUDIPOG [Neonatal growth - AUDIPOG [Internet]. [cited 29 Nov 2016]. Available from: http://www.audipog.net/courbes_morpho.php?langue=en ] formula for birthweight percentile considering maternal height, age, and prenatal weight, as well as infant sex, gestational age, and birth rank. Cases were matched to 106 controls based on study site, maternal age (± 2 years), parity, and infant sex. NMR spectroscopy was used to assess concentrations of four urinary metabolites that have been previously associated with FGR (tyrosine, acetate, formate, and trimethylamine) in first and second trimester urine samples. We fit multivariate conditional logistic regression models to estimate the odds of FGR in relation to urinary concentrations of these individual metabolites in the first and second trimesters. Exploratory analyses of custom binned spectroscopy results were run to consider other potentially related metabolites.

RESULTS

We found no significant association between the relative concentrations of each of the four metabolites and odds of FGR. Exploratory analyses did not reveal any significant differences in urinary metabolic profiles. Compared with controls, cases delivered earlier (38.6 vs 39.8, p < 0.001), and had lower birthweights (2527 g vs 3471 g, p < 0.001). Maternal BMI was similar between cases and controls.

CONCLUSIONS

First and second trimester concentrations of urinary metabolites (acetate, formate, trimethylamine and tyrosine) did not predict FGR. This inconsistency with previous studies highlights the need for more rigorous investigation and data collection in this area before metabolomics can be clinically applied to obstetrics.

Analysis of sequential hair segments reflects changes in the metabolome across the trimesters of pregnancy

The hair metabolome has been recognized as a valuable source of information in pregnancy research, as it provides stable metabolite information that could assist with studying biomarkers or metabolic mechanisms of pregnancy and its complications. We tested the hypothesis that hair segments could be used to reflect a metabolite profile containing information from both endogenous and exogenous compounds accumulated during the nine months of pregnancy. Segments of hair samples corresponding to the trimesters were collected from 175 pregnant women in New Zealand. The hair samples were analysed using gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry. In healthy pregnancies, 56 hair metabolites were significantly different between the first and second trimesters, while 62 metabolites were different between the first and third trimesters (p < 0.05). Additionally, three metabolites in the second trimester hair samples were significantly different between healthy controls and women who delivered small-for-gestational-age infants (p < 0.05), and ten metabolites in third trimester hair were significantly different between healthy controls and women with gestational diabetes mellitus (p < 0.01). The findings from this pilot study provide improved insight into the changes of the hair metabolome during pregnancy, as well as highlight the potential of the maternal hair metabolome to differentiate pregnancy complications from healthy pregnancies.

Non-invasive identification of protein biomarkers for early pregnancy diagnosis in the cheetah (Acinonyx jubatus)

Approximately 80% of cheetahs living in typical zoological collections never reproduce. In more than 60% of breedings, the female is confirmed to ovulate, but parturition fails to occur. It is unknown if these non-pregnant intervals of elevated progesterone (deemed luteal phases) are conception failures or a pregnancy terminating in embryonic/fetal loss. There have been recent advances in metabolic profiling and proteome analyses in many species with mass spectrometry used to identify ‘biomarkers’ and mechanisms indicative of specific physiological states (including pregnancy). Here, we hypothesized that protein expression in voided cheetah feces varied depending on pregnancy status. We: 1) identified the expansive protein profile present in fecal material of females; and 2) isolated proteins that may be candidates playing a role in early pregnancy establishment and diagnosis. Five hundred and seventy unique proteins were discovered among samples from pregnant (n = 8), non-pregnant, luteal phase (n = 5), and non-ovulatory control (n = 5) cheetahs. Four protein candidates were isolated that were significantly up-regulated and two were down-regulated in samples from pregnant compared to non-pregnant or control counterparts. One up-regulated candidate, immunoglobulin J chain (IGJ; an important component of the secretory immune system) was detected using a commercially available antibody via immunoblotting. Findings revealed that increased IGJ abundance could be used to detect pregnancy successfully in >80% of 23 assessed females within 4 weeks after mating. The discovery of a novel fecal pregnancy marker improves the ability to determine reproductive, especially gestational, status in cheetahs managed in an ex situ insurance and source population.

Spontaneous miscarriage in first trimester pregnancy is associated with altered urinary metabolite profile

Threatened miscarriage is the most common gynecological emergency, occurring in about 20% of pregnant women. Approximately one in four of these patients go on to have spontaneous miscarriage and the etiology of miscarriage still remains elusive. In a bid to identify possible biomarkers and novel treatment targets, many studies have been undertaken to elucidate the pathways that lead to a miscarriage. Luteal phase deficiency has been shown to contribute to miscarriages, and the measurement of serum progesterone as a prognostic marker and the prescription of progesterone supplementation has been proposed as possible diagnostic and treatment methods. However, luteal phase deficiency only accounts for 35% of miscarriages. In order to understand the other causes of spontaneous miscarriage and possible novel urine biomarkers for miscarriage, we looked at the changes in urinary metabolites in women with threatened miscarriage. To this end, we performed a case-control study of eighty patients who presented with threatened miscarriage between 6 and 10 weeks gestation. Urine metabolomics analyses of forty patients with spontaneous miscarriages and forty patients with ongoing pregnancies at 16 weeks gestation point to an impaired placental mitochondrial β-oxidation of fatty acids as the possible cause of spontaneous miscarriage. This study also highlighted the potential of urine metabolites as a non-invasive screening tool for the risk stratification of women presenting with threatened miscarriage.

Associations of cord blood metabolites with perinatal characteristics, newborn anthropometry, and cord blood hormones in project viva

CONTEXT

Metabolomics has emerged as a powerful tool to characterize biomarkers and elucidate physiological processes underlying adverse health outcomes. Little is known of these relationships during gestation and infancy, which are critical period for development of metabolic disease risk.

OBJECTIVES

To identify cord blood metabolite patterns associated with birth size; and to investigate relations of the birth size-associated metabolite patterns, and a branched chain amino acid (BCAA) metabolite pattern with a range of newborn and perinatal characteristics.

METHODS

Using untargeted mass-spectrometry, we quantified metabolites in cord blood of 126 mother-child pairs. After excluding 103 xenobiotics, we used principal components analysis (PCA) to consolidate the remaining 606 metabolites into principal components ("factors"). Next, we identified factors associated with gestational age-and sex-standardized birthweight z-score (BW/GA) and examined associations of the BW/GA-associated pattern(s) and the BCAA pattern with cord blood insulin, leptin, adiponectin, insulin-like growth factor (IGF)-1, IGF-2, and IGF binding protein 3 (IGFBP-3) using multivariable linear regression. Finally, we examined associations of maternal/perinatal characteristics with the cord blood metabolite patterns.

RESULTS

Mean BW/GA z-score was 0.27±0.98 units. About half of the infants were male (52.4%) and white (57.1%). Of the 6 factors identified from PCA, one was associated with higher BW/GA: Factor 5, which comprised metabolites involved in energy production (malate, succinate, fumarate) and nucleotide turnover (inosine 5-monophosphate, adenosine 5-monophosphate, cytidine 5-monophosphate) pathways. In multivariable analysis, Factor 5 was related to higher cord blood leptin (1.64 [95% CI: 0.42, 2.87] ng/mL) and IGF-1 even after adjusting for IGFBP-3 (3.35 [0.25, 6.44] ng/mL). The BCAA pattern was associated with higher BW/GA (0.20 [0.03, 0.36] z-scores) and IGFBP-3 (106.5 [44.7, 168.2] ng/mL). No maternal characteristics were associated with either metabolite pattern; however, infants born via Cesarean delivery exhibited a higher score for Factor 5, and gestation length was inversely associated with the BCAA pattern.

CONCLUSIONS

Metabolites in energy production and DNA/RNA turnover pathways in cord blood are associated with larger size at birth, and higher leptin and IGF-1. Similarly, the BCAA pattern was associated with larger birth size and IGFBP-3.

Importance of metabolomics analyses of maternal parameters and their influence on fetal growth

Metabolomics is the scientific study of chemical processes involving metabolites. Specifically, metabolomics is the systematic study of the unique chemical fingerprints that specifically conveys cell processes. Fetal growth aberrations, including fetal growth restriction and macrosomia, convey the highest risk of perinatal mortality and morbidity, as well as increasing the chance of developing chronic disease in later life. We searched the electronic database PubMed for preclinical as well as clinical controlled studies pertaining to metabolomics analyses of maternal parameters and their influence on fetal growth. It was observed clearly that metabolic profiling/metabolomics approaches in maternal urine samples provide information on early-life exposure and are potentially linked to child health outcomes, in addition to identifying new biomarkers of exposure. This review article is aimed to discuss intra- and inter-individual variations in maternal urine profiles during pregnancy, fetal growth outcomes and environmental sources of metabolic variations. The review concludes that metabolic profiling of mother is a useful tool for the evaluation of influences on the growth of the fetus.

Prediction of Small for Gestational Age Infants in Healthy Nulliparous Women Using Clinical and Ultrasound Risk Factors Combined with Early Pregnancy Biomarkers

Objective

Most small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks’ with ultrasound parameters at 20±1 weeks’ gestation.

Methods

Data from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE) cohort study were divided into Training (n = 3735) and Validation datasets (n = 1871). The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile), Normotensive-SGA (small for gestational age with a normotensive mother) and Hypertensive-SGA (small for gestational age with an hypertensive mother). The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler) variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve.

Results

633 (11.2%) infants were All-SGA, 465(8.2%) Normotensive-SGA and 168 (3%) Hypertensive-SGA. Area under the curve (95% Confidence Intervals) for All-SGA using 15±1 weeks’ clinical variables, 15±1 weeks’ clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks’ were: 0.63 (0.59–0.67), 0.64 (0.60–0.68) and 0.69 (0.66–0.73) respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57–0.66), 0.61 (0.56–0.66) and 0.68 (0.64–0.73) with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals) for Hypertensive-SGA were: 0.76 (0.70–0.82), 0.80 (0.75–0.86) and 0.84 (0.78–0.89) with minimal change in the Training datasets.

Conclusion

Models for prediction of small for gestational age, which combine biomarkers, clinical and ultrasound data from a cohort of low-risk nulliparous women achieved modest performance. Incorporation of biomarkers into the models resulted in no improvement in performance of prediction of All-SGA and Normotensive-SGA but a small improvement in prediction of Hypertensive-SGA. Our models currently have insufficient reliability for application in clinical practice however, they have potential utility in two-staged screening tests which include third trimester biomarkers and or fetal biometry.

Maternal urinary metabolic signatures of fetal growth and associated clinical and environmental factors in the INMA study

BACKGROUND

Maternal metabolism during pregnancy is a major determinant of the intra-uterine environment and fetal outcomes. Herein, we characterize the maternal urinary metabolome throughout pregnancy to identify maternal metabolic signatures of fetal growth in two subcohorts and explain potential sources of variation in metabolic profiles based on lifestyle and clinical data.

METHODS

We used ¹H nuclear magnetic resonance (NMR) spectroscopy to characterize maternal urine samples collected in the INMA birth cohort at the first (n = 412 and n = 394, respectively, in Gipuzkoa and Sabadell cohorts) and third trimesters of gestation (n = 417 and 469). Metabolic phenotypes that reflected longitudinal intra- and inter-individual variation were used to predict measures of fetal growth and birth weight.

RESULTS

A metabolic shift between the first and third trimesters of gestation was characterized by ¹H NMR signals arising predominantly from steroid by-products. We identified 10 significant and reproducible metabolic associations in the third trimester with estimated fetal, birth, and placental weight in two independent subcohorts. These included branched-chain amino acids; isoleucine, valine, leucine, alanine and 3 hydroxyisobutyrate (metabolite of valine), which were associated with a significant fetal weight increase at week 34 of up to 2.4 % in Gipuzkoa (P < 0.005) and 1 % in Sabadell (P < 0.05). Other metabolites included pregnancy-related hormone by-products of estrogens and progesterone, and the methyl donor choline. We could explain a total of 48-53 % of the total variance in birth weight of which urine metabolites had an independent predictive power of 12 % adjusting for all other lifestyle/clinical factors. First trimester metabolic phenotypes could not predict reproducibly weight at later stages of development. Physical activity, as well as other modifiable lifestyle/clinical factors, such as coffee consumption, vitamin D intake, and smoking, were identified as potential sources of metabolic variation during pregnancy.

CONCLUSIONS

Significant reproducible maternal urinary metabolic signatures of fetal growth and birth weight are identified for the first time and linked to modifiable lifestyle factors. This novel approach to prenatal screening, combining multiple risk factors, present a great opportunity to personalize pregnancy management and reduce newborn disease risk in later life.