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

Abnormal maternal apolipoprotein levels during pregnancy are risk factors for preterm birth in women with dichorionic twin pregnancies: A retrospective study

OBJECTIVE

In singleton-pregnant women, abnormal maternal apolipoprotein levels have been confirmed as a risk factor for preterm birth. However, there are currently no studies on the relationship of the related research in twin-pregnant women.

METHODS

This single-center retrospective study included 743 dichorionic twin-pregnant women who delivered between January 2019 and December 2020. Twins delivered before 37 weeks gestation were categorized as the preterm group, while those delivered at or after 37 weeks gestation were classified as the term group. Maternal serum apolipoprotein A1 (ApoA1) levels, apolipoprotein B (ApoB) levels, and the ApoB/ApoA1 ratio were measured in the first trimester(6-14 weeks), the second trimester(18-28 weeks) and the third trimester(after 28 weeks). We conducted SPSS analysis to evaluate the correlation between ApoA1 levels, ApoB levels, the ApoB/ApoA1 ratio and preterm birth.

RESULTS

Among the 743 included dichorionic twin-pregnant women, 53.57 % (398/743) delivered preterm. Compared with the term group, the ApoA1 levels in the third trimester were lower (p < 0.001), while the Apo B/ApoA1 ratio was higher in the second (p = 0.01) and third trimesters in the preterm group (p = 0.001). When preterm birth was categorized as iatrogenic and spontaneous preterm birth, the results were similar. In the analysis stratified by prepregnancy BMI, a higher risk of preterm birth was associated with low ApoA1 levels and a high Apo B/ApoA1 ratio in the second and third trimesters only among the subgroup of overweight/obese dichorionic twin-pregnant women.

CONCLUSIONS

Low ApoA1 levels and a high Apo B/ApoA1 ratio during the second and third trimesters were associated with a high incidence of preterm birth for overweight/obese dichorionic twin-pregnant women.

Simple and Fast Prediction of Gestational Diabetes Mellitus Based on Machine Learning and Near-Infrared Spectra of Serum: A Proof of Concept Study at Different Stages of Pregnancy

Gestational diabetes mellitus (GDM) is a hyperglycemic state that is typically diagnosed by an oral glucose tolerance test (OGTT), which is unpleasant, time-consuming, has low reproducibility, and results are tardy. The machine learning (ML) predictive models that have been proposed to improve GDM diagnosis are usually based on instrumental methods that take hours to produce a result. Near-infrared (NIR) spectroscopy is a simple, fast, and low-cost analytical technique that has never been assessed for the prediction of GDM. This study aims to develop ML predictive models for GDM based on NIR spectroscopy, and to evaluate their potential as early detection or alternative screening tools according to their predictive power and duration of analysis. Serum samples from the first trimester (before GDM diagnosis) and the second trimester (at the time of GDM diagnosis) of pregnancy were analyzed by NIR spectroscopy. Four spectral ranges were considered, and 80 mathematical pretreatments were tested for each. NIR data-based models were built with single- and multi-block ML techniques. Every model was subjected to double cross-validation. The best models for first and second trimester achieved areas under the receiver operating characteristic curve of 0.5768 ± 0.0635 and 0.8836 ± 0.0259, respectively. This is the first study reporting NIR-spectroscopy-based methods for the prediction of GDM. The developed methods allow for prediction of GDM from 10 µL of serum in only 32 min. They are simple, fast, and have a great potential for application in clinical practice, especially as alternative screening tools to the OGTT for GDM diagnosis.

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

BACKGROUND

Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. Socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women.

METHODS

We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities.

RESULTS

Five SEP sub-groups were identified and labelled 'Highest SEP' (48% WE and 52% SA), 'High-Medium SEP' (77% and 23%), 'Medium SEP' (56% and 44%) 'Low-Medium SEP' (21% and 79%), and 'Lowest SEP' (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for 'Medium SEP' and 0.25SD (0.18 to 0.32) for 'Lowest SEP'. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for 'Medium SEP', and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for 'Lowest SEP'.

CONCLUSIONS

We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Associations of polymetabolic risk of high maternal pre-pregnancy body mass index with pregnancy complications, birth outcomes, and early childhood neurodevelopment: findings from two pregnancy cohorts

BACKGROUND

A substantial proportion of maternal pregnancy complications, adverse birth outcomes and neurodevelopmental delay in children may be attributable to high maternal pre-pregnancy Body Mass Index (BMI). However, BMI alone is insufficient for the identification of all at-risk mothers and children as many women with non-obesity(< 30 kg/m2) or normal weight(18.5-24.99 kg/m2) and their children may suffer from adversities. Evidence suggests that BMI-related metabolic changes during pregnancy may predict adverse mother-child outcomes better than maternal anthropometric BMI.

METHODS

In a cohort of 425 mother-child dyads, we identified maternal BMI-defined metabolome based on associations of 95 metabolic measures measured three times during pregnancy with maternal pre-pregnancy BMI. We then examined whether maternal BMI-defined metabolome performed better than anthropometric BMI in predicting gestational diabetes, hypertensive disorders, gestational weight gain (GWG), Caesarian section delivery, child gestational age and weight at birth, preterm birth, admission to neonatal intensive care unit (NICU), and childhood neurodevelopment. Based on metabolic measures with the highest contributions to BMI-defined metabolome, including inflammatory and glycolysis-related measures, fatty acids, fluid balance, ketone bodies, lipids and amino acids, we created a set of maternal high BMI-related polymetabolic risk scores (PMRSs), and in an independent replication cohort of 489 mother-child dyads tested their performance in predicting the same set of mother-child outcomes in comparison to anthropometric BMI.

RESULTS

BMI-defined metabolome predicted all of the studied mother-child outcomes and improved their prediction over anthropometric BMI, except for gestational hypertension and GWG. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarian section delivery, admission to NICU, lower gestational age at birth, lower cognitive development score of the child, and improved their prediction over anthropometric BMI. BMI-related PMRSs predicted gestational diabetes, preeclampsia, Caesarean section delivery, NICU admission and child's lower gestational age at birth even at the levels of maternal non-obesity and normal weight.

CONCLUSIONS

Maternal BMI-defined metabolome improves the prediction of pregnancy complications, birth outcomes, and neurodevelopment in children over anthropometric BMI. The novel, BMI-related PMRSs generated based on the BMI-defined metabolome have the potential to become biomarkers identifying at-risk mothers and their children for timely targeted interventions even at the level of maternal non-obesity and normal weight.

The role of lipid dysregulation in gestational diabetes mellitus: Early prediction and postpartum prognosis

Gestational diabetes mellitus (GDM) is a pathological condition during pregnancy characterized by impaired glucose tolerance, and the failure of pancreatic beta-cells to respond appropriately to an increased insulin demand. However, while the majority of women with GDM will return to normoglycemia after delivery, they have up to a seven times higher risk of developing type 2 diabetes during midlife, compared with those with no history of GDM. Gestational diabetes mellitus also increases the risk of multiple metabolic disorders, including non-alcoholic fatty liver disease, obesity, and cardiovascular diseases. Lipid metabolism undergoes significant changes throughout the gestational period, and lipid dysregulation is strongly associated with GDM and the progression to future type 2 diabetes. In addition to common lipid variables, discovery-based omics techniques, such as metabolomics and lipidomics, have identified lipid biomarkers that correlate with GDM. These lipid species also show considerable potential in predicting the onset of GDM and subsequent type 2 diabetes post-delivery. This review aims to update the current knowledge of the role that lipids play in the onset of GDM, with a focus on potential lipid biomarkers or metabolic pathways. These biomarkers may be useful in establishing predictive models to accurately predict the future onset of GDM and type 2 diabetes, and early intervention may help to reduce the complications associated with GDM.

Untargeted metabolomics on first trimester serum implicates metabolic perturbations associated with BMI in development of hypertensive disorders: a discovery study

Goal

Body mass index (BMI) in early pregnancy is a critical risk factor for hypertensive disorders of pregnancy (HDP). The pathobiology of the interplay between BMI and HDP is not fully understood and represents the focus of this investigation.

Methods

BMI and 1st-trimester serum samples were obtained from the Global Alliance to Prevent Prematurity and Stillbirth repository for 154 women (105 without HDP and 49 with HDP). Metabotyping was conducted using ultra-high-performance liquid-chromatography high-resolution mass spectrometry (UHPLC HR-MS). Multivariable linear regression and logistic models were used to determine metabolites and pathway perturbations associated with BMI in women with and without HDP, and to determine metabolites and pathway perturbations associated with HDP for women in categories of obese, overweight, and normal weight based on the 1st trimester BMI. These outcome-associated signals were identified or annotated by matching against an in-house physical standards library and public database. Pathway analysis was conducted by the Mummichog algorithm in MetaboAnalyst.

Result

Vitamin D3 and lysine metabolism were enriched to associate with BMI for women with and without HDP. Tryptophan metabolism enrichment was associated with HDP in all the BMI categories. Pregnant women who developed HDP showed more metabolic perturbations with BMI (continuous) than those without HDP in their 1st-trimester serum. The HDP-associated pathways for women with normal weight indicated inflammation and immune responses. In contrast, the HDP-associated pathways for women of overweight and obese BMI indicated metabolic syndromes with disorders in glucose, protein, and amino acid, lipid and bile acid metabolism, and oxidative and inflammatory stress.

Conclusion

High first-trimester BMI indicates underlying metabolic syndromes, which play critical roles in HDP development. Vitamin D3 and tryptophan metabolism may be the targets to guide nutritional interventions to mitigate metabolic and inflammatory stress in pregnancy and reduce the onset of HDP.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

Effect of common pregnancy and perinatal complications on offspring metabolic traits across the life course: a multi-cohort study

BACKGROUND

Common pregnancy and perinatal complications are associated with offspring cardiometabolic risk factors. These complications may influence multiple metabolic traits in the offspring and these associations might differ with offspring age.

METHODS

We used data from eight population-based cohort studies to examine and compare associations of pre-eclampsia (PE), gestational hypertension (GH), gestational diabetes (GD), preterm birth (PTB), small (SGA) and large (LGA) for gestational age (vs. appropriate size for gestational age (AGA)) with up to 167 plasma/serum-based nuclear magnetic resonance-derived metabolic traits encompassing lipids, lipoproteins, fatty acids, amino acids, ketones, glycerides/phospholipids, glycolysis, fluid balance, and inflammation. Confounder-adjusted regression models were used to examine associations (adjusted for maternal education, parity age at pregnancy, ethnicity, pre/early pregnancy body mass index and smoking, and offspring sex and age at metabolic trait assessment), and results were combined using meta-analysis by five age categories representing different periods of the offspring life course: neonates (cord blood), infancy (mean ages: 1.1-1.6 years), childhood (4.2-7.5 years); adolescence (12.0-16.0 years), and adulthood (22.0-67.8 years).

RESULTS

Offspring numbers for each age category/analysis varied from 8925 adults (441 PTB) to 1181 infants (135 GD); 48.4% to 60.0% were females. Pregnancy complications (PE, GH, GD) were each associated with up to three metabolic traits in neonates (P≤0.001) with some evidence of persistence to older ages. PTB and SGA were associated with 32 and 12 metabolic traits in neonates respectively, which included an adjusted standardised mean difference of -0.89 standard deviation (SD) units for albumin with PTB (95% CI: -1.10 to -0.69, P=1.3×10-17) and -0.41 SD for total lipids in medium HDL with SGA (95% CI: -0.56 to -0.25, P=2.6×10-7), with some evidence of persistence to older ages. LGA was inversely associated with 19 metabolic traits including lower levels of cholesterol, lipoproteins, fatty acids, and amino acids, with associations emerging in adolescence, (e.g. -0.11 SD total fatty acids, 95% CI: -0.18 to -0.05, P=0.0009), and attenuating with older age across adulthood.

CONCLUSIONS

These reassuring findings suggest little evidence of wide-spread and long-term impact of common pregnancy and perinatal complications on offspring metabolic traits, with most associations only observed for newborns rather than older ages, and for perinatal rather than pregnancy complications.

Metabolic profiling of pregnancies complicated by preeclampsia: A longitudinal study

INTRODUCTION

Preeclampsia is associated with maternal metabolic disturbances, but longitudinal studies with comprehensive metabolic profiling are lacking. We aimed to determine metabolic profiles across gestation in women who developed preeclampsia compared with women with healthy pregnancies. We also explored the respective effects of body mass index (BMI) and preeclampsia on various metabolic measures.

MATERIAL AND METHODS

We measured 91 metabolites by high-throughput nuclear magnetic resonance spectroscopy at four time points (visits) during pregnancy (weeks 14-16, 22-24, 30-32 and 36-38). Samples were taken from a Norwegian pregnancy cohort. We fitted a linear regression model for each metabolic measure to compare women who developed preeclampsia (n = 38) and healthy controls (n = 70).

RESULTS

Among women who developed preeclampsia, 92% gave birth after 34 weeks of gestation. Compared to women with healthy pregnancies, women who developed preeclampsia had higher levels of several lipid-related metabolites at visit 1, whereas fewer differences were observed at visit 2. At visit 3, the pattern from visit 1 reappeared. At visit 4 the differences were larger in most subgroups of very-low-density lipoprotein particles, the smallest high-density lipoprotein, total lipids and triglycerides. Total fatty acids were also increased, of which monounsaturated fatty acids and saturated fatty acids showed more pronounced differences. Concentration of glycine tended to be lower in pregnancies with preeclampsia until visit 3, although this was not significant after correction for multiple testing. After adjustment for age, BMI, parity and gestational weight gain, all significant differences were attenuated at visits 1 and 2. The estimates were less affected by adjustment at visits 3 and 4.

CONCLUSIONS

In early pregnancy, the metabolic differences between preeclamptic and healthy pregnancies were primarily driven by maternal BMI, probably representing the women's pre-pregnancy metabolic status. In early third trimester, several weeks before clinical manifestation, the differences were less influenced by BMI, indicating preeclampsia-specific changes. Near term, women with preeclampsia developed an atherogenic metabolic profile, including elevated total lipids, very-low-density lipoprotein, triglycerides, and total fatty acids.

Emission of Industrial Air Pollution and Mortality Due to Respiratory Diseases: A Birth Cohort Study in Poland

BACKGROUND

Air pollution is a major risk factor for public health worldwide, but evidence linking this environmental problem with the mortality of children in Central Europe is limited.

OBJECTIVE

To investigate the relationship between air pollution due to the emission of industry-related particulate matter and mortality due to respiratory diseases under one year of age.

METHODS

A retrospective birth cohort analysis of the dataset including 2,277,585 children from all Polish counties was conducted, and the dataset was matched with 248 deaths from respiratory diseases under one year of age. Time to death during the first 365 days of life was used as a dependent variable. Harmful emission was described as total particle pollution (TPP) from industries. The survival analysis was performed using the Cox proportional hazards model for the emission of TPP at the place of residence of the mother and child, adjusted individual characteristics, demographic factors, and socioeconomic status related to the contextual level.

RESULTS

Infants born in areas with extremely high emission of TPP had a significantly higher risk of mortality due to respiratory diseases: hazard ratio (HR) = 1.781 [95% confidence interval (CI): 1.175, 2.697], p = 0.006, compared with those born in areas with the lowest emission levels. This effect was persistent when significant factors were adjusted at individual and contextual levels (HR = 1.959 [95% CI: 1.058, 3.628], p = 0.032). The increased risk of mortality was marked between the 50th and 150th days of life, coinciding with the highest exposure to TPP.

CONCLUSIONS

The emission of TPP from industries is associated with mortality due to respiratory diseases under one year of age. A considerable proportion of children's deaths could be prevented in Poland, especially in urban areas, if air pollution due to the emission of particle pollution is reduced.

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.

Associations between maternal mid-pregnancy apolipoprotein A-1, apolipoprotein B, apolipoprotein B/apolipoprotein A-1 ratio and preterm birth

BACKGROUND AND AIMS

Elevated lipid levels during pregnancy have been shown to be related to the risk of preterm birth. Despite the importance of apolipoprotein (Apo) in lipid metabolism and transportation, evidence regarding apolipoprotein levels during pregnancy and preterm birth is still limited. Therefore, we aim to investigate the associations between maternal ApoA-1, ApoB, ApoB/ApoA-1 ratio and preterm birth.

MATERIALS AND METH

Data were extracted from the information system of Guangdong Women and Children Hospital. Lipoprotein levels were tested using Beckman Coulter AU5800 in mid-pregnancy at a median gestational age of 18 w. Maternal serum ApoB, ApoA-1 and ApoB/ApoA-1 ratio were categorized into tertiles. Logistic regression models were performed to evaluate the odds ratios and 95% confidence intervals for preterm birth.

RESULTS

A total of 5,986 maternal-newborn pairs were included in this study. The rate of preterm birth was 5.7% (n = 344). The multivariate-adjusted ORs (95% CI) of preterm birth were 1.51 (1.06, 2.10) for individuals with high ApoB (>90th), 0.63 (0.38, 0.99) for those with low ApoB (<10th), and 1.64 (1.18, 2.24) for those with high ApoB/ApoA-1 (>90th). Subgroup analyses showed that the association of ApoB and preterm birth was only significant among women with pre-pregnancy BMI 18.5-24 kg/m² (OR = 1.36, 95% CI: 1.12-1.65), age at delivery ≥ 35 years (OR = 1.43, 95% CI: 1.12-1.83).

CONCLUSION

Elevated maternal ApoB level and ApoB/ApoA-1 ratio during mid-pregnancy were related to increased risk of preterm birth. Monitoring maternal serum apolipoprotein levels may help to identify the high-risk population of preterm birth.

Using Mendelian Randomisation to Prioritise Candidate Maternal Metabolic Traits Influencing Offspring Birthweight

Marked physiological changes in pregnancy are essential to support foetal growth; however, evidence on the role of specific maternal metabolic traits from human studies is limited. We integrated Mendelian randomisation (MR) and metabolomics data to probe the effect of 46 maternal metabolic traits on offspring birthweight (N = 210,267). We implemented univariable two-sample MR (UVMR) to identify candidate metabolic traits affecting offspring birthweight. We then applied two-sample multivariable MR (MVMR) to jointly estimate the potential direct causal effect for each candidate maternal metabolic trait. In the main analyses, UVMR indicated that higher maternal glucose was related to higher offspring birthweight (0.328 SD difference in mean birthweight per 1 SD difference in glucose (95% CI: 0.104, 0.414)), as were maternal glutamine (0.089 (95% CI: 0.033, 0.144)) and alanine (0.137 (95% CI: 0.036, 0.239)). In additional analyses, UVMR estimates were broadly consistent when selecting instruments from an independent data source, albeit imprecise for glutamine and alanine, and were attenuated for alanine when using other UVMR methods. MVMR results supported independent effects of these metabolites, with effect estimates consistent with those seen with the UVMR results. Among the remaining 43 metabolic traits, UVMR estimates indicated a null effect for most lipid-related traits and a high degree of uncertainty for other amino acids and ketone bodies. Our findings suggest that maternal gestational glucose and glutamine are causally related to offspring birthweight.

Maternal Body Mass Index, Early-Pregnancy Metabolite Profile, and Birthweight

CONTEXT

Maternal prepregnancy body mass index (BMI) has a strong influence on gestational metabolism, but detailed metabolic alterations are unknown.

OBJECTIVE

First, to examine the associations of maternal prepregnancy BMI with maternal early-pregnancy metabolite alterations. Second, to identify an early-pregnancy metabolite profile associated with birthweight in women with a higher prepregnancy BMI that improved prediction of birthweight compared to glucose and lipid concentrations.

DESIGN, SETTING, AND PARTICIPANTS

Prepregnancy BMI was obtained in a subgroup of 682 Dutch pregnant women from the Generation R prospective cohort study.

MAIN OUTCOME MEASURES

Maternal nonfasting targeted amino acids, nonesterified fatty acid, phospholipid, and carnitine concentrations measured in blood serum at mean gestational age of 12.8 weeks. Birthweight was obtained from medical records.

RESULTS

A higher prepregnancy BMI was associated with 72 altered amino acids, nonesterified fatty acid, phospholipid and carnitine concentrations, and 6 metabolite ratios reflecting Krebs cycle, inflammatory, oxidative stress, and lipid metabolic processes (P-values < 0.05). Using penalized regression models, a metabolite profile was selected including 15 metabolites and 4 metabolite ratios based on its association with birthweight in addition to prepregnancy BMI. The adjusted R2 of birthweight was 6.1% for prepregnancy BMI alone, 6.2% after addition of glucose and lipid concentrations, and 12.9% after addition of the metabolite profile.

CONCLUSIONS

A higher maternal prepregnancy BMI was associated with altered maternal early-pregnancy amino acids, nonesterified fatty acids, phospholipids, and carnitines. Using these metabolites, we identified a maternal metabolite profile that improved prediction of birthweight in women with a higher prepregnancy BMI compared to glucose and lipid concentrations.

Prediction of Healthy Pregnancy Outcomes in Women with Overweight and Obesity: The Role of Maternal Early-Pregnancy Metabolites

Women with obesity receive intensified antenatal care due to their increased risk of pregnancy complications, even though not all of these women develop complications. We developed a model based on maternal characteristics for prediction of healthy pregnancy outcomes in women with obesity or who are overweight. We assessed whether early-pregnancy metabolites improved prediction. In a population-based cohort study among a subsample of 1180 Dutch pregnant women with obesity or who are overweight, we developed a prediction model using 32 maternal socio-demographic, lifestyle, physical and pregnancy-related characteristics. We determined early-pregnancy amino acids, nonesterifed fatty acids, phospholipids and carnitines in blood serum using liquid chromatography-tandem mass spectrometry. A healthy pregnancy outcome was the absence of fetal death, gestational hypertension, preeclampsia, gestational diabetes, caesarian section, preterm birth, large-for-gestational-age at birth, macrosomia, postpartum weight retention and offspring overweight/obesity at 5 years. Maternal age, relationship status, parity, early-pregnancy body mass index, mid-pregnancy gestational weight gain, systolic blood pressure and estimated fetal weight were selected into the model using backward selection (area under the receiver operating characteristic curve: 0.65 (95% confidence interval 0.61 to 0.68)). Early-pregnancy metabolites did not improve model performance. Thus, in women with obesity or who are overweight, maternal characteristics can moderately predict a healthy pregnancy outcome. Maternal early-pregnancy metabolites have no incremental value in the prediction of a healthy pregnancy outcome.

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.

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.

Childhood overeating is associated with adverse cardiometabolic and inflammatory profiles in adolescence

Childhood eating behaviour contributes to the rise of obesity and related noncommunicable disease worldwide. However, we lack a deep understanding of biochemical alterations that can arise from aberrant eating behaviour. In this study, we prospectively associate longitudinal trajectories of childhood overeating, undereating, and fussy eating with metabolic markers at age 16 years to explore adolescent metabolic alterations related to specific eating patterns in the first 10 years of life. Data are from the Avon Longitudinal Study of Parents and Children (n = 3104). We measure 158 metabolic markers with a high-throughput (1H) NMR metabolomics platform. Increasing childhood overeating is prospectively associated with an adverse cardiometabolic profile (i.e., hyperlipidemia, hypercholesterolemia, hyperlipoproteinemia) in adolescence; whereas undereating and fussy eating are associated with lower concentrations of the amino acids glutamine and valine, suggesting a potential lack of micronutrients. Here, we show associations between early behavioural indicators of eating and metabolic markers.

Putrescine as a Novel Biomarker of Maternal Serum in First Trimester for the Prediction of Gestational Diabetes Mellitus: A Nested Case-Control Study

AIMS

Early identification of gestational diabetes mellitus (GDM) aims to reduce the risk of adverse maternal and perinatal outcomes. Currently, no acknowledged biomarker has proven clinically useful for the accurate prediction of GDM. In this study, we tested whether serum putrescine level changed in the first trimester and could improve the prediction of GDM.

METHODS

This study is a nested case-control study conducted in Beijing Obstetrics and Gynecology Hospital. We examined serum putrescine at 8-12 weeks pregnancy in 47 women with GDM and 47 age- and body mass index (BMI)-matched normoglycaemic women. Anthropometric, clinical and laboratory variables were obtained during the same period. The receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess the discrimination and calibration of the prediction models.

RESULTS

Serum putrescine in the first trimester was significantly higher in women who later developed GDM. When using putrescine alone to predict the risk of GDM, the AUC of the nomogram was 0.904 (sensitivity of 100% and specificity of 83%, 95% CI=0.832-0.976, P<0.001). When combined with traditional risk factors (prepregnant BMI and fasting blood glucose), the AUC was 0.951 (sensitivity of 89.4% and specificity of 91.5%, 95% CI=0.906-0.995, P<0.001).

CONCLUSION

This study revealed that GDM women had an elevated level of serum putrescine in the first trimester. Circulating putrescine may serve as a valuable predictive biomarker for GDM.