A maternal serum metabolite ratio predicts fetal growth restriction at term

Impact of combined consumption of fish oil and probiotics on the serum metabolome in pregnant women with overweight or obesity

BACKGROUND

If a pregnant woman is overweight, this can evoke metabolic alterations that may have health consequences for both mother and child.

METHODS

Pregnant women with overweight/obesity (n = 358) received fish oil+placebo, probiotics+placebo, fish oil+probiotics or placebo+placebo from early pregnancy onwards. The serum metabolome was analysed from fasting samples with a targeted NMR-approach in early and late pregnancy. GDM was diagnosed by OGTT.

FINDINGS

The intervention changed the metabolic profile of the women, but the effect was influenced by their GDM status. In women without GDM, the changes in nine lipids (FDR<0.05) in the fish oil+placebo-group differed when compared to the placebo+placebo-group. The combination of fish oil and probiotics induced changes in more metabolites, 46 of the lipid metabolites differed in women without GDM when compared to placebo+placebo-group; these included reduced increases in the concentrations and lipid constituents of VLDL-particles and less pronounced alterations in the ratios of various lipids in several lipoproteins. In women with GDM, no differences were detected in the changes of any metabolites due to any of the interventions when compared to the placebo+placebo-group (FDR<0.05).

INTERPRETATION

Fish oil and particularly the combination of fish oil and probiotics modified serum lipids in pregnant women with overweight or obesity, while no such effects were seen with probiotics alone. The effects were most evident in the lipid contents of VLDL and LDL only in women without GDM.

FUNDING

State Research Funding for university-level health research in the Turku University Hospital Expert Responsibility Area, Academy of Finland, the Diabetes Research Foundation, the Juho Vainio Foundation, Janssen Research & Development, LLC.

Detection and clinical outcome of small-for-gestational-age fetuses in the third trimester-A comparison between routine ultrasound examination and examination on indication

INTRODUCTION

Fetal growth restriction is a major risk factor for adverse perinatal outcome. As most of the growth-restricted fetuses are small for gestational age (SGA), an efficient antenatal screening method for SGA fetuses would have a major impact on perinatal health. The aim of this study was to compare the SGA prediction rate achieved with third-trimester routine ultrasound estimation of fetal weight (EFW) with that obtained using ultrasound examination on indication. The secondary aim was to evaluate the clinical outcome in relation to the SGA screening method.

MATERIAL AND METHODS

During 1995-2009, two perinatal centers in southern Sweden offered routine ultrasound examination at 32-34 gestational weeks to 99 265 women with singleton pregnancies. Of these, 59 452 (60%) underwent the ultrasound examination. The other population, comprising 24 868 pregnancies, was cared for in another three centers that used a risk-based method with ultrasound examinations on indication only. Of them, 5792 (23%) underwent ultrasound examination at 32-36 gestational weeks. The deviation in the EFW from the expected one was expressed as the EFW z-score, SGA EFW being defined as the EFW z-score less than -2. SGA prediction ability was assessed by receiver operating characteristic (ROC) curves. Crude and adjusted risk ratios were calculated for selected variables of perinatal outcome when comparing the populations.

RESULTS

The SGA prediction ability for routine ultrasound was high, area under the ROC curve was 0.90 (95% CI 0.89-0.91). For an EFW z-score of -1, the sensitivity was 67.3% and specificity was 90.5% among routinely screened pregnancies; corresponding numbers in the ultrasound on indication population were 34.3% and 96.6%. The screened population had a lower risk of preterm birth, birthweight z-score less than -3, and Apgar score less than 7 at 5 min with adjusted risk ratios 0.87 (95% CI 0.82-0.92), 0.75 (95% CI 0.61-0.92), and 0.77 (95% CI 0.68-0.87), respectively. No difference in perinatal mortality was detected. There were no differences in perinatal outcome between the two subcohorts of infants born SGA.

CONCLUSIONS

Third-trimester routine ultrasound improves the detection of SGA antenatally compared with ultrasound performed on indication, but no convincing improvement in perinatal outcome was identified.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

Circulating syndecan-1 is reduced in pregnancies with poor fetal growth and its secretion regulated by matrix metalloproteinases and the mitochondria

Fetal growth restriction is a leading cause of stillbirth that often remains undetected during pregnancy. Identifying novel biomarkers may improve detection of pregnancies at risk. This study aimed to assess syndecan-1 as a biomarker for small for gestational age (SGA) or fetal growth restricted (FGR) pregnancies and determine its molecular regulation. Circulating maternal syndecan-1 was measured in several cohorts; a large prospective cohort collected around 36 weeks’ gestation (n = 1206), a case control study from the Manchester Antenatal Vascular service (285 women sampled at 24–34 weeks’ gestation); two prospective cohorts collected on the day of delivery (36 + 3–41 + 3 weeks’ gestation, n = 562 and n = 405 respectively) and a cohort who delivered for preterm FGR (< 34 weeks). Circulating syndecan-1 was consistently reduced in women destined to deliver growth restricted infants and those delivering for preterm disease. Syndecan-1 secretion was reduced by hypoxia, and its loss impaired proliferation. Matrix metalloproteinases and mitochondrial electron transport chain inhibitors significantly reduced syndecan-1 secretion, an effect that was rescued by coadministration of succinate, a mitochondrial electron transport chain activator. In conclusion, circulating syndecan-1 is reduced among cases of term and preterm growth restriction and has potential for inclusion in multi-marker algorithms to improve detection of poorly grown fetuses.

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.

Association Between Iatrogenic Delivery for Suspected Fetal Growth Restriction and Childhood School Outcomes

IMPORTANCE

Timely delivery of infants suspected of having fetal growth restriction (FGR) is a balance between preventing stillbirth and minimizing prematurity, particularly because many infants with suspected FGR have normal growth.

OBJECTIVE

To explore the association between iatrogenic delivery for suspected FGR and childhood school outcomes.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective whole-population cohort study linking perinatal data from births 32 weeks' or more gestation between January 1, 2003, to December 31, 2013, to developmental and educational test scores at preparatory school, and at school grades 3, 5, and 7 in Victoria, Australia. Follow-up was concluded in 2019.

EXPOSURES

Suspicion or nonsuspicion of FGR, presence or absence of iatrogenic delivery (defined as early induction of labor or cesarean delivery prior to labor) for suspected FGR, and presence or absence of small for gestational age (SGA).

MAIN OUTCOMES AND MEASURES

The coprimary outcomes were being in the bottom 10th percentile on 2 or more of 5 developmental domains at school entry and being below the national minimum standard on 2 or more of 5 educational domains in grades 3, 5, or 7.

RESULTS

In the birth population of 705 937 infants, the mean gestation at birth was 39.1 (SD, 1.5) weeks and the mean birth weight was 3426 (SD, 517) grams. The birth population linked to 181 902 children with developmental results and 425 717 children with educational results. Compared with infants with severe SGA (birth weight <3rd percentile) not suspected of having FGR, infants with severe SGA delivered for suspected FGR were born earlier (mean gestation, 37.9 weeks vs 39.4 weeks). They also had a significantly increased risk of poor developmental outcome at school entry (16.2% vs 12.7%; absolute difference, 3.5% [95% CI, 0.5%-6.5%]); adjusted odds ratio [aOR], 1.36 [95% CI, 1.07-1.74]) and poor educational outcomes in grades 3, 5, and 7 (for example, in grade 7: 13.4% vs 10.5%; absolute difference, 2.9% [95% CI, 0.4%-5.5%]); aOR, 1.33 [95% CI, 1.04-1.70]). There was no significant difference between infants with normal growth (birth weight ≥10th percentile) delivered for suspected FGR and those not suspected of having FGR in developmental outcome (8.6% vs 8.1%; absolute difference, 0.5% [95% CI, -1.1% to 2.0%]); aOR, 1.17 [95% CI, 0.95-1.45]) or educational outcome in grade 3, 5 or 7, despite being born earlier (mean gestation, 38.0 weeks vs 39.1 weeks).

CONCLUSIONS AND RELEVANCE

In this exploratory study conducted in Victoria, Australia, iatrogenic delivery of infants with severe SGA due to suspected FGR was associated with poorer school outcomes compared with infants with severe SGA not suspected of having FGR. Iatrogenic delivery of infants with normal growth due to suspected FGR was not associated with poorer school outcomes compared with infants with normal growth not suspected of having FGR.

Comparison of dried blood spot and plasma sampling for untargeted metabolomics

INTRODUCTION

Untargeted metabolomics holds significant promise for biomarker detection and development. In resource-limited settings, a dried blood spot (DBS)-based platform would offer significant advantages over plasma-based approaches that require a cold supply chain.

OBJECTIVES

The primary goal of this study was to compare the ability of DBS- and plasma-based assays to characterize maternal metabolites. Utility of the two assays was also assessed in the context of a case-control predictive model in pregnant women living with HIV.

METHODS

Untargeted metabolomics was performed on archived paired maternal plasma and DBS from n = 79 women enrolled in a large clinical trial.

RESULTS

A total of 984 named biochemicals were detected across both plasma and DBS samples, of which 627 (63.7%), 260 (26.4%), and 97 (9.9%) were detected in both plasma and DBS, plasma alone, and DBS alone, respectively. Variation attributable to study individual (R² = 0.54, p < 0.001) exceeded that of the sample type (R² = 0.21, p < 0.001), suggesting that both plasma and DBS were capable of differentiating individual metabolomic profiles. Log-transformed metabolite abundances were strongly correlated (mean Spearman rho = 0.51) but showed low agreement (mean intraclass correlation of 0.15). However, following standardization, DBS and plasma metabolite profiles were strongly concordant (mean intraclass correlation of 0.52). Random forests classification models for cases versus controls identified distinct feature sets with comparable performance in plasma and DBS (86.5% versus 91.2% mean accuracy, respectively).

CONCLUSION

Maternal plasma and DBS samples yield distinct metabolite profiles highly predictive of the individual subject. In our case study, classification models showed similar performance albeit with distinct feature sets. Appropriate normalization and standardization methods are critical to leverage data from both sample types. Ultimately, the choice of sample type will likely depend on the compounds of interest as well as logistical demands.

Developing Novel Tests to Screen for Fetal Growth Restriction

Fetal growth restriction (FGR) is a major determinant of global morbidity and mortality. There is an unmet need for methods to stratify the pregnant population on the basis of FGR risk. Despite evolutionary divergence in mammalian reproduction, studies of genetically modified mice have identified biomarkers that have been validated in women, and a systematic screen for genes that control fetal growth in animals could help identify novel clinical biomarkers. Current approaches to biomarker identification using human samples include both targeted and discovery approaches (omics). Application of omic methods to the placenta and maternal blood has yielded promising results, but comes with logistical, experimental, and analytical challenges and all studies are limited by the lack of a gold standard for disease.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Routine Third Trimester Sonogram: Friend or Foe

Several risk factors for adverse pregnancy outcomes can be identified by a routine third trimester ultrasound scan. However, there is also potential for harm, anxiety, and additional health care costs through unnecessary intervention due to false positive results. The evidence base informing the balance of risks and benefits of universal screening is inadequate to fully inform decision making. However, data on the diagnostic effectiveness of universal ultrasound suggest that better methods are required to result in net benefit, with the exception of screening for presentation near term, where a clinical and economic case can be made for its implementation.

The RNA landscape of the human placenta in health and disease

The placenta is the interface between mother and fetus and inadequate function contributes to short and long-term ill-health. The placenta is absent from most large-scale RNA-Seq datasets. We therefore analyze long and small RNAs (~101 and 20 million reads per sample respectively) from 302 human placentas, including 94 cases of preeclampsia (PE) and 56 cases of fetal growth restriction (FGR). The placental transcriptome has the seventh lowest complexity of 50 human tissues: 271 genes account for 50% of all reads. We identify multiple circular RNAs and validate 6 of these by Sanger sequencing across the back-splice junction. Using large-scale mass spectrometry datasets, we find strong evidence of peptides produced by translation of two circular RNAs. We also identify novel piRNAs which are clustered on Chr1 and Chr14. PE and FGR are associated with multiple and overlapping differences in mRNA, lincRNA and circRNA but fewer consistent differences in small RNAs. Of the three protein coding genes differentially expressed in both PE and FGR, one encodes a secreted protein FSTL3 (follistatin-like 3). Elevated serum levels of FSTL3 in pregnant women are predictive of subsequent PE and FGR. To aid visualization of our placenta transcriptome data, we develop a web application ( https://www.obgyn.cam.ac.uk/placentome/ ).

Gestational age-dependent development of the neonatal metabolome

BACKGROUND

Prematurity is a severe pathophysiological condition, however, little is known about the gestational age-dependent development of the neonatal metabolome.

METHODS

Using an untargeted liquid chromatography-tandem mass spectrometry metabolomics protocol, we measured over 9000 metabolites in 298 neonatal residual heel prick dried blood spots retrieved from the Danish Neonatal Screening Biobank. By combining multiple state-of-the-art metabolome mining tools, we retrieved chemical structural information at a broad level for over 5000 (60%) metabolites and assessed their relation to gestational age.

RESULTS

A total of 1459 (~16%) metabolites were significantly correlated with gestational age (false discovery rate-adjusted P < 0.05), whereas 83 metabolites explained on average 48% of the variance in gestational age. Using a custom algorithm based on hypergeometric testing, we identified compound classes (617 metabolites) overrepresented with metabolites correlating with gestational age (P < 0.05). Metabolites significantly related to gestational age included bile acids, carnitines, polyamines, amino acid-derived compounds, nucleotides, phosphatidylcholines and dipeptides, as well as treatment-related metabolites, such as antibiotics and caffeine.

CONCLUSIONS

Our findings elucidate the gestational age-dependent development of the neonatal blood metabolome and suggest that the application of metabolomics tools has great potential to reveal novel biochemical underpinnings of disease and improve our understanding of complex pathophysiological mechanisms underlying prematurity-associated disorders.

IMPACT

A large variation in the neonatal dried blood spot metabolome from residual heel pricks stored at the Danish Neonatal Screening Biobank can be explained by gestational age. While previous studies have assessed the relation of selected metabolic markers to gestational age, this study assesses metabolome-wide changes related to prematurity. Using a combination of recently developed metabolome mining tools, we assess the relation of over 9000 metabolic features to gestational age. The ability to assess metabolome-wide changes related to prematurity in neonates could pave the way to finding novel biochemical underpinnings of health complications related to preterm birth.

The serum metabolome of COVID-19 patients is distinctive and predictive

BACKGROUND

Metabolism is critical for sustaining life, immunity and infection, but its role in COVID-19 is not fully understood.

METHODS

Seventy-nine COVID-19 patients, 78 healthy controls (HCs) and 30 COVID-19-like patients were recruited in a prospective cohort study. Samples were collected from COVID-19 patients with mild or severe symptoms on admission, patients who progressed from mild to severe symptoms, and patients who were followed from hospital admission to discharge. The metabolome was assayed using gas chromatography-mass spectrometry.

RESULTS

Serum butyric acid, 2-hydroxybutyric acid, l-glutamic acid, l-phenylalanine, l-serine, l-lactic acid, and cholesterol were enriched in COVID-19 and COVID-19-like patients versus HCs. Notably, d-fructose and succinic acid were enriched, and citric acid and 2-palmitoyl-glycerol were depleted in COVID-19 patients compared to COVID-19-like patients and HCs, and these four metabolites were not differentially distributed in non-COVID-19 groups. COVID-19 patients had enriched 4-deoxythreonic acid and depleted 1,5-anhydroglucitol compared to HCs and enriched oxalic acid and depleted phosphoric acid compared to COVID-19-like patients. A combination of d-fructose, citric acid and 2-palmitoyl-glycerol distinguished COVID-19 patients from HCs and COVID-19-like patients, with an area under the curve (AUC) > 0.92 after validation. The combination of 2-hydroxy-3-methylbutyric acid, 3-hydroxybutyric acid, cholesterol, succinic acid, L-ornithine, oleic acid and palmitelaidic acid predicted patients who progressed from mild to severe COVID-19, with an AUC of 0.969. After discharge, nearly one-third of metabolites were recovered in COVID-19 patients.

CONCLUSIONS

The serum metabolome of COVID-19 patients is distinctive and has important value in investigating pathogenesis, determining a diagnosis, predicting severe cases, and improving treatment.

Does detection of fetal growth restriction improve neonatal outcomes?

AIM

Timely delivery of fetal growth restriction (FGR) is a balance between avoiding stillbirth and minimising prematurity. We sought to assess the neonatal outcomes for babies suspected of FGR, both true and false positives.

METHODS

This population cohort study examined all singleton births in Victoria, Australia from 2000 to 2017 (n = 1 231 415). Neonatal morbidities associated with neonatal intensive care unit (NICU) admission were assessed for babies born ≥32 weeks' with severe FGR (<3rd centile) and babies with birthweight ≥10th centile who were iatrogenically delivered for suspected FGR.

RESULTS

Babies with severe FGR iatrogenically delivered for suspected FGR were more likely to require NICU admission than babies with severe FGR who were not detected (3.0% vs. 1.1%, P < 0.001). After adjusting for potential confounders, the odds of NICU admission were increased (adjusted odds ratio (aOR) = 3.00, 95% confidence interval = 2.45-3.67; P < 0.001). Rates of NICU admission were also higher in ≥10th centile babies iatrogenically delivered for suspected FGR than for ≥10th centile babies who entered labour spontaneously (1.8% vs. 0.5%, P < 0.001). After adjustments, the odds of NICU admission were increased (aOR = 3.91, 95% confidence interval = 3.40-4.49; P < 0.001). NICU admissions were associated with morbidities related to iatrogenic prematurity.

CONCLUSIONS

Detection and planned delivery of FGR reduces stillbirth but may be associated with increased neonatal morbidity related to iatrogenic prematurity.

Preventing harm: A balance measure for improving the detection of fetal growth restriction

BACKGROUND

Increasing the detection of fetal growth restriction (FGR), while reducing stillbirth, also leads to unnecessary early intervention, and associated morbidity, for normally grown babies who are incorrectly suspected of FGR.

AIMS

We sought to design a balance measure that addresses the specificity of FGR detection.

METHODS

A retrospective cohort study on all singleton births ≥32 weeks gestation in 2016 and 2017 in Victoria. We compared two balance measures for the detection of FGR, defined as the proportion of all babies iatrogenically delivered before 39 weeks gestation for suspected FGR that had a birthweight ≥10th centile (balance measure 1) or ≥25th centile (balance measure 2). Hospital level performance on each balance measure was derived and compared to an existing performance measure for severe FGR detection in Victoria.

RESULTS

Of the 38 hospitals analysed, 12 (32%) had a favourable performance on an existing indicator of FGR detection, seven (18%) hospitals had a favourable performance on balance measure 1, and 15 (39%) had a favourable performance on balance measure 2. There was a moderate correlation between hospital performance on the existing indicator and on balance measure 1 (r = 0.447, P = 0.005) but not balance measure 2 (r = -0.063, P = 0.71). There was no difference in perinatal mortality between high performing hospitals and low performing hospitals.

CONCLUSION

Introducing a balance measure into routine reporting may bring greater awareness to the unintended harm associated with increased detection of FGR.

Steroid disulfates - Sulfation double trouble

Sulfation pathways have recently come into the focus of biomedical research. For steroid hormones and related compounds, sulfation represents an additional layer of regulation as sulfated steroids are more water-soluble and tend to be biologically less active. For steroid diols, an additional sulfation is possible, carried out by the same sulfotransferases that catalyze the first sulfation step. The steroid disulfates that are formed are the focus of this review. We discuss both their biochemical production as well as their putative biological function. Steroid disulfates have also been linked to various clinical conditions in numerous untargeted metabolomics studies. New analytical techniques exploring the biosynthetic routes of steroid disulfates have led to novel insights, changing our understanding of sulfation in human biology. They promise a bright future for research into sulfation pathways, hopefully too for the diagnosis and treatment of several associated diseases.

Risk stratification for early-onset fetal growth restriction in women with abnormal serum biomarkers: a retrospective cohort study

Abnormal maternal serum biomarkers (AMSB), identified through the aneuploidy screening programme, are frequent incidental findings in pregnancy. They are associated with fetal growth restriction (FGR), but previous studies have not examined whether this association is with early-onset (< 34 weeks) or late-onset (> 34 weeks) FGR; as a result there is no consensus on management. The aims of this study were to determine the prevalence and phenotype of FGR in women with AMSB and test the predictive value of placental sonographic screening to predict early-onset FGR. 1196 pregnant women with AMSB underwent a 21-24 week "placental screen" comprising fetal and placental size, and uterine artery Doppler. Multivariable regression was used to calculate a predictive model for early-onset FGR (birthweight centile < 3rd/< 10th with absent umbilical end-diastolic flow, < 34 weeks). FGR prevalence was high (10.3%), however early-onset FGR was uncommon (2.3%). Placental screening effectively identified early-onset (area under the curve (AUC) 0.93, 95% confidence interval (CI) 0.87-1.00), but not late-onset FGR (AUC 0.70, 95% CI 0.64-0.75). Internal validation demonstrated robust performance for detection/exclusion of early-onset FGR. In this cohort, utilisation of our proposed algorithm with targeted fetal growth and Doppler surveillance, compared with universal comprehensive surveillance would have avoided 1044 scans, potentiating significant cost-saving for maternity services.

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.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks' gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts' extensive phenotype data from questionnaires, medical, educational and social record linkage, and other 'omics data.

Regulation of maternal-fetal metabolic communication

Pregnancy may be the most nutritionally sensitive stage in the life cycle, and improved metabolic health during gestation and early postnatal life can reduce the risk of chronic disease in adulthood. Successful pregnancy requires coordinated metabolic, hormonal, and immunological communication. In this review, maternal-fetal metabolic communication is defined as the bidirectional communication of nutritional status and metabolic demand by various modes including circulating metabolites, endocrine molecules, and other secreted factors. Emphasis is placed on metabolites as a means of maternal-fetal communication by synthesizing findings from studies in humans, non-human primates, domestic animals, rabbits, and rodents. In this review, fetal, placental, and maternal metabolic adaptations are discussed in turn. (1) Fetal macronutrient needs are summarized in terms of the physiological adaptations in place to ensure their proper allocation. (2) Placental metabolite transport and maternal physiological adaptations during gestation, including changes in energy budget, are also discussed. (3) Maternal nutrient limitation and metabolic disorders of pregnancy serve as case studies of the dynamic nature of maternal-fetal metabolic communication. The review concludes with a summary of recent research efforts to identify metabolites, endocrine molecules, and other secreted factors that mediate this communication, with particular emphasis on serum/plasma metabolomics in humans, non-human primates, and rodents. A better understanding of maternal-fetal metabolic communication in health and disease may reveal novel biomarkers and therapeutic targets for metabolic disorders of pregnancy.

Early-life exposure to widespread environmental toxicants and maternal-fetal health risk: A focus on metabolomic biomarkers

Prenatal exposure to widespread environmental toxicants is detrimental to maternal health and fetal development. The effects of environmental toxicants on maternal and fetal metabolic profile changes have not yet been summarized. This systematic review aims to summarize the current studies exploring the association between prenatal exposure to environmental toxicants and metabolic profile alterations in mother and fetus. We searched the MEDLINE (PubMed) electronic database for relevant literature conducted up to September 18, 2019 with some key terms. From the initial 155 articles, 15 articles met the inclusion and exclusion criteria, and consist of highly heterogeneous research methods. Seven studies assessed the effects of multiple environmental pollutants (metals, organic pollutants, nicotine, air pollutants) on the maternal urine and blood metabolomic profile; five studies evaluated the effects of arsenic, polychlorinated biphenyls (PCBs), nicotine, and ambient fine particulate matter (PM_2.5) on the cord blood metabolomic profile; and one study assessed the effects of smoking exposure on the amniotic fluid metabolomic profile. The alteration of metabolic pathways in these studies mainly involve energy metabolism, hormone metabolism, oxidative stress and inflammation. No population study investigated the association between environmental toxicants and placental metabolomics. This systematic review provides evidence that prenatal exposure to a variety of environmental pollutants can affect maternal and fetal metabolomic characteristics. Integration of environmental toxicant exposure and metabolomics data in maternal-fetal samples is helpful to understand the interaction between toxicants and metabolites, so as to reveal the pathogenesis of fetal disease or diseases of fetal origin.

Association of the functional ovarian reserve with serum metabolomic profiling by nuclear magnetic resonance spectroscopy: a cross-sectional study of ~ 400 women

BACKGROUND

Women with diminished ovarian reserve are known to have increased cardiovascular risk, whether there is a continuous association between the ovarian reserve biomarkers; anti-Müllerian hormone (AMH), antral follicle count (AFC) and cardio-metabolic risk factors are unknown.

METHODS

A cross-sectional study of 398 women intending to undergo IVF with pre-treatment early follicular AMH and AFC measurements. Serum lipids, lipoprotein subclasses and low-molecular-weight metabolites were quantified by NMR spectroscopy (155 metabolic measures). Associations were analysed using multivariable regression.

RESULTS

Participants were mean 35.5 (SD 4.43) years old and had a median AMH of 16 pmol/l (IQR 8.8, 28.0 pmol/l) and a median AFC of 12 (IQR 7.16). AMH showed positive associations with HDL, omega-6 and polyunsaturated fatty acids and the amino acids isoleucine, leucine and tyrosine, with effects ranging from 0.11 (95%CI 0.004 to 0.21) for total lipids in small HDL to 0.16 (0.06 to 0.26) for isoleucine, for a mean difference of one SD of metabolite per one SD increment in AMH, and negatively with acetate: - 0.31(- 0.22, - 0.004) SD per 1 SD AMH. AFC was positively associated with alanine, glutamine and glycine. Results were consistent, though less precisely estimated, when restricted to those women who were preparing for treatment because of their partner's infertility.

CONCLUSIONS

In women intending to have IVF, AMH and AFC were not associated with traditional lipid measured but were associated with a number of novel cardiovascular risk factors. Prospective studies will be required for replication, determination of causality and confirmation that ovarian reserve is impacting on metabolism rather than variation in metabolism is influencing ovarian reserve.

Identification of the optimal growth chart and threshold for the prediction of antepartum stillbirth

PURPOSE

To evaluate the effect of the choice growth chart and threshold used to define small for gestational age (SGA) on the predictive value of SGA for placenta-related or unexplained antepartum stillbirth.

METHODS

A retrospective cohort study of all women with a singleton pregnancy who gave birth > 24 week gestation in a single center (2000-2016). The exposure of interest was SGA, defined as birth weight < 10th or < 25th centile according to three fetal growth charts (Hadlock et al., Radiology 181:129-133, 1991; intergrowth-21st (IG21), WHO 2017, and a Canadian birthweight-based reference-Kramer et al., Pediatrics 108:E35, 2001). The outcome of interest was antepartum stillbirth due to placental dysfunction or unknown etiology. Cases of stillbirth attributed to other specific etiologies were excluded.

RESULTS

A total of 49,458 women were included in the cohort. There were 103 (0.21%) cases of stillbirth due to placental dysfunction or unknown etiology. For cases in the early stillbirth cluster (≤ 30 weeks), the detection rate was high and was similar for the three ultrasound-based fetal growth charts of Hadlock, IG21, and WHO (range 83.3-87.0%). In contrast, the detection rate of SGA for cases in the late stillbirth cluster (> 30 weeks) was low, being highest for WHO and Hadlock (36.7% and 34.7%, respectively), and lowest for IG21 (18.4%). Using a threshold of the 25th centile increased the detection rate for stillbirth by approximately 15-20% compared with that achieved by the 10th centile cutoff.

CONCLUSION

At > 30 week gestation, the Hadlock or WHO fetal growth charts provided the best balance between detection rate and false positive rate for stillbirth.

The pitfalls of using birthweight centile charts to audit care

Objectives

Timely delivery of fetal growth restriction (FGR) is important in reducing stillbirth. However, targeted earlier delivery of FGR preferentially removes smaller babies from later gestations, thereby right-shifting the distribution of birthweights at term. This artificially increases the birthweight cutoffs defining the lower centiles and redefines normally grown babies as small by population-based birthweight centiles. Our objective was to compare updated Australian national population-based birthweight centile charts over time with the prescriptive INTERGROWTH-21^st standard.

Methods

A retrospective descriptive study of all singleton births ≥34 weeks’ gestation in Victoria, Australia in five two-year epochs: 1983–84, 1993–94, 2003–04, 2013–14, and 2016–17. The birthweight cutoffs defining the 3^rd and 10^th centile from three Australian national population-based birthweight centile charts, for births in 1991–1994, in 1998–2007, and 2004–2013 respectively, were applied to each epoch to calculate the proportion of babies with birthweight <3^rd and <10^th centile. The same analysis was done using the INTERGROWTH-21^st birthweight standard. To assess change over gestation, proportions were also calculated at preterm, early term and late term gestations.

Results

From 1983–84 to 2016–17, the proportion of babies with birthweight <3^rd fell across all birthweight centile charts, from 3.1% to 1.7% using the oldest Australian chart, from 3.9% to 1.9% using the second oldest Australian chart, from 4.3% to 2.2% using the most recent Australian chart, and from 2.0% to 0.9% using the INTERGROWTH-21^st standard. A similar effect was evident for the <10^th centile. The effect was most obvious at term gestations. Updating the Australian population birthweight chart progressively right-shifted the birthweight distribution, changing the definition of small over time. The birthweight distribution of INTERGROWTH-21^st was left-shifted compared to the Australian charts.

Conclusions

Locally-derived population-based birthweight centiles are better for clinical audit of care but should not be updated. Prescriptive birthweight standards are less useful in defining ‘small’ due to their significant left-shift.

A Metabolomics Approach to Screening for Autism Risk in the Children's Autism Metabolome Project

Autism spectrum disorder (ASD) is biologically and behaviorally heterogeneous. Delayed diagnosis of ASD is common and problematic. The complexity of ASD and the low sensitivity of available screening tools are key factors in delayed diagnosis. Identification of biomarkers that reduce complexity through stratification into reliable subpopulations can assist in earlier diagnosis, provide insight into the biology of ASD, and potentially suggest targeted interventions. Quantitative metabolomic analysis was performed on plasma samples from 708 fasting children, aged 18 to 48 months, enrolled in the Children's Autism Metabolome Project (CAMP). The primary goal was to identify alterations in metabolism helpful in stratifying ASD subjects into subpopulations with shared metabolic phenotypes (i.e., metabotypes). Metabotypes associated with ASD were identified in a discovery set of 357 subjects. The reproducibility of the metabotypes was validated in an independent replication set of 351 CAMP subjects. Thirty-four candidate metabotypes that differentiated subsets of ASD from typically developing participants were identified with sensitivity of at least 5% and specificity greater than 95%. The 34 metabotypes formed six metabolic clusters based on ratios of either lactate or pyruvate, succinate, glycine, ornithine, 4-hydroxyproline, or α-ketoglutarate with other metabolites. Optimization of a subset of new and previously defined metabotypes into a screening battery resulted in 53% sensitivity (95% confidence interval [CI], 48%-57%) and 91% specificity (95% CI, 86%-94%). Thus, our metabolomic screening tool detects more than 50% of the autistic participants in the CAMP study. Further development of this metabolomic screening approach may facilitate earlier referral and diagnosis of ASD and, ultimately, more targeted treatments. LAY SUMMARY: Analysis of a selected set of metabolites in blood samples from children with autism and typically developing children identified reproducible differences in the metabolism of about half of the children with autism. Testing for these differences in blood samples can be used to help screen children as young as 18 months for risk of autism that, in turn, can facilitate earlier diagnoses. In addition, differences may lead to biological insights that produce more precise treatment options. We are exploring other blood-based molecules to determine if still a higher percentage of children with autism can be detected using this strategy. Autism Res 2020, 13: 1270-1285. © 2020 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.